import pandas as pd

import tensorflow.keras
from sklearn.linear_model import LinearRegression
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

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

x = x.iloc[:-600]
y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])


from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(2, interaction_only=True)
df = poly.fit_transform(x)

model = Sequential(
    [
        Dense(512, activation="relu", input_dim=2702),
        tensorflow.keras.layers.BatchNormalization(),
        Dense(512 // 2, activation="relu"),
        tensorflow.keras.layers.BatchNormalization(),
        Dense(512 // 4, activation="relu"),
        tensorflow.keras.layers.BatchNormalization(),
        Dense(512 // 8, activation="relu"),
        tensorflow.keras.layers.BatchNormalization(),
        Dense(32, activation="relu"),
        tensorflow.keras.layers.BatchNormalization(),
        Dense(1),
    ]
)

model.compile(
    loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]
)
model.fit(df, y, epochs=60)

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])
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]
poly = PolynomialFeatures(2, interaction_only=True)
x_test2 = poly.fit_transform(x_test)
pred = model.predict(x_test2)

out = pd.DataFrame(pred)
out.to_csv("test-A/out.tsv", sep="\t", header=False, index=False)