{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " \n", "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n", "###
Author: Archit Rungta\n", " \n", "##
Tutorial\n", "##
Imputing missing data with fancyimpute" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hi folks!\n", "\n", "Often in real world applications of data analysis, we run into the problem of missing data. This can happen due to a multitude of reasons such as:\n", " - The data was compiled from different sources/times \n", " - Corrupted during storage\n", " - Certain fields were optional\n", " - etc.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook has the following sections:\n", " 1. Introduction\n", " 2. The Problem\n", " 3. KNN Imputation\n", " 4. Comparison And Application\n", " 5. Summary\n", " 6. Further Reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we look at the problem of missing data in data analytics. Then, we categorize the different types of missing data and briefly discuss the specific issue presented by each specific type. Finally, we look at various methods of handling data imputation and compare their accuracy on a real-world dataset with logistic regression. We also look at the validity of a commonly held assumption about imputation techniques. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Introduction\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Broadly, missing data is classified into 3 categories. \n", " - Missing Completely At Random (MCAR)\n", " > Values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random\n", " - Missing At Random (MAR)\n", " >Missing at random (MAR) occurs when the missingness is not random, but where missingness can be fully accounted for by variables where there is complete information\n", " - Missing Not At Random (MNAR)\n", " >Missing not at random (MNAR) (also known as nonignorable nonresponse) is data that is neither MAR nor MCAR\n", " \n", "Data compilation from different sources is an example of MAR while data corruption is an example of MCAR. MNAR is not a problem we can fix with imputation because this is **non-ignorable non-response.** The only thing we can do about MNAR is to gather more information from different sources or ignore it all-together. As such we are not going to talk about MNAR anymore in this tutorial. \n", "\n", "All of the techniques that follow are applicable only for MCAR. However, in real world scenarios, MAR is more common. As such, we will treat MAR as MCAR only which gives a reasonably good approximation in practice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "##
The Problem\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with a toy example, \n", "\n", "\\begin{align}\n", "\\ y & = \\sin(x) x\\, \\text{for $|x|<=6$}\n", "\\end{align}\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np # vectors and matrices\n", "import pandas as pd # tables and data manipulations\n", "import matplotlib.pyplot as plt # plots\n", "import seaborn as sns # more plots\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-6,6)\n", "y = np.asarray([x1*np.sin(x1) for x1 in x])\n", "plt.scatter(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's delete some points on random to get an MCAR dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "missing_fraction = 0.3\n", "indices = np.random.randint(1,len(x)-1, size=int((1-missing_fraction)*len(x)))\n", "x_mcar = x[indices]\n", "y_mcar = y[indices]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x_mcar,y_mcar)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Throughout this tutorial, we will use MSE as an indicator of how good an imputation technique is when we have the original dataset and accuracy on predictions when we don't" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error as mse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try the easiest methods first:\n", " - Mean\n", " - Median" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.26262965947143" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred_mean = np.array(y)\n", "for ind in list(set(np.linspace(0,len(x)-1))-set(indices)):\n", " y_pred_mean[int(ind)] = np.mean(y_mcar)\n", "plt.scatter(x,y_pred_mean)\n", "mse(y_pred_mean,y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.2995309240897033" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred_median = np.array(y)\n", "for ind in list(set(np.linspace(0,len(x)-1))-set(indices)):\n", " y_pred_median[int(ind)] = np.median(y_mcar)\n", "plt.scatter(x,y_pred_median)\n", "mse(y_pred_median,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, this seems like pretty awful. Let's see what fancyimpute has to offer. \n", "**Note: You need TensorFlow**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: fancyimpute in c:\\programdata\\anaconda3\\lib\\site-packages (0.4.0)\n", "Requirement already satisfied: scikit-learn>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (0.19.1)\n", "Requirement already satisfied: numpy>=1.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (1.14.3)\n", "Requirement already satisfied: tensorflow in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (1.10.0)\n", "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (1.1.0)\n", "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (1.11.0)\n", "Requirement already satisfied: np-utils in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (0.5.5.2)\n", "Requirement already satisfied: cvxpy>=1.0.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (1.0.9)\n", "Requirement already satisfied: knnimpute in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (0.1.0)\n", "Requirement already satisfied: keras>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from fancyimpute) (2.2.2)\n", "Requirement already satisfied: termcolor>=1.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (1.1.0)\n", "Requirement already satisfied: protobuf>=3.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (3.6.0)\n", "Requirement already satisfied: setuptools<=39.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (39.1.0)\n", "Requirement already satisfied: astor>=0.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (0.7.1)\n", "Requirement already satisfied: tensorboard<1.11.0,>=1.10.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (1.10.0)\n", "Requirement already satisfied: grpcio>=1.8.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (1.12.1)\n", "Requirement already satisfied: absl-py>=0.1.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (0.5.0)\n", "Requirement already satisfied: gast>=0.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (0.2.0)\n", "Requirement already satisfied: wheel>=0.26 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow->fancyimpute) (0.31.1)\n", "Requirement already satisfied: future>=0.16 in c:\\programdata\\anaconda3\\lib\\site-packages (from np-utils->fancyimpute) (0.16.0)\n", "Requirement already satisfied: scs>=1.1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (2.0.2)\n", "Requirement already satisfied: multiprocess in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (0.70.6.1)\n", "Requirement already satisfied: toolz in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (0.9.0)\n", "Requirement already satisfied: osqp in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (0.4.1)\n", "Requirement already satisfied: fastcache in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (1.0.2)\n", "Requirement already satisfied: ecos>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from cvxpy>=1.0.6->fancyimpute) (2.0.5)\n", "Requirement already satisfied: pyyaml in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=2.0.0->fancyimpute) (3.12)\n", "Requirement already satisfied: h5py in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=2.0.0->fancyimpute) (2.7.1)\n", "Requirement already satisfied: keras_applications==1.0.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=2.0.0->fancyimpute) (1.0.4)\n", "Requirement already satisfied: keras_preprocessing==1.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=2.0.0->fancyimpute) (1.0.2)\n", "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<1.11.0,>=1.10.0->tensorflow->fancyimpute) (2.6.11)\n", "Requirement already satisfied: werkzeug>=0.11.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<1.11.0,>=1.10.0->tensorflow->fancyimpute) (0.14.1)\n", "Requirement already satisfied: dill>=0.2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from multiprocess->cvxpy>=1.0.6->fancyimpute) (0.2.8.2)\n", "Requirement already satisfied: pyreadline>=1.7.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from dill>=0.2.8.1->multiprocess->cvxpy>=1.0.6->fancyimpute) (2.1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 10.0.1, however version 18.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] } ], "source": [ "!pip install fancyimpute" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } ], "source": [ "import fancyimpute" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imputing row 1/50 with 0 missing, elapsed time: 0.004\n" ] } ], "source": [ "y_pred_knn = np.concatenate((np.array(y).reshape(-1,1), np.array(x).reshape(-1,1)), axis=1)\n", "for ind in indices:\n", " y_pred_knn[int(ind)] = [float(\"NaN\"), y_pred_knn[int(ind)][1]]\n", "y_pred_knn = fancyimpute.KNN(k=3).fit_transform(y_pred_knn)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "y_pred_knn_2 = [x[0] for x in y_pred_knn]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.11433938631068095" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x,y_pred_knn_2)\n", "mse(y_pred_knn_2,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, fancyimpute has performed much better than mean or median methods on this toy dataset. \n", "\n", "Next up, we get some in-depth understanding of how the KNN algorithm for fancyimpute works and apply it to some real datasets. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "##
