{ "cells": [ { "cell_type": "markdown", "id": "9e9ed0be", "metadata": {}, "source": [ "# Регрессия по датасету NASA Kepler Object of Interest\n", "\n", "В работе решается задача регрессии четырьмя способами:\n", "1. линейная регрессия по всем признакам;\n", "2. линейная регрессия после классического отбора признаков;\n", "3. нейросеть по всем признакам;\n", "4. ансамбль нейросетей по трём методам отбора признаков.\n", "\n", "Финальный текстовый вывод в ноутбуке не приводится. В конце печатаются все ключевые метрики для подготовки итогового вывода по фактическим результатам.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "2864671b", "metadata": {}, "outputs": [], "source": [ "import os\n", "import re\n", "import json\n", "import random\n", "import warnings\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.feature_selection import SelectKBest, f_regression, mutual_info_regression, RFE, SelectFromModel\n", "from sklearn.linear_model import LinearRegression, LassoCV\n", "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "warnings.filterwarnings('ignore')\n", "SEED = 42\n", "np.random.seed(SEED)\n", "random.seed(SEED)\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "0ce63254", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow доступен: True\n" ] } ], "source": [ "def set_global_seed(seed: int = 42):\n", " import os\n", " os.environ['PYTHONHASHSEED'] = str(seed)\n", " np.random.seed(seed)\n", " random.seed(seed)\n", " try:\n", " import tensorflow as tf\n", " tf.keras.utils.set_random_seed(seed)\n", " try:\n", " tf.config.experimental.enable_op_determinism()\n", " except Exception:\n", " pass\n", " return tf\n", " except Exception:\n", " return None\n", "\n", "TF = set_global_seed(SEED)\n", "USE_TF = TF is not None\n", "print('TensorFlow доступен:', USE_TF)\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "bba9e04a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Используемый файл: NASA Exoplanet.csv\n", "Форма датасета: (9564, 49)\n" ] }, { "data": { "text/html": [ "
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kepidkepoi_namekepler_namekoi_dispositionkoi_pdispositionkoi_scorekoi_fpflag_ntkoi_fpflag_sskoi_fpflag_cokoi_fpflag_ec...koi_steff_err2koi_sloggkoi_slogg_err1koi_slogg_err2koi_sradkoi_srad_err1koi_srad_err2radeckoi_kepmag
010797460K00752.01Kepler-227 bCONFIRMEDCANDIDATE1.0000000...-81.04.4670.064-0.0960.9270.105-0.061291.9342348.14165115.347
110797460K00752.02Kepler-227 cCONFIRMEDCANDIDATE0.9690000...-81.04.4670.064-0.0960.9270.105-0.061291.9342348.14165115.347
210811496K00753.01NaNCANDIDATECANDIDATE0.0000000...-176.04.5440.044-0.1760.8680.233-0.078297.0048248.13412915.436
310848459K00754.01NaNFALSE POSITIVEFALSE POSITIVE0.0000100...-174.04.5640.053-0.1680.7910.201-0.067285.5346148.28521015.597
410854555K00755.01Kepler-664 bCONFIRMEDCANDIDATE1.0000000...-211.04.4380.070-0.2101.0460.334-0.133288.7548848.22620015.509
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5 rows × 49 columns

\n", "
" ], "text/plain": [ " kepid kepoi_name kepler_name koi_disposition koi_pdisposition \\\n", "0 10797460 K00752.01 Kepler-227 b CONFIRMED CANDIDATE \n", "1 10797460 K00752.02 Kepler-227 c CONFIRMED CANDIDATE \n", "2 10811496 K00753.01 NaN CANDIDATE CANDIDATE \n", "3 10848459 K00754.01 NaN FALSE POSITIVE FALSE POSITIVE \n", "4 10854555 K00755.01 Kepler-664 b CONFIRMED CANDIDATE \n", "\n", " koi_score koi_fpflag_nt koi_fpflag_ss koi_fpflag_co koi_fpflag_ec ... \\\n", "0 1.000 0 0 0 0 ... \n", "1 0.969 0 0 0 0 ... \n", "2 0.000 0 0 0 0 ... \n", "3 0.000 0 1 0 0 ... \n", "4 1.000 0 0 0 0 ... \n", "\n", " koi_steff_err2 koi_slogg koi_slogg_err1 koi_slogg_err2 koi_srad \\\n", "0 -81.0 4.467 0.064 -0.096 0.927 \n", "1 -81.0 4.467 0.064 -0.096 0.927 \n", "2 -176.0 4.544 0.044 -0.176 0.868 \n", "3 -174.0 4.564 0.053 -0.168 0.791 \n", "4 -211.0 4.438 0.070 -0.210 1.046 \n", "\n", " koi_srad_err1 koi_srad_err2 ra dec koi_kepmag \n", "0 0.105 -0.061 291.93423 48.141651 15.347 \n", "1 0.105 -0.061 291.93423 48.141651 15.347 \n", "2 0.233 -0.078 297.00482 48.134129 15.436 \n", "3 0.201 -0.067 285.53461 48.285210 15.597 \n", "4 0.334 -0.133 288.75488 48.226200 15.509 \n", "\n", "[5 rows x 49 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Типы данных:\n" ] }, { "data": { "text/html": [ "
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dtype
kepidint64
kepoi_nameobject
kepler_nameobject
koi_dispositionobject
koi_pdispositionobject
koi_scorefloat64
koi_fpflag_ntint64
koi_fpflag_ssint64
koi_fpflag_coint64
koi_fpflag_ecint64
koi_periodfloat64
koi_period_err1float64
koi_period_err2float64
koi_time0bkfloat64
koi_time0bk_err1float64
koi_time0bk_err2float64
koi_impactfloat64
koi_impact_err1float64
koi_impact_err2float64
koi_durationfloat64
koi_duration_err1float64
koi_duration_err2float64
koi_depthfloat64
koi_depth_err1float64
koi_depth_err2float64
koi_pradfloat64
koi_prad_err1float64
koi_prad_err2float64
koi_teqfloat64
koi_teq_err1float64
koi_teq_err2float64
koi_insolfloat64
koi_insol_err1float64
koi_insol_err2float64
koi_model_snrfloat64
koi_tce_plnt_numfloat64
koi_tce_delivnameobject
koi_stefffloat64
koi_steff_err1float64
koi_steff_err2float64
koi_sloggfloat64
koi_slogg_err1float64
koi_slogg_err2float64
koi_sradfloat64
koi_srad_err1float64
koi_srad_err2float64
rafloat64
decfloat64
koi_kepmagfloat64
