not assume equal_cov__FASHION_MNIST - DPER - SOFT IMPUTE.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"not assume equal_cov__FASHION_MNIST - DPER - SOFT IMPUTE.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"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":"3.8.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"01Q1wGI3uIdg"},"source":["## libraries and function "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"iEdO9dJPION8","executionInfo":{"status":"ok","timestamp":1632427814507,"user_tz":-120,"elapsed":21250,"user":{"displayName":"Thu Nguyễn","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjaRl8WgKOjLdrEa69IqFm__Rq7udZ763hhFDl9=s64","userId":"04714273295929622355"}},"outputId":"39c9bd21-3a06-47e0-c880-0628cb29ad9c"},"source":["# !pip install impyute\n","!pip install fancyimpute\n","from sklearn import datasets\n","from sklearn.preprocessing import LabelEncoder, StandardScaler\n","from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as skLDA\n","from sklearn.experimental import enable_iterative_imputer\n","from sklearn.impute import IterativeImputer\n","from scipy import stats\n","import numpy as np\n","# import impyute as impy\n","from fancyimpute import  SoftImpute\n","import pandas as pd\n","import time \n","# from missingpy import MissForest"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting fancyimpute\n","  Downloading fancyimpute-0.6.1-py3-none-any.whl (30 kB)\n","Collecting knnimpute>=0.1.0\n","  Downloading knnimpute-0.1.0.tar.gz (8.3 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np.var(arr)\n","\n","def musMLE(X,y,G):\n","    n,p = X.shape[0], X.shape[1]\n","    f = lambda g: np.nanmean(X[y==g,:],axis=0)\n","    musMLE = np.array([f(g) for g in range(G)])    \n","    return musMLE.T\n","\n","def Smle(X,y,musMLE,g):\n","    '''\n","    function to compute the covariance matrix for the g-th class\n","    X: input, should be a numpy array\n","    y: label\n","    G: number of classes\n","    g: class index\n","    output:\n","    - mus: each row is a class mean\n","    - S: common covariance matrix of class 1,2,..., G \n","    '''\n","    epsilon = 1e-5 # define epsilon to put r down to 0 if r < epsilon\n","    Xg, yg = X[y==g,:], y[y==g]\n","    n,p = Xg.shape[0], Xg.shape[1] \n"," \n","    S = np.diag([diag_term(Xg,i) for i in range(p)]) \n","\n","    for i in range(p):      \n","      for j in range(i):\n","        if ((S[i,i] == 0.) | (S[j,j] == 0.)):\n","          S[i,j] = S[j,i] = 0.\n","          continue\n","\n","        mat = Xg[:,[i,j]]\n","\n","        # drop rows with NA\n","        idx = ~np.isnan(mat).any(axis=1)\n","        mat, y_arr = mat[idx], yg[idx]\n","        A = mg = len(y_arr) \n","\n","        s11 = mg*np.var(mat[:,0])\n","        s22 = mg*np.var(mat[:,1])\n","        s12 = sum((mat[:,0]-musMLE[i,g])*(mat[:,1]-musMLE[j,g]))\n","        B = S[i,i]*S[j,j]*A - s22 * S[i,i] - s11 * S[j,j]\n","        coefficient = [-A, s12, B, s12*S[i,i]*S[j,j]]\n","        r = np.roots(coefficient)\n","        r = r[abs(np.imag(r)) < epsilon]\n","        r = np.real(r)\n","        r[abs(r) < epsilon] = 0\n"," \n","        if len(r)>1:\n","          condi_var = S[j,j] - r**2/S[i,i]\n","          eta = -A*np.log(condi_var)-(S[j,j]-2*r/S[i,i]*s12 + r**2/S[i,i]**2*s11)/condi_var\n","          r = r[eta == max(eta[~np.isnan(eta)])]\n","\n","        if len(r) > 1: \n","          if sum(r==0.0) == len(r):\n","            r = 0.