import pandas as pd

# import tensorflow.keras

import numpy as np
import pandas as pd
import xgboost as xg

import tensorflow.keras
from keras.layers import Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor

in_columns = ["id_stacji", "nazwa_stacji", "typ_zbioru", "rok", "miesiąc"]
df = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
df_test = pd.read_csv("dev-0/in.tsv", names=in_columns, sep="\t")

df = pd.concat([df, df_test])
y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])
df["rainfall"] = y["rainfall"]
x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)
df_train = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")

x_test = pd.concat([x_test, df_train])

# grouped_multiple_years = df.groupby(['id_stacji', 'rok']).agg({'rainfall': ['mean', 'min', 'max']})
# grouped_multiple_months = df.groupby(['id_stacji', 'miesiąc']).agg({'rainfall': ['mean', 'min', 'max']})
# flat = grouped_multiple_years.reset_index()
# for index, row in flat.iterrows():
#     df[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_mean"] = row["rainfall"]["mean"]
#     df[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_max"] = row["rainfall"]["min"]
#     df[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_min"] = row["rainfall"]["max"]
#     x_test[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_mean"] = row["rainfall"]["mean"]
#     x_test[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_max"] = row["rainfall"]["min"]
#     x_test[f"{row['id_stacji'].values[0]}_{row['rok'].values[0]}_min"] = row["rainfall"]["max"]
#
# # flat2 = grouped_multiple_months.reset_index()
# # for index, row in flat2.iterrows():
# #     df[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_mean"] = row["rainfall"]["mean"]
# #     df[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_max"] = row["rainfall"]["min"]
# #     df[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_min"] = row["rainfall"]["max"]
# #     x_test[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_mean"] = row["rainfall"]["mean"]
# #     x_test[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_max"] = row["rainfall"]["min"]
#     x_test[f"{row['id_stacji'].values[0]}_{row['miesiąc'].values[0]}_min"] = row["rainfall"]["max"]

x = pd.get_dummies(df, columns=["id_stacji", "rok", "miesiąc"])
x = x.drop(["nazwa_stacji", "typ_zbioru", "rainfall"], axis=1)
x = x.iloc[:-600]

model = Sequential(
    [
        Dense(2048, activation="relu", input_dim=73),
        Dense(1024, activation="relu"),
        Dense(512, activation="relu"),
        # tensorflow.keras.layers.BatchNormalization(),
        Dense(256, activation="relu"),
        # tensorflow.keras.layers.BatchNormalization(),
        Dense(128, activation="relu"),
        # tensorflow.keras.layers.BatchNormalization(),
        Dense(64, activation="relu"),
        # tensorflow.keras.layers.BatchNormalization(),
        Dense(32, activation="relu"),
        # tensorflow.keras.layers.BatchNormalization(),
        Dense(1, activation="linear"),
    ]
)

# input = tensorflow.keras.layers.Input(shape=x.shape[1:])
# hidden1 = tensorflow.keras.layers.Dense(1024, activation='relu')(input)
# hidden2 = tensorflow.keras.layers.Dense(512, activation='relu')(hidden1)
# hidden3 = tensorflow.keras.layers.Dense(256, activation='relu')(hidden2)
# hidden4 = tensorflow.keras.layers.Dense(128, activation='relu')(hidden3)
# concat = tensorflow.keras.layers.Concatenate()([input, hidden4])
# output = tensorflow.keras.layers.Dense(1, activation="linear")(concat)
# model = tensorflow.keras.models.Model(inputs=[input], outputs=[output])

model.compile(
    loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]
)

# estimator = KerasRegressor(build_fn=model, epochs=100, batch_size=10, verbose=0)
# estimator.fit(x, y)
model.fit(x, y, epochs=100)
exit()


x_test = x_test.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
x_test = pd.get_dummies(x_test, columns=["id_stacji", "rok", "miesiąc"])
x_test = x_test.iloc[:-8760]
pred = model.predict(x_test)
# pred = estimator.predict(x_test)
out = pd.DataFrame(pred)
out.to_csv("test-A/out2.tsv", sep="\t", header=False, index=False)