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" ], "text/plain": [ " dtype\n", "kepid int64\n", "kepoi_name object\n", "kepler_name object\n", "koi_disposition object\n", "koi_pdisposition object\n", "koi_score float64\n", "koi_fpflag_nt int64\n", "koi_fpflag_ss int64\n", "koi_fpflag_co int64\n", "koi_fpflag_ec int64\n", "koi_period float64\n", "koi_period_err1 float64\n", "koi_period_err2 float64\n", "koi_time0bk float64\n", "koi_time0bk_err1 float64\n", "koi_time0bk_err2 float64\n", "koi_impact float64\n", "koi_impact_err1 float64\n", "koi_impact_err2 float64\n", "koi_duration float64\n", "koi_duration_err1 float64\n", "koi_duration_err2 float64\n", "koi_depth float64\n", "koi_depth_err1 float64\n", "koi_depth_err2 float64\n", "koi_prad float64\n", "koi_prad_err1 float64\n", "koi_prad_err2 float64\n", "koi_teq float64\n", "koi_teq_err1 float64\n", "koi_teq_err2 float64\n", "koi_insol float64\n", "koi_insol_err1 float64\n", "koi_insol_err2 float64\n", "koi_model_snr float64\n", "koi_tce_plnt_num float64\n", "koi_tce_delivname object\n", "koi_steff float64\n", "koi_steff_err1 float64\n", "koi_steff_err2 float64\n", "koi_slogg float64\n", "koi_slogg_err1 float64\n", "koi_slogg_err2 float64\n", "koi_srad float64\n", "koi_srad_err1 float64\n", "koi_srad_err2 float64\n", "ra float64\n", "dec float64\n", "koi_kepmag float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Поиск CSV-файла\n", "candidate_paths = [\n", " Path('NASA Exoplanet.csv'),\n", " Path('/mnt/data/NASA Exoplanet.csv'),\n", " Path('/home/konnilol/Documents/uni/mmo/pr13/NASA Exoplanet.csv')\n", "]\n", "\n", "csv_path = None\n", "for p in candidate_paths:\n", " if p.exists():\n", " csv_path = p\n", " break\n", "\n", "if csv_path is None:\n", " # поиск похожего файла в текущей директории и /mnt/data\n", " search_roots = [Path('.'), Path('/mnt/data')]\n", " for root in search_roots:\n", " if root.exists():\n", " for p in root.glob('*.csv'):\n", " name = p.name.lower()\n", " if ('nasa' in name or 'exoplanet' in name or 'koi' in name) and p.exists():\n", " csv_path = p\n", " break\n", " if csv_path is not None:\n", " break\n", "\n", "if csv_path is None:\n", " raise FileNotFoundError('CSV-файл не найден. Поместите файл NASA Exoplanet.csv рядом с ноутбуком или укажите путь вручную.')\n", "\n", "print('Используемый файл:', csv_path)\n", "df = pd.read_csv(csv_path)\n", "print('Форма датасета:', df.shape)\n", "display(df.head())\n", "print('Типы данных:')\n", "display(df.dtypes.rename('dtype').to_frame())\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a27f51cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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original_namenormalized_name
0kepidkepid
1kepoi_namekepoi_name
2kepler_namekepler_name
3koi_dispositionkoi_disposition
4koi_pdispositionkoi_pdisposition
5koi_scorekoi_score
6koi_fpflag_ntkoi_fpflag_nt
7koi_fpflag_sskoi_fpflag_ss
8koi_fpflag_cokoi_fpflag_co
9koi_fpflag_eckoi_fpflag_ec
10koi_periodkoi_period
11koi_period_err1koi_period_err1
12koi_period_err2koi_period_err2
13koi_time0bkkoi_time0bk
14koi_time0bk_err1koi_time0bk_err1
15koi_time0bk_err2koi_time0bk_err2
16koi_impactkoi_impact
17koi_impact_err1koi_impact_err1
18koi_impact_err2koi_impact_err2
19koi_durationkoi_duration
20koi_duration_err1koi_duration_err1
21koi_duration_err2koi_duration_err2
22koi_depthkoi_depth
23koi_depth_err1koi_depth_err1
24koi_depth_err2koi_depth_err2
25koi_pradkoi_prad
26koi_prad_err1koi_prad_err1
27koi_prad_err2koi_prad_err2
28koi_teqkoi_teq
29koi_teq_err1koi_teq_err1
30koi_teq_err2koi_teq_err2
31koi_insolkoi_insol
32koi_insol_err1koi_insol_err1
33koi_insol_err2koi_insol_err2
34koi_model_snrkoi_model_snr
35koi_tce_plnt_numkoi_tce_plnt_num
36koi_tce_delivnamekoi_tce_delivname
37koi_steffkoi_steff
38koi_steff_err1koi_steff_err1
39koi_steff_err2koi_steff_err2
40koi_sloggkoi_slogg
41koi_slogg_err1koi_slogg_err1
42koi_slogg_err2koi_slogg_err2
43koi_sradkoi_srad
44koi_srad_err1koi_srad_err1
45koi_srad_err2koi_srad_err2
46rara
47decdec
48koi_kepmagkoi_kepmag
\n", "
" ], "text/plain": [ " original_name normalized_name\n", "0 kepid kepid\n", "1 kepoi_name kepoi_name\n", "2 kepler_name kepler_name\n", "3 koi_disposition koi_disposition\n", "4 koi_pdisposition koi_pdisposition\n", "5 koi_score koi_score\n", "6 koi_fpflag_nt koi_fpflag_nt\n", "7 koi_fpflag_ss koi_fpflag_ss\n", "8 koi_fpflag_co koi_fpflag_co\n", "9 koi_fpflag_ec koi_fpflag_ec\n", "10 koi_period koi_period\n", "11 koi_period_err1 koi_period_err1\n", "12 koi_period_err2 koi_period_err2\n", "13 koi_time0bk koi_time0bk\n", "14 koi_time0bk_err1 koi_time0bk_err1\n", "15 koi_time0bk_err2 koi_time0bk_err2\n", "16 koi_impact koi_impact\n", "17 koi_impact_err1 koi_impact_err1\n", "18 koi_impact_err2 koi_impact_err2\n", "19 koi_duration koi_duration\n", "20 koi_duration_err1 koi_duration_err1\n", "21 koi_duration_err2 koi_duration_err2\n", "22 koi_depth koi_depth\n", "23 koi_depth_err1 koi_depth_err1\n", "24 koi_depth_err2 koi_depth_err2\n", "25 koi_prad koi_prad\n", "26 koi_prad_err1 koi_prad_err1\n", "27 koi_prad_err2 koi_prad_err2\n", "28 koi_teq koi_teq\n", "29 koi_teq_err1 koi_teq_err1\n", "30 koi_teq_err2 koi_teq_err2\n", "31 koi_insol koi_insol\n", "32 koi_insol_err1 koi_insol_err1\n", "33 koi_insol_err2 koi_insol_err2\n", "34 koi_model_snr koi_model_snr\n", "35 koi_tce_plnt_num koi_tce_plnt_num\n", "36 koi_tce_delivname koi_tce_delivname\n", "37 koi_steff koi_steff\n", "38 koi_steff_err1 koi_steff_err1\n", "39 koi_steff_err2 koi_steff_err2\n", "40 koi_slogg koi_slogg\n", "41 koi_slogg_err1 koi_slogg_err1\n", "42 koi_slogg_err2 koi_slogg_err2\n", "43 koi_srad koi_srad\n", "44 koi_srad_err1 koi_srad_err1\n", "45 koi_srad_err2 koi_srad_err2\n", "46 ra ra\n", "47 dec dec\n", "48 koi_kepmag koi_kepmag" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def normalize_name(name: str) -> str:\n", " name = str(name).strip().lower()\n", " name = name.replace('°', 'deg')\n", " name = re.sub(r'[^a-z0-9]+', '_', name)\n", " name = re.sub(r'_+', '_', name).strip('_')\n", " return name\n", "\n", "original_columns = df.columns.tolist()\n", "normalized_columns = [normalize_name(c) for c in original_columns]\n", "col_map = dict(zip(original_columns, normalized_columns))\n", "\n", "df = df.rename(columns=col_map)\n", "display(pd.DataFrame({'original_name': original_columns, 'normalized_name': normalized_columns}))\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "12ce029a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Выбранная зависимая переменная: koi_period\n" ] } ], "source": [ "# Автоматический выбор зависимой переменной для регрессии\n", "# Приоритет отдаётся содержательной непрерывной переменной радиуса планеты.