KNN Imputation\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method used for classification and regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The assumption behind using KNN for missing values is that a point value can be approximated by the values of the points that are closest to it, based on other variables.\n", "\n", "The fancyimpute KNN algorithm works by calculating the k nearest neighbors which have the missing features available and then weights them based on Euclidean distance from the target row. The missing value is then calculated as a weighted mean from these neighboring rows.\n", "\n", "Below is an implementation for k = 2. Because we know our data is sorted we can code this much more efficiently. However, this isn't a general implementation. We also ignore the possibility that both of the closest neighbors can be on the same side to reduce the complexity of the code." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "y_cust = np.array(y)\n", "for ind in indices:\n", " low1 = ind-1\n", " while low1 in indices:\n", " low1 = low1 - 1\n", " high1 = ind + 1\n", " while high1 in indices:\n", " high1 = high1 + 1\n", " d1 = 1/(ind - low1)\n", " d2 = 1/(high1 - ind)\n", " y_cust[ind] = (d1*y_cust[low1]+d2*y_cust[high1])/(d1+d2)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.034650998762531186" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x,y_cust)\n", "mse(y_cust,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Comparison and Application\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the Pima Indians Diabetes database for our example use case. This is an example of a MAR dataset but we will treat it as MCAR to make the best out of what we have. You can download the data from - https://www.kaggle.com/kumargh/pimaindiansdiabetescsv" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('pima-indians-diabetes.csv',header=None)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8\n", "0 6 148 72 35 0 33.6 0.627 50 1\n", "1 1 85 66 29 0 26.6 0.351 31 0\n", "2 8 183 64 0 0 23.3 0.672 32 1\n", "3 1 89 66 23 94 28.1 0.167 21 0\n", "4 0 137 40 35 168 43.1 2.288 33 1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " 0. Number of times pregnant\n", " 1. Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n", " 2. Diastolic blood pressure (mm Hg)\n", " 3. Triceps skin fold thickness (mm)\n", " 4. 2-Hour serum insulin (mu U/ml)\n", " 5. Body mass index (weight in kg/(height in m)^2)\n", " 6. Diabetes pedigree function\n", " 7. Age (years)\n", " 8. Class variable (0 or 1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, a person cannot have triceps sking fold thickness as 0 mm. This is a missing value and we need to replace 0 with NaN to let our algorithms know that it's a missing value.\n", "\n", "By reading the descriptions we can be sure that columns 1,2,3,4,5,6 and 7 cannot have zero values. As such, we will mark 0s as missing. \n", "\n", "Also, imputing functions work better with scaled features so we will use MinMaxScaler to scale every feature between 0 to 1." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 5\n", "2 35\n", "3 227\n", "4 374\n", "5 11\n", "6 0\n", "7 0\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df[[1,2,3,4,5,6,7]] == 0).sum()\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "\n", "df = pd.DataFrame(data=MinMaxScaler().fit_transform(df.values), columns=df.columns, index=df.index)\n", "df[[1,2,3,4,5,6,7]] = df[[1,2,3,4,5,6,7]].replace(0,float('NaN'))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "0 0.352941 0.743719 0.590164 0.353535 NaN 0.500745 0.234415 \n", "1 0.058824 0.427136 0.540984 0.292929 NaN 0.396423 0.116567 \n", "2 0.470588 0.919598 0.524590 NaN NaN 0.347243 0.253629 \n", "3 0.058824 0.447236 0.540984 0.232323 0.111111 0.418778 0.038002 \n", "4 0.000000 0.688442 0.327869 0.353535 0.198582 0.642325 0.943638 \n", "\n", " 7 8 \n", "0 0.483333 1.0 \n", "1 0.166667 0.0 \n", "2 0.183333 1.0 \n", "3 NaN 0.0 \n", "4 0.200000 1.0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "fancyimpute offers many different forms of imputation methods, however, we are only comparing the four mentioned below. You can read about all of these at https://pypi.org/project/fancyimpute/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will compare Logistic Regression using four different imputation methods:\n", " - KNN\n", " - Mean\n", " - IterativeImputer\n", " - SoftImpute" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will first construct the dataframe for the bottom three because for KNN we need to find the optimum value of the hyperparameter. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[SoftImpute] Max Singular Value of X_init = 31.698472\n", "[SoftImpute] Iter 1: observed MAE=0.017449 rank=9\n", "[SoftImpute] Iter 2: observed MAE=0.017573 rank=9\n", "[SoftImpute] Iter 3: observed MAE=0.017683 rank=9\n", "[SoftImpute] Iter 4: observed MAE=0.017781 rank=9\n", "[SoftImpute] Iter 5: observed MAE=0.017869 rank=9\n", "[SoftImpute] Iter 6: observed MAE=0.017944 rank=9\n", "[SoftImpute] Iter 7: observed MAE=0.018009 rank=9\n", "[SoftImpute] Iter 8: observed MAE=0.018064 rank=9\n", "[SoftImpute] Iter 9: observed MAE=0.018111 rank=9\n", "[SoftImpute] Iter 10: observed MAE=0.018151 rank=9\n", "[SoftImpute] Iter 11: observed MAE=0.018186 rank=9\n", "[SoftImpute] Iter 12: observed MAE=0.018216 rank=9\n", "[SoftImpute] Iter 13: observed MAE=0.018242 rank=9\n", "[SoftImpute] Iter 14: observed MAE=0.018265 rank=9\n", "[SoftImpute] Iter 15: observed MAE=0.018285 rank=9\n", "[SoftImpute] Iter 16: observed MAE=0.018301 rank=9\n", "[SoftImpute] Iter 17: observed MAE=0.018315 rank=9\n", "[SoftImpute] Iter 18: observed MAE=0.018326 rank=9\n", "[SoftImpute] Iter 19: observed MAE=0.018337 rank=9\n", "[SoftImpute] Iter 20: observed MAE=0.018346 rank=9\n", "[SoftImpute] Iter 21: observed MAE=0.018353 rank=9\n", "[SoftImpute] Iter 22: observed MAE=0.018360 rank=9\n", "[SoftImpute] Iter 23: observed MAE=0.018366 rank=9\n", "[SoftImpute] Iter 24: observed MAE=0.018371 rank=9\n", "[SoftImpute] Iter 25: observed MAE=0.018375 rank=9\n", "[SoftImpute] Iter 26: observed MAE=0.018379 rank=9\n", "[SoftImpute] Iter 27: observed MAE=0.018382 rank=9\n", "[SoftImpute] Iter 28: observed MAE=0.018385 rank=9\n", "[SoftImpute] Iter 29: observed MAE=0.018387 rank=9\n", "[SoftImpute] Iter 30: observed MAE=0.018389 rank=9\n", "[SoftImpute] Iter 31: observed MAE=0.018391 rank=9\n", "[SoftImpute] Iter 32: observed MAE=0.018392 rank=9\n", "[SoftImpute] Iter 33: observed MAE=0.018393 rank=9\n", "[SoftImpute] Iter 34: observed MAE=0.018394 rank=9\n", "[SoftImpute] Iter 35: observed MAE=0.018395 rank=9\n", "[SoftImpute] Iter 36: observed MAE=0.018396 rank=9\n", "[SoftImpute] Iter 37: observed MAE=0.018397 rank=9\n", "[SoftImpute] Iter 38: observed MAE=0.018398 rank=9\n", "[SoftImpute] Iter 39: observed MAE=0.018398 rank=9\n", "[SoftImpute] Iter 40: observed MAE=0.018399 rank=9\n", "[SoftImpute] Stopped after iteration 40 for lambda=0.633969\n" ] } ], "source": [ "df_mean=pd.DataFrame(data=fancyimpute.SimpleFill().fit_transform(df.values), columns=df.columns, index=df.index)\n", "df_iterative=pd.DataFrame(data=fancyimpute.IterativeImputer().fit_transform(df.values), columns=df.columns, index=df.index)\n", "df_soft=pd.DataFrame(data=fancyimpute.SoftImpute().fit_transform(df.values), columns=df.columns, index=df.index)\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "logisticRegr = LogisticRegression()\n", "validation_split = 0.8\n", "input_columns = [0,1,2,3,4,5,6,7]\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7597402597402597" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logisticRegr.fit(df_mean[:int(len(df)*validation_split)][input_columns], df[:int(len(df)*validation_split)][8].values )\n", "mean_score = logisticRegr.score(df_mean[int(len(df)*validation_split):][input_columns], df[int(len(df)*validation_split):][8].values )\n", "mean_score" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7727272727272727" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logisticRegr = LogisticRegression()\n", "\n", "logisticRegr.fit(df_iterative[:int(len(df)*validation_split)][input_columns], df[:int(len(df)*validation_split)][8].values )\n", "iter_score = logisticRegr.score(df_iterative[int(len(df)*validation_split):][input_columns], df[int(len(df)*validation_split):][8].values )\n", "iter_score" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7727272727272727" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logisticRegr = LogisticRegression()\n", "\n", "logisticRegr.fit(df_soft[:int(len(df)*validation_split)][input_columns], df[:int(len(df)*validation_split)][8].values )\n", "soft_score = logisticRegr.score(df_soft[int(len(df)*validation_split):][input_columns], df[int(len(df)*validation_split):][8].values )\n", "soft_score" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imputing row 1/768 with 1 missing, elapsed time: 