{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse par composantes principales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Données IRIS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
      "       [4.9, 3. , 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.3, 0.2],\n",
      "       [4.6, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.6, 1.4, 0.2],\n",
      "       [5.4, 3.9, 1.7, 0.4],\n",
      "       [4.6, 3.4, 1.4, 0.3],\n",
      "       [5. , 3.4, 1.5, 0.2],\n",
      "       [4.4, 2.9, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.1],\n",
      "       [5.4, 3.7, 1.5, 0.2],\n",
      "       [4.8, 3.4, 1.6, 0.2],\n",
      "       [4.8, 3. , 1.4, 0.1],\n",
      "       [4.3, 3. , 1.1, 0.1],\n",
      "       [5.8, 4. , 1.2, 0.2],\n",
      "       [5.7, 4.4, 1.5, 0.4],\n",
      "       [5.4, 3.9, 1.3, 0.4],\n",
      "       [5.1, 3.5, 1.4, 0.3],\n",
      "       [5.7, 3.8, 1.7, 0.3],\n",
      "       [5.1, 3.8, 1.5, 0.3],\n",
      "       [5.4, 3.4, 1.7, 0.2],\n",
      "       [5.1, 3.7, 1.5, 0.4],\n",
      "       [4.6, 3.6, 1. , 0.2],\n",
      "       [5.1, 3.3, 1.7, 0.5],\n",
      "       [4.8, 3.4, 1.9, 0.2],\n",
      "       [5. , 3. , 1.6, 0.2],\n",
      "       [5. , 3.4, 1.6, 0.4],\n",
      "       [5.2, 3.5, 1.5, 0.2],\n",
      "       [5.2, 3.4, 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.6, 0.2],\n",
      "       [4.8, 3.1, 1.6, 0.2],\n",
      "       [5.4, 3.4, 1.5, 0.4],\n",
      "       [5.2, 4.1, 1.5, 0.1],\n",
      "       [5.5, 4.2, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.6, 1.4, 0.1],\n",
      "       [4.4, 3. , 1.3, 0.2],\n",
      "       [5.1, 3.4, 1.5, 0.2],\n",
      "       [5. , 3.5, 1.3, 0.3],\n",
      "       [4.5, 2.3, 1.3, 0.3],\n",
      "       [4.4, 3.2, 1.3, 0.2],\n",
      "       [5. , 3.5, 1.6, 0.6],\n",
      "       [5.1, 3.8, 1.9, 0.4],\n",
      "       [4.8, 3. , 1.4, 0.3],\n",
      "       [5.1, 3.8, 1.6, 0.2],\n",
      "       [4.6, 3.2, 1.4, 0.2],\n",
      "       [5.3, 3.7, 1.5, 0.2],\n",
      "       [5. , 3.3, 1.4, 0.2],\n",
      "       [7. , 3.2, 4.7, 1.4],\n",
      "       [6.4, 3.2, 4.5, 1.5],\n",
      "       [6.9, 3.1, 4.9, 1.5],\n",
      "       [5.5, 2.3, 4. , 1.3],\n",
      "       [6.5, 2.8, 4.6, 1.5],\n",
      "       [5.7, 2.8, 4.5, 1.3],\n",
      "       [6.3, 3.3, 4.7, 1.6],\n",
      "       [4.9, 2.4, 3.3, 1. ],\n",
      "       [6.6, 2.9, 4.6, 1.3],\n",
      "       [5.2, 2.7, 3.9, 1.4],\n",
      "       [5. , 2. , 3.5, 1. ],\n",
      "       [5.9, 3. , 4.2, 1.5],\n",
      "       [6. , 2.2, 4. , 1. ],\n",
      "       [6.1, 2.9, 4.7, 1.4],\n",
      "       [5.6, 2.9, 3.6, 1.3],\n",
      "       [6.7, 3.1, 4.4, 1.4],\n",
      "       [5.6, 3. , 4.5, 1.5],\n",
      "       [5.8, 2.7, 4.1, 1. ],\n",
      "       [6.2, 2.2, 4.5, 1.5],\n",
      "       [5.6, 2.5, 3.9, 1.1],\n",
      "       [5.9, 3.2, 4.8, 1.8],\n",
      "       [6.1, 2.8, 4. , 1.3],\n",
      "       [6.3, 2.5, 4.9, 1.5],\n",
      "       [6.1, 2.8, 4.7, 1.2],\n",
      "       [6.4, 2.9, 4.3, 1.3],\n",
      "       [6.6, 3. , 4.4, 1.4],\n",
      "       [6.8, 2.8, 4.8, 1.4],\n",
      "       [6.7, 3. , 5. , 1.7],\n",
      "       [6. , 2.9, 4.5, 1.5],\n",
      "       [5.7, 2.6, 3.5, 1. ],\n",
      "       [5.5, 2.4, 3.8, 1.1],\n",
      "       [5.5, 2.4, 3.7, 1. ],\n",
      "       [5.8, 2.7, 3.9, 1.2],\n",
      "       [6. , 2.7, 5.1, 1.6],\n",
      "       [5.4, 3. , 4.5, 1.5],\n",
      "       [6. , 3.4, 4.5, 1.6],\n",
      "       [6.7, 3.1, 4.7, 1.5],\n",
      "       [6.3, 2.3, 4.4, 1.3],\n",
      "       [5.6, 3. , 4.1, 1.3],\n",
      "       [5.5, 2.5, 4. , 1.3],\n",
      "       [5.5, 2.6, 4.4, 1.2],\n",
      "       [6.1, 3. , 4.6, 1.4],\n",
      "       [5.8, 2.6, 4. , 1.2],\n",
      "       [5. , 2.3, 3.3, 1. ],\n",
      "       [5.6, 2.7, 4.2, 1.3],\n",
      "       [5.7, 3. , 4.2, 1.2],\n",
      "       [5.7, 2.9, 4.2, 1.3],\n",
      "       [6.2, 2.9, 4.3, 1.3],\n",
      "       [5.1, 2.5, 3. , 1.1],\n",
      "       [5.7, 2.8, 4.1, 1.3],\n",