\n", "priority_targets = [\n", " 'koi_prad',\n", " 'koi_period',\n", " 'koi_duration',\n", " 'koi_depth',\n", " 'koi_teq',\n", " 'koi_insol',\n", " 'koi_kepmag'\n", "]\n", "\n", "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n", "missing_share = df.isna().mean()\n", "\n", "non_feature_patterns = [\n", " 'err1', 'err2', 'score', 'flag', 'kepid', 'ra', 'dec', 'tce_plnt_num'\n", "]\n", "\n", "candidate_numeric = [\n", " c for c in numeric_cols\n", " if missing_share[c] < 0.35 and not any(p in c for p in non_feature_patterns)\n", "]\n", "\n", "selected_target = None\n", "for c in priority_targets:\n", " if c in candidate_numeric:\n", " selected_target = c\n", " break\n", "\n", "if selected_target is None:\n", " # fallback: содержательная числовая переменная с умеренной вариативностью\n", " candidate_stats = []\n", " for c in candidate_numeric:\n", " nunique = df[c].nunique(dropna=True)\n", " if nunique >= 20:\n", " candidate_stats.append((c, missing_share[c], nunique))\n", " if not candidate_stats:\n", " raise ValueError('Не удалось автоматически определить зависимую переменную.')\n", " candidate_stats = sorted(candidate_stats, key=lambda x: (x[1], -x[2]))\n", " selected_target = candidate_stats[0][0]\n", "\n", "print('Выбранная зависимая переменная:', selected_target)\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "825f0181", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Исключённые столбцы: ['kepid', 'kepler_name', 'kepoi_name', 'koi_period', 'koi_period_err1', 'koi_period_err2', 'koi_teq_err1', 'koi_teq_err2']\n", "Форма X: (9564, 41)\n", "Доля пропусков в y: 0.0\n" ] } ], "source": [ "# Удаление явных идентификаторов и утечек\n", "id_like = [c for c in df.columns if c in ['kepid', 'kepoi_name', 'kepler_name'] or c.endswith('_name')]\n", "\n", "# Исключаем переменные, напрямую характеризующие целевую переменную ошибками измерения или служебными версиями\n", "leak_like = [c for c in df.columns if c.startswith(selected_target + '_err')]\n", "\n", "# Отдельно исключаем полностью/почти полностью пропущенные признаки\n", "high_na_cols = df.columns[df.isna().mean() > 0.70].tolist()\n", "\n", "exclude_cols = sorted(set(id_like + leak_like + [selected_target] + high_na_cols))\n", "X = df.drop(columns=exclude_cols, errors='ignore').copy()\n", "y = df[selected_target].copy()\n", "\n", "# Оставляем только строки с известным target\n", "mask = y.notna()\n", "X = X.loc[mask].reset_index(drop=True)\n", "y = y.loc[mask].reset_index(drop=True)\n", "\n", "print('Исключённые столбцы:', exclude_cols)\n", "print('Форма X:', X.shape)\n", "print('Доля пропусков в y:', df[selected_target].isna().mean())\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "bc01ce3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Удалены константные признаки: []\n", "Итоговая форма X: (9564, 41)\n", "Числовые признаки: 38\n", "Категориальные признаки: 3\n" ] }, { "data": { "text/html": [ "
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numeric_featurescategorical_features
0koi_scorekoi_disposition
1koi_fpflag_ntkoi_pdisposition
2koi_fpflag_sskoi_tce_delivname
3koi_fpflag_coNaN
4koi_fpflag_ecNaN
5koi_time0bkNaN
6koi_time0bk_err1NaN
7koi_time0bk_err2NaN
8koi_impactNaN
9koi_impact_err1NaN
10koi_impact_err2NaN
11koi_durationNaN
12koi_duration_err1NaN
13koi_duration_err2NaN
14koi_depthNaN
15koi_depth_err1NaN
16koi_depth_err2NaN
17koi_pradNaN
18koi_prad_err1NaN
19koi_prad_err2NaN
20koi_teqNaN
21koi_insolNaN
22koi_insol_err1NaN
23koi_insol_err2NaN
24koi_model_snrNaN
25koi_tce_plnt_numNaN
26koi_steffNaN
27koi_steff_err1NaN
28koi_steff_err2NaN
29koi_sloggNaN
30koi_slogg_err1NaN
31koi_slogg_err2NaN
32koi_sradNaN
33koi_srad_err1NaN
34koi_srad_err2NaN
35raNaN
36decNaN
37koi_kepmagNaN
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" ], "text/plain": [ " numeric_features categorical_features\n", "0 koi_score koi_disposition\n", "1 koi_fpflag_nt koi_pdisposition\n", "2 koi_fpflag_ss koi_tce_delivname\n", "3 koi_fpflag_co NaN\n", "4 koi_fpflag_ec NaN\n", "5 koi_time0bk NaN\n", "6 koi_time0bk_err1 NaN\n", "7 koi_time0bk_err2 NaN\n", "8 koi_impact NaN\n", "9 koi_impact_err1 NaN\n", "10 koi_impact_err2 NaN\n", "11 koi_duration NaN\n", "12 koi_duration_err1 NaN\n", "13 koi_duration_err2 NaN\n", "14 koi_depth NaN\n", "15 koi_depth_err1 NaN\n", "16 koi_depth_err2 NaN\n", "17 koi_prad NaN\n", "18 koi_prad_err1 NaN\n", "19 koi_prad_err2 NaN\n", "20 koi_teq NaN\n", "21 koi_insol NaN\n", "22 koi_insol_err1 NaN\n", "23 koi_insol_err2 NaN\n", "24 koi_model_snr NaN\n", "25 koi_tce_plnt_num NaN\n", "26 koi_steff NaN\n", "27 koi_steff_err1 NaN\n", "28 koi_steff_err2 NaN\n", "29 koi_slogg NaN\n", "30 koi_slogg_err1 NaN\n", "31 koi_slogg_err2 NaN\n", "32 koi_srad NaN\n", "33 koi_srad_err1 NaN\n", "34 koi_srad_err2 NaN\n", "35 ra NaN\n", "36 dec NaN\n", "37 koi_kepmag NaN" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Огрублённая очистка: убираем константные и почти константные признаки\n", "low_var_cols = []\n", "for c in X.columns:\n", " nunique = X[c].nunique(dropna=True)\n", " if nunique <= 1:\n", " low_var_cols.append(c)\n", "\n", "X = X.drop(columns=low_var_cols, errors='ignore')\n", "print('Удалены константные признаки:', low_var_cols)\n", "print('Итоговая форма X:', X.shape)\n", "\n", "numeric_features = X.select_dtypes(include=[np.number]).columns.tolist()\n", "categorical_features = [c for c in X.columns if c not in numeric_features]\n", "\n", "print('Числовые признаки:', len(numeric_features))\n", "print('Категориальные признаки:', len(categorical_features))\n", "display(pd.DataFrame({'numeric_features': pd.Series(numeric_features), 'categorical_features': pd.Series(categorical_features)}))\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "5717a571", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train: (7651, 41) Test: (1913, 41) Val для НС: (1531, 41)\n" ] } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=SEED\n", ")\n", "\n", "# небольшой validation для нейросетей\n", "X_train_nn, X_val_nn, y_train_nn, y_val_nn = train_test_split(\n", " X_train, y_train, test_size=0.2, random_state=SEED\n", ")\n", "\n", "print('Train:', X_train.shape, 'Test:', X_test.shape, 'Val для НС:', X_val_nn.shape)\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "ee5c29f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размерность после кодирования: 46\n" ] }, { "data": { "text/html": [ "
