LSTMtest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, SimpleRNN,LSTM,Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58317946, 0.62840121, 0.63104902, 0.68452775, 0.77727067,\n",
       "       0.79486465, 0.79378462, 0.76918789, 0.75939797, 0.73866843,\n",
       "       0.70508309, 0.7013901 , 0.72037766, 0.65324182, 0.61073755,\n",
       "       0.58913702, 0.57272759, 0.58074069, 0.50928474, 0.50646274,\n",
       "       0.53123367, 0.5114448 , 0.49068042, 0.50541755, 0.48350347,\n",
       "       0.49433857, 0.52433544, 0.50221231, 0.46162422, 0.47831237,\n",
       "       0.46040484, 0.51165383, 0.49844964, 0.44504059, 0.38020416,\n",
       "       0.40528865, 0.4005853 , 0.36992649, 0.29233878, 0.30118803,\n",
       "       0.3707278 , 0.3865798 , 0.43563391, 0.44786259, 0.42068773,\n",
       "       0.40981779, 0.39650908, 0.3533777 , 0.38894889, 0.32324147,\n",
       "       0.38288681, 0.40487057, 0.29320977, 0.35707069, 0.35734941,\n",
       "       0.37581437, 0.34755949, 0.31373027, 0.32048915, 0.36240114,\n",
       "       0.35916106, 0.3849075 , 0.38382747, 0.36375989, 0.39807686,\n",
       "       0.42061805, 0.43971013, 0.36477023, 0.33278751, 0.41542696,\n",
       "       0.38138871, 0.36825419, 0.37302721, 0.40417378, 0.42810856,\n",
       "       0.41309271, 0.39469742, 0.38034352, 0.32595896, 0.29275685,\n",
       "       0.32940808, 0.274919  , 0.24844093, 0.2268404 , 0.24871965,\n",
       "       0.26512908, 0.25965927, 0.18287287, 0.20050169, 0.24750026,\n",
       "       0.21471623, 0.21370588, 0.17388426, 0.17015643, 0.16451242,\n",
       "       0.15716127, 0.1422151 , 0.16691635, 0.12611922, 0.10403094,\n",
       "       0.07967808, 0.09458942, 0.1153538 , 0.12510887, 0.02710518,\n",
       "       0.07410375, 0.06497579, 0.05560394, 0.05013413, 0.07821482,\n",
       "       0.04201651, 0.01721074, 0.00456398, 0.        , 0.00369299,\n",
       "       0.04476884, 0.11012786, 0.12862767, 0.12040553, 0.16555761,\n",
       "       0.18475421, 0.14712748, 0.18451033, 0.21130195, 0.172421  ,\n",
       "       0.12695537, 0.12779152, 0.12820959, 0.12737345, 0.15486186,\n",
       "       0.23405219, 0.19078145, 0.16660279, 0.15252761, 0.16416402,\n",
       "       0.10936139, 0.14360868, 0.15716127, 0.15050692, 0.15911229,\n",
       "       0.25920635, 0.28895934, 0.22405323, 0.48942619, 0.52085148,\n",
       "       0.54548305, 0.59718496, 0.58213427, 0.59039125, 0.66230011,\n",
       "       0.66181235, 0.59133192, 0.56063826, 0.54262621, 0.53206982,\n",
       "       0.53746995, 0.56130021, 0.58317946, 0.66020973, 0.6850155 ,\n",
       "       0.61700868, 0.60551162, 0.65787548, 0.61850678, 0.64115249,\n",
       "       0.64780685, 0.6408041 , 0.65076821, 0.70375919, 0.7254294 ,\n",
       "       0.74497439, 0.72260739, 0.75364944, 0.72947079, 0.72009894,\n",
       "       0.74162979, 0.67755984, 0.73483608, 0.76113995, 0.72117897,\n",
       "       0.72828624, 0.68658328, 0.7302721 , 0.74664669, 0.76089607,\n",
       "       0.75688952, 0.75082744, 0.78193917, 0.80576943, 0.8262551 ,\n",
       "       0.82207435, 0.83252622, 0.85050343, 0.86151273, 0.852768  ,\n",
       "       0.86248824, 0.96982894, 1.        , 0.97846915, 0.92168066,\n",
       "       0.92715047, 0.92718531, 0.92728983, 0.88520364, 0.91851026,\n",
       "       0.91579277, 0.8993485 , 0.90739644, 0.90823259, 0.8961781 ,\n",
       "       0.8624534 , 0.85245445, 0.83820507, 0.82987841, 0.80225064,\n",
       "       0.79643243, 0.66557503, 0.70870641, 0.6982197 , 0.71313103])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('graph.csv')\n",
    "#data=data[\"Sunspots\"]\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "column=['X']\n",
    "data=scaler.fit_transform(data[column]).flatten()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=len(data)\n",
    "split=int(n*0.9)\n",
    "train_data=data[range(split)]\n",
    "test_data=data[split:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "198"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_units=50\n",
    "time_steps=2\n",
    "input_shape=(time_steps,1)\n",
    "y_ind=np.arange(time_steps,len(train_data),time_steps)\n",
    "y_train=train_data[y_ind]\n",
    "x_train=train_data[range(len(y_train)*time_steps)]\n",
    "x_train=np.reshape(x_train,(len(y_train),time_steps,1))\n",
    "\n",
    "y_ind=np.arange(time_steps,len(test_data),time_steps)\n",
    "y_test=test_data[y_ind]\n",
    "x_test=train_data[range(len(y_test)*time_steps)]\n",
    "x_test=np.reshape(x_test,(len(y_test),time_steps,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(98, 2, 1)\n",
      "(98,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "\n",
    "model.add(LSTM(hidden_units,return_sequences=True,input_shape=input_shape))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(hidden_units,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(hidden_units,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(hidden_units))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "4/4 [==============================] - 5s 13ms/step - loss: 0.2354\n",
      "Epoch 2/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2146\n",
      "Epoch 3/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1922\n",
      "Epoch 4/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1656\n",
      "Epoch 5/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1365\n",
      "Epoch 6/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1036\n",
      "Epoch 7/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0702\n",
      "Epoch 8/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0503\n",
