From 41497aa039368d05f2475de8650220670a8281ac Mon Sep 17 00:00:00 2001
From: syropiatovvv <syrslava@yandex.ru>
Date: Wed, 15 Oct 2025 11:23:17 +0300
Subject: [PATCH] =?UTF-8?q?=D0=B1=D0=BB=D0=BE=D0=BA=D0=BD=D0=BE=D1=82=20re?=
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---
 research/research.py | 173 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 173 insertions(+)
 create mode 100644 research/research.py

diff --git a/research/research.py b/research/research.py
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index 0000000..6213f39
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+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     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
+
+# %%
+import os
+import pathlib
+import pickle
+
+# %%
+import sklearn.compose
+import sklearn.ensemble
+import sklearn.metrics
+import sklearn.model_selection
+import sklearn.pipeline
+import sklearn.preprocessing
+
+# %%
+# %% 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.
+
+# %%
+BASE_PATH = pathlib.Path('..')
+
+# %%
+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