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num__koi_scorenum__koi_fpflag_ntnum__koi_fpflag_ssnum__koi_fpflag_conum__koi_fpflag_ecnum__koi_time0bknum__koi_time0bk_err1num__koi_time0bk_err2num__koi_impactnum__koi_impact_err1...num__decnum__koi_kepmagcat__koi_disposition_CANDIDATEcat__koi_disposition_CONFIRMEDcat__koi_disposition_FALSE POSITIVEcat__koi_pdisposition_CANDIDATEcat__koi_pdisposition_FALSE POSITIVEcat__koi_tce_delivname_q1_q16_tcecat__koi_tce_delivname_q1_q17_dr24_tcecat__koi_tce_delivname_q1_q17_dr25_tce
8117-1.0187820.145800-0.5556602.025852.752699-0.507685-0.1362870.136287-0.127040-0.177027...-0.0378890.2154070.00.01.00.01.00.00.01.0
52451.237579-0.041934-0.555660-0.49362-0.363280-0.379101-0.3241700.324170-0.177811-0.160815...1.634841-0.3514690.01.00.01.00.00.00.01.0
58651.244369-0.041934-0.5556602.02585-0.363280-0.501236-0.2826150.282615-0.039601-0.196209...-1.055615-2.7592460.00.01.00.01.00.00.01.0
4050-1.018782-0.0419341.7996612.02585-0.3632801.415000-0.2277970.2277970.045300-0.198492...-0.5912220.8213290.00.01.00.01.00.00.01.0
51301.235316-0.041934-0.555660-0.49362-0.363280-0.4851490.140895-0.140895-0.199248-0.151567...0.7764891.2168401.00.00.01.00.00.00.01.0
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5 rows × 46 columns

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" ], "text/plain": [ " num__koi_score num__koi_fpflag_nt num__koi_fpflag_ss \\\n", "8117 -1.018782 0.145800 -0.555660 \n", "5245 1.237579 -0.041934 -0.555660 \n", "5865 1.244369 -0.041934 -0.555660 \n", "4050 -1.018782 -0.041934 1.799661 \n", "5130 1.235316 -0.041934 -0.555660 \n", "\n", " num__koi_fpflag_co num__koi_fpflag_ec num__koi_time0bk \\\n", "8117 2.02585 2.752699 -0.507685 \n", "5245 -0.49362 -0.363280 -0.379101 \n", "5865 2.02585 -0.363280 -0.501236 \n", "4050 2.02585 -0.363280 1.415000 \n", "5130 -0.49362 -0.363280 -0.485149 \n", "\n", " num__koi_time0bk_err1 num__koi_time0bk_err2 num__koi_impact \\\n", "8117 -0.136287 0.136287 -0.127040 \n", "5245 -0.324170 0.324170 -0.177811 \n", "5865 -0.282615 0.282615 -0.039601 \n", "4050 -0.227797 0.227797 0.045300 \n", "5130 0.140895 -0.140895 -0.199248 \n", "\n", " num__koi_impact_err1 ... num__dec num__koi_kepmag \\\n", "8117 -0.177027 ... -0.037889 0.215407 \n", "5245 -0.160815 ... 1.634841 -0.351469 \n", "5865 -0.196209 ... -1.055615 -2.759246 \n", "4050 -0.198492 ... -0.591222 0.821329 \n", "5130 -0.151567 ... 0.776489 1.216840 \n", "\n", " cat__koi_disposition_CANDIDATE cat__koi_disposition_CONFIRMED \\\n", "8117 0.0 0.0 \n", "5245 0.0 1.0 \n", "5865 0.0 0.0 \n", "4050 0.0 0.0 \n", "5130 1.0 0.0 \n", "\n", " cat__koi_disposition_FALSE POSITIVE cat__koi_pdisposition_CANDIDATE \\\n", "8117 1.0 0.0 \n", "5245 0.0 1.0 \n", "5865 1.0 0.0 \n", "4050 1.0 0.0 \n", "5130 0.0 1.0 \n", "\n", " cat__koi_pdisposition_FALSE POSITIVE cat__koi_tce_delivname_q1_q16_tce \\\n", "8117 1.0 0.0 \n", "5245 0.0 0.0 \n", "5865 1.0 0.0 \n", "4050 1.0 0.0 \n", "5130 0.0 0.0 \n", "\n", " cat__koi_tce_delivname_q1_q17_dr24_tce \\\n", "8117 0.0 \n", "5245 0.0 \n", "5865 0.0 \n", "4050 0.0 \n", "5130 0.0 \n", "\n", " cat__koi_tce_delivname_q1_q17_dr25_tce \n", "8117 1.0 \n", "5245 1.0 \n", "5865 1.0 \n", "4050 1.0 \n", "5130 1.0 \n", "\n", "[5 rows x 46 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def build_preprocessor(num_cols, cat_cols):\n", " num_pipe = Pipeline([\n", " ('imputer', SimpleImputer(strategy='median')),\n", " ('scaler', StandardScaler())\n", " ])\n", " cat_pipe = Pipeline([\n", " ('imputer', SimpleImputer(strategy='most_frequent')),\n", " ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", " ])\n", " return ColumnTransformer([\n", " ('num', num_pipe, num_cols),\n", " ('cat', cat_pipe, cat_cols)\n", " ], remainder='drop')\n", "\n", "base_preprocessor = build_preprocessor(numeric_features, categorical_features)\n", "X_train_proc = base_preprocessor.fit_transform(X_train)\n", "X_test_proc = base_preprocessor.transform(X_test)\n", "X_train_proc_df = pd.DataFrame(X_train_proc, columns=base_preprocessor.get_feature_names_out(), index=X_train.index)\n", "X_test_proc_df = pd.DataFrame(X_test_proc, columns=base_preprocessor.get_feature_names_out(), index=X_test.index)\n", "\n", "X_train_nn_proc = base_preprocessor.transform(X_train_nn)\n", "X_val_nn_proc = base_preprocessor.transform(X_val_nn)\n", "\n", "print('Размерность после кодирования:', X_train_proc_df.shape[1])\n", "display(X_train_proc_df.head())\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "0ad93d0e", "metadata": {}, "outputs": [], "source": [ "def rmse(y_true, y_pred):\n", " return float(np.sqrt(mean_squared_error(y_true, y_pred)))\n", "\n", "def regression_metrics(y_true, y_pred, prefix=''):\n", " return {\n", " f'{prefix}r2': float(r2_score(y_true, y_pred)),\n", " f'{prefix}rmse': rmse(y_true, y_pred),\n", " f'{prefix}mae': float(mean_absolute_error(y_true, y_pred))\n", " }\n", "\n", "results = []\n", "selected_feature_sets = {}\n" ] }, { "cell_type": "markdown", "id": "25d13735", "metadata": {}, "source": [ "## 1. Линейная регрессия по всем признакам\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "9ce9ca75", "metadata": {}, "outputs": [], "source": [ "lin_all = LinearRegression()\n", "lin_all.fit(X_train_proc_df, y_train)\n", "pred_lin_all = lin_all.predict(X_test_proc_df)\n", "\n", "metrics_lin_all = regression_metrics(y_test, pred_lin_all)\n", "results.append({\n", " 'model': 'Линейная регрессия по всем признакам',\n", " **metrics_lin_all,\n", " 'n_original_features': X.shape[1],\n", " 'n_processed_features': X_train_proc_df.shape[1]\n", "})\n" ] }, { "cell_type": "markdown", "id": "78bcb86b", "metadata": {}, "source": [ "## 2. Классический отбор признаков + линейная регрессия\n", "\n", "В качестве классического метода отбора используется **LassoCV**. Отбор выполняется по уже преобразованным признакам.\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "35dfeb63", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Число отобранных преобразованных признаков: 46\n" ] }, { "data": { "text/html": [ "
