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ium_434788/IUM_5_434788_wersja_Jupyter.ipynb

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Zadanie Domowe Zajęcia 5 (Jupyter + Docker)
2021-04-17 18:33:36 +02:00
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "IUM_5_434788.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"source": [
"# Dostępna jest również wersja na Dockerze"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "markdown",
"metadata": {
"id": "tY8oIUexCAg2"
},
"source": [
"# 0. Imports and downloading the Data Frame"
]
},
{
"cell_type": "code",
"metadata": {
"id": "AlE65Fo32mGf"
},
"source": [
"from tensorflow.keras.models import Sequential, load_model\n",
"from tensorflow.keras.layers import Dense\n",
"from sklearn.metrics import accuracy_score\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hFXots4qa8Sz"
},
"source": [
"### 0.1. Wyczytanie pliku csv z mojego repo"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 470
},
"id": "8y82qyYTCN3H",
"outputId": "d4c552d5-27fc-4c9c-f2c3-87bfa6d4325d"
},
"source": [
"!curl -OL https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv\n",
"\n",
"wine=pd.read_csv('winequality-red.csv')\n",
"wine"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 98k 0 98k 0 0 75449 0 --:--:-- 0:00:01 --:--:-- 75449\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.4</td>\n",
" <td>0.700</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.8</td>\n",
" <td>0.880</td>\n",
" <td>0.00</td>\n",
" <td>2.6</td>\n",
" <td>0.098</td>\n",
" <td>25.0</td>\n",
" <td>67.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.20</td>\n",
" <td>0.68</td>\n",
" <td>9.8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.8</td>\n",
" <td>0.760</td>\n",
" <td>0.04</td>\n",
" <td>2.3</td>\n",
" <td>0.092</td>\n",
" <td>15.0</td>\n",
" <td>54.0</td>\n",
" <td>0.99700</td>\n",
" <td>3.26</td>\n",
" <td>0.65</td>\n",
" <td>9.8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.2</td>\n",
" <td>0.280</td>\n",
" <td>0.56</td>\n",
" <td>1.9</td>\n",
" <td>0.075</td>\n",
" <td>17.0</td>\n",
" <td>60.0</td>\n",
" <td>0.99800</td>\n",
" <td>3.16</td>\n",
" <td>0.58</td>\n",
" <td>9.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7.4</td>\n",
" <td>0.700</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1594</th>\n",
" <td>6.2</td>\n",
" <td>0.600</td>\n",
" <td>0.08</td>\n",
" <td>2.0</td>\n",
" <td>0.090</td>\n",
" <td>32.0</td>\n",
" <td>44.0</td>\n",
" <td>0.99490</td>\n",
" <td>3.45</td>\n",
" <td>0.58</td>\n",
" <td>10.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1595</th>\n",
" <td>5.9</td>\n",
" <td>0.550</td>\n",
" <td>0.10</td>\n",
" <td>2.2</td>\n",
" <td>0.062</td>\n",
" <td>39.0</td>\n",
" <td>51.0</td>\n",
" <td>0.99512</td>\n",
" <td>3.52</td>\n",
" <td>0.76</td>\n",
" <td>11.2</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1596</th>\n",
" <td>6.3</td>\n",
" <td>0.510</td>\n",
" <td>0.13</td>\n",
" <td>2.3</td>\n",
" <td>0.076</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>0.99574</td>\n",
" <td>3.42</td>\n",
" <td>0.75</td>\n",
" <td>11.0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1597</th>\n",
" <td>5.9</td>\n",
" <td>0.645</td>\n",
" <td>0.12</td>\n",
" <td>2.0</td>\n",
" <td>0.075</td>\n",
" <td>32.0</td>\n",
" <td>44.0</td>\n",
" <td>0.99547</td>\n",
" <td>3.57</td>\n",
" <td>0.71</td>\n",
" <td>10.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1598</th>\n",
" <td>6.0</td>\n",
" <td>0.310</td>\n",
" <td>0.47</td>\n",
" <td>3.6</td>\n",
" <td>0.067</td>\n",
" <td>18.0</td>\n",
" <td>42.0</td>\n",
" <td>0.99549</td>\n",
" <td>3.39</td>\n",
" <td>0.66</td>\n",
" <td>11.0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1599 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid ... sulphates alcohol quality\n",
"0 7.4 0.700 0.00 ... 0.56 9.4 5\n",
"1 7.8 0.880 0.00 ... 0.68 9.8 5\n",
"2 7.8 0.760 0.04 ... 0.65 9.8 5\n",
"3 11.2 0.280 0.56 ... 0.58 9.8 6\n",
"4 7.4 0.700 0.00 ... 0.56 9.4 5\n",
"... ... ... ... ... ... ... ...\n",
"1594 6.2 0.600 0.08 ... 0.58 10.5 5\n",
"1595 5.9 0.550 0.10 ... 0.76 11.2 6\n",
"1596 6.3 0.510 0.13 ... 0.75 11.0 6\n",
"1597 5.9 0.645 0.12 ... 0.71 10.2 5\n",
"1598 6.0 0.310 0.47 ... 0.66 11.0 6\n",
"\n",
"[1599 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 71
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i1z5M-qvCanz"
},
"source": [
"# 1. Analiza zbioru"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n-qmc_JJbDbY"
