ANN_CLASSIFIER.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d8afd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics._plot.confusion_matrix import plot_confusion_matrix\n",
    "\n",
    "df= pd.read_excel('output_params_Phase.xlsx')\n",
    "\n",
    "# # FCC=0, FCC+BCC=1, BCC=2\n",
    "\n",
    "data= df.drop('Phase',axis='columns')\n",
    "target= df['Phase']\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=[0, 1])\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(data = data_scaled, columns =['Ni', 'Al', 'Co', 'Fe', 'Cr', 'Δsₘᵢₓ', 'VEC', 'δ', 'Δχ', 'ΔHₘᵢₓ', 'Ω','Tm'])\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xtrain,xtest,ytrain,ytest= train_test_split(data,target,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1d39542",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1943d2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "import tensorflow\n",
    "tensorflow.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "747ffb1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain= xtrain.to_numpy()\n",
    "xtest = xtest.to_numpy()\n",
    "ytrain= ytrain.to_numpy()\n",
    "ytest = ytest.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4e7aeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes):\n",
    "    layers = []\n",
    "    \n",
    "    nodes_increment = (last_layer_nodes - first_layer_nodes)/ (n_layers-1)\n",
    "    nodes = first_layer_nodes\n",
    "    for i in range(1, n_layers+1):\n",
    "        layers.append(math.ceil(nodes))\n",
    "        nodes = nodes + nodes_increment\n",
    "    \n",
    "    return layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "757c865f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createmodel(n_layers, first_layer_nodes, last_layer_nodes, activation_func, loss_func):\n",
    "    clf = Sequential()\n",
    "    n_nodes = FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes)\n",
    "    for i in range(1, n_layers):\n",
    "        if i==1:\n",
    "            clf.add(Dense(first_layer_nodes,input_dim=12, activation=activation_func))\n",
    "        else:\n",
    "            clf.add(Dense(n_nodes[i-1], activation=activation_func))\n",
    "            \n",
    "    #Final layer\n",
    "    clf.add(Dense(3, activation='softmax'))\n",
    "    clf.compile(optimizer='adam', loss=loss_func, metrics = [\"accuracy\"])\n",
    "    \n",
    "    return clf\n",
    "\n",
    "#Wrap model/classifier into scikit-learn\n",
    "clf =  KerasClassifier(build_fn=createmodel, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37b483d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## OPTIMIZED PARAMETERS WERE FOUND BY THIS\n",
    "\n",
    "# activation_funcs = ['tanh','relu','sigmoid'] \n",
    "# loss_funcs = ['categorical_crossentropy']\n",
    "# param_grid = dict(n_layers=[5,6], first_layer_nodes = [32,16], last_layer_nodes = [4],  activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [1000], epochs = [100])\n",
    "# grid = GridSearchCV(estimator = clf, param_grid = param_grid)\n",
    "\n",
    "#  BEST PARAMS:\n",
    "# {'activation_func': 'tanh',\n",
    "#  'batch_size': 1000,\n",
    "#  'epochs': 100,\n",
    "#  'first_layer_nodes': 32,\n",
    "#  'last_layer_nodes': 4,\n",
    "#  'loss_func': 'categorical_crossentropy',\n",
    "#  'n_layers': 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f54892d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "activation_funcs = ['tanh'] \n",
    "loss_funcs = ['categorical_crossentropy']\n",
    "param_grid = dict(n_layers=[6], first_layer_nodes = [32], last_layer_nodes = [4],  activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [1000], epochs = [100])\n",
    "grid = GridSearchCV(estimator = clf, param_grid = param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb201354",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
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      "Epoch 7/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.3657 - accuracy: 0.9215\n",
      "Epoch 8/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.3239 - accuracy: 0.9413\n",
      "Epoch 9/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.2916 - accuracy: 0.9491\n",
      "Epoch 10/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.2658 - accuracy: 0.9526\n",
      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 15/100\n",
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      "Epoch 19/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.1548 - accuracy: 0.9636\n",
      "Epoch 20/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.1500 - accuracy: 0.9615\n",
      "Epoch 21/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.1444 - accuracy: 0.9621\n",
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      "9/9 [==============================] - 0s 7ms/step - loss: 0.1341 - accuracy: 0.9628\n",
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      "9/9 [==============================] - 0s 6ms/step - loss: 0.1281 - accuracy: 0.9633\n",
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      "9/9 [==============================] - 0s 9ms/step - loss: 0.1187 - accuracy: 0.9639\n",
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      "9/9 [==============================] - 0s 8ms/step - loss: 0.1165 - accuracy: 0.9663\n",
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      "9/9 [==============================] - 0s 9ms/step - loss: 0.1135 - accuracy: 0.9675\n",
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      "9/9 [==============================] - 0s 7ms/step - loss: 0.0966 - accuracy: 0.9722\n",
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      "Epoch 80/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0679 - accuracy: 0.9790\n",
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      "9/9 [==============================] - 0s 6ms/step - loss: 0.0704 - accuracy: 0.9773\n",
      "Epoch 82/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0674 - accuracy: 0.9804\n",
      "Epoch 83/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9803: 0s - loss: 0.0663 - accuracy: 0.98\n",
      "Epoch 84/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 12ms/step - loss: 0.0665 - accuracy: 0.9796\n",
      "Epoch 85/100\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.0664 - accuracy: 0.9792\n",
      "Epoch 86/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0647 - accuracy: 0.9803\n",
      "Epoch 87/100\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0653 - accuracy: 0.9795\n",
      "Epoch 88/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0658 - accuracy: 0.9786\n",
      "Epoch 89/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0634 - accuracy: 0.9798\n",
      "Epoch 90/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0627 - accuracy: 0.9818\n",
      "Epoch 91/100\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0666 - accuracy: 0.9785\n",
      "Epoch 92/100\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0660 - accuracy: 0.9788\n",
      "Epoch 93/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0621 - accuracy: 0.9818\n",
      "Epoch 94/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0617 - accuracy: 0.9807\n",
      "Epoch 95/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0612 - accuracy: 0.9819\n",
      "Epoch 96/100\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 0.0616 - accuracy: 0.9815\n",
      "Epoch 97/100\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 0.0602 - accuracy: 0.9819\n",
      "Epoch 98/100\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0623 - accuracy: 0.9806\n",
      "Epoch 99/100\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 0.0676 - accuracy: 0.9775\n",
      "Epoch 100/100\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0614 - accuracy: 0.9807\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 0.0580 - accuracy: 0.9804\n",
      "Epoch 1/100\n",
      "9/9 [==============================] - 1s 5ms/step - loss: 1.1386 - accuracy: 0.4082\n",
      "Epoch 2/100\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 1.0404 - accuracy: 0.4565\n",
      "Epoch 3/100\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 0.9615 - accuracy: 0.4688\n",
      "Epoch 4/100\n",
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      "Epoch 28/100\n",
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "9/9 [==============================] - 0s 5ms/step - loss: 0.2481 - accuracy: 0.9343\n",
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      "Epoch 94/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0634 - accuracy: 0.9801\n",
      "Epoch 95/100\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0614 - accuracy: 0.9818\n",
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      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x000002329BA3AD30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 0.0519 - accuracy: 0.9814\n",
      "Epoch 1/100\n",
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      "Epoch 67/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0630 - accuracy: 0.9803\n",
      "Epoch 68/100\n",
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      "11/11 [==============================] - 0s 10ms/step - loss: 0.0585 - accuracy: 0.9813 0s - loss: 0.0541 - accuracy: 0.98\n",
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      "Epoch 88/100\n",
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      "Epoch 91/100\n",
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      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0545 - accuracy: 0.9842\n",
      "Epoch 93/100\n",
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      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0543 - accuracy: 0.9842\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0546 - accuracy: 0.9843\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0553 - accuracy: 0.9832\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0568 - accuracy: 0.9812\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0586 - accuracy: 0.9807\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 0.0549 - accuracy: 0.9836\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0528 - accuracy: 0.9849 0s - loss: 0.0529 - accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasClassifier object at 0x00000232F351B160>,\n",
       "             param_grid={'activation_func': ['tanh'], 'batch_size': [1000],\n",
       "                         'epochs': [100], 'first_layer_nodes': [32],\n",
       "                         'last_layer_nodes': [4],\n",
       "                         'loss_func': ['categorical_crossentropy'],\n",
       "                         'n_layers': [6]})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79c9111d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.982490062713623"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35ac68b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x00000233BFC47B80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "3/3 [==============================] - 1s 5ms/step - loss: 0.0436 - accuracy: 0.9859\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.98591548204422"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "038c555c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0523 - accuracy: 0.9841\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9840555787086487"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.score(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1856d776",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\anubh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:455: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "ypred= grid.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8dbc3ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, ..., 2, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd54d1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred,title='ANN CONFUSION MATRIX', normalize=False, figsize=(8,6))\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "              (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('ANN_cm.