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selected_processed_features_lasso
0num__koi_score
1num__koi_fpflag_nt
2num__koi_fpflag_ss
3num__koi_fpflag_co
4num__koi_fpflag_ec
5num__koi_time0bk
6num__koi_time0bk_err1
7num__koi_time0bk_err2
8num__koi_impact
9num__koi_impact_err1
10num__koi_impact_err2
11num__koi_duration
12num__koi_duration_err1
13num__koi_duration_err2
14num__koi_depth
15num__koi_depth_err1
16num__koi_depth_err2
17num__koi_prad
18num__koi_prad_err1
19num__koi_prad_err2
20num__koi_teq
21num__koi_insol
22num__koi_insol_err1
23num__koi_insol_err2
24num__koi_model_snr
25num__koi_tce_plnt_num
26num__koi_steff
27num__koi_steff_err1
28num__koi_steff_err2
29num__koi_slogg
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" ], "text/plain": [ " selected_processed_features_lasso\n", "0 num__koi_score\n", "1 num__koi_fpflag_nt\n", "2 num__koi_fpflag_ss\n", "3 num__koi_fpflag_co\n", "4 num__koi_fpflag_ec\n", "5 num__koi_time0bk\n", "6 num__koi_time0bk_err1\n", "7 num__koi_time0bk_err2\n", "8 num__koi_impact\n", "9 num__koi_impact_err1\n", "10 num__koi_impact_err2\n", "11 num__koi_duration\n", "12 num__koi_duration_err1\n", "13 num__koi_duration_err2\n", "14 num__koi_depth\n", "15 num__koi_depth_err1\n", "16 num__koi_depth_err2\n", "17 num__koi_prad\n", "18 num__koi_prad_err1\n", "19 num__koi_prad_err2\n", "20 num__koi_teq\n", "21 num__koi_insol\n", "22 num__koi_insol_err1\n", "23 num__koi_insol_err2\n", "24 num__koi_model_snr\n", "25 num__koi_tce_plnt_num\n", "26 num__koi_steff\n", "27 num__koi_steff_err1\n", "28 num__koi_steff_err2\n", "29 num__koi_slogg" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lasso_selector = LassoCV(cv=5, random_state=SEED, max_iter=5000)\n", "lasso_selector.fit(X_train_proc_df, y_train)\n", "selector = SelectFromModel(lasso_selector, prefit=True, threshold='median')\n", "\n", "mask_sel = selector.get_support()\n", "selected_processed_lasso = X_train_proc_df.columns[mask_sel].tolist()\n", "if len(selected_processed_lasso) < 5:\n", " # fallback: top-k по абсолютному коэффициенту\n", " coef_abs = pd.Series(np.abs(lasso_selector.coef_), index=X_train_proc_df.columns).sort_values(ascending=False)\n", " selected_processed_lasso = coef_abs.head(min(15, len(coef_abs))).index.tolist()\n", "\n", "selected_feature_sets['lasso'] = selected_processed_lasso\n", "\n", "lin_sel = LinearRegression()\n", "lin_sel.fit(X_train_proc_df[selected_processed_lasso], y_train)\n", "pred_lin_sel = lin_sel.predict(X_test_proc_df[selected_processed_lasso])\n", "\n", "metrics_lin_sel = regression_metrics(y_test, pred_lin_sel)\n", "results.append({\n", " 'model': 'Lasso-отбор + линейная регрессия',\n", " **metrics_lin_sel,\n", " 'n_original_features': X.shape[1],\n", " 'n_processed_features': len(selected_processed_lasso)\n", "})\n", "\n", "print('Число отобранных преобразованных признаков:', len(selected_processed_lasso))\n", "display(pd.DataFrame({'selected_processed_features_lasso': pd.Series(selected_processed_lasso)}).head(30))\n" ] }, { "cell_type": "markdown", "id": "dcaeaa87", "metadata": {}, "source": [ "## 3. Нейросеть по всем признакам\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "26332c33", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1773499680.203072 138433 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7154 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:06:00.0, compute capability: 8.6\n", "2026-03-14 17:48:00.673146: E tensorflow/core/framework/node_def_util.cc:680] NodeDef mentions attribute use_unbounded_threadpool which is not in the op definition: Op handle:variant; attr=f:func; attr=Targuments:list(type),min=0; attr=output_types:list(type),min=1; attr=output_shapes:list(shape),min=1; attr=use_inter_op_parallelism:bool,default=true; attr=preserve_cardinality:bool,default=false; attr=force_synchronous:bool,default=false; attr=metadata:string,default=\"\"> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node ParallelMapDatasetV2/_15}}\n", "2026-03-14 17:48:14.427126: E tensorflow/core/framework/node_def_util.cc:680] NodeDef mentions attribute use_unbounded_threadpool which is not in the op definition: Op handle:variant; attr=f:func; attr=Targuments:list(type),min=0; attr=output_types:list(type),min=1; attr=output_shapes:list(shape),min=1; attr=use_inter_op_parallelism:bool,default=true; attr=preserve_cardinality:bool,default=false; attr=force_synchronous:bool,default=false; attr=metadata:string,default=\"\"> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node ParallelMapDatasetV2/_14}}\n" ] }, { "data": { "text/html": [ "
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lossmaermseval_lossval_maeval_rmseepoch
751948.21289116.76424044.13856510933792.0103.2030873306.62841876
761898.58984416.91413743.57281110932898.0103.6155623306.49316477
771894.84167516.58187343.52977810934878.0102.8891833306.79272578
781789.61877416.16287642.30388610931300.0103.0772553306.25146579
791848.36694316.26519042.99263810930978.0102.8885803306.20263780
\n", "