},
"source": [
"### 1.1. Heatmap by zbada korelacje. Z początku zastanawiałem się, czy nie wykorzystać tylko kolumn wysoko skorelowanych z 'Quality', jednak koniec końców model będzie się opierać o wszystkie kolumny"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 462
},
"id": "KGx2vKgO5L1b",
"outputId": "2a1e4e18-d4aa-4317-9a6c-ed8712f5f7b8"
},
"source": [
"plt.figure(figsize=(10,6))\n",
"sns.heatmap(wine.corr(),annot=True)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YJlKPvbbCjiA"
},
"source": [
"# 2. Normalizacja i podział zbioru na Test/Train"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fSb2f0BRbdtI"
},
"source": [
"### 2.1. 'y' to pojedyńcza kolumna z wartościami 'quality'"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nASk0bFA25Qs",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e7cdc203-361b-4f72-ef97-919e24cac7a9"
},
"source": [
"y = wine.quality\n",
"y.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 5\n",
"1 5\n",
"2 5\n",
"3 6\n",
"4 5\n",
"Name: quality, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 116
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Syf1hf1qbvAc"
},
"source": [
"### 2.2. 'x' to wszystkie kolumny poza 'quality'"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "DRh8_4RaC2eV",
"outputId": "08284f79-b5e5-4c5d-a56b-924a90179768"
},
"source": [
"x = wine.drop(['quality'], axis= 1)\n",
"x.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"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>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.4</td>\n",
" <td>0.70</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.9978</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.8</td>\n",
" <td>0.88</td>\n",
" <td>0.00</td>\n",
" <td>2.6</td>\n",
" <td>0.098</td>\n",
" <td>25.0</td>\n",
" <td>67.0</td>\n",
" <td>0.9968</td>\n",
" <td>3.20</td>\n",
" <td>0.68</td>\n",
" <td>9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.8</td>\n",
" <td>0.76</td>\n",
" <td>0.04</td>\n",
" <td>2.3</td>\n",
" <td>0.092</td>\n",
" <td>15.0</td>\n",
" <td>54.0</td>\n",
" <td>0.9970</td>\n",
" <td>3.26</td>\n",
" <td>0.65</td>\n",
" <td>9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.2</td>\n",
" <td>0.28</td>\n",
" <td>0.56</td>\n",
" <td>1.9</td>\n",
" <td>0.075</td>\n",
" <td>17.0</td>\n",
" <td>60.0</td>\n",
" <td>0.9980</td>\n",
" <td>3.16</td>\n",
" <td>0.58</td>\n",
" <td>9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7.4</td>\n",
" <td>0.70</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.9978</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid ... pH sulphates alcohol\n",
"0 7.4 0.70 0.00 ... 3.51 0.56 9.4\n",
"1 7.8 0.88 0.00 ... 3.20 0.68 9.8\n",
"2 7.8 0.76 0.04 ... 3.26 0.65 9.8\n",
"3 11.2 0.28 0.56 ... 3.16 0.58 9.8\n",
"4 7.4 0.70 0.00 ... 3.51 0.56 9.4\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 117
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sjRN2mU9b0S4"
},
"source": [
"### 2.3. Normalizacja wartości w x (do przedziału 0-1)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "aYSFPw7e58uC",
"outputId": "8dfa36f2-e26b-4d0b-98c6-ba079c868e7a"
},
"source": [
"x=((x-x.min())/(x.max()-x.min()))\n",
"x.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.247788</td>\n",
" <td>0.397260</td>\n",
" <td>0.00</td>\n",
" <td>0.068493</td>\n",
" <td>0.106845</td>\n",
" <td>0.140845</td>\n",
" <td>0.098940</td>\n",
" <td>0.567548</td>\n",
" <td>0.606299</td>\n",
" <td>0.137725</td>\n",
" <td>0.153846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.283186</td>\n",
" <td>0.520548</td>\n",
" <td>0.00</td>\n",
" <td>0.116438</td>\n",
" <td>0.143573</td>\n",
" <td>0.338028</td>\n",
" <td>0.215548</td>\n",
" <td>0.494126</td>\n",
" <td>0.362205</td>\n",
" <td>0.209581</td>\n",
" <td>0.215385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.283186</td>\n",
" <td>0.438356</td>\n",
" <td>0.04</td>\n",
" <td>0.095890</td>\n",
" <td>0.133556</td>\n",
" <td>0.197183</td>\n",
" <td>0.169611</td>\n",
" <td>0.508811</td>\n",
" <td>0.409449</td>\n",
" <td>0.191617</td>\n",
" <td>0.215385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.584071</td>\n",
" <td>0.109589</td>\n",
" <td>0.56</td>\n",
" <td>0.068493</td>\n",
" <td>0.105175</td>\n",
" <td>0.225352</td>\n",
" <td>0.190813</td>\n",
" <td>0.582232</td>\n",
" <td>0.330709</td>\n",
" <td>0.149701</td>\n",
" <td>0.215385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.247788</td>\n",
" <td>0.397260</td>\n",
" <td>0.00</td>\n",
" <td>0.068493</td>\n",
" <td>0.106845</td>\n",
" <td>0.140845</td>\n",
" <td>0.098940</td>\n",
" <td>0.567548</td>\n",
" <td>0.606299</td>\n",
" <td>0.137725</td>\n",
" <td>0.153846</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid ... pH sulphates alcohol\n",
"0 0.247788 0.397260 0.00 ... 0.606299 0.137725 0.153846\n",
"1 0.283186 0.520548 0.00 ... 0.362205 0.209581 0.215385\n",
"2 0.283186 0.438356 0.04 ... 0.409449 0.191617 0.215385\n",
"3 0.584071 0.109589 0.56 ... 0.330709 0.149701 0.215385\n",
"4 0.247788 0.397260 0.00 ... 0.606299 0.137725 0.153846\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 118
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5w0PCwRDb7Qu"
},
"source": [
"### 2.4. Podział na zbiory testowe i treningowe (1:4)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uhN2kywv3psP"
},
"source": [
"x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "l0iJsRfe9uqK",
"outputId": "f9d41a73-d769-4ae3-85e8-dcaac4ecf8fe"
},
"source": [
"x_train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"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>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>0.274336</td>\n",
" <td>0.407534</td>\n",
" <td>0.01</td>\n",
" <td>0.082192</td>\n",
" <td>0.086811</td>\n",
" <td>0.422535</td>\n",
" <td>0.130742</td>\n",
" <td>0.267254</td>\n",
" <td>0.527559</td>\n",
" <td>0.143713</td>\n",
" <td>0.523077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.584071</td>\n",
" <td>0.109589</td>\n",
" <td>0.56</td>\n",
" <td>0.068493</td>\n",
" <td>0.105175</td>\n",
" <td>0.225352</td>\n",
" <td>0.190813</td>\n",
" <td>0.582232</td>\n",
" <td>0.330709</td>\n",
" <td>0.149701</td>\n",
" <td>0.215385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.000000</td>\n",
" <td>0.273973</td>\n",
" <td>0.15</td>\n",
" <td>0.082192</td>\n",
" <td>0.070117</td>\n",
" <td>0.098592</td>\n",
" <td>0.208481</td>\n",
" <td>0.244493</td>\n",
" <td>0.913386</td>\n",
" <td>0.137725</td>\n",
" <td>0.723077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>780</th>\n",
" <td>0.212389</td>\n",
" <td>0.308219</td>\n",
" <td>0.00</td>\n",
" <td>0.075342</td>\n",
" <td>0.297162</td>\n",
" <td>0.154930</td>\n",
" <td>0.137809</td>\n",
" <td>0.491189</td>\n",
" <td>0.448819</td>\n",
" <td>0.161677</td>\n",
" <td>0.153846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>0.230088</td>\n",
" <td>0.198630</td>\n",
" <td>0.30</td>\n",
" <td>0.082192</td>\n",
" <td>0.118531</td>\n",
" <td>0.478873</td>\n",
" <td>0.233216</td>\n",
" <td>0.508811</td>\n",
" <td>0.551181</td>\n",
" <td>0.113772</td>\n",
" <td>0.153846</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity ... sulphates alcohol\n",
"870 0.274336 0.407534 ... 0.143713 0.523077\n",
"3 0.584071 0.109589 ... 0.149701 0.215385\n",
"45 0.000000 0.273973 ... 0.137725 0.723077\n",
"780 0.212389 0.308219 ... 0.161677 0.153846\n",
"976 0.230088 0.198630 ... 0.113772 0.153846\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 120
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Eml7m1efDZcB"
},
"source": [
"# 3. Model i jego trening (Tensorflow.Keras)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yw5LC_8g4W_z"
},
"source": [
"def regression_model():\n",
" model = Sequential()\n",
" model.add(Dense(32,activation = \"relu\", input_shape = (x_train.shape[1],)))\n",
" model.add(Dense(64,activation = \"relu\"))\n",
" model.add(Dense(1,activation = \"relu\"))\n",
" \n",
" model.compile(optimizer = \"adam\", loss = \"mean_squared_error\")\n",
" return model"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UZex-gc2-fma"
},
"source": [
"model = regression_model()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p3Soo5N94nYK",
"outputId": "a05d5732-643c-4616-dbb1-9e31699d6278"
},
"source": [
"model.fit(x_train, y_train, epochs = 600, verbose = 1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 27.0722\n",
"Epoch 2/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 7.8550\n",
"Epoch 3/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 1.1584\n",
"Epoch 4/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.9741\n",
"Epoch 5/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.9378\n",
"Epoch 6/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.8014\n",
"Epoch 7/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.7171\n",
"Epoch 8/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.6538\n",
"Epoch 9/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.7108\n",
"Epoch 10/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.6757\n",
"Epoch 11/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.6143\n",
"Epoch 12/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.5839\n",
"Epoch 13/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5464\n",
"Epoch 14/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5382\n",
"Epoch 15/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5355\n",
"Epoch 16/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5039\n",
"Epoch 17/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5245\n",
"Epoch 18/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.5104\n",
"Epoch 19/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4445\n",
"Epoch 20/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4727\n",
"Epoch 21/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4921\n",
"Epoch 22/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4548\n",
"Epoch 23/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4569\n",
"Epoch 24/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4608\n",
"Epoch 25/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4281\n",
"Epoch 26/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4640\n",
"Epoch 27/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4457\n",
"Epoch 28/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4267\n",
"Epoch 29/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4892\n",
"Epoch 30/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4421\n",
"Epoch 31/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3928\n",
"Epoch 32/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4320\n",
"Epoch 33/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4135\n",
"Epoch 34/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4090\n",
"Epoch 35/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3813\n",
"Epoch 36/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3866\n",
"Epoch 37/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3960\n",
"Epoch 38/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3816\n",
"Epoch 39/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3651\n",
"Epoch 40/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4080\n",
"Epoch 41/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4022\n",
"Epoch 42/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3986\n",
"Epoch 43/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3999\n",
"Epoch 44/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3736\n",
"Epoch 45/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3777\n",
"Epoch 46/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3688\n",
"Epoch 47/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3823\n",
"Epoch 48/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.4036\n",
"Epoch 49/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3661\n",
"Epoch 50/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3900\n",
"Epoch 51/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3591\n",
"Epoch 52/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3651\n",
"Epoch 53/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3740\n",
"Epoch 54/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4092\n",
"Epoch 55/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4019\n",
"Epoch 56/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3838\n",
"Epoch 57/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3893\n",
"Epoch 58/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4165\n",
"Epoch 59/600\n",
"40/40 [==============================] - 0s 1ms/step - loss: 0.3723\n",
"Epoch 60/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4293\n",
"Epoch 61/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3633\n",
"Epoch 62/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3889\n",
"Epoch 63/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.4001\n",
"Epoch 64/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3785\n",
"Epoch 65/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3762\n",
"Epoch 66/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3975\n",
"Epoch 67/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3809\n",
"Epoch 68/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3740\n",
"Epoch 69/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3885\n",
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"40/40 [==============================] - 0s 2ms/step - loss: 0.2929\n",
"Epoch 510/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2884\n",
"Epoch 511/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2762\n",
"Epoch 512/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2800\n",
"Epoch 513/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2846\n",
"Epoch 514/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3170\n",
"Epoch 515/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2864\n",
"Epoch 516/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2761\n",
"Epoch 517/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2875\n",
"Epoch 518/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2767\n",
"Epoch 519/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2742\n",
"Epoch 520/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2869\n",
"Epoch 521/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2894\n",
"Epoch 522/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2789\n",
"Epoch 523/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2738\n",
"Epoch 524/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2797\n",
"Epoch 525/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3122\n",
"Epoch 526/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3101\n",
"Epoch 527/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2728\n",
"Epoch 528/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2959\n",
"Epoch 529/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3053\n",
"Epoch 530/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2888\n",
"Epoch 531/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2808\n",
"Epoch 532/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2743\n",
"Epoch 533/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2703\n",
"Epoch 534/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2816\n",
"Epoch 535/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2798\n",
"Epoch 536/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2982\n",
"Epoch 537/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2832\n",
"Epoch 538/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2650\n",
"Epoch 539/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2898\n",
"Epoch 540/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2876\n",
"Epoch 541/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2916\n",
"Epoch 542/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2800\n",
"Epoch 543/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2743\n",
"Epoch 544/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2603\n",
"Epoch 545/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2856\n",
"Epoch 546/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2960\n",
"Epoch 547/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2805\n",
"Epoch 548/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2860\n",
"Epoch 549/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2891\n",
"Epoch 550/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2796\n",
"Epoch 551/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2686\n",
"Epoch 552/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2687\n",
"Epoch 553/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2856\n",
"Epoch 554/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2830\n",
"Epoch 555/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2727\n",
"Epoch 556/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2916\n",
"Epoch 557/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2840\n",
"Epoch 558/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2884\n",
"Epoch 559/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2808\n",
"Epoch 560/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2696\n",
"Epoch 561/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2733\n",
"Epoch 562/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2811\n",
"Epoch 563/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2844\n",
"Epoch 564/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2977\n",
"Epoch 565/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3177\n",
"Epoch 566/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2673\n",
"Epoch 567/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2814\n",
"Epoch 568/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2773\n",