      "       [6.3, 3.3, 6. , 2.5],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [7.1, 3. , 5.9, 2.1],\n",
      "       [6.3, 2.9, 5.6, 1.8],\n",
      "       [6.5, 3. , 5.8, 2.2],\n",
      "       [7.6, 3. , 6.6, 2.1],\n",
      "       [4.9, 2.5, 4.5, 1.7],\n",
      "       [7.3, 2.9, 6.3, 1.8],\n",
      "       [6.7, 2.5, 5.8, 1.8],\n",
      "       [7.2, 3.6, 6.1, 2.5],\n",
      "       [6.5, 3.2, 5.1, 2. ],\n",
      "       [6.4, 2.7, 5.3, 1.9],\n",
      "       [6.8, 3. , 5.5, 2.1],\n",
      "       [5.7, 2.5, 5. , 2. ],\n",
      "       [5.8, 2.8, 5.1, 2.4],\n",
      "       [6.4, 3.2, 5.3, 2.3],\n",
      "       [6.5, 3. , 5.5, 1.8],\n",
      "       [7.7, 3.8, 6.7, 2.2],\n",
      "       [7.7, 2.6, 6.9, 2.3],\n",
      "       [6. , 2.2, 5. , 1.5],\n",
      "       [6.9, 3.2, 5.7, 2.3],\n",
      "       [5.6, 2.8, 4.9, 2. ],\n",
      "       [7.7, 2.8, 6.7, 2. ],\n",
      "       [6.3, 2.7, 4.9, 1.8],\n",
      "       [6.7, 3.3, 5.7, 2.1],\n",
      "       [7.2, 3.2, 6. , 1.8],\n",
      "       [6.2, 2.8, 4.8, 1.8],\n",
      "       [6.1, 3. , 4.9, 1.8],\n",
      "       [6.4, 2.8, 5.6, 2.1],\n",
      "       [7.2, 3. , 5.8, 1.6],\n",
      "       [7.4, 2.8, 6.1, 1.9],\n",
      "       [7.9, 3.8, 6.4, 2. ],\n",
      "       [6.4, 2.8, 5.6, 2.2],\n",
      "       [6.3, 2.8, 5.1, 1.5],\n",
      "       [6.1, 2.6, 5.6, 1.4],\n",
      "       [7.7, 3. , 6.1, 2.3],\n",
      "       [6.3, 3.4, 5.6, 2.4],\n",
      "       [6.4, 3.1, 5.5, 1.8],\n",
      "       [6. , 3. , 4.8, 1.8],\n",
      "       [6.9, 3.1, 5.4, 2.1],\n",
      "       [6.7, 3.1, 5.6, 2.4],\n",
      "       [6.9, 3.1, 5.1, 2.3],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [6.8, 3.2, 5.9, 2.3],\n",
      "       [6.7, 3.3, 5.7, 2.5],\n",
      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'frame': None, 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 150 (50 in each of three classes)\\n:Number of Attributes: 4 numeric, predictive attributes and the class\\n:Attribute Information:\\n    - sepal length in cm\\n    - sepal width in cm\\n    - petal length in cm\\n    - petal width in cm\\n    - class:\\n            - Iris-Setosa\\n            - Iris-Versicolour\\n            - Iris-Virginica\\n\\n:Summary Statistics:\\n\\n============== ==== ==== ======= ===== ====================\\n                Min  Max   Mean    SD   Class Correlation\\n============== ==== ==== ======= ===== ====================\\nsepal length:   4.3  7.9   5.84   0.83    0.7826\\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n============== ==== ==== ======= ===== ====================\\n\\n:Missing Attribute Values: None\\n:Class Distribution: 33.3% for each of 3 classes.\\n:Creator: R.A. Fisher\\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n:Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. dropdown:: References\\n\\n  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n    Mathematical Statistics\" (John Wiley, NY, 1950).\\n  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n    Structure and Classification Rule for Recognition in Partially Exposed\\n    Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n    Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n    on Information Theory, May 1972, 431-433.\\n  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n    conceptual clustering system finds 3 classes in the data.\\n  - Many, many more ...\\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "#print(X.shape)\n",
    "#print(y.shape)\n",
    "\n",
    "print(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalisation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-9.00681170e-01  1.01900435e+00 -1.34022653e+00 -1.31544430e+00]\n",
      " [-1.14301691e+00 -1.31979479e-01 -1.34022653e+00 -1.31544430e+00]\n",
      " [-1.38535265e+00  3.28414053e-01 -1.39706395e+00 -1.31544430e+00]\n",
      " [-1.50652052e+00  9.82172869e-02 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  1.24920112e+00 -1.34022653e+00 -1.31544430e+00]\n",
      " [-5.37177559e-01  1.93979142e+00 -1.16971425e+00 -1.05217993e+00]\n",
      " [-1.50652052e+00  7.88807586e-01 -1.34022653e+00 -1.18381211e+00]\n",