" ], "text/plain": [ " loss mae rmse val_loss val_mae val_rmse \\\n", "75 1948.212891 16.764240 44.138565 10933792.0 103.203087 3306.628418 \n", "76 1898.589844 16.914137 43.572811 10932898.0 103.615562 3306.493164 \n", "77 1894.841675 16.581873 43.529778 10934878.0 102.889183 3306.792725 \n", "78 1789.618774 16.162876 42.303886 10931300.0 103.077255 3306.251465 \n", "79 1848.366943 16.265190 42.992638 10930978.0 102.888580 3306.202637 \n", "\n", " epoch \n", "75 76 \n", "76 77 \n", "77 78 \n", "78 79 \n", "79 80 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def build_dense_regressor(input_dim: int, seed: int = 42):\n", " if not USE_TF:\n", " raise RuntimeError('TensorFlow недоступен, нейросетевые модели не могут быть обучены.')\n", " tf = TF\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Input(shape=(input_dim,)),\n", " tf.keras.layers.Dense(128, activation='relu'),\n", " tf.keras.layers.Dropout(0.15),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.Dense(1)\n", " ])\n", " model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),\n", " loss='mse',\n", " metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse'), tf.keras.metrics.MeanAbsoluteError(name='mae')]\n", " )\n", " return model\n", "\n", "def train_nn_model(Xtr, ytr, Xval, yval, Xte, yte, label='model'):\n", " model = build_dense_regressor(Xtr.shape[1], SEED)\n", " tf = TF\n", " callbacks = [\n", " tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", " ]\n", " history = model.fit(\n", " Xtr, ytr,\n", " validation_data=(Xval, yval),\n", " epochs=80,\n", " batch_size=128,\n", " verbose=0,\n", " callbacks=callbacks\n", " )\n", " pred_test = model.predict(Xte, verbose=0).ravel()\n", " pred_val = model.predict(Xval, verbose=0).ravel()\n", " metrics_test = regression_metrics(yte, pred_test)\n", " metrics_val = regression_metrics(yval, pred_val, prefix='val_')\n", " hist_df = pd.DataFrame(history.history)\n", " hist_df['epoch'] = np.arange(1, len(hist_df) + 1)\n", " return model, hist_df, pred_test, {**metrics_test, **metrics_val}\n", "\n", "nn_all_model, nn_all_history, pred_nn_all, metrics_nn_all = train_nn_model(\n", " X_train_nn_proc, y_train_nn.to_numpy(),\n", " X_val_nn_proc, y_val_nn.to_numpy(),\n", " X_test_proc, y_test.to_numpy(),\n", " label='nn_all'\n", ")\n", "\n", "results.append({\n", " 'model': 'Нейросеть по всем признакам',\n", " 'r2': metrics_nn_all['r2'],\n", " 'rmse': metrics_nn_all['rmse'],\n", " 'mae': metrics_nn_all['mae'],\n", " 'n_original_features': X.shape[1],\n", " 'n_processed_features': X_train_proc_df.shape[1]\n", "})\n", "\n", "display(nn_all_history.tail())\n" ] }, { "cell_type": "markdown", "id": "67e05285", "metadata": {}, "source": [ "## 4. Ансамбль нейросетей по трём методам отбора признаков\n", "\n", "Используются три метода отбора на преобразованных признаках:\n", "- **f_regression**;\n", "- **mutual information**;\n", "- **Random Forest importance**.\n", "\n", "Для каждого набора обучается нейросеть с той же архитектурой. Итоговый прогноз ансамбля — среднее по предсказаниям трёх сетей.\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "31794ea7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Число признаков в каждом подмножестве: 11\n", "lasso -> 46\n", "f_regression -> 11\n", "mutual_info -> 11\n", "random_forest -> 11\n" ] } ], "source": [ "# Подготовка преобразованных наборов для отбора\n", "X_train_base = X_train_proc_df.copy()\n", "X_test_base = X_test_proc_df.copy()\n", "X_train_nn_base = pd.DataFrame(X_train_nn_proc, columns=X_train_proc_df.columns, index=X_train_nn.index)\n", "X_val_nn_base = pd.DataFrame(X_val_nn_proc, columns=X_train_proc_df.columns, index=X_val_nn.index)\n", "\n", "k_top = max(10, min(30, X_train_base.shape[1] // 4))\n", "print('Число признаков в каждом подмножестве:', k_top)\n", "\n", "# Метод 1: f_regression\n", "sel_f = SelectKBest(score_func=f_regression, k=min(k_top, X_train_base.shape[1]))\n", "sel_f.fit(X_train_base, y_train)\n", "features_f = X_train_base.columns[sel_f.get_support()].tolist()\n", "selected_feature_sets['f_regression'] = features_f\n", "\n", "# Метод 2: mutual information\n", "sel_mi = SelectKBest(score_func=mutual_info_regression, k=min(k_top, X_train_base.shape[1]))\n", "sel_mi.fit(X_train_base, y_train)\n", "features_mi = X_train_base.columns[sel_mi.get_support()].tolist()\n", "selected_feature_sets['mutual_info'] = features_mi\n", "\n", "# Метод 3: Random Forest importance\n", "rf = RandomForestRegressor(n_estimators=200, random_state=SEED, n_jobs=-1)\n", "rf.fit(X_train_base, y_train)\n", "rf_imp = pd.Series(rf.feature_importances_, index=X_train_base.columns).sort_values(ascending=False)\n", "features_rf = rf_imp.head(min(k_top, len(rf_imp))).index.tolist()\n", "selected_feature_sets['random_forest'] = features_rf\n", "\n", "for method_name, features in selected_feature_sets.items():\n", " print(method_name, '->', len(features))\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "b3a8a39a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-03-14 17:48:31.472575: E tensorflow/core/framework/node_def_util.cc:680] NodeDef mentions attribute use_unbounded_threadpool which is not in the op definition: Op handle:variant; attr=f:func; attr=Targuments:list(type),min=0; attr=output_types:list(type),min=1; attr=output_shapes:list(shape),min=1; attr=use_inter_op_parallelism:bool,default=true; attr=preserve_cardinality:bool,default=false; attr=force_synchronous:bool,default=false; attr=metadata:string,default=\"\"> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node ParallelMapDatasetV2/_14}}\n", "2026-03-14 17:48:44.434714: E tensorflow/core/framework/node_def_util.cc:680] NodeDef mentions attribute use_unbounded_threadpool which is not in the op definition: Op handle:variant; attr=f:func; attr=Targuments:list(type),min=0; attr=output_types:list(type),min=1; attr=output_shapes:list(shape),min=1; attr=use_inter_op_parallelism:bool,default=true; attr=preserve_cardinality:bool,default=false; attr=force_synchronous:bool,default=false; attr=metadata:string,default=\"\"> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node ParallelMapDatasetV2/_14}}\n", "2026-03-14 17:48:50.749088: E tensorflow/core/framework/node_def_util.cc:680] NodeDef mentions attribute use_unbounded_threadpool which is not in the op definition: Op handle:variant; attr=f:func; attr=Targuments:list(type),min=0; attr=output_types:list(type),min=1; attr=output_shapes:list(shape),min=1; attr=use_inter_op_parallelism:bool,default=true; attr=preserve_cardinality:bool,default=false; attr=force_synchronous:bool,default=false; attr=metadata:string,default=\"\"> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node ParallelMapDatasetV2/_14}}\n" ] }, { "data": { "text/html": [ "
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methodr2rmsemaen_features