0.265\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.275\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.278\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.282\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.287\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.292\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.297\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.281\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.288\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.293\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.298\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.305\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.309\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.315\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.321\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.256\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.261\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.266\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.270\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.274\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.280\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.288\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.264\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.274\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.279\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.283\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.287\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.291\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.295\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.261\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.266\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.271\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.275\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.279\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.283\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.295\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.266\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.276\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.280\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.284\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.289\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.293\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.298\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.280\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.285\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.289\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.293\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.296\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.300\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.305\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.309\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.269\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.274\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.279\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.283\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.286\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.291\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.297\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.302\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.263\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.274\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.278\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.282\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.286\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.294\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.272\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.278\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.283\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.287\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.290\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.297\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.302\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.311\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.269\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.273\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.277\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.282\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.286\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.294\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.301\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.259\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.272\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.275\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.278\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.283\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.289\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.259\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.265\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.273\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.276\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.280\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.290\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.260\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.266\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.270\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.274\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.279\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.289\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.293\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.261\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.277\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.282\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.287\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.298\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.305\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.261\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.266\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.276\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.280\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.295\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.254\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.258\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.269\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.273\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.277\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.282\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.286\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.253\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.258\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.265\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.268\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.277\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.282\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.287\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.249\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.254\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.259\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.264\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.268\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.273\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.277\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.283\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Imputing row 1/768 with 1 missing, elapsed time: 0.265\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.270\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.274\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.278\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.282\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.286\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.294\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.267\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.278\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.284\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.288\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.292\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.299\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.304\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.268\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.273\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.277\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.282\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.286\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.296\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.301\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.257\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.273\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.278\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.283\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.289\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.295\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.262\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.267\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.272\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.276\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.279\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.288\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.292\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.264\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.273\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.278\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.281\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.286\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.290\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.295\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.257\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.269\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.274\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.281\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.285\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.289\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.294\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.246\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.252\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.256\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.261\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.270\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.275\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.280\n", "Imputing