\n","          else:  \n","            w = np.cov(mat, rowvar=False)  \n","            #r = r[w[0,1]*r>=0]\n","            r = r[np.abs(r-w[0,1]).argmin()] # select r that is closet to w[0,1] \n","\n","        S[i,j] = S[j,i] = r\n","    return S"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XhPzOEDaePDB"},"source":["## Individual compute error function "]},{"cell_type":"code","metadata":{"id":"XXAyfAX6eUko","executionInfo":{"status":"ok","timestamp":1632427815166,"user_tz":-120,"elapsed":5,"user":{"displayName":"Thu Nguyễn","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjaRl8WgKOjLdrEa69IqFm__Rq7udZ763hhFDl9=s64","userId":"04714273295929622355"}}},"source":["def err(mus, S, mus_est, S_est):\n","  er = [np.linalg.norm(mus_est-mus)/mus.size,\n","        np.linalg.norm(S_est.flatten().flatten()-S.flatten())/S.size]  \n","  return np.mean(er)  \n","\n","def generate_nan(Xtrain, missing_rate):\n","  Xshape = Xtrain.shape\n","  na_id = np.random.randint(0,Xtrain.size,round(missing_rate*Xtrain.size))\n","  Xtr_nan = Xtrain.flatten()\n","  Xtr_nan[na_id] = np.nan \n","  return Xtr_nan.reshape(Xshape) "],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"-3ehJtbOKb8u","executionInfo":{"status":"ok","timestamp":1632427815166,"user_tz":-120,"elapsed":4,"user":{"displayName":"Thu Nguyễn","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjaRl8WgKOjLdrEa69IqFm__Rq7udZ763hhFDl9=s64","userId":"04714273295929622355"}}},"source":["def compute_err_mle(Xtrain, ytrain, G, missing_rate, runs = 10):\n","  e_rate = []\n","  run_time = []\n","  for i in  range(runs):\n","    Xtr_nan = generate_nan(Xtrain, missing_rate)\n","    \n","    scaler = StandardScaler()\n","    scaler.fit(Xtr_nan)\n","    Xtr_nan = scaler.transform(Xtr_nan)\n","    Xtrain = scaler.transform(Xtrain)\n","    \n","    # estimate parameters from full data\n","    # each row is a mean of a class\n","    mus = np.array([np.mean(Xtrain[ytrain==g,:], axis=0) for g in np.arange(G)])\n","    S = np.array([np.cov(Xtrain[ytrain==g,:],rowvar =False) \n","             for g in np.arange(G)])\n","    # MLEs approach\n","    start = time.time()\n","    mus_mle = musMLE(Xtr_nan,y,G)\n","    S_mle = np.array([Smle(Xtr_nan,y,mus_mle, g) for g in range(G)])   \n","    mle_err = err(mus, S, mus_mle.T, S_mle)\n","    mle_time = time.time()-start  \n","    e_rate.append(mle_err)\n","    run_time.append(mle_time)\n","  e_rate = np.asarray(e_rate)\n","  return np.mean(e_rate), np.std(e_rate), np.mean(np.asarray(run_time))"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rclni2O8rvDt","executionInfo":{"status":"ok","timestamp":1632427815167,"user_tz":-120,"elapsed":5,"user":{"displayName":"Thu Nguyễn","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjaRl8WgKOjLdrEa69IqFm__Rq7udZ763hhFDl9=s64","userId":"04714273295929622355"}}},"source":["def compute_err_soft(Xtrain, ytrain, G, missing_rate, runs = 10):\n","  e_rate = []\n","  run_time = []\n","  for i in  range(runs):\n","    Xtr_nan = generate_nan(Xtrain, missing_rate)\n","    \n","    scaler = StandardScaler()\n","    scaler.fit(Xtr_nan)\n","    Xtr_nan = scaler.transform(Xtr_nan)\n","    Xtrain = scaler.transform(Xtrain)\n","    \n","    # estimate parameters from full data\n","    # each row is a mean of a class\n","    mus = np.array([np.mean(Xtrain[ytrain==g,:], axis=0) for g in np.arange(G)])\n","    S = np.array([np.cov(Xtrain[ytrain==g,:],rowvar =False) \n","             for g in np.arange(G)])\n","\n","    start = time.time()\n","    Xtr_softimpute = SoftImpute(max_iters = 100).fit_transform(Xtr_nan)\n","    mus_softimpute = np.asarray([np.mean(Xtr_softimpute[ytrain==g,:], axis=0\n","                                         ) for g in np.arange(G)])\n","    S_softimpute = np.asarray([np.cov(Xtr_softimpute[ytrain==g,:], rowvar =False) \n","             for g in np.arange(G)])\n","    print('mus soft impute', mus_softimpute)\n","    print('S soft impute', S_softimpute)\n","    softimpute_err =  err(mus, S, mus_softimpute, S_softimpute)\n","    softimpute_time = time.time()-start\n","    e_rate.append(softimpute_err)\n","    run_time.append(softimpute_time)\n","\n","  e_rate = np.asarray(e_rate)\n","  return np.mean(e_rate), np.std(e_rate),np.mean(np.asarray(run_time))"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xxFKee5wgcJk"},"source":["### FASHION MNIST"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tOG38sMNgeTR","executionInfo":{"status":"ok","timestamp":1632427681564,"user_tz":-120,"elapsed":5654,"user":{"displayName":"Thu Nguyễn","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjaRl8WgKOjLdrEa69IqFm__Rq7udZ763hhFDl9=s64","userId":"04714273295929622355"}},"outputId":"fcaa161b-c694-48bd-b48e-6a9e93f93326"},"source":["import tensorflow as tf\n","import tensorflow_datasets as tfds\n","\n","fashion_mnist = tf.keras.datasets.fashion_mnist\n","(Xtrain, ytrain), (Xtest, ytest) = fashion_mnist.load_data()\n","Xtrain = Xtrain.astype(float).reshape((60000,784))\n","Xtest = Xtest.astype(float).reshape((10000,784))\n","\n","X = np.vstack((Xtrain, Xtest))\n","y = np.hstack((ytrain, ytest))\n","\n","# set random seed and shuffle the data\n","np.random.seed(1)\n","idx = np.arange(len(y))\n","np.random.shuffle(idx)\n","X, y = X[idx,:], y[idx]  \n","\n","X.shape, y.shape "],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","32768/29515 [=================================] - 0s 0us/step\n","40960/29515 [=========================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26427392/26421880 [==============================] - 0s 0us/step\n","26435584/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","16384/5148 [===============================================================================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4423680/4422102 [==============================] - 0s 0us/step\n","4431872/4422102 [==============================] - 0s 0us/step\n"]},{"output_type":"execute_result","data":{"text/plain":["((70000, 784), (70000,))"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"HMwOTYJTgnDP"},"source":["G = 10\n","mle_err = np.array([compute_err_mle(X, y, G, .2, runs = 10),\n","                    compute_err_mle(X, y, G, .35, runs = 10),\n","                    compute_err_mle(X, y, G, .5, runs = 10),\n","                    compute_err_mle(X, y, G, .65, runs = 10),\n","                    compute_err_mle(X, y, G, .8, runs = 10)])\n","print('fashion mnist: soft - dper error')\n","print(mle_err.round(3))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3ZBvjMfTKb81","outputId":"090aa0c1-376d-4538-861d-b755172e33db"},"source":["G = 10\n","soft_err = np.array([compute_err_soft(X, y, G, .2, runs = 10),\n","                    compute_err_soft(X, y, G, .35, runs = 10),\n","                    compute_err_soft(X, y, G, .5, runs = 10),\n","                    compute_err_soft(X, y, G, .65, runs = 10),\n","                    compute_err_soft(X, y, G, .8, runs = 10)])\n","print('fashion mnist: soft - impute error')\n","print(soft_err.round(3))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[2.89677844e-05, 6.16749044e+03],\n","       [3.95722202e-05, 5.05816724e+03],\n","       [5.04169202e-05, 4.26305263e+03],\n","       [5.89859441e-05, 3.49414068e+03],\n","       [8.19900695e-05, 2.91714304e+03]])"]},"metadata":{"tags":[]},"execution_count":21}]}]}