0f_regression0.72486970.78222924.18527211
1mutual_info0.48069797.24455728.83085111
2random_forest0.61348283.89570632.71278311
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" ], "text/plain": [ " method r2 rmse mae n_features\n", "0 f_regression 0.724869 70.782229 24.185272 11\n", "1 mutual_info 0.480697 97.244557 28.830851 11\n", "2 random_forest 0.613482 83.895706 32.712783 11" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ensemble_predictions = []\n", "ensemble_histories = {}\n", "ensemble_member_metrics = []\n", "\n", "for method_name in ['f_regression', 'mutual_info', 'random_forest']:\n", " feats = selected_feature_sets[method_name]\n", " model_i, hist_i, pred_i, metrics_i = train_nn_model(\n", " X_train_nn_base[feats].to_numpy(), y_train_nn.to_numpy(),\n", " X_val_nn_base[feats].to_numpy(), y_val_nn.to_numpy(),\n", " X_test_base[feats].to_numpy(), y_test.to_numpy(),\n", " label=method_name\n", " )\n", " ensemble_predictions.append(pred_i)\n", " ensemble_histories[method_name] = hist_i\n", " ensemble_member_metrics.append({\n", " 'method': method_name,\n", " 'r2': metrics_i['r2'],\n", " 'rmse': metrics_i['rmse'],\n", " 'mae': metrics_i['mae'],\n", " 'n_features': len(feats)\n", " })\n", "\n", "ensemble_pred = np.mean(np.column_stack(ensemble_predictions), axis=1)\n", "metrics_ensemble = regression_metrics(y_test, ensemble_pred)\n", "\n", "results.append({\n", " 'model': 'Ансамбль нейросетей по 3 методам отбора признаков',\n", " **metrics_ensemble,\n", " 'n_original_features': X.shape[1],\n", " 'n_processed_features': np.mean([len(selected_feature_sets[m]) for m in ['f_regression', 'mutual_info', 'random_forest']])\n", "})\n", "\n", "display(pd.DataFrame(ensemble_member_metrics))\n" ] }, { "cell_type": "markdown", "id": "d28a4f29", "metadata": {}, "source": [ "## Сравнение подходов\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "d7894845", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelr2rmsemaen_original_featuresn_processed_features
0Ансамбль нейросетей по 3 методам отбора признаков0.68494475.74410425.2480014111.0
1Нейросеть по всем признакам0.55446890.07296420.0872474146.0
2Линейная регрессия по всем признакам0.061597130.72221686.1566054146.0
3Lasso-отбор + линейная регрессия0.061597130.72221686.1566054146.0
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" ], "text/plain": [ " model r2 rmse \\\n", "0 Ансамбль нейросетей по 3 методам отбора признаков 0.684944 75.744104 \n", "1 Нейросеть по всем признакам 0.554468 90.072964 \n", "2 Линейная регрессия по всем признакам 0.061597 130.722216 \n", "3 Lasso-отбор + линейная регрессия 0.061597 130.722216 \n", "\n", " mae n_original_features n_processed_features \n", "0 25.248001 41 11.0 \n", "1 20.087247 41 46.0 \n", "2 86.156605 41 46.0 \n", "3 86.156605 41 46.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Лучшая модель по максимальному R2 и минимальному RMSE:\n" ] }, { "data": { "text/html": [ "
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modelr2rmsemaen_original_featuresn_processed_features
0Ансамбль нейросетей по 3 методам отбора признаков0.68494475.74410425.2480014111.0
\n", "
" ], "text/plain": [ " model r2 rmse \\\n", "0 Ансамбль нейросетей по 3 методам отбора признаков 0.684944 75.744104 \n", "\n", " mae n_original_features n_processed_features \n", "0 25.248001 41 11.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_df = pd.DataFrame(results)\n", "results_df = results_df.sort_values(['r2', 'rmse'], ascending=[False, True]).reset_index(drop=True)\n", "display(results_df)\n", "\n", "best_model_row = results_df.iloc[[0]].copy()\n", "print('Лучшая модель по максимальному R2 и минимальному RMSE:')\n", "display(best_model_row)\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "a775dd2d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "plt.bar(results_df['model'], results_df['r2'])\n", "plt.xticks(rotation=20, ha='right')\n", "plt.title('Сравнение моделей по R2 на тестовой выборке')\n", "plt.ylabel('R2')\n", "plt.show()\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.bar(results_df['model'], results_df['rmse'])\n", "plt.xticks(rotation=20, ha='right')\n", "plt.title('Сравнение моделей по RMSE на тестовой выборке')\n", "plt.ylabel('RMSE')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "f4e8c3fc", "metadata": {}, "source": [ "## Финальная ячейка с ключевой информацией для вывода\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "9f16bc5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== КЛЮЧЕВАЯ ИНФОРМАЦИЯ ДЛЯ ВЫВОДА ===\n", "Зависимая переменная: koi_period\n", "Число исходных признаков: 41\n", "Число признаков после кодирования: 46\n", "Метрики моделей на тестовой выборке:\n" ] }, { "data": { "text/html": [ "
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modelr2rmsemaen_original_featuresn_processed_features
0Ансамбль нейросетей по 3 методам отбора признаков0.68494475.74410425.2480014111.0
1Нейросеть по всем признакам0.55446890.07296420.0872474146.0
2Линейная регрессия по всем признакам0.061597130.72221686.1566054146.0
3Lasso-отбор + линейная регрессия0.061597130.72221686.1566054146.0
\n", "
" ], "text/plain": [ " model r2 rmse \\\n", "0 Ансамбль нейросетей по 3 методам отбора признаков 0.684944 75.744104 \n", "1 Нейросеть по всем признакам 0.554468 90.072964 \n", "2 Линейная регрессия по всем признакам 0.061597 130.722216 \n", "3 Lasso-отбор + линейная регрессия 0.061597 130.722216 \n", "\n", " mae n_original_features n_processed_features \n", "0 25.248001 41 11.0 \n", "1 20.087247 41 46.0 \n", "2 86.156605 41 46.0 \n", "3 86.156605 41 46.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Лучшая модель:\n" ] }, { "data": { "text/html": [ "
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modelr2rmsemaen_original_featuresn_processed_features
0Ансамбль нейросетей по 3 методам отбора признаков0.68494475.74410425.2480014111.0