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd3a79b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(ytest, ypred,title='ANN CONFUSION MATRIX', normalize=True, figsize=(8,6),cmap='Greens')\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "              (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('normalized_cm_ann.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "847703ef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0523 - accuracy: 0.9841\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.5280 - accuracy: 0.8791\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 0.5208 - accuracy: 0.8827\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.5278 - accuracy: 0.8821\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 0.5178 - accuracy: 0.8779\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.5249 - accuracy: 0.8798\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.9193 - accuracy: 0.8030\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 0.9217 - accuracy: 0.8012\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.9122 - accuracy: 0.8011\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.8819 - accuracy: 0.8099\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.9179 - accuracy: 0.8029\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0725 - accuracy: 0.9751\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0727 - accuracy: 0.9739\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0712 - accuracy: 0.9756\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0738 - accuracy: 0.9743\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0757 - accuracy: 0.9751\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.4402 - accuracy: 0.9038\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.4398 - accuracy: 0.9019\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4403 - accuracy: 0.9028\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4379 - accuracy: 0.9029\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.4248 - accuracy: 0.9045\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.6367 - accuracy: 0.8791\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.6373 - accuracy: 0.8767\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6445 - accuracy: 0.8756\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.6244 - accuracy: 0.8792\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.6222 - accuracy: 0.8806\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1240 - accuracy: 0.9644\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1242 - accuracy: 0.9625\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.1279 - accuracy: 0.9623\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1333 - accuracy: 0.9617\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1341 - accuracy: 0.9617\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 0.5201 - accuracy: 0.8795\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.4968 - accuracy: 0.8844\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.5180 - accuracy: 0.8810\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.4820 - accuracy: 0.8895\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.5157 - accuracy: 0.8809\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6526 - accuracy: 0.8461\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6384 - accuracy: 0.8453\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6408 - accuracy: 0.8496\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6151 - accuracy: 0.8519\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.6424 - accuracy: 0.8492\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1139 - accuracy: 0.9614\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.1060 - accuracy: 0.9629\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.1041 - accuracy: 0.9661\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1010 - accuracy: 0.9650\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1104 - accuracy: 0.9621\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 0.2515 - accuracy: 0.9303\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.2370 - accuracy: 0.9312\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.2392 - accuracy: 0.9326\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 0.2258 - accuracy: 0.9339\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.2417 - accuracy: 0.9296\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0830 - accuracy: 0.9715\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0847 - accuracy: 0.9699\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0796 - accuracy: 0.9732\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0803 - accuracy: 0.9718\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0807 - accuracy: 0.9700\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.4144 - accuracy: 0.8978\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4030 - accuracy: 0.8977\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.4015 - accuracy: 0.9002\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3796 - accuracy: 0.9038\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 0.4005 - accuracy: 0.9019\n",
      "       importance\n",
      "Al       0.180417\n",
      "δ        0.135635\n",
      "Cr       0.105820\n",
      "Ni       0.103727\n",
      "VEC      0.101027\n",
      "Tm       0.083772\n",
      "Fe       0.080876\n",
      "ΔHₘᵢₓ    0.052548\n",
      "Δsₘᵢₓ    0.021540\n",
      "Δχ       0.020561\n",
      "Ω        0.012775\n",
      "Co       0.009273\n"
     ]
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "\n",
    "pi= permutation_importance(grid, xtrain, ytrain,random_state=42)\n",
    "result=pi.importances_mean\n",
    "\n",
    "fi = pd.DataFrame(result, index= data.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "807fab6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=fi.index,y='importance',data=fi)\n",
    "plt.savefig('FI_ANN.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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