      " [-1.02184904e+00  7.88807586e-01 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.74885626e+00 -3.62176246e-01 -1.34022653e+00 -1.31544430e+00]\n",
      " [-1.14301691e+00  9.82172869e-02 -1.28338910e+00 -1.44707648e+00]\n",
      " [-5.37177559e-01  1.47939788e+00 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.26418478e+00  7.88807586e-01 -1.22655167e+00 -1.31544430e+00]\n",
      " [-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.44707648e+00]\n",
      " [-1.87002413e+00 -1.31979479e-01 -1.51073881e+00 -1.44707648e+00]\n",
      " [-5.25060772e-02  2.16998818e+00 -1.45390138e+00 -1.31544430e+00]\n",
      " [-1.73673948e-01  3.09077525e+00 -1.28338910e+00 -1.05217993e+00]\n",
      " [-5.37177559e-01  1.93979142e+00 -1.39706395e+00 -1.05217993e+00]\n",
      " [-9.00681170e-01  1.01900435e+00 -1.34022653e+00 -1.18381211e+00]\n",
      " [-1.73673948e-01  1.70959465e+00 -1.16971425e+00 -1.18381211e+00]\n",
      " [-9.00681170e-01  1.70959465e+00 -1.28338910e+00 -1.18381211e+00]\n",
      " [-5.37177559e-01  7.88807586e-01 -1.16971425e+00 -1.31544430e+00]\n",
      " [-9.00681170e-01  1.47939788e+00 -1.28338910e+00 -1.05217993e+00]\n",
      " [-1.50652052e+00  1.24920112e+00 -1.56757623e+00 -1.31544430e+00]\n",
      " [-9.00681170e-01  5.58610819e-01 -1.16971425e+00 -9.20547742e-01]\n",
      " [-1.26418478e+00  7.88807586e-01 -1.05603939e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00 -1.31979479e-01 -1.22655167e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  7.88807586e-01 -1.22655167e+00 -1.05217993e+00]\n",
      " [-7.79513300e-01  1.01900435e+00 -1.28338910e+00 -1.31544430e+00]\n",
      " [-7.79513300e-01  7.88807586e-01 -1.34022653e+00 -1.31544430e+00]\n",
      " [-1.38535265e+00  3.28414053e-01 -1.22655167e+00 -1.31544430e+00]\n",
      " [-1.26418478e+00  9.82172869e-02 -1.22655167e+00 -1.31544430e+00]\n",
      " [-5.37177559e-01  7.88807586e-01 -1.28338910e+00 -1.05217993e+00]\n",
      " [-7.79513300e-01  2.40018495e+00 -1.28338910e+00 -1.44707648e+00]\n",
      " [-4.16009689e-01  2.63038172e+00 -1.34022653e+00 -1.31544430e+00]\n",
      " [-1.14301691e+00  9.82172869e-02 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  3.28414053e-01 -1.45390138e+00 -1.31544430e+00]\n",
      " [-4.16009689e-01  1.01900435e+00 -1.39706395e+00 -1.31544430e+00]\n",
      " [-1.14301691e+00  1.24920112e+00 -1.34022653e+00 -1.44707648e+00]\n",
      " [-1.74885626e+00 -1.31979479e-01 -1.39706395e+00 -1.31544430e+00]\n",
      " [-9.00681170e-01  7.88807586e-01 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  1.01900435e+00 -1.39706395e+00 -1.18381211e+00]\n",
      " [-1.62768839e+00 -1.74335684e+00 -1.39706395e+00 -1.18381211e+00]\n",
      " [-1.74885626e+00  3.28414053e-01 -1.39706395e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  1.01900435e+00 -1.22655167e+00 -7.88915558e-01]\n",
      " [-9.00681170e-01  1.70959465e+00 -1.05603939e+00 -1.05217993e+00]\n",
      " [-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.18381211e+00]\n",
      " [-9.00681170e-01  1.70959465e+00 -1.22655167e+00 -1.31544430e+00]\n",
      " [-1.50652052e+00  3.28414053e-01 -1.34022653e+00 -1.31544430e+00]\n",
      " [-6.58345429e-01  1.47939788e+00 -1.28338910e+00 -1.31544430e+00]\n",
      " [-1.02184904e+00  5.58610819e-01 -1.34022653e+00 -1.31544430e+00]\n",
      " [ 1.40150837e+00  3.28414053e-01  5.35408562e-01  2.64141916e-01]\n",
      " [ 6.74501145e-01  3.28414053e-01  4.21733708e-01  3.95774101e-01]\n",
      " [ 1.28034050e+00  9.82172869e-02  6.49083415e-01  3.95774101e-01]\n",
      " [-4.16009689e-01 -1.74335684e+00  1.37546573e-01  1.32509732e-01]\n",
      " [ 7.95669016e-01 -5.92373012e-01  4.78571135e-01  3.95774101e-01]\n",
      " [-1.73673948e-01 -5.92373012e-01  4.21733708e-01  1.32509732e-01]\n",
      " [ 5.53333275e-01  5.58610819e-01  5.35408562e-01  5.27406285e-01]\n",
      " [-1.14301691e+00 -1.51316008e+00 -2.60315415e-01 -2.62386821e-01]\n",
      " [ 9.16836886e-01 -3.62176246e-01  4.78571135e-01  1.32509732e-01]\n",
      " [-7.79513300e-01 -8.22569778e-01  8.07091462e-02  2.64141916e-01]\n",