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" ], "text/plain": [ " model r2 rmse \\\n", "0 Ансамбль нейросетей по 3 методам отбора признаков 0.684944 75.744104 \n", "\n", " mae n_original_features n_processed_features \n", "0 25.248001 41 11.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Признаки, отобранные классическим методом Lasso:\n", "['num__koi_score', 'num__koi_fpflag_nt', 'num__koi_fpflag_ss', 'num__koi_fpflag_co', 'num__koi_fpflag_ec', 'num__koi_time0bk', 'num__koi_time0bk_err1', 'num__koi_time0bk_err2', 'num__koi_impact', 'num__koi_impact_err1', 'num__koi_impact_err2', 'num__koi_duration', 'num__koi_duration_err1', 'num__koi_duration_err2', 'num__koi_depth', 'num__koi_depth_err1', 'num__koi_depth_err2', 'num__koi_prad', 'num__koi_prad_err1', 'num__koi_prad_err2', 'num__koi_teq', 'num__koi_insol', 'num__koi_insol_err1', 'num__koi_insol_err2', 'num__koi_model_snr', 'num__koi_tce_plnt_num', 'num__koi_steff', 'num__koi_steff_err1', 'num__koi_steff_err2', 'num__koi_slogg', 'num__koi_slogg_err1', 'num__koi_slogg_err2', 'num__koi_srad', 'num__koi_srad_err1', 'num__koi_srad_err2', 'num__ra', 'num__dec', 'num__koi_kepmag', 'cat__koi_disposition_CANDIDATE', 'cat__koi_disposition_CONFIRMED', 'cat__koi_disposition_FALSE POSITIVE', 'cat__koi_pdisposition_CANDIDATE', 'cat__koi_pdisposition_FALSE POSITIVE', 'cat__koi_tce_delivname_q1_q16_tce', 'cat__koi_tce_delivname_q1_q17_dr24_tce', 'cat__koi_tce_delivname_q1_q17_dr25_tce']\n", "Количество: 46\n", "Размеры наборов признаков для ансамбля:\n", "f_regression: 11\n", "mutual_info: 11\n", "random_forest: 11\n", "Первые 20 признаков по методам отбора ансамбля:\n", "f_regression\n", "['num__koi_fpflag_ss', 'num__koi_fpflag_co', 'num__koi_time0bk', 'num__koi_duration', 'num__koi_duration_err1', 'num__koi_duration_err2', 'num__koi_teq', 'cat__koi_disposition_CANDIDATE', 'cat__koi_disposition_CONFIRMED', 'cat__koi_tce_delivname_q1_q16_tce', 'cat__koi_tce_delivname_q1_q17_dr25_tce']\n", "mutual_info\n", "['num__koi_score', 'num__koi_time0bk', 'num__koi_duration', 'num__koi_duration_err1', 'num__koi_duration_err2', 'num__koi_depth_err1', 'num__koi_depth_err2', 'num__koi_teq', 'num__koi_insol', 'num__koi_insol_err1', 'num__koi_insol_err2']\n", "random_forest\n", "['num__koi_insol', 'num__koi_teq', 'num__koi_time0bk', 'num__koi_srad', 'num__koi_srad_err1', 'num__koi_duration_err2', 'num__koi_duration_err1', 'num__koi_steff', 'num__koi_insol_err2', 'num__koi_insol_err1', 'num__koi_slogg']\n", "Последние эпохи обучения нейросети по всем признакам:\n" ] }, { "data": { "text/html": [ "
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lossmaermseval_lossval_maeval_rmseepoch
751948.21289116.76424044.13856510933792.0103.2030873306.62841876
761898.58984416.91413743.57281110932898.0103.6155623306.49316477
771894.84167516.58187343.52977810934878.0102.8891833306.79272578
781789.61877416.16287642.30388610931300.0103.0772553306.25146579
791848.36694316.26519042.99263810930978.0102.8885803306.20263780
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" ], "text/plain": [ " loss mae rmse val_loss val_mae val_rmse \\\n", "75 1948.212891 16.764240 44.138565 10933792.0 103.203087 3306.628418 \n", "76 1898.589844 16.914137 43.572811 10932898.0 103.615562 3306.493164 \n", "77 1894.841675 16.581873 43.529778 10934878.0 102.889183 3306.792725 \n", "78 1789.618774 16.162876 42.303886 10931300.0 103.077255 3306.251465 \n", "79 1848.366943 16.265190 42.992638 10930978.0 102.888580 3306.202637 \n", "\n", " epoch \n", "75 76 \n", "76 77 \n", "77 78 \n", "78 79 \n", "79 80 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Метрики отдельных нейросетей ансамбля:\n" ] }, { "data": { "text/html": [ "
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methodr2rmsemaen_features
0f_regression0.72486970.78222924.18527211
1mutual_info0.48069797.24455728.83085111
2random_forest0.61348283.89570632.71278311
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" ], "text/plain": [ " method r2 rmse mae n_features\n", "0 f_regression 0.724869 70.782229 24.185272 11\n", "1 mutual_info 0.480697 97.244557 28.830851 11\n", "2 random_forest 0.613482 83.895706 32.712783 11" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('=== КЛЮЧЕВАЯ ИНФОРМАЦИЯ ДЛЯ ВЫВОДА ===')\n", "print('Зависимая переменная:', selected_target)\n", "print('Число исходных признаков:', X.shape[1])\n", "print('Число признаков после кодирования:', X_train_proc_df.shape[1])\n", "\n", "print('Метрики моделей на тестовой выборке:')\n", "display(results_df)\n", "\n", "print('Лучшая модель:')\n", "display(best_model_row)\n", "\n", "print('Признаки, отобранные классическим методом Lasso:')\n", "print(selected_processed_lasso[:50])\n", "print('Количество:', len(selected_processed_lasso))\n", "\n", "print('Размеры наборов признаков для ансамбля:')\n", "for method_name in ['f_regression', 'mutual_info', 'random_forest']:\n", " print(f'{method_name}:', len(selected_feature_sets[method_name]))\n", "\n", "print('Первые 20 признаков по методам отбора ансамбля:')\n", "for method_name in ['f_regression', 'mutual_info', 'random_forest']:\n", " print('' + method_name)\n", " print(selected_feature_sets[method_name][:20])\n", "\n", "print('Последние эпохи обучения нейросети по всем признакам:')\n", "display(nn_all_history.tail())\n", "\n", "print('Метрики отдельных нейросетей ансамбля:')\n", "display(pd.DataFrame(ensemble_member_metrics))\n" ] }, { "cell_type": "markdown", "id": "7c11b042", "metadata": {}, "source": [ "Итог\n", "\n", "Зависимой переменной выбрана koi_period. Сравнены четыре подхода: линейная регрессия по всем признакам, линейная регрессия после Lasso-отбора, нейросеть по всем признакам и ансамбль нейросетей по признакам, отобранным тремя методами (f_regression, mutual_info, random_forest).\n", "\n", "Лучший результат показал ансамбль нейросетей (R² = 0.685, RMSE ≈ 75.7), использующий 11 наиболее информативных признаков, что существенно лучше нейросети по всем признакам (R² = 0.554) и значительно превосходит линейные модели (R² ≈ 0.062).\n", "\n", "Следовательно, ансамбль нейросетей с предварительным отбором признаков является наиболее эффективным подходом для данной задачи регрессии." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }