# ---
# jupyter:
#   jupytext:
#     formats: py:percent,ipynb
#     text_representation:
#       extension: .py
#       format_name: percent
#       format_version: '1.3'
#       jupytext_version: 1.17.3
#   kernelspec:
#     display_name: .venv
#     language: python
#     name: python3
# ---

# %%
from typing import Optional

# %%
# %% tags=["parameters"]
data_path: Optional[str] = None
# Полный путь к файлу (CSV) с исходным датасетом. Если не установлен, ищется файл в `data/<data_relpath>`.
data_relpath: str = 'cars.csv'
# Путь к файлу (CSV) с исходным датасетом относительно директории данных `data`. Игнорируется, если установлен data_path.

data_aug_pickle_path: Optional[str] = None
# Полный путь к файлу (pickle) для сохранения очищенного датасета. Если не установлен, используется `data/<data_aug_pickle_relpath>`.
data_aug_pickle_relpath: str = 'cars.aug.pickle'
# Путь к файлу (pickle) для сохранения очищенного датасета относительно директории данных `data`. Игнорируется, если установлен data_aug_pickle_path.

mlflow_tracking_server_uri: str = 'http://localhost:5000'
mlflow_registry_uri: Optional[str] = None

mlflow_experiment_name: str = 'Current price predicion for used cars'
mlflow_experiment_new: bool = False
mlflow_run_name: str = 'Baseline model'

# %%
import os
import pathlib
import pickle

# %%
import mlflow
import mlflow.models
import mlflow.sklearn
import sklearn.compose
import sklearn.ensemble
import sklearn.metrics
import sklearn.model_selection
import sklearn.pipeline
import sklearn.preprocessing

# %%
BASE_PATH = pathlib.Path('..')

# %%
mlflow.set_tracking_uri(mlflow_tracking_server_uri)
if mlflow_registry_uri is not None:
    mlflow.set_registry_uri(mlflow_registry_uri)

# %%
DATA_PATH = (
    pathlib.Path(os.path.dirname(data_path))
    if data_path is not None
    else (BASE_PATH / 'data')
)

# %%
with open(
    (
    data_aug_pickle_path
    if data_aug_pickle_path is not None
    else (DATA_PATH / data_aug_pickle_relpath)
    ),
    'rb',
) as input_file:
    df_orig = pickle.load(input_file)

# %%
df_orig.head(0x10)

# %%
len(df_orig)

# %%
df_orig.info()

# %%
feature_columns = (
    'selling_price',
    'driven_kms',
    'fuel_type',
    'selling_type',
    'transmission',
    #'owner',
    'age',
)

target_columns = (
    'present_price',
)

# %%
features_to_scale_to_standard_columns = (
    'selling_price',
    'driven_kms',
    'age',
)
assert all(
    (col in df_orig.select_dtypes(('number',)).columns)
    for col in features_to_scale_to_standard_columns
)

features_to_encode_one_hot_columns = (
    'fuel_type',
    'selling_type',
    'transmission',
    #'owner',
)
assert all(
    (col in df_orig.select_dtypes(('category', 'object')).columns)
    for col in features_to_encode_one_hot_columns
)

# %%
df_orig_features = df_orig[list(feature_columns)]
df_target = df_orig[list(target_columns)]

# %%
DF_TEST_PORTION = 0.25

# %%
df_orig_features_train, df_orig_features_test, df_target_train, df_target_test = (
    sklearn.model_selection.train_test_split(
        df_orig_features, df_target, test_size=DF_TEST_PORTION, random_state=0x7AE6,
    )
)

# %%
tuple(map(len, (df_target_train, df_target_test)))

# %%
preprocess_transformer = sklearn.compose.ColumnTransformer(
    [
        ('scale_to_standard', sklearn.preprocessing.StandardScaler(), features_to_scale_to_standard_columns),
        (
            #'encode_categoricals_one_hot',
            'encode_categoricals_wrt_target',
            #sklearn.preprocessing.OneHotEncoder(),
            sklearn.preprocessing.TargetEncoder(
                target_type='continuous', smooth='auto', cv=3, shuffle=True, random_state=0x2ED6,
            ),
            features_to_encode_one_hot_columns,
        ),
    ],
    remainder='drop',
)

# %%
regressor = sklearn.ensemble.RandomForestRegressor(
    10, criterion='squared_error', max_features='sqrt', random_state=0x016B,
)

# %%
pipeline = sklearn.pipeline.Pipeline([
    ('preprocess', preprocess_transformer),
    ('regress', regressor),
])

# %%
pipeline

# %%
_ = pipeline.fit(df_orig_features_train, df_target_train.iloc[:, 0])

# %%
target_test_predicted = pipeline.predict(df_orig_features_test)

# %%
metrics = {
    'mse': sklearn.metrics.mean_squared_error(df_target_test, target_test_predicted),
    'mae': sklearn.metrics.mean_absolute_error(df_target_test, target_test_predicted),
    'mape': sklearn.metrics.mean_absolute_percentage_error(df_target_test, target_test_predicted),
}

# %%
metrics

# %%
MODEL_PIP_REQUIREMENTS_PATH = BASE_PATH / 'requirements.txt'
MODEL_COMMENTS_FILE_PATH = BASE_PATH / 'comment.txt'

# %%
MODEL_INOUT_EXAMPLE_SIZE = 0x10

# %%
model_inout_example = (df_orig_features.head(MODEL_INOUT_EXAMPLE_SIZE), df_target.head(MODEL_INOUT_EXAMPLE_SIZE))

# %%
mlflow_model_signature = mlflow.models.infer_signature(model_input=model_inout_example[0], model_output=model_inout_example[1])

# %%
mlflow_model_signature

# %%
model_params = pipeline.get_params()

# %%
model_params

# %%
if mlflow_experiment_new:
    experiment = mlflow.get_experiment(mlflow.create_experiment(mlflow_experiment_name))
else:
    experiment = mlflow.set_experiment(experiment_name=mlflow_experiment_name)

# %%
with mlflow.start_run(experiment_id=experiment.experiment_id, run_name=mlflow_run_name):
    _ = mlflow.sklearn.log_model(
        pipeline,
        'model',
        signature=mlflow_model_signature,
        input_example=model_inout_example[0],
        pip_requirements=str(MODEL_PIP_REQUIREMENTS_PATH),
    )
    _ = mlflow.log_params(model_params)
    _ = mlflow.log_metrics({k: float(v) for k, v in metrics.items()})
    if MODEL_COMMENTS_FILE_PATH.exists():
        mlflow.log_artifact(str(MODEL_COMMENTS_FILE_PATH))