# import sys
# import pandas as pd
# from sklearn.model_selection import train_test_split
# from sklearn.preprocessing import StandardScaler, OneHotEncoder
# from sklearn.compose import ColumnTransformer
# from sklearn.pipeline import Pipeline
# from tensorflow.keras.models import Sequential
# from tensorflow.keras.layers import Dense
# import tensorflow as tf
#
# data = pd.read_csv('./data/train.csv')
#
# data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
#
# features = data[['Sex', 'Age', 'BodyweightKg']]
# target = data['TotalKg']
#
# X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
#
# preprocessor = ColumnTransformer(
#     transformers=[
#         ('num', StandardScaler(), ['Age', 'BodyweightKg']),
#         ('cat', OneHotEncoder(), ['Sex'])
#     ]
# )
#
# pipeline = Pipeline(steps=[
#     ('preprocessor', preprocessor),
#     ('model', Sequential([
#         Dense(64, activation='relu', input_dim=4),
#         Dense(64, activation='relu'),
#         Dense(1)
#     ]))
# ])
#
# pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
#
# pipeline.fit(X_train, y_train, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
#
# pipeline['model'].save('powerlifting_model.h5')

import sys
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import tensorflow as tf

data = pd.read_csv('./data/train.csv')

features_idx = [1, 4, 7]  # Sex, Age, BodyweightKg
target_idx = 25  # TotalKg

data = data.iloc[:, [1, 4, 7, 25]].dropna()

features = data.iloc[:, features_idx]
target = data.iloc[:, target_idx]

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), [1, 2]),  # Age, BodyweightKg
        ('cat', OneHotEncoder(), [0])  # Sex
    ]
)

pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('model', Sequential([
        Dense(64, activation='relu', input_dim=4),
        Dense(64, activation='relu'),
        Dense(1)
    ]))
])

pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])

pipeline.fit(X_train, y_train, model__epochs=int(sys.argv[1]), model__validation_split=0.1)

pipeline['model'].save('powerlifting_model.h5')