row 1/768 with 1 missing, elapsed time: 0.256\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.260\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.265\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.271\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.275\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.280\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.284\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.288\n" ] } ], "source": [ "results_knn = []\n", "\n", "for k in range(2,30):\n", " df_knn=pd.DataFrame(data=fancyimpute.KNN(k=k).fit_transform(df.values), columns=df.columns, index=df.index)\n", " logisticRegr.fit(df_knn[:int(len(df)*validation_split)][input_columns], df[:int(len(df)*validation_split)][8].values )\n", " results_knn.append(logisticRegr.score(df_knn[int(len(df)*validation_split):][input_columns], df[int(len(df)*validation_split):][8].values ))\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(results_knn)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarising the results:\n", " - Mean Imputation - 75.97%\n", " - Iterative Imputer - 77.27%\n", " - Soft Imputer - 77.27%\n", " - KNN Imputation - 80.52%" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is often claimed that mean imputation is just as good as the fancier methods such as KNN when used in conjunction with more complicated models. To test it, we build a simple neural network and train it with mean imputed data and compare results with KNN imputed data. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: keras in c:\\programdata\\anaconda3\\lib\\site-packages (2.2.2)\n", "Requirement already satisfied: numpy>=1.9.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (1.14.3)\n", "Requirement already satisfied: scipy>=0.14 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (1.1.0)\n", "Requirement already satisfied: six>=1.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (1.11.0)\n", "Requirement already satisfied: pyyaml in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (3.12)\n", "Requirement already satisfied: h5py in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (2.7.1)\n", "Requirement already satisfied: keras_applications==1.0.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (1.0.4)\n", "Requirement already satisfied: keras_preprocessing==1.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from keras) (1.0.2)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 10.0.1, however version 18.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] } ], "source": [ "!pip install keras" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 614 samples, validate on 154 samples\n", "Epoch 1/400\n", "614/614 [==============================] - 1s 900us/step - loss: 0.7004 - acc: 0.3941 - val_loss: 0.6877 - val_acc: 0.6039\n", "Epoch 2/400\n", "614/614 [==============================] - 0s 95us/step - loss: 0.6769 - acc: 0.7101 - val_loss: 0.6661 - val_acc: 0.6494\n", "Epoch 3/400\n", "614/614 [==============================] - 0s 101us/step - loss: 0.6599 - acc: 0.6612 - val_loss: 0.6540 - val_acc: 0.6429\n", "Epoch 4/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.6512 - acc: 0.6531 - val_loss: 0.6494 - val_acc: 0.6429\n", "Epoch 5/400\n", "614/614 [==============================] - 0s 83us/step - loss: 0.6459 - acc: 0.6531 - val_loss: 0.6456 - val_acc: 0.6429\n", "Epoch 6/400\n", "614/614 [==============================] - 0s 79us/step - loss: 0.6415 - acc: 0.6531 - val_loss: 0.6418 - val_acc: 0.6429\n", "Epoch 7/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.6368 - acc: 0.6547 - val_loss: 0.6370 - val_acc: 0.6429\n", "Epoch 8/400\n", "614/614 [==============================] - 0s 122us/step - loss: 0.6322 - acc: 0.6564 - val_loss: 0.6321 - val_acc: 0.6429\n", "Epoch 9/400\n", "614/614 [==============================] - 0s 101us/step - loss: 0.6276 - acc: 0.6564 - val_loss: 0.6271 - val_acc: 0.6429\n", "Epoch 10/400\n", "614/614 [==============================] - 0s 64us/step - loss: 0.6234 - acc: 0.6564 - val_loss: 0.6211 - val_acc: 0.6429\n", "Epoch 11/400\n", "614/614 [==============================] - 0s 104us/step - loss: 0.6189 - acc: 0.6661 - val_loss: 0.6168 - val_acc: 0.6364\n", "Epoch 12/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.6145 - acc: 0.6743 - val_loss: 0.6117 - val_acc: 0.6364\n", "Epoch 13/400\n", "614/614 [==============================] - 0s 95us/step - loss: 0.6100 - acc: 0.6824 - val_loss: 0.6082 - val_acc: 0.6364\n", "Epoch 14/400\n", "614/614 [==============================] - 0s 64us/step - loss: 0.6065 - acc: 0.6775 - val_loss: 0.6041 - val_acc: 0.6623\n", "Epoch 15/400\n", "614/614 [==============================] - 0s 99us/step - loss: 0.6024 - acc: 0.6808 - val_loss: 0.5998 - val_acc: 0.6688\n", "Epoch 16/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.5985 - acc: 0.7052 - val_loss: 0.5964 - val_acc: 0.6623\n", "Epoch 17/400\n", "614/614 [==============================] - 0s 109us/step - loss: 0.5947 - acc: 0.6954 - val_loss: 0.5924 - val_acc: 0.6688\n", "Epoch 18/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.5914 - acc: 0.7068 - val_loss: 0.5893 - val_acc: 0.6688\n", "Epoch 19/400\n", "614/614 [==============================] - 0s 86us/step - loss: 0.5867 - acc: 0.7052 - val_loss: 0.5867 - val_acc: 0.6623\n", "Epoch 20/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.5824 - acc: 0.6987 - val_loss: 0.5799 - val_acc: 0.7013\n", "Epoch 21/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.5794 - acc: 0.7101 - val_loss: 0.5757 - val_acc: 0.7013\n", "Epoch 22/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.5756 - acc: 0.7020 - val_loss: 0.5717 - val_acc: 0.7143\n", "Epoch 23/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.5717 - acc: 0.7085 - val_loss: 0.5682 - val_acc: 0.6948\n", "Epoch 24/400\n", "614/614 [==============================] - 0s 81us/step - loss: 0.5690 - acc: 0.7134 - val_loss: 0.5639 - val_acc: 0.7338\n", "Epoch 25/400\n", "614/614 [==============================] - 0s 104us/step - loss: 0.5654 - acc: 0.7134 - val_loss: 0.5612 - val_acc: 0.7468\n", "Epoch 26/400\n", "614/614 [==============================] - 0s 141us/step - loss: 0.5634 - acc: 0.7215 - val_loss: 0.5567 - val_acc: 0.7338\n", "Epoch 27/400\n", "614/614 [==============================] - 0s 133us/step - loss: 0.5593 - acc: 0.7215 - val_loss: 0.5553 - val_acc: 0.7078\n", "Epoch 28/400\n", "614/614 [==============================] - 0s 119us/step - loss: 0.5554 - acc: 0.7231 - val_loss: 0.5530 - val_acc: 0.7078\n", "Epoch 29/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.5532 - acc: 0.7248 - val_loss: 0.5458 - val_acc: 0.7468\n", "Epoch 30/400\n", "614/614 [==============================] - 0s 125us/step - loss: 0.5496 - acc: 0.7296 - val_loss: 0.5441 - val_acc: 0.7338\n", "Epoch 31/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.5472 - acc: 0.7296 - val_loss: 0.5458 - val_acc: 0.7078\n", "Epoch 32/400\n", "614/614 [==============================] - 0s 127us/step - loss: 0.5446 - acc: 0.7313 - val_loss: 0.5372 - val_acc: 0.7532\n", "Epoch 33/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.5411 - acc: 0.7296 - val_loss: 0.5397 - val_acc: 0.7143\n", "Epoch 34/400\n", "614/614 [==============================] - 0s 78us/step - loss: 0.5390 - acc: 0.7313 - val_loss: 0.5316 - val_acc: 0.7468\n", "Epoch 35/400\n", "614/614 [==============================] - 0s 97us/step - loss: 0.5352 - acc: 0.7394 - val_loss: 0.5296 - val_acc: 0.7727\n", "Epoch 36/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.5346 - acc: 0.7394 - val_loss: 0.5299 - val_acc: 0.7338\n", "Epoch 37/400\n", "614/614 [==============================] - 0s 132us/step - loss: 0.5313 - acc: 0.7427 - val_loss: 0.5311 - val_acc: 0.7338\n", "Epoch 38/400\n", "614/614 [==============================] - 0s 143us/step - loss: 0.5307 - acc: 0.7476 - val_loss: 0.5254 - val_acc: 0.7403\n", "Epoch 39/400\n", "614/614 [==============================] - 0s 109us/step - loss: 0.5275 - acc: 0.7394 - val_loss: 0.5234 - val_acc: 0.7403\n", "Epoch 40/400\n", "614/614 [==============================] - 0s 81us/step - loss: 0.5255 - acc: 0.7410 - val_loss: 0.5183 - val_acc: 0.7727\n", "Epoch 41/400\n", "614/614 [==============================] - 0s 112us/step - loss: 0.5240 - acc: 0.7410 - val_loss: 0.5213 - val_acc: 0.7403\n", "Epoch 42/400\n", "614/614 [==============================] - 0s 68us/step - loss: 0.5211 - acc: 0.7476 - val_loss: 0.5145 - val_acc: 0.7662\n", "Epoch 43/400\n", "614/614 [==============================] - 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0s 63us/step - loss: 0.4550 - acc: 0.7801 - val_loss: 0.4744 - val_acc: 0.7922\n", "Epoch 274/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4577 - acc: 0.7720 - val_loss: 0.4698 - val_acc: 0.7532\n", "Epoch 275/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.4570 - acc: 0.7818 - val_loss: 0.4699 - val_acc: 0.7662\n", "Epoch 276/400\n", "614/614 [==============================] - 0s 76us/step - loss: 0.4554 - acc: 0.7769 - val_loss: 0.4703 - val_acc: 0.7532\n", "Epoch 277/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4569 - acc: 0.7769 - val_loss: 0.4723 - val_acc: 0.7532\n", "Epoch 278/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4550 - acc: 0.7801 - val_loss: 0.4689 - val_acc: 0.7532\n", "Epoch 279/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4528 - acc: 0.7785 - val_loss: 0.4681 - val_acc: 0.7403\n", "Epoch 280/400\n", "614/614 [==============================] - 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0s 58us/step - loss: 0.4533 - acc: 0.7801 - val_loss: 0.4861 - val_acc: 0.7662\n", "Epoch 288/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4585 - acc: 0.7850 - val_loss: 0.4709 - val_acc: 0.7532\n", "Epoch 289/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4542 - acc: 0.7801 - val_loss: 0.4747 - val_acc: 0.7922\n", "Epoch 290/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4559 - acc: 0.7834 - val_loss: 0.4712 - val_acc: 0.7532\n", "Epoch 291/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4555 - acc: 0.7769 - val_loss: 0.4722 - val_acc: 0.7532\n", "Epoch 292/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4529 - acc: 0.7769 - val_loss: 0.4789 - val_acc: 0.7468\n", "Epoch 293/400\n", "614/614 [==============================] - 0s 68us/step - loss: 0.4524 - acc: 0.7818 - val_loss: 0.4778 - val_acc: 0.7532\n", "Epoch 294/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4555 - acc: 0.7818 - val_loss: 0.4711 - val_acc: 0.7532\n", "Epoch 295/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4543 - acc: 0.7850 - val_loss: 0.4719 - val_acc: 0.7792\n", "Epoch 296/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4530 - acc: 0.7769 - val_loss: 0.4721 - val_acc: 0.7532\n", "Epoch 297/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4549 - acc: 0.7834 - val_loss: 0.4745 - val_acc: 0.7532\n", "Epoch 298/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4518 - acc: 0.7785 - val_loss: 0.4731 - val_acc: 0.7597\n", "Epoch 299/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4559 - acc: 0.7834 - val_loss: 0.4717 - val_acc: 0.7532\n", "Epoch 300/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4525 - acc: 0.7866 - val_loss: 0.4724 - val_acc: 0.7532\n", "Epoch 301/400\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "614/614 [==============================] - 0s 57us/step - loss: 0.4534 - acc: 0.7834 - val_loss: 0.4781 - val_acc: 0.7468\n", "Epoch 302/400\n", "614/614 [==============================] - 0s 75us/step - loss: 0.4550 - acc: 0.7834 - val_loss: 0.4759 - val_acc: 0.7532\n", "Epoch 303/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4548 - acc: 0.7818 - val_loss: 0.4698 - val_acc: 0.7468\n", "Epoch 304/400\n", "614/614 [==============================] - 0s 84us/step - loss: 0.4546 - acc: 0.7801 - val_loss: 0.4701 - val_acc: 0.7468\n", "Epoch 305/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4524 - acc: 0.7769 - val_loss: 0.4749 - val_acc: 0.7857\n", "Epoch 306/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.4535 - acc: 0.7752 - val_loss: 0.4692 - val_acc: 0.7468\n", "Epoch 307/400\n", "614/614 [==============================] - 0s 80us/step - loss: 0.4543 - acc: 0.7834 - val_loss: 0.4681 - val_acc: 0.7468\n", "Epoch 308/400\n", "614/614 [==============================] - 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0s 60us/step - loss: 0.4508 - acc: 0.7818 - val_loss: 0.4843 - val_acc: 0.7597\n", "Epoch 316/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4537 - acc: 0.7866 - val_loss: 0.4712 - val_acc: 0.7532\n", "Epoch 317/400\n", "614/614 [==============================] - 0s 84us/step - loss: 0.4519 - acc: 0.7769 - val_loss: 0.4705 - val_acc: 0.7597\n", "Epoch 318/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.4553 - acc: 0.7818 - val_loss: 0.4738 - val_acc: 0.7597\n", "Epoch 319/400\n", "614/614 [==============================] - 0s 80us/step - loss: 0.4533 - acc: 0.7866 - val_loss: 0.4720 - val_acc: 0.7532\n", "Epoch 320/400\n", "614/614 [==============================] - 0s 93us/step - loss: 0.4540 - acc: 0.7834 - val_loss: 0.4714 - val_acc: 0.7468\n", "Epoch 321/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4539 - acc: 0.7818 - val_loss: 0.4691 - val_acc: 0.7468\n", "Epoch 322/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4532 - acc: 0.7818 - val_loss: 0.4791 - val_acc: 0.7468\n", "Epoch 323/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4530 - acc: 0.7834 - val_loss: 0.4707 - val_acc: 0.7792\n", "Epoch 324/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4500 - acc: 0.7915 - val_loss: 0.4760 - val_acc: 0.7987\n", "Epoch 325/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4520 - acc: 0.7834 - val_loss: 0.4767 - val_acc: 0.7597\n", "Epoch 326/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4528 - acc: 0.7850 - val_loss: 0.4725 - val_acc: 0.7468\n", "Epoch 327/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4484 - acc: 0.7850 - val_loss: 0.4887 - val_acc: 0.7662\n", "Epoch 328/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4539 - acc: 0.7866 - val_loss: 0.4699 - val_acc: 0.7468\n", "Epoch 329/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.4534 - acc: 0.7915 - val_loss: 0.4729 - val_acc: 0.7532\n", "Epoch 330/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4527 - acc: 0.7801 - val_loss: 0.4766 - val_acc: 0.7857\n", "Epoch 331/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4520 - acc: 0.7834 - val_loss: 0.4794 - val_acc: 0.7468\n", "Epoch 332/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4543 - acc: 0.7752 - val_loss: 0.4733 - val_acc: 0.7662\n", "Epoch 333/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4497 - acc: 0.7834 - val_loss: 0.4794 - val_acc: 0.7532\n", "Epoch 334/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4512 - acc: 0.7818 - val_loss: 0.4696 - val_acc: 0.7468\n", "Epoch 335/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4547 - acc: 0.7834 - val_loss: 0.4693 - val_acc: 0.7468\n", "Epoch 336/400\n", "614/614 [==============================] - 0s 42us/step - loss: 0.4516 - acc: 0.7785 - val_loss: 0.4763 - val_acc: 0.7532\n", "Epoch 337/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4532 - acc: 0.7818 - val_loss: 0.4878 - val_acc: 0.7662\n", "Epoch 338/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4555 - acc: 0.7899 - val_loss: 0.4730 - val_acc: 0.7468\n", "Epoch 339/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4540 - acc: 0.7769 - val_loss: 0.4718 - val_acc: 0.7468\n", "Epoch 340/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4528 - acc: 0.7866 - val_loss: 0.4720 - val_acc: 0.7532\n", "Epoch 341/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4494 - acc: 0.7769 - val_loss: 0.4749 - val_acc: 0.7597\n", "Epoch 342/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4502 - acc: 0.7818 - val_loss: 0.4741 - val_acc: 0.7857\n", "Epoch 343/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4497 - acc: 0.7866 - val_loss: 0.4964 - val_acc: 0.7727\n", "Epoch 344/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4553 - acc: 0.7883 - val_loss: 0.4690 - val_acc: 0.7468\n", "Epoch 345/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4503 - acc: 0.7883 - val_loss: 0.4684 - val_acc: 0.7468\n", "Epoch 346/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4476 - acc: 0.7899 - val_loss: 0.4751 - val_acc: 0.7597\n", "Epoch 347/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4502 - acc: 0.7866 - val_loss: 0.4692 - val_acc: 0.7468\n", "Epoch 348/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4503 - acc: 0.7883 - val_loss: 0.4687 - val_acc: 0.7468\n", "Epoch 349/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4505 - acc: 0.7834 - val_loss: 0.4785 - val_acc: 0.7532\n", "Epoch 350/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4503 - acc: 0.7834 - val_loss: 0.4750 - val_acc: 0.7662\n", "Epoch 351/400\n", "614/614 [==============================] - 0s 44us/step - loss: 0.4522 - acc: 0.7785 - val_loss: 0.4727 - val_acc: 0.7403\n", "Epoch 352/400\n", "614/614 [==============================] - 0s 44us/step - loss: 0.4533 - acc: 0.7883 - val_loss: 0.4752 - val_acc: 0.7532\n", "Epoch 353/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4492 - acc: 0.7899 - val_loss: 0.4744 - val_acc: 0.7597\n", "Epoch 354/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4461 - acc: 0.7834 - val_loss: 0.4729 - val_acc: 0.7468\n", "Epoch 355/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4507 - acc: 0.7834 - val_loss: 0.4771 - val_acc: 0.7597\n", "Epoch 356/400\n", "614/614 [==============================] - 0s 39us/step - loss: 0.4516 - acc: 0.7769 - val_loss: 0.4947 - val_acc: 0.7727\n", "Epoch 357/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4522 - acc: 0.7850 - val_loss: 0.4714 - val_acc: 0.7468\n", "Epoch 358/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4506 - acc: 0.7883 - val_loss: 0.4806 - val_acc: 0.7597\n", "Epoch 359/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4511 - acc: 0.7818 - val_loss: 0.4750 - val_acc: 0.7597\n", "Epoch 360/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4488 - acc: 0.7850 - val_loss: 0.4758 - val_acc: 0.7597\n", "Epoch 361/400\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "614/614 [==============================] - 0s 49us/step - loss: 0.4521 - acc: 0.7752 - val_loss: 0.4712 - val_acc: 0.7468\n", "Epoch 362/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4488 - acc: 0.7801 - val_loss: 0.4692 - val_acc: 0.7532\n", "Epoch 363/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4502 - acc: 0.7834 - val_loss: 0.4714 - val_acc: 0.7468\n", "Epoch 364/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4507 - acc: 0.7866 - val_loss: 0.4716 - val_acc: 0.7597\n", "Epoch 365/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4507 - acc: 0.7834 - val_loss: 0.4774 - val_acc: 0.7597\n", "Epoch 366/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4493 - acc: 0.7834 - val_loss: 0.4731 - val_acc: 0.7468\n", "Epoch 367/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4472 - acc: 0.7883 - val_loss: 0.4734 - val_acc: 0.7468\n", "Epoch 368/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4498 - acc: 0.7899 - val_loss: 0.4769 - val_acc: 0.7922\n", "Epoch 369/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.4495 - acc: 0.7850 - val_loss: 0.4713 - val_acc: 0.7468\n", "Epoch 370/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4492 - acc: 0.7834 - val_loss: 0.4819 - val_acc: 0.7597\n", "Epoch 371/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4475 - acc: 0.7964 - val_loss: 0.4706 - val_acc: 0.7468\n", "Epoch 372/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4502 - acc: 0.7785 - val_loss: 0.4707 - val_acc: 0.7468\n", "Epoch 373/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4502 - acc: 0.7801 - val_loss: 0.4694 - val_acc: 0.7468\n", "Epoch 374/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4503 - acc: 0.7801 - val_loss: 0.4769 - val_acc: 0.7532\n", "Epoch 375/400\n", "614/614 [==============================] - 0s 84us/step - loss: 0.4486 - acc: 0.7915 - val_loss: 0.4722 - val_acc: 0.7468\n", "Epoch 376/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4499 - acc: 0.7850 - val_loss: 0.4748 - val_acc: 0.7597\n", "Epoch 377/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4498 - acc: 0.7883 - val_loss: 0.4762 - val_acc: 0.7857\n", "Epoch 378/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4495 - acc: 0.7801 - val_loss: 0.4713 - val_acc: 0.7468\n", "Epoch 379/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4477 - acc: 0.7850 - val_loss: 0.4723 - val_acc: 0.7468\n", "Epoch 380/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4483 - acc: 0.7883 - val_loss: 0.4812 - val_acc: 0.7597\n", "Epoch 381/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4511 - acc: 0.7964 - val_loss: 0.4754 - val_acc: 0.7597\n", "Epoch 382/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4491 - acc: 0.7834 - val_loss: 0.4747 - val_acc: 0.7468\n", "Epoch 383/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4497 - acc: 0.7866 - val_loss: 0.4747 - val_acc: 0.7403\n", "Epoch 384/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4492 - acc: 0.7866 - val_loss: 0.4737 - val_acc: 0.7662\n", "Epoch 385/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4477 - acc: 0.7801 - val_loss: 0.4731 - val_acc: 0.7468\n", "Epoch 386/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.4474 - acc: 0.7866 - val_loss: 0.4731 - val_acc: 0.7532\n", "Epoch 387/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4505 - acc: 0.7883 - val_loss: 0.4725 - val_acc: 0.7468\n", "Epoch 388/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4480 - acc: 0.7850 - val_loss: 0.4723 - val_acc: 0.7468\n", "Epoch 389/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4505 - acc: 0.7818 - val_loss: 0.4710 - val_acc: 0.7468\n", "Epoch 390/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4496 - acc: 0.7932 - val_loss: 0.4723 - val_acc: 0.7662\n", "Epoch 391/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4470 - acc: 0.7883 - val_loss: 0.4721 - val_acc: 0.7532\n", "Epoch 392/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4489 - acc: 0.7850 - val_loss: 0.4793 - val_acc: 0.7662\n", "Epoch 393/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4490 - acc: 0.7948 - val_loss: 0.4751 - val_acc: 0.7468\n", "Epoch 394/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4484 - acc: 0.7899 - val_loss: 0.4733 - val_acc: 0.7468\n", "Epoch 395/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4484 - acc: 0.7964 - val_loss: 0.4740 - val_acc: 0.7532\n", "Epoch 396/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4471 - acc: 0.7899 - val_loss: 0.4717 - val_acc: 0.7532\n", "Epoch 397/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4488 - acc: 0.7883 - val_loss: 0.4746 - val_acc: 0.7468\n", "Epoch 398/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4460 - acc: 0.7850 - val_loss: 0.4725 - val_acc: 0.7468\n", "Epoch 399/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.4459 - acc: 0.7866 - val_loss: 0.4745 - val_acc: 0.7597\n", "Epoch 400/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4473 - acc: 0.7818 - val_loss: 0.4706 - val_acc: 0.7468\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", "\n", "model = Sequential()\n", "model.add(Dense(10,activation='relu', input_dim=8))\n", "\n", "model.add(Dense(10,activation='relu'))\n", "\n", "model.add(Dense(1, activation='sigmoid'))\n", "\n", "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", "model.fit(df_mean[input_columns], df[8], batch_size=32, epochs=400, validation_split=0.2)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imputing row 1/768 with 1 missing, elapsed time: 0.252\n", "Imputing row 101/768 with 2 missing, elapsed time: 0.256\n", "Imputing row 201/768 with 2 missing, elapsed time: 0.259\n", "Imputing row 301/768 with 3 missing, elapsed time: 0.261\n", "Imputing row 401/768 with 2 missing, elapsed time: 0.264\n", "Imputing row 501/768 with 1 missing, elapsed time: 0.267\n", "Imputing row 601/768 with 1 missing, elapsed time: 0.275\n", "Imputing row 701/768 with 0 missing, elapsed time: 0.283\n", "Train on 614 samples, validate on 154 samples\n", "Epoch 1/400\n", "614/614 [==============================] - 0s 650us/step - loss: 0.7430 - acc: 0.3469 - val_loss: 0.7083 - val_acc: 0.3636\n", "Epoch 2/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.6932 - acc: 0.4739 - val_loss: 0.6788 - val_acc: 0.6623\n", "Epoch 3/400\n", "614/614 [==============================] - 0s 73us/step - loss: 0.6685 - acc: 0.6710 - val_loss: 0.6619 - val_acc: 0.6688\n", "Epoch 4/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.6541 - acc: 0.6596 - val_loss: 0.6524 - val_acc: 0.6494\n", "Epoch 5/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.6453 - acc: 0.6580 - val_loss: 0.6458 - val_acc: 0.6429\n", "Epoch 6/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.6390 - acc: 0.6580 - val_loss: 0.6399 - val_acc: 0.6429\n", "Epoch 7/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.6332 - acc: 0.6564 - val_loss: 0.6343 - val_acc: 0.6429\n", "Epoch 8/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.6284 - acc: 0.6580 - val_loss: 0.6295 - val_acc: 0.6494\n", "Epoch 9/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.6247 - acc: 0.6547 - val_loss: 0.6252 - val_acc: 0.6494\n", "Epoch 10/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.6204 - acc: 0.6564 - val_loss: 0.6198 - val_acc: 0.6494\n", "Epoch 11/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.6164 - acc: 0.6612 - val_loss: 0.6158 - val_acc: 0.6494\n", "Epoch 12/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.6118 - acc: 0.6596 - val_loss: 0.6100 - val_acc: 0.6623\n", "Epoch 13/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.6073 - acc: 0.6645 - val_loss: 0.6044 - val_acc: 0.6623\n", "Epoch 14/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.6028 - acc: 0.6694 - val_loss: 0.6006 - val_acc: 0.6688\n", "Epoch 15/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.5985 - acc: 0.6661 - val_loss: 0.5963 - val_acc: 0.6688\n", "Epoch 16/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.5945 - acc: 0.6629 - val_loss: 0.5893 - val_acc: 0.6753\n", "Epoch 17/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.5887 - acc: 0.6710 - val_loss: 0.5832 - val_acc: 0.6948\n", "Epoch 18/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.5837 - acc: 0.6759 - val_loss: 0.5776 - val_acc: 0.7013\n", "Epoch 19/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.5789 - acc: 0.6840 - val_loss: 0.5729 - val_acc: 0.7013\n", "Epoch 20/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5745 - acc: 0.6824 - val_loss: 0.5676 - val_acc: 0.7143\n", "Epoch 21/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.5696 - acc: 0.6857 - val_loss: 0.5619 - val_acc: 0.7338\n", "Epoch 22/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.5648 - acc: 0.6922 - val_loss: 0.5574 - val_acc: 0.7338\n", "Epoch 23/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.5605 - acc: 0.7068 - val_loss: 0.5525 - val_acc: 0.7403\n", "Epoch 24/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.5558 - acc: 0.7166 - val_loss: 0.5488 - val_acc: 0.7273\n", "Epoch 25/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5517 - acc: 0.7101 - val_loss: 0.5438 - val_acc: 0.7403\n", "Epoch 26/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.5481 - acc: 0.7329 - val_loss: 