update check_pred

Author: abap34

other/check_pred/lstmconfusion_matrix.png

@@ -1,47 +1,22 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import roc_auc_score, roc_curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix, accuracy_score\n",
     "import seaborn as sns\n",
     "sns.set()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -71,6 +46,10 @@
     "    val_pred = pd.read_csv('../../logs/{}/val_pred.csv'.format(model)).iloc[:, 1]\n",
     "    auc = roc_auc_score(train_y, val_pred)\n",
     "    acc = accuracy_score(train_y, np.round(val_pred))\n",
+    "    cm = confusion_matrix(train_y, np.round(val_pred))\n",
+    "    sns.heatmap(cm, annot=True, cmap='Blues')\n",
+    "    plt.savefig(model + 'confusion_matrix.png')\n",
+    "    plt.clf()\n",
     "    fpr, tpr, thresholds = roc_curve(train_y, val_pred)\n",
     "    label='auc = {:.3f} acc={:.3f}'.format(auc, acc)\n",
     "    plt.plot(fpr, tpr, label=label)\n",