      "Epoch 9/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0458\n",
      "Epoch 10/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0443\n",
      "Epoch 11/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0412\n",
      "Epoch 12/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0416\n",
      "Epoch 13/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0422\n",
      "Epoch 14/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0382\n",
      "Epoch 15/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0332\n",
      "Epoch 16/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0334\n",
      "Epoch 17/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0313\n",
      "Epoch 18/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0290\n",
      "Epoch 19/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0203\n",
      "Epoch 20/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0203\n",
      "Epoch 21/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0133\n",
      "Epoch 22/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0138\n",
      "Epoch 23/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0132\n",
      "Epoch 24/50\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0113\n",
      "Epoch 25/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0083\n",
      "Epoch 26/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0097\n",
      "Epoch 27/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0071\n",
      "Epoch 28/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0072\n",
      "Epoch 29/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0086\n",
      "Epoch 30/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0069\n",
      "Epoch 31/50\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0063\n",
      "Epoch 32/50\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0112\n",
      "Epoch 33/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0111\n",
      "Epoch 34/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0114\n",
      "Epoch 35/50\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0078\n",
      "Epoch 36/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0060\n",
      "Epoch 37/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0089\n",
      "Epoch 38/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0086\n",
      "Epoch 39/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0068\n",
      "Epoch 40/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0109\n",
      "Epoch 41/50\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0064\n",
      "Epoch 42/50\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0056\n",
      "Epoch 43/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0082\n",
      "Epoch 44/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0083\n",
      "Epoch 45/50\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0070\n",
      "Epoch 46/50\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0086\n",
      "Epoch 47/50\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0079\n",
      "Epoch 48/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0113\n",
      "Epoch 49/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0063\n",
      "Epoch 50/50\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0076\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26d659e04c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=50,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 2ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n"
     ]
    }
   ],
   "source": [
    "train_pred=model.predict(x_train)\n",
    "test_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_error(trainY, testY, train_predict, test_predict):    \n",
    "    # Error of predictions\n",
    "    train_rmse = math.sqrt(mean_squared_error(trainY, train_predict))\n",
    "    test_rmse = math.sqrt(mean_squared_error(testY, test_predict))\n",
    "    # Print RMSE\n",
    "    print('Train RMSE: %.3f RMSE' % (train_rmse))\n",
    "    print('Test RMSE: %.3f RMSE' % (test_rmse))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.062 RMSE\n",
      "Test RMSE: 0.217 RMSE\n"
     ]
    }
   ],
   "source": [
    "print_error(y_train,y_test,train_pred,test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_result(trainY, testY, train_predict, test_predict):\n",
    "    actual = np.append(trainY, testY)\n",
    "    predictions = np.append(train_predict, test_predict)\n",
    "    rows = len(actual)\n",
    "    plt.figure(figsize=(20, 5), dpi=100)\n",
    "    plt.plot(range(rows), actual)\n",
    "    plt.plot(range(rows), predictions)\n",
    "    plt.axvline(x=len(trainY), color='r')\n",
    "    plt.legend(['Actual', 'Predictions'])\n",
    "    plt.xlabel('Observation number after given time steps')\n",
    "    plt.ylabel('Sunspots scaled')\n",
    "    plt.title('Actual and Predicted Values. The Red Line Separates The Training And Test Examples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_result(y_train,y_test,train_pred,test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Y=%{x}<br>X=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "x": [
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          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Graph"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Y"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "X"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph=pd.read_csv('graph.csv')\n",
    "fig=px.line(graph,x=graph['Y'],y=graph['X'],title='Graph')\n",
    "fig.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "base",
   "language": "python",
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