0.5402 - val_acc: 0.7273\n", "Epoch 27/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.5455 - acc: 0.7231 - val_loss: 0.5356 - val_acc: 0.7468\n", "Epoch 28/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5416 - acc: 0.7345 - val_loss: 0.5326 - val_acc: 0.7532\n", "Epoch 29/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.5384 - acc: 0.7378 - val_loss: 0.5291 - val_acc: 0.7468\n", "Epoch 30/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.5349 - acc: 0.7427 - val_loss: 0.5254 - val_acc: 0.7532\n", "Epoch 31/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.5316 - acc: 0.7394 - val_loss: 0.5222 - val_acc: 0.7468\n", "Epoch 32/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5288 - acc: 0.7476 - val_loss: 0.5188 - val_acc: 0.7468\n", "Epoch 33/400\n", "614/614 [==============================] - 0s 44us/step - loss: 0.5248 - acc: 0.7459 - val_loss: 0.5162 - val_acc: 0.7403\n", "Epoch 34/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5220 - acc: 0.7427 - val_loss: 0.5126 - val_acc: 0.7597\n", "Epoch 35/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.5177 - acc: 0.7459 - val_loss: 0.5099 - val_acc: 0.7532\n", "Epoch 36/400\n", "614/614 [==============================] - 0s 44us/step - loss: 0.5156 - acc: 0.7476 - val_loss: 0.5061 - val_acc: 0.7792\n", "Epoch 37/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.5119 - acc: 0.7508 - val_loss: 0.5042 - val_acc: 0.7857\n", "Epoch 38/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.5098 - acc: 0.7541 - val_loss: 0.5026 - val_acc: 0.7922\n", "Epoch 39/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.5072 - acc: 0.7573 - val_loss: 0.4995 - val_acc: 0.7857\n", "Epoch 40/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.5043 - acc: 0.7524 - val_loss: 0.4968 - val_acc: 0.7857\n", "Epoch 41/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.5012 - acc: 0.7687 - val_loss: 0.4948 - val_acc: 0.7922\n", "Epoch 42/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4993 - acc: 0.7573 - val_loss: 0.4928 - val_acc: 0.7857\n", "Epoch 43/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4957 - acc: 0.7671 - val_loss: 0.4925 - val_acc: 0.7468\n", "Epoch 44/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4948 - acc: 0.7557 - val_loss: 0.4878 - val_acc: 0.7987\n", "Epoch 45/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4921 - acc: 0.7573 - val_loss: 0.4861 - val_acc: 0.7857\n", "Epoch 46/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4909 - acc: 0.7606 - val_loss: 0.4847 - val_acc: 0.8052\n", "Epoch 47/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4887 - acc: 0.7655 - val_loss: 0.4840 - val_acc: 0.7857\n", "Epoch 48/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4868 - acc: 0.7573 - val_loss: 0.4821 - val_acc: 0.7857\n", "Epoch 49/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4855 - acc: 0.7638 - val_loss: 0.4794 - val_acc: 0.7792\n", "Epoch 50/400\n", "614/614 [==============================] - 0s 42us/step - loss: 0.4834 - acc: 0.7638 - val_loss: 0.4787 - val_acc: 0.7857\n", "Epoch 51/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4818 - acc: 0.7638 - val_loss: 0.4760 - val_acc: 0.7857\n", "Epoch 52/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.4800 - acc: 0.7769 - val_loss: 0.4749 - val_acc: 0.7987\n", "Epoch 53/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4782 - acc: 0.7704 - val_loss: 0.4742 - val_acc: 0.7987\n", "Epoch 54/400\n", "614/614 [==============================] - 0s 42us/step - loss: 0.4768 - acc: 0.7720 - val_loss: 0.4722 - val_acc: 0.7792\n", "Epoch 55/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4758 - acc: 0.7671 - val_loss: 0.4706 - val_acc: 0.7857\n", "Epoch 56/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.4747 - acc: 0.7687 - val_loss: 0.4701 - val_acc: 0.7857\n", "Epoch 57/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4736 - acc: 0.7736 - val_loss: 0.4688 - val_acc: 0.7922\n", "Epoch 58/400\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "614/614 [==============================] - 0s 72us/step - loss: 0.4727 - acc: 0.7704 - val_loss: 0.4723 - val_acc: 0.7792\n", "Epoch 59/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4705 - acc: 0.7769 - val_loss: 0.4683 - val_acc: 0.7857\n", "Epoch 60/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4691 - acc: 0.7752 - val_loss: 0.4730 - val_acc: 0.7662\n", "Epoch 61/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.4696 - acc: 0.7785 - val_loss: 0.4665 - val_acc: 0.7857\n", "Epoch 62/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4691 - acc: 0.7720 - val_loss: 0.4647 - val_acc: 0.7922\n", "Epoch 63/400\n", "614/614 [==============================] - 0s 78us/step - loss: 0.4669 - acc: 0.7752 - val_loss: 0.4671 - val_acc: 0.7792\n", "Epoch 64/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4667 - acc: 0.7769 - val_loss: 0.4632 - val_acc: 0.7987\n", "Epoch 65/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4657 - acc: 0.7720 - val_loss: 0.4626 - val_acc: 0.8052\n", "Epoch 66/400\n", "614/614 [==============================] - 0s 76us/step - loss: 0.4644 - acc: 0.7752 - val_loss: 0.4618 - val_acc: 0.7987\n", "Epoch 67/400\n", "614/614 [==============================] - 0s 93us/step - loss: 0.4632 - acc: 0.7769 - val_loss: 0.4607 - val_acc: 0.7987\n", "Epoch 68/400\n", "614/614 [==============================] - 0s 78us/step - loss: 0.4615 - acc: 0.7785 - val_loss: 0.4689 - val_acc: 0.7727\n", "Epoch 69/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4641 - acc: 0.7801 - val_loss: 0.4622 - val_acc: 0.7857\n", "Epoch 70/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4613 - acc: 0.7834 - val_loss: 0.4583 - val_acc: 0.7922\n", "Epoch 71/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4617 - acc: 0.7785 - val_loss: 0.4580 - val_acc: 0.7987\n", "Epoch 72/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.4606 - acc: 0.7736 - val_loss: 0.4575 - val_acc: 0.8052\n", "Epoch 73/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4607 - acc: 0.7752 - val_loss: 0.4582 - val_acc: 0.7987\n", "Epoch 74/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4590 - acc: 0.7801 - val_loss: 0.4563 - val_acc: 0.7987\n", "Epoch 75/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4585 - acc: 0.7834 - val_loss: 0.4567 - val_acc: 0.7987\n", "Epoch 76/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.4573 - acc: 0.7752 - val_loss: 0.4556 - val_acc: 0.7987\n", "Epoch 77/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.4568 - acc: 0.7801 - val_loss: 0.4541 - val_acc: 0.7922\n", "Epoch 78/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4569 - acc: 0.7785 - val_loss: 0.4539 - val_acc: 0.7987\n", "Epoch 79/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4553 - acc: 0.7818 - val_loss: 0.4532 - val_acc: 0.7987\n", "Epoch 80/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4559 - acc: 0.7818 - val_loss: 0.4521 - val_acc: 0.7987\n", "Epoch 81/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4542 - acc: 0.7801 - val_loss: 0.4558 - val_acc: 0.8052\n", "Epoch 82/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4539 - acc: 0.7818 - val_loss: 0.4517 - val_acc: 0.7922\n", "Epoch 83/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4536 - acc: 0.7801 - val_loss: 0.4508 - val_acc: 0.7987\n", "Epoch 84/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.4527 - acc: 0.7801 - val_loss: 0.4505 - val_acc: 0.8052\n", "Epoch 85/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4509 - acc: 0.7883 - val_loss: 0.4493 - val_acc: 0.7922\n", "Epoch 86/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4517 - acc: 0.7801 - val_loss: 0.4504 - val_acc: 0.8052\n", "Epoch 87/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.4500 - acc: 0.7801 - val_loss: 0.4512 - val_acc: 0.8052\n", "Epoch 88/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.4508 - acc: 0.7785 - val_loss: 0.4487 - val_acc: 0.8052\n", "Epoch 89/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4496 - acc: 0.7801 - val_loss: 0.4552 - val_acc: 0.7922\n", "Epoch 90/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4489 - acc: 0.7850 - val_loss: 0.4489 - val_acc: 0.8117\n", "Epoch 91/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.4491 - acc: 0.7834 - val_loss: 0.4456 - val_acc: 0.8052\n", "Epoch 92/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.4492 - acc: 0.7818 - val_loss: 0.4453 - val_acc: 0.8052\n", "Epoch 93/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.4480 - acc: 0.7850 - val_loss: 0.4523 - val_acc: 0.7987\n", "Epoch 94/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4486 - acc: 0.7818 - val_loss: 0.4491 - val_acc: 0.8052\n", "Epoch 95/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4479 - acc: 0.7834 - val_loss: 0.4450 - val_acc: 0.8052\n", "Epoch 96/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4467 - acc: 0.7818 - val_loss: 0.4448 - val_acc: 0.8052\n", "Epoch 97/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4467 - acc: 0.7883 - val_loss: 0.4480 - val_acc: 0.8052\n", "Epoch 98/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4453 - acc: 0.7834 - val_loss: 0.4441 - val_acc: 0.7922\n", "Epoch 99/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.4457 - acc: 0.7899 - val_loss: 0.4426 - val_acc: 0.8052\n", "Epoch 100/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4451 - acc: 0.7801 - val_loss: 0.4453 - val_acc: 0.8052\n", "Epoch 101/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.4454 - acc: 0.7899 - val_loss: 0.4452 - val_acc: 0.8052\n", "Epoch 