diff --git a/Dockerfile b/Dockerfile new file mode 100755 index 0000000000000000000000000000000000000000..4ac5f7c7b76216267a2f13c47da2e80a8df14811 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,10 @@ +FROM node + +RUN apt update -y && apt upgrade -y + +WORKDIR /usr/app +COPY ./ /usr/app + +RUN cd face-api/examples/examples-browser/ && npm i + +CMD cd face-api/examples/examples-browser/ && npm start \ No newline at end of file diff --git a/README.md b/README.md old mode 100644 new mode 100755 index d7b85a10dfb0551759111394d03e57de8d8be106..c1f4ecb402247cd79d2acf8273bc9527c59bf027 --- a/README.md +++ b/README.md @@ -1,2 +1,42 @@ # ia_project + +- Richard PLANCHON +- Aziz M'HIRSI + + +# Perceptron + +lancer le fichier via jupiter notebook (wine_ia.ipynb)[wine_ia.ipynb] + +# Utilisation d'une IA deep learning + +## Installation + +### Avec Docker + +`cd face-api` + +#### build + +`docker build -t ia_project_pr172488_ma174899 .` + +#### run + +`docker run -d -p 1234:1234 ia_project_pr172488_ma174899` + +### Sans Docker + +`cd face-api.js/examples/` + +#### build + +`npm i` + +#### run + +`npm start` + +## Test + +(http://localhost:1234/)[http://localhost:1234/] \ No newline at end of file diff --git a/wine_ia.ipynb b/wine_ia.ipynb new file mode 100755 index 0000000000000000000000000000000000000000..c3c12833a1bc6a95d14e0d68bd68c2a5c41d5aa5 --- /dev/null +++ b/wine_ia.ipynb @@ -0,0 +1,377 @@ +{ + "cells":[ + { + "cell_type":"code", + "source":[ + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.datasets import load_wine\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.preprocessing import StandardScaler" + ], + "execution_count":1, + "outputs":[ + + ], + "metadata":{ + "jupyter":{ + "source_hidden":false, + "outputs_hidden":false + }, + "datalore":{ + "type":"CODE", + "sheet_delimiter":false + } + } + }, + { + "cell_type":"code", + "source":[ + "wine = load_wine()\n", + "data = pd.DataFrame(wine.data,columns=wine.feature_names)\n", + "data['class'] = pd.Series(wine.target)\n", + "\n", + "print(\"----------------------------------------------\")\n", + "print(\"------Loading and printing first 5 rows-------\")\n", + "print(\"----------------------------------------------\")\n", + "print(data.head())\n", + "\n", + "print(\"----------------------------------------------\")\n", + "print(\"---------Shape of data (Rows*Columns)---------\")\n", + "print(\"----------------------------------------------\")\n", + "print(data.shape)\n", + "\n", + "print(\"----------------------------------------------\")\n", + "print(\"----------Number of Samples per class---------\")\n", + "print(\"----------------------------------------------\")\n", + "print(data['class'].value_counts())\n", + "\n", + "print(\"----------------------------------------------\")\n", + "print(\"------Describing data like mean,std etc-------\")\n", + "print(\"----------------------------------------------\")\n", + "print(data.describe())\n", + "\n", + "print(\"----------------------------------------------\")\n", + "print(\"------------Features\/Columns names------------\")\n", + "print(\"----------------------------------------------\")\n", + "print(data.columns)" + ], + "execution_count":2, + "outputs":[ + { + "name":"stdout", + "text":[ + "----------------------------------------------\n", + "------Loading and printing first 5 rows-------\n", + "----------------------------------------------\n", + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280\/od315_of_diluted_wines proline class \n", + "0 3.92 1065.0 0 \n", + "1 3.40 1050.0 0 \n", + "2 3.17 1185.0 0 \n", + "3 3.45 1480.0 0 \n", + "4 2.93 735.0 0 \n", + "----------------------------------------------\n", + "---------Shape of data (Rows*Columns)---------\n", + "----------------------------------------------\n", + "(178, 14)\n", + "----------------------------------------------\n", + "----------Number of Samples per class---------\n", + "----------------------------------------------\n", + "1 71\n", + "0 59\n", + "2 48\n", + "Name: class, dtype: int64\n", + "----------------------------------------------\n", + "------Describing data like mean,std etc-------\n", + "----------------------------------------------\n", + " alcohol malic_acid ash alcalinity_of_ash magnesium \\\n", + "count 178.000000 178.000000 178.000000 178.000000 178.000000 \n", + "mean 13.000618 2.336348 2.366517 19.494944 99.741573 \n", + "std 0.811827 1.117146 0.274344 3.339564 14.282484 \n", + "min 11.030000 0.740000 1.360000 10.600000 70.000000 \n", + "25% 12.362500 1.602500 2.210000 17.200000 88.000000 \n", + "50% 13.050000 1.865000 2.360000 19.500000 98.000000 \n", + "75% 13.677500 3.082500 2.557500 21.500000 107.000000 \n", + "max 14.830000 5.800000 3.230000 30.000000 162.000000 \n", + "\n", + " total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n", + "count 178.000000 178.000000 178.000000 178.000000 \n", + "mean 2.295112 2.029270 0.361854 1.590899 \n", + "std 0.625851 0.998859 0.124453 0.572359 \n", + "min 0.980000 0.340000 0.130000 0.410000 \n", + "25% 1.742500 1.205000 0.270000 1.250000 \n", + "50% 2.355000 2.135000 0.340000 1.555000 \n", + "75% 2.800000 2.875000 0.437500 1.950000 \n", + "max 3.880000 5.080000 0.660000 3.580000 \n", + "\n", + " color_intensity hue od280\/od315_of_diluted_wines proline \\\n", + "count 178.000000 178.000000 178.000000 178.000000 \n", + "mean 5.058090 0.957449 2.611685 746.893258 \n", + "std 2.318286 0.228572 0.709990 314.907474 \n", + "min 1.280000 0.480000 1.270000 278.000000 \n", + "25% 3.220000 0.782500 1.937500 500.500000 \n", + "50% 4.690000 0.965000 2.780000 673.500000 \n", + "75% 6.200000 1.120000 3.170000 985.000000 \n", + "max 13.000000 1.710000 4.000000 1680.000000 \n", + "\n", + " class \n", + "count 178.000000 \n", + "mean 0.938202 \n", + "std 0.775035 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 1.000000 \n", + "75% 2.000000 \n", + "max 2.000000 \n", + "----------------------------------------------\n", + "------------Features\/Columns names------------\n", + "----------------------------------------------\n", + "Index(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n", + " 'total_phenols', 'flavanoids', 'nonflavanoid_phenols',\n", + " 'proanthocyanins', 'color_intensity', 'hue',\n", + " 'od280\/od315_of_diluted_wines', 'proline', 'class'],\n", + " dtype='object')\n" + ], + "output_type":"stream" + } + ], + "metadata":{ + "jupyter":{ + "source_hidden":false, + "outputs_hidden":false + }, + "datalore":{ + "type":"CODE", + "sheet_delimiter":false + } + } + }, + { + "cell_type":"code", + "source":[ + "def readWine(name, delimiter=\",\", split=\"\\n\"):\n", + " f = open(name, \"r\")\n", + " lines = f.read().split(split)\n", + " result = np.zeros((len(lines) - 1,len(lines[0].split(delimiter)) - 1))\n", + " y = np.zeros((len(lines) - 1,))\n", + " for l in range(len(lines) - 1):\n", + " entity = lines[l].split(delimiter)\n", + " if entity != ['']:\n", + " e = np.zeros((len(entity) - 1,))\n", + " y[l] = int(entity[len(entity) - 1])\n", + " for i in range(len(entity) - 1):\n", + " e[i] = float(entity[i])\n", + " result[l] = e\n", + " return result, y.astype(int)" + ], + "execution_count":3, + "outputs":[ + + ], + "metadata":{ + "jupyter":{ + "source_hidden":false, + "outputs_hidden":false + }, + "datalore":{ + "type":"CODE", + "sheet_delimiter":false + } + } + }, + { + "cell_type":"code", + "source":[ + "X = data.iloc[:, :-1].values\n", + "y = data.iloc[:, -1].values\n", + "\n", + "clf = MLPClassifier(max_iter=1000) # classifier mlp\n", + "X_train, y_train, = X, y # donnée de test\n", + "X_test, y_test = readWine(\"test.txt\") # donnée de test créé via un algo\n", + "sc = StandardScaler()\n", + "X_train = sc.fit_transform(X_train)\n", + "X_test = sc.transform(X_test)\n", + "\n", + "def transform(y): # permet de transformer le y en une sortie avec 3 neurones\n", + " new_y = np.zeros((len(y), 3))\n", + " for i in range(len(y)):\n", + " new_y[i][y[i]] = 1\n", + " return new_y\n", + "\n", + "clf.fit(X_train, transform(y_train)) # apprentissage\n", + "score = clf.score(X_test, transform(y_test)) # précision\n", + "print(\"Score : \" + str(score))\n", + "plt.scatter(\n", + " X_train[:, 0], # alcool\n", + " X_train[:, 1], #acide\n", + " c=transform(y_train), \n", + " edgecolors=\"k\"\n", + ")\n", + "plt.title(\"Donnée d'apprentissage\")\n", + "plt.show()\n", + "plt.close()\n", + "plt.title(\"Donnée de test\")\n", + "plt.scatter(\n", + " X_test[:, 0],\n", + " X_test[:, 1],\n", + " c=transform(y_test),\n", + " edgecolors=\"k\",\n", + " alpha=0.6,\n", + ")\n", + "\n", + "plt.show()\n", + "plt.close()\n" + ], + "execution_count":4, + "outputs":[ + { + "name":"stdout", + "text":[ + "Score : 0.9966666666666667\n" + ], + "output_type":"stream" + }, + { + "data":{ + "image\/png":[ + 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\n" + ] + }, + "metadata":{ + "image\/png":{ + + } + }, + "output_type":"display_data" + } + ], + "metadata":{ + "jupyter":{ + "source_hidden":false, + "outputs_hidden":false + }, + "datalore":{ + "type":"CODE", + "sheet_delimiter":false + } + } + }, + { + "cell_type":"code", + "source":[ + "def write_test(): # mon script permettant de généré des data de test\n", + " from sklearn.datasets import load_wine\n", + " import numpy as np\n", + " import pandas as pd\n", + "\n", + " wine = load_wine() # je genère les donnée de vin\n", + " data = pd.DataFrame(wine.data, columns=wine.feature_names)\n", + " data['class'] = pd.Series(wine.target)\n", + "\n", + "\n", + " def randomVine(class_type, n=1, data=data): # permet de choisir n vins aléatoirement\n", + " items = data[data['class'] == class_type]\n", + " items.sample(frac=0.7)\n", + " return items.sample(n).to_records(index=False)\n", + "\n", + " def chooseAttr(d1, d2): # choisi aléatoirement l'attribut en fonction de 2 attribu\n", + " rand = np.random.randint(0, 3)\n", + " if rand == 0:\n", + " return d1\n", + " elif rand == 1:\n", + " return d2\n", + " else:\n", + " return (d1 + d2)\/2\n", + "\n", + "\n", + " def createWine(class_type, n=1): # création de n vin de test\n", + " results = np.zeros((n, 13),)\n", + " for i in range(n):\n", + " w1, w2 = randomVine(class_type, 2) # je prend 2 vin\n", + " n_wine = np.zeros(13,)\n", + " for attr in range(len(w1) - 1):\n", + " n_wine[attr] = chooseAttr(w1[attr], w2[attr]) # pour chaque attribu de génère le nouvelle attribu\n", + " results[i] = n_wine\n", + " return results\n", + "\n", + " def writeWine(shape, name=\"test.txt\"): # écrire dans un fichier les vin\n", + " f = open(name, \"a\")\n", + " for i in range(len(shape)):\n", + " wines = createWine(i, shape[i]) # shape est un tuple (0, 1, 2) permettant de chosir combien de vin de type 0, 1 et 2 que l'on veut\n", + " for wine in wines:\n", + " x_arrstr = np.char.mod('%.2f', wine)\n", + " x_str = \",\".join(x_arrstr) + \",\" + str(i) +\"\\n\"\n", + " f.write(x_str)\n", + " f.close()\n", + "\n", + " def readWine(name, delimiter=\",\", split=\"\\n\"): # me permet de lire le vin écris dans le fichier\n", + " f = open(name, \"r\")\n", + " lines = f.read().split(split)\n", + " result = np.zeros((len(lines) - 1,len(lines[0].split(delimiter)) - 1))\n", + " y = np.zeros((len(lines) - 1,))\n", + " for l in range(len(lines) - 1):\n", + " entity = lines[l].split(delimiter)\n", + " if entity != ['']:\n", + " e = np.zeros((len(entity) - 1,))\n", + " y[l] = int(entity[len(entity) - 1])\n", + " for i in range(len(entity) - 1):\n", + " e[i] = float(entity[i])\n", + " result[l] = e\n", + " return result, y.astype(int)\n", + "\n", + "\n", + " writeWine((100, 100, 100)) # je créé 100 vin de chaque type\n", + "\n", + " f = open(\"test.txt\", \"r\")\n", + " wines, y = readWine(\"test.txt\")\n", + " print(y)\n", + "\n", + "# write_test()" + ], + "execution_count":5, + "outputs":[ + + ], + "metadata":{ + "jupyter":{ + "source_hidden":false, + "outputs_hidden":false + }, + "datalore":{ + "type":"CODE", + "sheet_delimiter":false + } + } + } + ], + "metadata":{ + "datalore":{ + "version":1, + "computation_mode":"JUPYTER", + "package_manager":"pip", + "base_environment":"default", + "packages":[ + + ] + } + }, + "nbformat":4, + "nbformat_minor":4 +} \ No newline at end of file