import importlib.util
import random
from pathlib import Path

import numpy as np
import pandas as pd
from fea_share_preprocess import preprocess_script
from sklearn.metrics import log_loss

# Set random seed for reproducibility
SEED = 42
random.seed(SEED)
np.random.seed(SEED)
DIRNAME = Path(__file__).absolute().resolve().parent


# Support various method for metrics calculation
def compute_metrics_for_classification(y_true, y_pred):
    """Compute log loss for classification."""
    all_classes = np.unique(y_true)
    logloss = log_loss(y_true, y_pred, labels=all_classes)
    return logloss


def import_module_from_path(module_name, module_path):
    spec = importlib.util.spec_from_file_location(module_name, module_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


# 1) Preprocess the data
X_train, X_valid, y_train, y_valid, X_test, status_encoder, test_ids = preprocess_script()


# 2) Auto feature engineering
X_train_l, X_valid_l = [], []
X_test_l = []

for f in DIRNAME.glob("feature/feat*.py"):
    cls = import_module_from_path(f.stem, f).feature_engineering_cls()
    cls.fit(X_train)
    X_train_f = cls.transform(X_train.copy())
    X_valid_f = cls.transform(X_valid.copy())
    X_test_f = cls.transform(X_test.copy())

    if X_train_f.shape[-1] == X_valid_f.shape[-1] and X_train_f.shape[-1] == X_test_f.shape[-1]:
        X_train_l.append(X_train_f)
        X_valid_l.append(X_valid_f)
        X_test_l.append(X_test_f)

X_train = pd.concat(X_train_l, axis=1)
X_valid = pd.concat(X_valid_l, axis=1)
X_test = pd.concat(X_test_l, axis=1)

print(X_train.shape, X_valid.shape, X_test.shape)

# Handle inf and -inf values
X_train.replace([np.inf, -np.inf], np.nan, inplace=True)
X_valid.replace([np.inf, -np.inf], np.nan, inplace=True)
X_test.replace([np.inf, -np.inf], np.nan, inplace=True)

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy="mean")

X_train = pd.DataFrame(imputer.fit_transform(X_train), columns=X_train.columns)
X_valid = pd.DataFrame(imputer.transform(X_valid), columns=X_valid.columns)
X_test = pd.DataFrame(imputer.transform(X_test), columns=X_test.columns)

# Remove duplicate columns
X_train = X_train.loc[:, ~X_train.columns.duplicated()]
X_valid = X_valid.loc[:, ~X_valid.columns.duplicated()]
X_test = X_test.loc[:, ~X_test.columns.duplicated()]


# 3) Train the model
model_l = []  # list[tuple[model, predict_func,]]
for f in DIRNAME.glob("model/model*.py"):
    select_python_path = f.with_name(f.stem.replace("model", "select") + f.suffix)
    select_m = import_module_from_path(select_python_path.stem, select_python_path)
    X_train_selected = select_m.select(X_train.copy())
    X_valid_selected = select_m.select(X_valid.copy())

    m = import_module_from_path(f.stem, f)
    model_l.append((m.fit(X_train_selected, y_train, X_valid_selected, y_valid), m.predict, select_m))

# 4) Evaluate the model on the validation set
metrics_all = []
for model, predict_func, select_m in model_l:
    X_valid_selected = select_m.select(X_valid.copy())
    y_valid_pred = predict_func(model, X_valid_selected)
    logloss = compute_metrics_for_classification(y_valid, y_valid_pred)
    print(f"log_loss on valid set: {logloss}")
    metrics_all.append(logloss)

# 5) Save the validation accuracy
min_index = np.argmin(metrics_all)
pd.Series(data=[metrics_all[min_index]], index=["log_loss"]).to_csv("submission_score.csv")

# 6) Make predictions on the test set and save them
X_test_selected = model_l[min_index][2].select(X_test.copy())
y_test_pred = model_l[min_index][1](model_l[min_index][0], X_test_selected)

class_labels = ["Status_" + label for label in status_encoder.classes_]

submission_result = pd.DataFrame(y_test_pred, columns=class_labels)
submission_result.insert(0, "id", test_ids)

submission_result.to_csv("submission.csv", index=False)
