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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Начало лабы №7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка даанных"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
" 'SP_open', 'SP_high', 'SP_low', 'SP_close', 'SP_Ajclose', 'SP_volume',\n",
" 'DJ_open', 'DJ_high', 'DJ_low', 'DJ_close', 'DJ_Ajclose', 'DJ_volume',\n",
" 'EG_open', 'EG_high', 'EG_low', 'EG_close', 'EG_Ajclose', 'EG_volume',\n",
" 'EU_Price', 'EU_open', 'EU_high', 'EU_low', 'EU_Trend', 'OF_Price',\n",
" 'OF_Open', 'OF_High', 'OF_Low', 'OF_Volume', 'OF_Trend', 'OS_Price',\n",
" 'OS_Open', 'OS_High', 'OS_Low', 'OS_Trend', 'SF_Price', 'SF_Open',\n",
" 'SF_High', 'SF_Low', 'SF_Volume', 'SF_Trend', 'USB_Price', 'USB_Open',\n",
" 'USB_High', 'USB_Low', 'USB_Trend', 'PLT_Price', 'PLT_Open', 'PLT_High',\n",
" 'PLT_Low', 'PLT_Trend', 'PLD_Price', 'PLD_Open', 'PLD_High', 'PLD_Low',\n",
" 'PLD_Trend', 'RHO_PRICE', 'USDI_Price', 'USDI_Open', 'USDI_High',\n",
" 'USDI_Low', 'USDI_Volume', 'USDI_Trend', 'GDX_Open', 'GDX_High',\n",
" 'GDX_Low', 'GDX_Close', 'GDX_Adj Close', 'GDX_Volume', 'USO_Open',\n",
" 'USO_High', 'USO_Low', 'USO_Close', 'USO_Adj Close', 'USO_Volume'],\n",
" dtype='object')\n"
]
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" <th>1</th>\n",
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" <td>51.029999</td>\n",
" <td>51.169998</td>\n",
" <td>48.490578</td>\n",
" <td>15120200</td>\n",
" <td>36.389999</td>\n",
" <td>36.450001</td>\n",
" <td>35.930000</td>\n",
" <td>36.200001</td>\n",
" <td>36.200001</td>\n",
" <td>7418200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011-12-20</td>\n",
" <td>156.820007</td>\n",
" <td>157.429993</td>\n",
" <td>156.580002</td>\n",
" <td>156.979996</td>\n",
" <td>156.979996</td>\n",
" <td>9136300</td>\n",
" <td>122.180000</td>\n",
" <td>124.139999</td>\n",
" <td>120.370003</td>\n",
" <td>...</td>\n",
" <td>52.369999</td>\n",
" <td>52.990002</td>\n",
" <td>50.215282</td>\n",
" <td>11644900</td>\n",
" <td>37.299999</td>\n",
" <td>37.610001</td>\n",
" <td>37.220001</td>\n",
" <td>37.560001</td>\n",
" <td>37.560001</td>\n",
" <td>10041600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2011-12-21</td>\n",
" <td>156.979996</td>\n",
" <td>157.529999</td>\n",
" <td>156.130005</td>\n",
" <td>157.160004</td>\n",
" <td>157.160004</td>\n",
" <td>11996100</td>\n",
" <td>123.930000</td>\n",
" <td>124.360001</td>\n",
" <td>122.750000</td>\n",
" <td>...</td>\n",
" <td>52.419998</td>\n",
" <td>52.959999</td>\n",
" <td>50.186852</td>\n",
" <td>8724300</td>\n",
" <td>37.669998</td>\n",
" <td>38.240002</td>\n",
" <td>37.520000</td>\n",
" <td>38.110001</td>\n",
" <td>38.110001</td>\n",
" <td>10728000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 2011-12-15 154.740005 154.949997 151.710007 152.330002 152.330002 \n",
"1 2011-12-16 154.309998 155.369995 153.899994 155.229996 155.229996 \n",
"2 2011-12-19 155.479996 155.860001 154.360001 154.869995 154.869995 \n",
"3 2011-12-20 156.820007 157.429993 156.580002 156.979996 156.979996 \n",
"4 2011-12-21 156.979996 157.529999 156.130005 157.160004 157.160004 \n",
"\n",
" Volume SP_open SP_high SP_low ... GDX_Low GDX_Close \\\n",
"0 21521900 123.029999 123.199997 121.989998 ... 51.570000 51.680000 \n",
"1 18124300 122.230003 122.949997 121.300003 ... 52.040001 52.680000 \n",
"2 12547200 122.059998 122.320000 120.029999 ... 51.029999 51.169998 \n",
"3 9136300 122.180000 124.139999 120.370003 ... 52.369999 52.990002 \n",
"4 11996100 123.930000 124.360001 122.750000 ... 52.419998 52.959999 \n",
"\n",
" GDX_Adj Close GDX_Volume USO_Open USO_High USO_Low USO_Close \\\n",
"0 48.973877 20605600 36.900002 36.939999 36.049999 36.130001 \n",
"1 49.921513 16285400 36.180000 36.500000 35.730000 36.270000 \n",
"2 48.490578 15120200 36.389999 36.450001 35.930000 36.200001 \n",
"3 50.215282 11644900 37.299999 37.610001 37.220001 37.560001 \n",
"4 50.186852 8724300 37.669998 38.240002 37.520000 38.110001 \n",
"\n",
" USO_Adj Close USO_Volume \n",
"0 36.130001 12616700 \n",
"1 36.270000 12578800 \n",
"2 36.200001 7418200 \n",
"3 37.560001 10041600 \n",
"4 38.110001 10728000 \n",
"\n",
"[5 rows x 81 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"..//static//csv//FINAL_USO.csv\")\n",
"print(df.columns)\n",
"display(df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Создание лингвистических переменных**\n",
"\n",
"Входные переменные: OF_Price (цены на нефть) и SF_Price (цена на с е р е б р о
"Выходная переменная: Adj Close (цена)."
]
},
{
"cell_type": "code",
2025-02-15 12:11:09 +04:00
"execution_count": 16,
2025-02-13 23:26:33 +04:00
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from skfuzzy import control as ctrl\n",
"\n",
"\n",
"# Инициализация лингвистических переменных\n",
"oil_price = ctrl.Antecedent(np.arange(df['OF_Price'].min(), df['OF_Price'].max(), 10), \"oil_price\")\n",
"silver_price = ctrl.Antecedent(np.arange(df['SF_Price'].min(), df['SF_Price'].max(), 1000), \"silver_price\")\n",
"adj_close = ctrl.Consequent(np.arange(df['Adj Close'].min(), df['Adj Close'].max(), 10), \"adj_close\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Формирование нечетких переменных для лингвистических переменных и их визуализация**"
]
},
{
"cell_type": "code",
2025-02-15 12:11:09 +04:00
"execution_count": null,
2025-02-13 23:26:33 +04:00
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
" fig.show()\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import skfuzzy as fuzz\n",
"\n",
"oil_price['low'] = fuzz.zmf(oil_price.universe, 40, 50)\n",
"oil_price['average'] = fuzz.trapmf(oil_price.universe, [60, 70, 80, 90])\n",
"oil_price['high'] = fuzz.smf(oil_price.universe, 100, 120)\n",
"\n",
"silver_price['low'] = fuzz.zmf(silver_price.universe, 35000, 45000)\n",
"silver_price['average'] = fuzz.trapmf(silver_price.universe, [35000, 45000, 50000, 60000])\n",
"silver_price['high'] = fuzz.smf(silver_price.universe, 50000, 60000)\n",
"\n",
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"adj_close['low'] = fuzz.zmf(adj_close.universe,110, 135)\n",
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"adj_close['average'] = fuzz.trapmf(adj_close.universe, [135, 145, 155, 165])\n",
"adj_close['high'] = fuzz.smf(adj_close.universe, 160, 170)\n",
"\n",
"oil_price.view()\n",
"silver_price.view()\n",
"adj_close.view()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Формирование и визуализация базы нечетких правил**\n"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 640x480 with 1 Axes>, <Axes: >)"
]
},
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"execution_count": 18,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import skfuzzy as fuzz\n",
"from skfuzzy import control as ctrl\n",
"# Нечеткие правила\n",
"rule1 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"low\"], adj_close[\"low\"])\n",
"rule2 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"average\"], adj_close[\"low\"])\n",
"rule3 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"high\"], adj_close[\"low\"])\n",
"rule4 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"low\"], adj_close[\"low\"])\n",
"rule5 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"average\"], adj_close[\"low\"])\n",
"rule6 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"high\"], adj_close[\"low\"])\n",
"rule7 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"low\"], adj_close[\"average\"])\n",
"rule8 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"average\"], adj_close[\"high\"])\n",
"rule9 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"high\"], adj_close[\"high\"])\n",
"rule1.view()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы**"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [],
"source": [
"price_ctrl = ctrl.ControlSystem(\n",
" [\n",
" rule1,\n",
" rule2,\n",
" rule3,\n",
" rule4,\n",
" rule5,\n",
" rule6,\n",
" rule7,\n",
" rule8,\n",
" rule9,\n",
" ]\n",
")\n",
"\n",
"# Создание симулятора нечеткой системы\n",
"price_sim = ctrl.ControlSystemSimulation(price_ctrl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Пример расчета выходной переменной adj_close на основе входных переменных silver_price и oil_price** \\\n",
"Система также формирует подробный журнал выполнения процесса нечеткого логического вывода"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=============\n",
" Antecedents \n",
"=============\n",
"Antecedent: silver_price = 60000\n",
" - low : 0.0\n",
" - average : 0.014110000000000011\n",
" - high : 0.99765774\n",
"Antecedent: oil_price = 30\n",
" - low : 1.0\n",
" - average : 0.0\n",
" - high : 0.0\n",
"\n",
"=======\n",
" Rules \n",
"=======\n",
"RULE #0:\n",
" IF silver_price[low] AND oil_price[low] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[low] : 0.0\n",
" - oil_price[low] : 1.0\n",
" silver_price[low] AND oil_price[low] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.0\n",
"\n",
"RULE #1:\n",
" IF silver_price[low] AND oil_price[average] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[low] : 0.0\n",
" - oil_price[average] : 0.0\n",
" silver_price[low] AND oil_price[average] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.0\n",
"\n",
"RULE #2:\n",
" IF silver_price[low] AND oil_price[high] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[low] : 0.0\n",
" - oil_price[high] : 0.0\n",
" silver_price[low] AND oil_price[high] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.0\n",
"\n",
"RULE #3:\n",
" IF silver_price[average] AND oil_price[low] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[average] : 0.014110000000000011\n",
" - oil_price[low] : 1.0\n",
" silver_price[average] AND oil_price[low] = 0.014110000000000011\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.014110000000000011\n",
"\n",
"RULE #4:\n",
" IF silver_price[average] AND oil_price[average] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[average] : 0.014110000000000011\n",
" - oil_price[average] : 0.0\n",
" silver_price[average] AND oil_price[average] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.0\n",
"\n",
"RULE #5:\n",
" IF silver_price[average] AND oil_price[high] THEN adj_close[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[average] : 0.014110000000000011\n",
" - oil_price[high] : 0.0\n",
" silver_price[average] AND oil_price[high] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[low] : 0.0\n",
"\n",
"RULE #6:\n",
" IF silver_price[high] AND oil_price[low] THEN adj_close[average]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[high] : 0.99765774\n",
" - oil_price[low] : 1.0\n",
" silver_price[high] AND oil_price[low] = 0.99765774\n",
" Activation (THEN-clause):\n",
" adj_close[average] : 0.99765774\n",
"\n",
"RULE #7:\n",
" IF silver_price[high] AND oil_price[average] THEN adj_close[high]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[high] : 0.99765774\n",
" - oil_price[average] : 0.0\n",
" silver_price[high] AND oil_price[average] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[high] : 0.0\n",
"\n",
"RULE #8:\n",
" IF silver_price[high] AND oil_price[high] THEN adj_close[high]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - silver_price[high] : 0.99765774\n",
" - oil_price[high] : 0.0\n",
" silver_price[high] AND oil_price[high] = 0.0\n",
" Activation (THEN-clause):\n",
" adj_close[high] : 0.0\n",
"\n",
"\n",
"==============================\n",
" Intermediaries and Conquests \n",
"==============================\n",
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"Consequent: adj_close = 149.2413379761956\n",
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" low:\n",
" Accumulate using accumulation_max : 0.014110000000000011\n",
" average:\n",
" Accumulate using accumulation_max : 0.99765774\n",
" high:\n",
" Accumulate using accumulation_max : 0.0\n",
"\n"
]
},
{
"data": {
"text/plain": [
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"np.float64(149.2413379761956)"
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]
},
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"execution_count": 20,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"price_sim.input['silver_price'] = 60000\n",
"price_sim.input['oil_price'] = 30\n",
"price_sim.compute()\n",
"\n",
"price_sim.print_state()\n",
"\n",
"price_sim.output[\"adj_close\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Визуализация функции принадлежности для выходной переменной adj_close** \\\n",
"Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"adj_close.view(sim=price_sim)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X**"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
"outputs": [],
"source": [
"def fuzzy_pred(row):\n",
" price_sim.input[\"silver_price\"] = row[\"SF_Price\"]\n",
" price_sim.input[\"oil_price\"] = row[\"OF_Price\"]\n",
" price_sim.compute()\n",
" return price_sim.output[\"adj_close\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Создадим выборки**"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: 1030\n",
"Размер контрольной выборки: 344\n",
"Размер тестовой выборки: 344\n"
]
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"df=pd.read_csv(\"..//static//csv//FINAL_USO.csv\")\n",
"# Разделение данных на обучающую и временную выборки\n",
"train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
"\n",
"# Разделение остатка на контрольную и тестовую выборки\n",
"val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
"\n",
"# Проверка размеров выборок\n",
"print(\"Размер обучающей выборки:\", len(train_df))\n",
"print(\"Размер контрольной выборки:\", len(val_df))\n",
"print(\"Размер тестовой выборки:\", len(test_df))\n",
"\n",
"# Сохранение выборок в файлы\n",
"train_df.to_csv(\"..//static//csv//train_data.csv\", index=False)\n",
"val_df.to_csv(\"..//static//csv//val_data.csv\", index=False)\n",
"test_df.to_csv(\"..//static//csv//test_data.csv\", index=False)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Тестирование нечеткой системы на обучающей выборке"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
{
"name": "index",
"rawType": "int64",
"type": "integer"
},
{
"name": "Adj Close",
"rawType": "float64",
"type": "float"
},
{
"name": "Adj_Pred",
"rawType": "float64",
"type": "float"
}
],
"conversionMethod": "pd.DataFrame",
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"ref": "395d9ad8-c2bc-4e96-abd7-aef80b9b35aa",
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[
"7",
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],
[
"8",
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],
[
"9",
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],
[
"10",
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"113.14420001833811"
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],
[
"11",
"116.720001",
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"119.85874650696019"
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],
[
"12",
"133.919998",
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"117.72446713035714"
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],
[
"13",
"124.389999",
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"114.49943272887351"
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],
[
"14",
"124.230003",
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"117.6826810238513"
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]
],
"shape": {
"columns": 2,
"rows": 15
}
},
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adj Close</th>\n",
" <th>Adj_Pred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>168.000000</td>\n",
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" <td>130.562212</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>112.570000</td>\n",
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" <td>114.718604</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>152.619995</td>\n",
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" <td>128.227957</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>114.099998</td>\n",
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" <td>113.722111</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>122.370003</td>\n",
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" <td>114.119637</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>110.739998</td>\n",
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" <td>118.652335</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>120.339996</td>\n",
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" <td>114.292079</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>108.529999</td>\n",
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" <td>117.657836</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>155.990005</td>\n",
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" <td>119.316656</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>152.619995</td>\n",
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" <td>122.122485</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>114.690002</td>\n",
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" <td>113.144200</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>116.720001</td>\n",
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" <td>119.858747</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>133.919998</td>\n",
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" <td>117.724467</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>124.389999</td>\n",
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" <td>114.499433</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>124.230003</td>\n",
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" <td>117.682681</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Adj Close Adj_Pred\n",
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"0 168.000000 130.562212\n",
"1 112.570000 114.718604\n",
"2 152.619995 128.227957\n",
"3 114.099998 113.722111\n",
"4 122.370003 114.119637\n",
"5 110.739998 118.652335\n",
"6 120.339996 114.292079\n",
"7 108.529999 117.657836\n",
"8 155.990005 119.316656\n",
"9 152.619995 122.122485\n",
"10 114.690002 113.144200\n",
"11 116.720001 119.858747\n",
"12 133.919998 117.724467\n",
"13 124.389999 114.499433\n",
"14 124.230003 117.682681"
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]
},
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"execution_count": 24,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n",
"\n",
"result_train = train_df.copy()\n",
"\n",
"\n",
"result_train[\"Adj_Pred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
"selected_cm=result_train[['Adj Close','Adj_Pred']]\n",
"selected_cm.head(15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Тестирование нечеткой системы на тестовой выборке"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
{
"name": "index",
"rawType": "int64",
"type": "integer"
},
{
"name": "Adj Close",
"rawType": "float64",
"type": "float"
},
{
"name": "Adj_Pred",
"rawType": "float64",
"type": "float"
}
],
"conversionMethod": "pd.DataFrame",
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"ref": "70457667-97bb-4889-ae06-9d87735e8bc3",
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"rows": [
[
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"1",
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[
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adj Close</th>\n",
" <th>Adj_Pred</th>\n",
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" <th>0</th>\n",
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
" <td>114.777997</td>\n",
2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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" <td>171.020004</td>\n",
2025-02-15 12:11:09 +04:00
" <td>134.490239</td>\n",
2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
" <td>115.509442</td>\n",
2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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"text/plain": [
" Adj Close Adj_Pred\n",
2025-02-15 12:11:09 +04:00
"0 160.539993 126.992652\n",
"1 121.610001 120.454951\n",
"2 126.160004 116.328038\n",
"3 160.990005 137.572396\n",
2025-02-13 23:26:33 +04:00
"4 173.610001 166.750349\n",
2025-02-15 12:11:09 +04:00
"5 118.970001 115.560821\n",
"6 126.680000 116.500640\n",
"7 117.519997 114.245149\n",
"8 126.730003 113.759291\n",
"9 120.309998 114.218952\n",
"10 114.419998 113.384163\n",
"11 124.540001 113.769799\n",
"12 115.430000 117.661916\n",
"13 118.220001 113.783256\n",
"14 121.050003 120.408936\n",
"15 106.220001 115.897552\n",
"16 109.139999 118.876038\n",
"17 112.239998 114.777997\n",
"18 122.879997 116.584875\n",
"19 117.290001 119.730281\n",
"20 127.400002 114.648531\n",
"21 171.020004 134.490239\n",
"22 118.120003 115.509442\n",
"23 119.430000 115.422122\n",
"24 115.800003 113.634607"
2025-02-13 23:26:33 +04:00
]
},
2025-02-15 12:11:09 +04:00
"execution_count": 25,
2025-02-13 23:26:33 +04:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"test_df=pd.read_csv(\"..//static//csv//test_data.csv\")\n",
"result_test = test_df.copy()\n",
"\n",
"result_test[\"Adj_Pred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
"\n",
"selected_cm=result_test[['Adj Close','Adj_Pred']]\n",
"selected_cm.head(25)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Тестирование нечёткой системы на контрольной выборке"
]
},
{
"cell_type": "code",
2025-02-15 12:11:09 +04:00
"execution_count": 26,
2025-02-13 23:26:33 +04:00
"metadata": {},
"outputs": [
{
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
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2025-02-13 23:26:33 +04:00
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2025-02-15 12:11:09 +04:00
" <td>118.467985</td>\n",
2025-02-13 23:26:33 +04:00
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>161.509995</td>\n",
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" <td>131.978154</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>120.589996</td>\n",
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" <td>118.541233</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>120.959999</td>\n",
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" <td>115.595202</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>115.989998</td>\n",
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" <td>113.315718</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>120.989998</td>\n",
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" <td>115.774836</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>168.789993</td>\n",
" <td>166.732904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>114.290001</td>\n",
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" <td>119.828262</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>114.209999</td>\n",
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" <td>119.669656</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>115.050003</td>\n",
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" <td>112.657356</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>118.860001</td>\n",
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" <td>114.268250</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>120.050003</td>\n",
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" <td>115.009975</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Adj Close Adj_Pred\n",
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"0 117.589996 116.428542\n",
"1 121.650002 117.405395\n",
"2 166.339996 139.323579\n",
"3 116.309998 113.383973\n",
"4 115.199997 120.439259\n",
"5 126.940002 113.266077\n",
"6 127.480003 115.502123\n",
"7 120.779999 118.493430\n",
"8 151.619995 128.041918\n",
"9 118.290001 115.096439\n",
"10 122.860001 114.249403\n",
"11 118.360001 113.150825\n",
"12 123.320000 115.117051\n",
"13 120.650002 118.467985\n",
"14 161.509995 131.978154\n",
"15 120.589996 118.541233\n",
"16 120.959999 115.595202\n",
"17 115.989998 113.315718\n",
"18 120.989998 115.774836\n",
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"19 168.789993 166.732904\n",
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"20 114.290001 119.828262\n",
"21 114.209999 119.669656\n",
"22 115.050003 112.657356\n",
"23 118.860001 114.268250\n",
"24 120.050003 115.009975"
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]
},
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"execution_count": 26,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"val_df=pd.read_csv(\"..//static//csv//val_data.csv\")\n",
"result_val = val_df.copy()\n",
"\n",
"result_val[\"Adj_Pred\"] = result_val.apply(fuzzy_pred, axis=1)\n",
"\n",
"selected_cm=result_val[['Adj Close','Adj_Pred']]\n",
"selected_cm.head(25)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Оценка результатов на основе метрик для задачи регрессии"
]
},
{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"{'RMSE_train': 14.063806361503557,\n",
" 'RMSE_test': 13.63382917388284,\n",
" 'RMAE_test': 3.152710529049624,\n",
" 'R2_test': 0.35443568923237845}"
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]
},
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"execution_count": 27,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import math\n",
"from sklearn import metrics\n",
"\n",
"\n",
"rmetrics = {}\n",
"rmetrics[\"RMSE_train\"] = math.sqrt(\n",
" metrics.mean_squared_error(result_train[\"Adj Close\"], result_train[\"Adj_Pred\"])\n",
")\n",
"rmetrics[\"RMSE_test\"] = math.sqrt(\n",
" metrics.mean_squared_error(result_test[\"Adj Close\"], result_test[\"Adj_Pred\"])\n",
")\n",
"rmetrics[\"RMAE_test\"] = math.sqrt(\n",
" metrics.mean_absolute_error(result_test[\"Adj Close\"], result_test[\"Adj_Pred\"])\n",
")\n",
"rmetrics[\"R2_test\"] = metrics.r2_score(\n",
" result_test[\"Adj Close\"], result_test[\"Adj_Pred\"]\n",
")\n",
"\n",
"rmetrics"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