      " [-1.02184904e+00 -2.43394714e+00 -1.46640561e-01 -2.62386821e-01]\n",
      " [ 6.86617933e-02 -1.31979479e-01  2.51221427e-01  3.95774101e-01]\n",
      " [ 1.89829664e-01 -1.97355361e+00  1.37546573e-01 -2.62386821e-01]\n",
      " [ 3.10997534e-01 -3.62176246e-01  5.35408562e-01  2.64141916e-01]\n",
      " [-2.94841818e-01 -3.62176246e-01 -8.98031345e-02  1.32509732e-01]\n",
      " [ 1.03800476e+00  9.82172869e-02  3.64896281e-01  2.64141916e-01]\n",
      " [-2.94841818e-01 -1.31979479e-01  4.21733708e-01  3.95774101e-01]\n",
      " [-5.25060772e-02 -8.22569778e-01  1.94384000e-01 -2.62386821e-01]\n",
      " [ 4.32165405e-01 -1.97355361e+00  4.21733708e-01  3.95774101e-01]\n",
      " [-2.94841818e-01 -1.28296331e+00  8.07091462e-02 -1.30754636e-01]\n",
      " [ 6.86617933e-02  3.28414053e-01  5.92245988e-01  7.90670654e-01]\n",
      " [ 3.10997534e-01 -5.92373012e-01  1.37546573e-01  1.32509732e-01]\n",
      " [ 5.53333275e-01 -1.28296331e+00  6.49083415e-01  3.95774101e-01]\n",
      " [ 3.10997534e-01 -5.92373012e-01  5.35408562e-01  8.77547895e-04]\n",
      " [ 6.74501145e-01 -3.62176246e-01  3.08058854e-01  1.32509732e-01]\n",
      " [ 9.16836886e-01 -1.31979479e-01  3.64896281e-01  2.64141916e-01]\n",
      " [ 1.15917263e+00 -5.92373012e-01  5.92245988e-01  2.64141916e-01]\n",
      " [ 1.03800476e+00 -1.31979479e-01  7.05920842e-01  6.59038469e-01]\n",
      " [ 1.89829664e-01 -3.62176246e-01  4.21733708e-01  3.95774101e-01]\n",
      " [-1.73673948e-01 -1.05276654e+00 -1.46640561e-01 -2.62386821e-01]\n",
      " [-4.16009689e-01 -1.51316008e+00  2.38717193e-02 -1.30754636e-01]\n",
      " [-4.16009689e-01 -1.51316008e+00 -3.29657076e-02 -2.62386821e-01]\n",
      " [-5.25060772e-02 -8.22569778e-01  8.07091462e-02  8.77547895e-04]\n",
      " [ 1.89829664e-01 -8.22569778e-01  7.62758269e-01  5.27406285e-01]\n",
      " [-5.37177559e-01 -1.31979479e-01  4.21733708e-01  3.95774101e-01]\n",
      " [ 1.89829664e-01  7.88807586e-01  4.21733708e-01  5.27406285e-01]\n",
      " [ 1.03800476e+00  9.82172869e-02  5.35408562e-01  3.95774101e-01]\n",
      " [ 5.53333275e-01 -1.74335684e+00  3.64896281e-01  1.32509732e-01]\n",
      " [-2.94841818e-01 -1.31979479e-01  1.94384000e-01  1.32509732e-01]\n",
      " [-4.16009689e-01 -1.28296331e+00  1.37546573e-01  1.32509732e-01]\n",
      " [-4.16009689e-01 -1.05276654e+00  3.64896281e-01  8.77547895e-04]\n",
      " [ 3.10997534e-01 -1.31979479e-01  4.78571135e-01  2.64141916e-01]\n",
      " [-5.25060772e-02 -1.05276654e+00  1.37546573e-01  8.77547895e-04]\n",
      " [-1.02184904e+00 -1.74335684e+00 -2.60315415e-01 -2.62386821e-01]\n",
      " [-2.94841818e-01 -8.22569778e-01  2.51221427e-01  1.32509732e-01]\n",
      " [-1.73673948e-01 -1.31979479e-01  2.51221427e-01  8.77547895e-04]\n",
      " [-1.73673948e-01 -3.62176246e-01  2.51221427e-01  1.32509732e-01]\n",
      " [ 4.32165405e-01 -3.62176246e-01  3.08058854e-01  1.32509732e-01]\n",
      " [-9.00681170e-01 -1.28296331e+00 -4.30827696e-01 -1.30754636e-01]\n",
      " [-1.73673948e-01 -5.92373012e-01  1.94384000e-01  1.32509732e-01]\n",
      " [ 5.53333275e-01  5.58610819e-01  1.27429511e+00  1.71209594e+00]\n",
      " [-5.25060772e-02 -8.22569778e-01  7.62758269e-01  9.22302838e-01]\n",
      " [ 1.52267624e+00 -1.31979479e-01  1.21745768e+00  1.18556721e+00]\n",
      " [ 5.53333275e-01 -3.62176246e-01  1.04694540e+00  7.90670654e-01]\n",
      " [ 7.95669016e-01 -1.31979479e-01  1.16062026e+00  1.31719939e+00]\n",
      " [ 2.12851559e+00 -1.31979479e-01  1.61531967e+00  1.18556721e+00]\n",
      " [-1.14301691e+00 -1.28296331e+00  4.21733708e-01  6.59038469e-01]\n",
      " [ 1.76501198e+00 -3.62176246e-01  1.44480739e+00  7.90670654e-01]\n",
      " [ 1.03800476e+00 -1.28296331e+00  1.16062026e+00  7.90670654e-01]\n",
      " [ 1.64384411e+00  1.24920112e+00  1.33113254e+00  1.71209594e+00]\n",
      " [ 7.95669016e-01  3.28414053e-01  7.62758269e-01  1.05393502e+00]\n",
      " [ 6.74501145e-01 -8.22569778e-01  8.76433123e-01  9.22302838e-01]\n",
      " [ 1.15917263e+00 -1.31979479e-01  9.90107977e-01  1.18556721e+00]\n",