102/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.4447 - acc: 0.7801 - val_loss: 0.4420 - val_acc: 0.8052\n", "Epoch 103/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.4440 - acc: 0.7818 - val_loss: 0.4431 - val_acc: 0.8052\n", "Epoch 104/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.4431 - acc: 0.7866 - val_loss: 0.4407 - val_acc: 0.8052\n", "Epoch 105/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.4430 - acc: 0.7818 - val_loss: 0.4414 - val_acc: 0.8052\n", "Epoch 106/400\n", "614/614 [==============================] - 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0s 58us/step - loss: 0.3885 - acc: 0.8111 - val_loss: 0.3814 - val_acc: 0.8506\n", "Epoch 352/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.3884 - acc: 0.8094 - val_loss: 0.3791 - val_acc: 0.8506\n", "Epoch 353/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.3882 - acc: 0.8046 - val_loss: 0.3817 - val_acc: 0.8636\n", "Epoch 354/400\n", "614/614 [==============================] - 0s 58us/step - loss: 0.3879 - acc: 0.8111 - val_loss: 0.3840 - val_acc: 0.8312\n", "Epoch 355/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.3889 - acc: 0.8094 - val_loss: 0.3797 - val_acc: 0.8636\n", "Epoch 356/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.3887 - acc: 0.8094 - val_loss: 0.3809 - val_acc: 0.8506\n", "Epoch 357/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.3870 - acc: 0.8127 - val_loss: 0.3824 - val_acc: 0.8636\n", "Epoch 358/400\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "614/614 [==============================] - 0s 47us/step - loss: 0.3880 - acc: 0.8078 - val_loss: 0.3802 - val_acc: 0.8636\n", "Epoch 359/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.3878 - acc: 0.8143 - val_loss: 0.3905 - val_acc: 0.8117\n", "Epoch 360/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.3892 - acc: 0.8046 - val_loss: 0.3812 - val_acc: 0.8506\n", "Epoch 361/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.3885 - acc: 0.8111 - val_loss: 0.3883 - val_acc: 0.8247\n", "Epoch 362/400\n", "614/614 [==============================] - 0s 45us/step - loss: 0.3863 - acc: 0.8078 - val_loss: 0.3794 - val_acc: 0.8636\n", "Epoch 363/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.3876 - acc: 0.8062 - val_loss: 0.3799 - val_acc: 0.8636\n", "Epoch 364/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.3872 - acc: 0.8078 - val_loss: 0.3853 - val_acc: 0.8377\n", "Epoch 365/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.3866 - acc: 0.8143 - val_loss: 0.3815 - val_acc: 0.8377\n", "Epoch 366/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.3875 - acc: 0.8111 - val_loss: 0.3925 - val_acc: 0.8117\n", "Epoch 367/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.3884 - acc: 0.8127 - val_loss: 0.3802 - val_acc: 0.8506\n", "Epoch 368/400\n", "614/614 [==============================] - 0s 71us/step - loss: 0.3873 - acc: 0.8094 - val_loss: 0.3796 - val_acc: 0.8636\n", "Epoch 369/400\n", "614/614 [==============================] - 0s 70us/step - loss: 0.3885 - acc: 0.8127 - val_loss: 0.3783 - val_acc: 0.8636\n", "Epoch 370/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.3879 - acc: 0.8127 - val_loss: 0.3843 - val_acc: 0.8377\n", "Epoch 371/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.3868 - acc: 0.8062 - val_loss: 0.3809 - val_acc: 0.8636\n", "Epoch 372/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.3862 - acc: 0.8127 - val_loss: 0.3805 - val_acc: 0.8636\n", "Epoch 373/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.3874 - acc: 0.8062 - val_loss: 0.3824 - val_acc: 0.8442\n", "Epoch 374/400\n", "614/614 [==============================] - 0s 44us/step - loss: 0.3866 - acc: 0.8111 - val_loss: 0.3816 - val_acc: 0.8377\n", "Epoch 375/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.3875 - acc: 0.8143 - val_loss: 0.3792 - val_acc: 0.8442\n", "Epoch 376/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.3852 - acc: 0.8062 - val_loss: 0.3800 - val_acc: 0.8701\n", "Epoch 377/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.3854 - acc: 0.8078 - val_loss: 0.3818 - val_acc: 0.8506\n", "Epoch 378/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.3860 - acc: 0.8111 - val_loss: 0.3793 - val_acc: 0.8377\n", "Epoch 379/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.3854 - acc: 0.8046 - val_loss: 0.3791 - val_acc: 0.8636\n", "Epoch 380/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.3862 - acc: 0.8111 - val_loss: 0.3784 - val_acc: 0.8701\n", "Epoch 381/400\n", "614/614 [==============================] - 0s 63us/step - loss: 0.3856 - acc: 0.8176 - val_loss: 0.3826 - val_acc: 0.8442\n", "Epoch 382/400\n", "614/614 [==============================] - 0s 67us/step - loss: 0.3869 - acc: 0.8062 - val_loss: 0.3780 - val_acc: 0.8636\n", "Epoch 383/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.3851 - acc: 0.8127 - val_loss: 0.3777 - val_acc: 0.8506\n", "Epoch 384/400\n", "614/614 [==============================] - 0s 54us/step - loss: 0.3849 - acc: 0.8111 - val_loss: 0.3762 - val_acc: 0.8506\n", "Epoch 385/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.3859 - acc: 0.8127 - val_loss: 0.3809 - val_acc: 0.8377\n", "Epoch 386/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.3847 - acc: 0.8078 - val_loss: 0.3787 - val_acc: 0.8442\n", "Epoch 387/400\n", "614/614 [==============================] - 0s 65us/step - loss: 0.3831 - acc: 0.8143 - val_loss: 0.3795 - val_acc: 0.8442\n", "Epoch 388/400\n", "614/614 [==============================] - 0s 55us/step - loss: 0.3846 - acc: 0.8111 - val_loss: 0.3777 - val_acc: 0.8442\n", "Epoch 389/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.3851 - acc: 0.8094 - val_loss: 0.3811 - val_acc: 0.8506\n", "Epoch 390/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.3842 - acc: 0.8078 - val_loss: 0.3810 - val_acc: 0.8377\n", "Epoch 391/400\n", "614/614 [==============================] - 0s 62us/step - loss: 0.3859 - acc: 0.8111 - val_loss: 0.3838 - val_acc: 0.8442\n", "Epoch 392/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.3854 - acc: 0.8127 - val_loss: 0.3820 - val_acc: 0.8442\n", "Epoch 393/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.3846 - acc: 0.8094 - val_loss: 0.3791 - val_acc: 0.8442\n", "Epoch 394/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.3851 - acc: 0.8127 - val_loss: 0.3820 - val_acc: 0.8506\n", "Epoch 395/400\n", "614/614 [==============================] - 0s 50us/step - loss: 0.3861 - acc: 0.8094 - val_loss: 0.3812 - val_acc: 0.8442\n", "Epoch 396/400\n", "614/614 [==============================] - 0s 47us/step - loss: 0.3858 - acc: 0.8143 - val_loss: 0.3780 - val_acc: 0.8701\n", "Epoch 397/400\n", "614/614 [==============================] - 0s 49us/step - loss: 0.3839 - acc: 0.8143 - val_loss: 0.3788 - val_acc: 0.8636\n", "Epoch 398/400\n", "614/614 [==============================] - 0s 60us/step - loss: 0.3847 - acc: 0.8143 - val_loss: 0.3765 - val_acc: 0.8506\n", "Epoch 399/400\n", "614/614 [==============================] - 0s 57us/step - loss: 0.3838 - acc: 0.8111 - val_loss: 0.3781 - val_acc: 0.8442\n", "Epoch 400/400\n", "614/614 [==============================] - 0s 52us/step - loss: 0.3841 - acc: 0.8143 - val_loss: 0.3851 - val_acc: 0.8506\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_knn=pd.DataFrame(data=fancyimpute.KNN(k=8).fit_transform(df.values), columns=df.columns, index=df.index)\n", "model = Sequential()\n", "model.add(Dense(10,activation='relu', input_dim=8))\n", "\n", "model.add(Dense(10,activation='relu'))\n", "\n", "model.add(Dense(1, activation='sigmoid'))\n", "\n", "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", "model.fit(df_knn[input_columns], df[8], batch_size=32, epochs=400, validation_split=0.2)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_4 (Dense) (None, 10) 90 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 10) 110 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 1) 11 \n", "=================================================================\n", "Total params: 211\n", "Trainable params: 211\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As evident from this highly unscientific test, the common wisdom that mean imputation is just as good is not necessarily true. Even with this overkill of a model, KNN imputed data performs significantly better than mean imputed data(0.8701 - epoch 396 vs 0.7987 - epoch 324 in this run)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Summary\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Missing data is broadly classified into three categories: MCAR, MAR and MNAR. We show the abysmal performance of mean imputation and median imputation with a toy example. Next, we create an intuitive understanding of KNN imputation and write sample code for its implementation. \n", "\n", "Finally, we apply the techniques to Pima Indian Diabetes set and use four different imputation strategies. We show the superiority of KNN imputation technique over other imputation strategies for both logistic regression and neural networks, discrediting a common belief about imputation techniques." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Further Reading\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - https://pypi.org/project/fancyimpute/\n", " - https://github.com/iskandr/fancyimpute/tree/master/fancyimpute\n", " - https://github.com/iskandr/knnimpute/blob/master/knnimpute/few_observed_entries.py\n", " - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959387/" ] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }