from pprint import pprint
import sklearn.metrics
import autosklearn.regression

import datetime
import importlib
import os
import sys

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import yaml

from sklearn import linear_model, svm, kernel_ridge, gaussian_process
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer

model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki

model_input.dropna(axis=1, how="all", inplace=True)
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)

categorical_feature_colnames = ["gender", "startlanguage"]
categorical_feature_colnames += [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_features = model_input[categorical_feature_colnames].copy()
mode_categorical_features = categorical_features.mode().iloc[0]
categorical_features = categorical_features.fillna(mode_categorical_features)
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
if not categorical_features.empty:
    categorical_features = pd.get_dummies(categorical_features)
numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
model_in = pd.concat([numerical_features, categorical_features], axis=1)

index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_in.set_index(index_columns, inplace=True)

X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.30)

automl = autosklearn.regression.AutoSklearnRegressor(
    time_left_for_this_task=7200,
    per_run_time_limit=120
)
automl.fit(X_train, y_train, dataset_name='straw')

print(automl.leaderboard())
pprint(automl.show_models(), indent=4)

train_predictions = automl.predict(X_train)
print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
test_predictions = automl.predict(X_test)
print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))

import sys
sys.exit()