Keras shampoo sales conv1d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Month', 'Sales'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#df=pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/shampoo.csv')\n",
    "#df.to_csv('shampoo.csv')\n",
    "data=pd.read_csv('shampoo.csv')\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1d convolutions extract 1d patches and the patch matches to an output\n"
     ]
    }
   ],
   "source": [
    "n_timesteps=5\n",
    "n_features=1\n",
    "print(\"1d convolutions extract 1d patches and the patch matches to an output\")\n",
    "model=models.Sequential()\n",
    "model.add(layers.Conv1D(64,kernel_size=2,activation='relu',input_shape=(n_timesteps,n_features)))\n",
    "\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://machinelearningmastery.com/how-to-develop-convolutional-neural-network-models-for-time-series-forecasting/\n",
    "def split_sequence(sequence, n_steps):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(sequence)):\n",
    "        # find the end of this pattern\n",
    "        end_ix = i + n_steps\n",
    "        # check if we are beyond the sequence\n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "        # gather input and output parts of the pattern\n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X=data.Sales.copy()\n",
    "#y=data.Sales.rolling(5).mean().copy()\n",
    "\n",
    "n_steps=5\n",
    "X, y = split_sequence(data.Sales, n_steps)\n",
    "\n",
    "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
    "#X=X.values.reshape(X.shape[0],X.shape[1],1)\n",
    "\n",
    "#print(X,y)\n",
    "\n",
    "\t# evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80/1000\n",
      "31/31 [==============================] - 0s 97us/step - loss: 4516.4512 - acc: 0.0000e+00\n",
      "Epoch 81/1000\n",
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      "Epoch 90/1000\n",
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     ]
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 238/1000\n",
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     ]
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 475/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 554/1000\n",
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     ]
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     "text": [
      "Epoch 633/1000\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 712/1000\n",
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     ]
    },
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     "text": [
      "Epoch 792/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 871/1000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 950/1000\n",
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      "Epoch 956/1000\n",
      "31/31 [==============================] - 0s 161us/step - loss: 694.1576 - acc: 0.0000e+00\n",
      "Epoch 957/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 691.7562 - acc: 0.0000e+00\n",
      "Epoch 958/1000\n",
      "31/31 [==============================] - 0s 33us/step - loss: 685.7935 - acc: 0.0000e+00\n",
      "Epoch 959/1000\n",
      "31/31 [==============================] - 0s 97us/step - loss: 692.5709 - acc: 0.0000e+00\n",
      "Epoch 960/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 691.8767 - acc: 0.0000e+00\n",
      "Epoch 961/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 678.6574 - acc: 0.0000e+00\n",
      "Epoch 962/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 690.9824 - acc: 0.0000e+00\n",
      "Epoch 963/1000\n",
      "31/31 [==============================] - 0s 97us/step - loss: 687.8939 - acc: 0.0000e+00\n",
      "Epoch 964/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 682.9797 - acc: 0.0000e+00\n",
      "Epoch 965/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 706.1128 - acc: 0.0000e+00\n",
      "Epoch 966/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 688.4469 - acc: 0.0000e+00\n",
      "Epoch 967/1000\n",
      "31/31 [==============================] - 0s 96us/step - loss: 687.8040 - acc: 0.0000e+00\n",
      "Epoch 968/1000\n",
      "31/31 [==============================] - 0s 31us/step - loss: 690.0323 - acc: 0.0000e+00\n",
      "Epoch 969/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 669.9620 - acc: 0.0000e+00\n",
      "Epoch 970/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 685.7555 - acc: 0.0000e+00\n",
      "Epoch 971/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 683.3751 - acc: 0.0000e+00\n",
      "Epoch 972/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 672.0031 - acc: 0.0000e+00\n",
      "Epoch 973/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 674.5056 - acc: 0.0000e+00\n",
      "Epoch 974/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 666.2466 - acc: 0.0000e+00\n",
      "Epoch 975/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 662.5940 - acc: 0.0000e+00\n",
      "Epoch 976/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 665.3375 - acc: 0.0000e+00\n",
      "Epoch 977/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 659.9601 - acc: 0.0000e+00\n",
      "Epoch 978/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 666.0447 - acc: 0.0000e+00\n",
      "Epoch 979/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 660.3392 - acc: 0.0000e+00\n",
      "Epoch 980/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 659.9628 - acc: 0.0000e+00\n",
      "Epoch 981/1000\n",
      "31/31 [==============================] - 0s 61us/step - loss: 658.0098 - acc: 0.0000e+00\n",
      "Epoch 982/1000\n",
      "31/31 [==============================] - 0s 129us/step - loss: 654.9473 - acc: 0.0000e+00\n",
      "Epoch 983/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 654.8088 - acc: 0.0000e+00\n",
      "Epoch 984/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 653.4597 - acc: 0.0000e+00\n",
      "Epoch 985/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 653.6467 - acc: 0.0000e+00\n",
      "Epoch 986/1000\n",
      "31/31 [==============================] - 0s 97us/step - loss: 652.1686 - acc: 0.0000e+00\n",
      "Epoch 987/1000\n",
      "31/31 [==============================] - 0s 64us/step - loss: 652.2063 - acc: 0.0000e+00\n",
      "Epoch 988/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 649.8590 - acc: 0.0000e+00\n",
      "Epoch 989/1000\n",
      "31/31 [==============================] - 0s 63us/step - loss: 654.4005 - acc: 0.0000e+00\n",
      "Epoch 990/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 647.8909 - acc: 0.0000e+00\n",
      "Epoch 991/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 650.4567 - acc: 0.0000e+00\n",
      "Epoch 992/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 646.7337 - acc: 0.0000e+00\n",
      "Epoch 993/1000\n",
      "31/31 [==============================] - 0s 66us/step - loss: 645.1971 - acc: 0.0000e+00\n",
      "Epoch 994/1000\n",
      "31/31 [==============================] - 0s 96us/step - loss: 645.7093 - acc: 0.0000e+00\n",
      "Epoch 995/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 647.3074 - acc: 0.0000e+00\n",
      "Epoch 996/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 644.4858 - acc: 0.0000e+00\n",
      "Epoch 997/1000\n",
      "31/31 [==============================] - 0s 32us/step - loss: 646.4146 - acc: 0.0000e+00\n",
      "Epoch 998/1000\n",
      "31/31 [==============================] - 0s 65us/step - loss: 648.6891 - acc: 0.0000e+00\n",
      "Epoch 999/1000\n",
      "31/31 [==============================] - 0s 33us/step - loss: 646.7276 - acc: 0.0000e+00\n",
      "Epoch 1000/1000\n",
      "31/31 [==============================] - 0s 31us/step - loss: 644.6047 - acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mse', metrics=['acc'])\n",
    "history=model.fit(X,y, epochs=1000, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "      \n",
    "y_pred=model.predict(X).flatten()           \n",
    "\n",
    "data.Sales.plot()\n",
    "plt.plot(y_pred)\n",
    "plt.title('Predicted Sales using conv1d')\n",
    "plt.ylabel(\"Shampoo Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Sales.plot()\n",
    "plt.plot(y)\n",
    "plt.title(\"Actual Shampo Sales\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "hist_dict=history.history\n",
    "epochs=range(1,len(hist_dict['loss'])+1)\n",
    "#print(hist_dict)\n",
    "plt.plot(epochs,hist_dict['loss'],'b',label='loss')\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}