      " [-1.73673948e-01 -1.28296331e+00  7.05920842e-01  1.05393502e+00]\n",
      " [-5.25060772e-02 -5.92373012e-01  7.62758269e-01  1.58046376e+00]\n",
      " [ 6.74501145e-01  3.28414053e-01  8.76433123e-01  1.44883158e+00]\n",
      " [ 7.95669016e-01 -1.31979479e-01  9.90107977e-01  7.90670654e-01]\n",
      " [ 2.24968346e+00  1.70959465e+00  1.67215710e+00  1.31719939e+00]\n",
      " [ 2.24968346e+00 -1.05276654e+00  1.78583195e+00  1.44883158e+00]\n",
      " [ 1.89829664e-01 -1.97355361e+00  7.05920842e-01  3.95774101e-01]\n",
      " [ 1.28034050e+00  3.28414053e-01  1.10378283e+00  1.44883158e+00]\n",
      " [-2.94841818e-01 -5.92373012e-01  6.49083415e-01  1.05393502e+00]\n",
      " [ 2.24968346e+00 -5.92373012e-01  1.67215710e+00  1.05393502e+00]\n",
      " [ 5.53333275e-01 -8.22569778e-01  6.49083415e-01  7.90670654e-01]\n",
      " [ 1.03800476e+00  5.58610819e-01  1.10378283e+00  1.18556721e+00]\n",
      " [ 1.64384411e+00  3.28414053e-01  1.27429511e+00  7.90670654e-01]\n",
      " [ 4.32165405e-01 -5.92373012e-01  5.92245988e-01  7.90670654e-01]\n",
      " [ 3.10997534e-01 -1.31979479e-01  6.49083415e-01  7.90670654e-01]\n",
      " [ 6.74501145e-01 -5.92373012e-01  1.04694540e+00  1.18556721e+00]\n",
      " [ 1.64384411e+00 -1.31979479e-01  1.16062026e+00  5.27406285e-01]\n",
      " [ 1.88617985e+00 -5.92373012e-01  1.33113254e+00  9.22302838e-01]\n",
      " [ 2.49201920e+00  1.70959465e+00  1.50164482e+00  1.05393502e+00]\n",
      " [ 6.74501145e-01 -5.92373012e-01  1.04694540e+00  1.31719939e+00]\n",
      " [ 5.53333275e-01 -5.92373012e-01  7.62758269e-01  3.95774101e-01]\n",
      " [ 3.10997534e-01 -1.05276654e+00  1.04694540e+00  2.64141916e-01]\n",
      " [ 2.24968346e+00 -1.31979479e-01  1.33113254e+00  1.44883158e+00]\n",
      " [ 5.53333275e-01  7.88807586e-01  1.04694540e+00  1.58046376e+00]\n",
      " [ 6.74501145e-01  9.82172869e-02  9.90107977e-01  7.90670654e-01]\n",
      " [ 1.89829664e-01 -1.31979479e-01  5.92245988e-01  7.90670654e-01]\n",
      " [ 1.28034050e+00  9.82172869e-02  9.33270550e-01  1.18556721e+00]\n",
      " [ 1.03800476e+00  9.82172869e-02  1.04694540e+00  1.58046376e+00]\n",
      " [ 1.28034050e+00  9.82172869e-02  7.62758269e-01  1.44883158e+00]\n",
      " [-5.25060772e-02 -8.22569778e-01  7.62758269e-01  9.22302838e-01]\n",
      " [ 1.15917263e+00  3.28414053e-01  1.21745768e+00  1.44883158e+00]\n",
      " [ 1.03800476e+00  5.58610819e-01  1.10378283e+00  1.71209594e+00]\n",
      " [ 1.03800476e+00 -1.31979479e-01  8.19595696e-01  1.44883158e+00]\n",
      " [ 5.53333275e-01 -1.28296331e+00  7.05920842e-01  9.22302838e-01]\n",
      " [ 7.95669016e-01 -1.31979479e-01  8.19595696e-01  1.05393502e+00]\n",
      " [ 4.32165405e-01  7.88807586e-01  9.33270550e-01  1.44883158e+00]\n",
      " [ 6.86617933e-02 -1.31979479e-01  7.62758269e-01  7.90670654e-01]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "Xscaled = scale(X)\n",
    "print(Xscaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importez la fonction `PCA` donnée par  `sklearn.decomposition`.\n",
    "\n",
    "Regarder la documentation https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html \n",
    "et faites une analyse en composantes principales sur vos données normalisées en utilisant deux composantes. \n",
    "Stockez les deux composantes principales dans une variable `pc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.52106591 -0.26934744  0.5804131   0.56485654]\n",
      " [ 0.37741762  0.92329566  0.02449161  0.06694199]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# n = min(Xscaled.shape[0], Xscaled.shape[1])\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pc = pca.fit_transform(Xscaled)\n",
    "\n",
    "print(pca.components_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trouver la fraction de la variance dans les données expliquée par ces 2 composantes principales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9581320720000166)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traçons la nouvelle disposition des données par rapport aux deux composantes principales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x284924f9e80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15,9)\n",