"Epoch 569/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2661\n",
"Epoch 570/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2860\n",
"Epoch 571/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2632\n",
"Epoch 572/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2537\n",
"Epoch 573/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2910\n",
"Epoch 574/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2541\n",
"Epoch 575/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2707\n",
"Epoch 576/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2525\n",
"Epoch 577/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2689\n",
"Epoch 578/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2751\n",
"Epoch 579/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2747\n",
"Epoch 580/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2754\n",
"Epoch 581/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2862\n",
"Epoch 582/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2617\n",
"Epoch 583/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.3128\n",
"Epoch 584/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2916\n",
"Epoch 585/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2729\n",
"Epoch 586/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2752\n",
"Epoch 587/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2752\n",
"Epoch 588/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2822\n",
"Epoch 589/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2671\n",
"Epoch 590/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2904\n",
"Epoch 591/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2853\n",
"Epoch 592/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2986\n",
"Epoch 593/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2821\n",
"Epoch 594/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2926\n",
"Epoch 595/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2931\n",
"Epoch 596/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2743\n",
"Epoch 597/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2620\n",
"Epoch 598/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2616\n",
"Epoch 599/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2621\n",
"Epoch 600/600\n",
"40/40 [==============================] - 0s 2ms/step - loss: 0.2799\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f21b42280d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 135
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QRsJLVS2cOfM"
},
"source": [
"# 4. Predykcje, Pokrycie, Precyzja i F-Score"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1KlMEBYj4zYC",
"outputId": "92d33ba6-ca81-4a3e-a880-212a3dee07cc"
},
"source": [
"y_pred = model.predict(x_test)\n",
"\n",
"y_pred[:5]"
],
"execution_count": 143,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[5.5316496],\n",
" [5.08223 ],\n",
" [4.947891 ],\n",
" [6.1343417],\n",
" [5.526009 ]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 143
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y7QI0nqhBKeq",
"outputId": "6cbee1b5-525e-4bae-e121-f557023d0852"
},
"source": [
"y_pred = np.around(y_pred, decimals=0)\n",
"\n",
"y_pred[:5]"
],
"execution_count": 144,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[6.],\n",
" [5.],\n",
" [5.],\n",
" [6.],\n",
" [6.]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 144
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iPDHtbA6AC-P",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0b7bed7f-6431-4458-c9b4-d1b1f61471b9"
},
"source": [
"accuracy_score(y_test, y_pred)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.603125"
]
},
"metadata": {
"tags": []
},
"execution_count": 138
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EUAaNOJaAlsk",
"outputId": "3bbc97d1-df61-4e2e-aec9-dc75407df371"
},
"source": [
"from sklearn.metrics import classification_report\n",
"print(classification_report(y_test,y_pred)) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 1.0 0.00 0.00 0.00 0\n",
" 3.0 0.00 0.00 0.00 1\n",
" 4.0 0.00 0.00 0.00 6\n",
" 5.0 0.75 0.62 0.68 152\n",
" 6.0 0.49 0.70 0.58 115\n",
" 7.0 0.66 0.47 0.55 40\n",
" 8.0 0.00 0.00 0.00 6\n",
"\n",
" accuracy 0.60 320\n",
" macro avg 0.27 0.26 0.26 320\n",
"weighted avg 0.61 0.60 0.60 320\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "t8OqcubbIIJU"
},
"source": [],
"execution_count": null,
"outputs": []
}
]
}
Reference in New Issue Copy Permalink