    "\n",
    "for i in range(3): # i=0,1,2 (types des fleurs)\n",
    "    idx = (y == i)\n",
    "   \n",
    "    plt.scatter(pc[idx,0], pc[idx,1], label=iris.target_names[i])\n",
    "    \n",
    "   \n",
    "plt.title('2 component PCA')\n",
    "plt.xlabel('1st principal component')\n",
    "plt.ylabel('2nd principal component')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous observons que les données sont assez bien séparées. Mais si nous traçons toutes les variables les unes contre les autres, nous constatons qu'il existe également des paires de variables similaires, voire mieux séparées. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (15,9)\n",
    "fig, ax = plt.subplots(3,3)\n",
    "\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        if i < j:\n",
    "            for k in range(3):\n",
    "                idx = (y == k)\n",
    "                ax[i][j-1].scatter(X[idx,i], X[idx,j], label=iris.target_names[k])\n",
    "                ax[i][j-1].set_xlabel(iris.feature_names[i])\n",
    "                ax[i][j-1].set_ylabel(iris.feature_names[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effectuer une analyse discriminante linéaire en utilisant les 2 composantes principales ci-dessus.\n",
    "Quelle proportion des * données d'entraînement * est classée correctement?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "clf = LDA()\n",
    "clf.fit(pc, y)\n",
    "clf.score(pc,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparez avec les données initiales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "clf = LDA()\n",
    "clf.fit(Xscaled, y)\n",
    "clf.score(Xscaled,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse en composantes principales sur un portefeuille "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from pandas_datareader import DataReader\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "#from matplotlib.pylab import rcParams\n",
    "#rcParams['figure.figsize'] = 12, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On construit un portefeuille de 10 actifs : IBM, MSFT, FB, T, INTC, ABX, NEM, AU, AEM, GFI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lione\\AppData\\Local\\Temp\\ipykernel_46484\\1266330452.py:6: FutureWarning: YF.download() has changed argument auto_adjust default to True\n",
      "  portfolio_returns = yf.download(tickers, start, end).loc[:,'Close'].pct_change()[1:]\n",
      "[*********************100%***********************]  9 of 9 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ticker           AEM        AU       GFI       IBM      INTC      META  \\\n",
      "Date                                                                     \n",
      "2015-09-02 -0.021136  0.000000 -0.050167  0.016611  0.028037  0.030494   \n",
      "2015-09-03 -0.035140  0.007884  0.007042  0.011927  0.016783 -0.019357   \n",
      "2015-09-04  0.000877 -0.009127  0.010490 -0.020984 -0.019257  0.001248   \n",
      "2015-09-08 -0.006576  0.026316 -0.010381  0.024565  0.034362  0.014389   \n",
      "2015-09-09 -0.048985 -0.037179 -0.016451 -0.014806 -0.008814  0.010164   \n",
      "...              ...       ...       ...       ...       ...       ...   \n",
      "2016-10-25  0.026921  0.023686  0.036855  0.002059 -0.004538 -0.007428   \n",
      "2016-10-26 -0.023997 -0.023138 -0.033176  0.006164 -0.005128 -0.009449   \n",
      "2016-10-27  0.007645 -0.011103 -0.012255  0.010144 -0.003150 -0.010302   \n",
      "2016-10-28  0.012918  0.011976  0.004963 -0.004826 -0.002011  0.012337   \n",
      "2016-10-31  0.028340  0.016272  0.024691  0.007077  0.003742 -0.002285   \n",
      "\n",
      "Ticker          MSFT       NEM         T  \n",
      "Date                                      \n",
      "2015-09-02  0.036824 -0.031082  0.015471  \n",
      "2015-09-03  0.003229 -0.001234  0.006703  \n",
      "2015-09-04 -0.020460 -0.030265 -0.014528  \n",
      "2015-09-08  0.030040  0.018182  0.017813  \n",
      "2015-09-09 -0.018683 -0.021303 -0.010863  \n",
      "...              ...       ...       ...  \n",
      "2016-10-25 -0.000164  0.020926 -0.004341  \n",
      "2016-10-26 -0.005902 -0.011205 -0.007357  \n",
      "2016-10-27 -0.008742 -0.038419  0.002471  \n",
      "2016-10-28 -0.003827  0.016959 -0.000274  \n",
      "2016-10-31  0.000835  0.046919  0.007669  \n",
      "\n",
      "[294 rows x 9 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "tickers = ['IBM','MSFT', 'META', 'T', 'INTC','NEM', 'AU', 'AEM', 'GFI']\n",
    "portfolio_returns = pd.DataFrame()\n",
    "\n",
    "\n",
    "start, end = dt.datetime(2015, 9, 1), dt.datetime(2016, 11, 1)\n",
    "portfolio_returns = yf.download(tickers, start, end).loc[:,'Close'].pct_change()[1:]\n",
    "\n",
    "\n",
    "print(portfolio_returns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of timestamps is 294.\n",
      "The number of stocks is 9.\n",
      "[[-0.02113588  0.         -0.05016721 ...  0.03682429 -0.03108226\n",
      "   0.01547057]\n",
      " [-0.03513954  0.0078843   0.00704234 ...  0.003229   -0.00123362\n",
      "   0.0067033 ]\n",
      " [ 0.00087741 -0.00912651  0.01048959 ... -0.02045994 -0.03026547\n",
      "  -0.01452778]\n",
      " ...\n",
      " [ 0.00764474 -0.01110287 -0.01225476 ... -0.00874158 -0.03841894\n",
      "   0.00247085]\n",
      " [ 0.01291772  0.01197604  0.0049628  ... -0.00382703  0.01695901\n",
      "  -0.00027405]\n",
      " [ 0.02833961  0.01627219  0.02469122 ...  0.0008351   0.0469191\n",
      "   0.00766911]]\n",
      "67.13% of the variance is explained by the first 2 PCs\n",
      "[[ 0.48981997  0.4855935   0.46510703  0.11405771  0.07399805  0.02854275\n",
      "   0.03996444  0.48823719  0.22063211]\n",
      " [-0.10407773 -0.107373   -0.1348654   0.44624456  0.49179232  0.38821907\n",
      "   0.49730219 -0.0571833   0.3422906 ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "num_pc = 2\n",
    "\n",
    "X = np.asarray(portfolio_returns)\n",
    "\n",
    "[n,m] = X.shape\n",
    "print('The number of timestamps is {}.'.format(n))\n",
    "print('The number of stocks is {}.'.format(m))\n",
    "print(X)\n",
    "\n",
    "X=scale(X)\n",
    "pca = PCA(n_components=num_pc)\n",
    "\n",
    "pc = pca.fit_transform(X)\n",
    "\n",
    "percentage_explained_variance_ratio =  pca.explained_variance_ratio_\n",
    "percentage_sum = np.sum(percentage_explained_variance_ratio)\n",
    "percentage_cum= np.cumsum(percentage_explained_variance_ratio)\n",
    "\n",
    "\n",
    "print('{0:.2f}% of the variance is explained by the first 2 PCs'.format(percentage_sum*100))\n",
    "\n",
    "pca_components = pca.components_\n",
    "\n",
    "\n",
    "print(pca_components)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notez que la plus grande partie de la variance des rendements de ces actifs peut être expliquée par les deux premières composantes principales.\n",
    "Nous rassemblons maintenant les deux premières composantes principales et en traçons les contributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,len(percentage_explained_variance_ratio)+1,1)\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(x, percentage_explained_variance_ratio*100, align = \"center\")\n",
    "plt.title('Contribution of principal components',fontsize = 16)\n",
    "plt.xlabel('principal components',fontsize = 16)\n",
    "plt.ylabel('percentage',fontsize = 16)\n",
    "plt.xticks(x,fontsize = 16) \n",
    "plt.yticks(fontsize = 16)\n",
    "plt.xlim([0, num_pc+1])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, percentage_cum*100,'ro-')\n",
    "plt.xlabel('principal components',fontsize = 16)\n",
    "plt.ylabel('percentage',fontsize = 16)\n",
    "plt.title('Cumulative contribution of principal components',fontsize = 16)\n",
    "plt.xticks(x,fontsize = 16) \n",
    "plt.yticks(fontsize = 16)\n",
    "plt.xlim([1, num_pc])\n",
    "plt.ylim([50,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "À partir de ces composantes principales, nous pouvons construire des \"facteurs de risque statistiques\", similaires aux facteurs de risque plus conventionnels. Celles-ci devraient nous donner une idée de la part des rendements du portefeuille provenant de certaines caractéristiques statistiques non observables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.41589163 -0.19220526]\n",
      " [ 0.60043354  0.01944583]\n",
      " [ 0.70142055  0.22025992]\n",
      " [-0.03946288 -0.01892128]\n",
      " [-0.0562441   0.04181034]\n",
      " [-0.07469913  0.04543184]\n",
      " [-0.07270401  0.053181  ]\n",
      " [ 0.4078595  -0.22102769]\n",
      " [ 0.00754018 -0.00915527]]\n"
     ]
    }
   ],
   "source": [
    "factor_returns = ??? # faire passer les données dans la nouvelle base\n",
    "\n",
    "print(factor_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "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>factor 1</th>\n",
       "      <th>factor 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AEM</th>\n",
       "      <td>0.415892</td>\n",
       "      <td>-0.192205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>0.600434</td>\n",
       "      <td>0.019446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFI</th>\n",
       "      <td>0.701421</td>\n",
       "      <td>0.220260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>-0.039463</td>\n",
       "      <td>-0.018921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTC</th>\n",
       "      <td>-0.056244</td>\n",
       "      <td>0.041810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>META</th>\n",
       "      <td>-0.074699</td>\n",
       "      <td>0.045432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>-0.072704</td>\n",
       "      <td>0.053181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEM</th>\n",
       "      <td>0.407859</td>\n",
       "      <td>-0.221028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.007540</td>\n",
       "      <td>-0.009155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      factor 1  factor 2\n",
       "AEM   0.415892 -0.192205\n",
       "AU    0.600434  0.019446\n",
       "GFI   0.701421  0.220260\n",
       "IBM  -0.039463 -0.018921\n",
       "INTC -0.056244  0.041810\n",
       "META -0.074699  0.045432\n",
       "MSFT -0.072704  0.053181\n",
       "NEM   0.407859 -0.221028\n",
       "T     0.007540 -0.009155"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_exposures = pd.DataFrame(index=[\"factor 1\", \"factor 2\"], \n",
    "                                columns=portfolio_returns.columns,\n",
    "                                data = factor_returns.T).T\n",
    "\n",
    "factor_exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = factor_exposures.index\n",
    "data = factor_exposures.values\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplots_adjust(bottom = 0.1)\n",
    "plt.scatter(data[:, 0], data[:, 1], marker='o', s=300, c='m',cmap=plt.get_cmap('Spectral'))\n",
    "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
    "plt.xlabel('factor exposure of PC1')\n",
    "plt.ylabel('factor exposure of PC2')\n",
    "\n",
    "for label, x, y in zip(labels, data[:, 0], data[:, 1]):\n",
    "    plt.annotate(\n",
    "        label,\n",
    "        xy=(x, y), xytext=(-20, 20),\n",
    "        textcoords='offset points', ha='right', va='bottom',\n",
    "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
    "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La création de facteurs de risque statistiques nous permet de décomposer davantage les rendements d'un portefeuille pour avoir une meilleure idée du risque. Cela peut être utilisé comme une étape supplémentaire après l'attribution de performance avec des facteurs de risque plus courants pour tenter de prendre en compte des risques inconnus supplémentaires."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**: Les deux compsantes principales cachées ont séparé les données en deux groupes. Si on regarde plus en détail on voit que les 5 indices sur la gauche du graphique sont reliés au secteur d'innovation technologique et les 5 autres sont liés à des entreprises de recherche minière d'or. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}