stock-prediction-pytorch.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/stock-time-series-20050101-to-20171231/CAT_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/NKE_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/MRK_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/MCD_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/AMZN_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/BA_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/TRV_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/JNJ_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/GS_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/HD_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/CSCO_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/GOOGL_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/XOM_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/PG_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/MSFT_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/UNH_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/WMT_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/all_stocks_2017-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/MMM_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/GE_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/AAPL_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/UTX_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/VZ_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/AXP_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/PFE_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/DIS_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/INTC_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/AABA_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/KO_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/CVX_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/all_stocks_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/JPM_2006-01-01_to_2018-01-01.csv\n/kaggle/input/stock-time-series-20050101-to-20171231/IBM_2006-01-01_to_2018-01-01.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"filepath = '/kaggle/input/stock-time-series-20050101-to-20171231/AMZN_2006-01-01_to_2018-01-01.csv'\ndata = pd.read_csv(filepath)\ndata = data.sort_values('Date')\ndata.head()","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"         Date   Open   High    Low  Close   Volume  Name\n0  2006-01-03  47.47  47.85  46.25  47.58  7582127  AMZN\n1  2006-01-04  47.48  47.73  46.69  47.25  7440914  AMZN\n2  2006-01-05  47.16  48.20  47.11  47.65  5417258  AMZN\n3  2006-01-06  47.97  48.58  47.32  47.87  6154285  AMZN\n4  2006-01-09  46.55  47.10  46.40  47.08  8945056  AMZN","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2006-01-03</td>\n      <td>47.47</td>\n      <td>47.85</td>\n      <td>46.25</td>\n      <td>47.58</td>\n      <td>7582127</td>\n      <td>AMZN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2006-01-04</td>\n      <td>47.48</td>\n      <td>47.73</td>\n      <td>46.69</td>\n      <td>47.25</td>\n      <td>7440914</td>\n      <td>AMZN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2006-01-05</td>\n      <td>47.16</td>\n      <td>48.20</td>\n      <td>47.11</td>\n      <td>47.65</td>\n      <td>5417258</td>\n      <td>AMZN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2006-01-06</td>\n      <td>47.97</td>\n      <td>48.58</td>\n      <td>47.32</td>\n      <td>47.87</td>\n      <td>6154285</td>\n      <td>AMZN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2006-01-09</td>\n      <td>46.55</td>\n      <td>47.10</td>\n      <td>46.40</td>\n      <td>47.08</td>\n      <td>8945056</td>\n      <td>AMZN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.set_style(\"darkgrid\")\nplt.figure(figsize = (15,9))\nplt.plot(data[['Close']])\nplt.xticks(range(0,data.shape[0],500),data['Date'].loc[::500],rotation=45)\nplt.title(\"Amazon Stock Price\",fontsize=18, fontweight='bold')\nplt.xlabel('Date',fontsize=18)\nplt.ylabel('Close Price (USD)',fontsize=18)\nplt.show()","execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x648 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Normalize data"},{"metadata":{"trusted":true},"cell_type":"code","source":"price = data[['Close']]\nprice.info()","execution_count":4,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 3019 entries, 0 to 3018\nData columns (total 1 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   Close   3019 non-null   float64\ndtypes: float64(1)\nmemory usage: 47.2 KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler(feature_range=(-1, 1))\nprice['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))","execution_count":5,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  after removing the cwd from sys.path.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def split_data(stock, lookback):\n    data_raw = stock.to_numpy() # convert to numpy array\n    data = []\n    \n    # create all possible sequences of length seq_len\n    for index in range(len(data_raw) - lookback): \n        data.append(data_raw[index: index + lookback])\n    \n    data = np.array(data);\n    test_set_size = int(np.round(0.2*data.shape[0]));\n    train_set_size = data.shape[0] - (test_set_size);\n    \n    x_train = data[:train_set_size,:-1,:]\n    y_train = data[:train_set_size,-1,:]\n    \n    x_test = data[train_set_size:,:-1]\n    y_test = data[train_set_size:,-1,:]\n    \n    return [x_train, y_train, x_test, y_test]","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lookback = 20 # choose sequence length\nx_train, y_train, x_test, y_test = split_data(price, lookback)\nprint('x_train.shape = ',x_train.shape)\nprint('y_train.shape = ',y_train.shape)\nprint('x_test.shape = ',x_test.shape)\nprint('y_test.shape = ',y_test.shape)","execution_count":7,"outputs":[{"output_type":"stream","text":"x_train.shape =  (2399, 19, 1)\ny_train.shape =  (2399, 1)\nx_test.shape =  (600, 19, 1)\ny_test.shape =  (600, 1)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\nx_train = torch.from_numpy(x_train).type(torch.Tensor)\nx_test = torch.from_numpy(x_test).type(torch.Tensor)\ny_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\ny_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)\ny_train_gru = torch.from_numpy(y_train).type(torch.Tensor)\ny_test_gru = torch.from_numpy(y_test).type(torch.Tensor)","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"input_dim = 1\nhidden_dim = 32\nnum_layers = 2\noutput_dim = 1\nnum_epochs = 100","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class LSTM(nn.Module):\n    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n        super(LSTM, self).__init__()\n        self.hidden_dim = hidden_dim\n        self.num_layers = num_layers\n        \n        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n        self.fc = nn.Linear(hidden_dim, output_dim)\n\n    def forward(self, x):\n        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n        out = self.fc(out[:, -1, :]) \n        return out","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\ncriterion = torch.nn.MSELoss(reduction='mean')\noptimiser = torch.optim.Adam(model.parameters(), lr=0.01)","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import time\n\nhist = np.zeros(num_epochs)\nstart_time = time.time()\nlstm = []\n\nfor t in range(num_epochs):\n    y_train_pred = model(x_train)\n\n    loss = criterion(y_train_pred, y_train_lstm)\n    print(\"Epoch \", t, \"MSE: \", loss.item())\n    hist[t] = loss.item()\n\n    optimiser.zero_grad()\n    loss.backward()\n    optimiser.step()\n    \ntraining_time = time.time()-start_time\nprint(\"Training time: {}\".format(training_time))","execution_count":12,"outputs":[{"output_type":"stream","text":"Epoch  0 MSE:  0.4865466058254242\nEpoch  1 MSE:  0.2529647946357727\nEpoch  2 MSE:  0.07670584321022034\nEpoch  3 MSE:  0.08061616122722626\nEpoch  4 MSE:  0.0728728398680687\nEpoch  5 MSE:  0.036570146679878235\nEpoch  6 MSE:  0.03921389952301979\nEpoch  7 MSE:  0.051150813698768616\nEpoch  8 MSE:  0.0552743561565876\nEpoch  9 MSE:  0.05105438455939293\nEpoch  10 MSE:  0.04311574995517731\nEpoch  11 MSE:  0.03640219196677208\nEpoch  12 MSE:  0.03420165926218033\nEpoch  13 MSE:  0.03661046922206879\nEpoch  14 MSE:  0.0403180867433548\nEpoch  15 MSE:  0.04138927161693573\nEpoch  16 MSE:  0.038899581879377365\nEpoch  17 MSE:  0.03505545109510422\nEpoch  18 MSE:  0.03240696340799332\nEpoch  19 MSE:  0.0318516343832016\nEpoch  20 MSE:  0.03267236799001694\nEpoch  21 MSE:  0.03354232758283615\nEpoch  22 MSE:  0.033403411507606506\nEpoch  23 MSE:  0.031889867037534714\nEpoch  24 MSE:  0.029382795095443726\nEpoch  25 MSE:  0.026800883933901787\nEpoch  26 MSE:  0.02511834166944027\nEpoch  27 MSE:  0.0245656855404377\nEpoch  28 MSE:  0.023906638845801353\nEpoch  29 MSE:  0.02121603488922119\nEpoch  30 MSE:  0.016356877982616425\nEpoch  31 MSE:  0.011770094744861126\nEpoch  32 MSE:  0.009110278449952602\nEpoch  33 MSE:  0.00485796807333827\nEpoch  34 MSE:  0.001033932901918888\nEpoch  35 MSE:  0.008409264497458935\nEpoch  36 MSE:  0.007406735792756081\nEpoch  37 MSE:  0.006567967124283314\nEpoch  38 MSE:  0.0014230306260287762\nEpoch  39 MSE:  0.0021099932491779327\nEpoch  40 MSE:  0.0015720365336164832\nEpoch  41 MSE:  0.0016657636733725667\nEpoch  42 MSE:  0.0030368396546691656\nEpoch  43 MSE:  0.003612309694290161\nEpoch  44 MSE:  0.003122493391856551\nEpoch  45 MSE:  0.002742383163422346\nEpoch  46 MSE:  0.0028223947156220675\nEpoch  47 MSE:  0.0023306936491280794\nEpoch  48 MSE:  0.0012681314256042242\nEpoch  49 MSE:  0.0008671208051964641\nEpoch  50 MSE:  0.0009132063714787364\nEpoch  51 MSE:  0.0005733488360419869\nEpoch  52 MSE:  0.0007088698912411928\nEpoch  53 MSE:  0.0013648530002683401\nEpoch  54 MSE:  0.0011903468985110521\nEpoch  55 MSE:  0.0011568984482437372\nEpoch  56 MSE:  0.0010970501461997628\nEpoch  57 MSE:  0.0005704837967641652\nEpoch  58 MSE:  0.0004419001052156091\nEpoch  59 MSE:  0.0005215902347117662\nEpoch  60 MSE:  0.00042426466825418174\nEpoch  61 MSE:  0.0005228922236710787\nEpoch  62 MSE:  0.000712079752702266\nEpoch  63 MSE:  0.0006843477603979409\nEpoch  64 MSE:  0.0006083109765313566\nEpoch  65 MSE:  0.0006218703929334879\nEpoch  66 MSE:  0.0005256570875644684\nEpoch  67 MSE:  0.00036799561348743737\nEpoch  68 MSE:  0.00035654642852023244\nEpoch  69 MSE:  0.0003623665252234787\nEpoch  70 MSE:  0.00032693278626538813\nEpoch  71 MSE:  0.0003996879968326539\nEpoch  72 MSE:  0.00045216147555038333\nEpoch  73 MSE:  0.00040842671296559274\nEpoch  74 MSE:  0.0004072848241776228\nEpoch  75 MSE:  0.00037482453626580536\nEpoch  76 MSE:  0.0003051102685276419\nEpoch  77 MSE:  0.00030595294083468616\nEpoch  78 MSE:  0.00031369453063234687\nEpoch  79 MSE:  0.00030453145154751837\nEpoch  80 MSE:  0.00033302296651527286\nEpoch  81 MSE:  0.0003480718587525189\nEpoch  82 MSE:  0.00032887322595342994\nEpoch  83 MSE:  0.0003243198152631521\nEpoch  84 MSE:  0.0003137537860311568\nEpoch  85 MSE:  0.0002858175430446863\nEpoch  86 MSE:  0.00028325579478405416\nEpoch  87 MSE:  0.0002874464262276888\nEpoch  88 MSE:  0.0002829113509505987\nEpoch  89 MSE:  0.00029456798802129924\nEpoch  90 MSE:  0.0002989061176776886\nEpoch  91 MSE:  0.0002890276082325727\nEpoch  92 MSE:  0.00028736222884617746\nEpoch  93 MSE:  0.00027887034229934216\nEpoch  94 MSE:  0.00026899322983808815\nEpoch  95 MSE:  0.0002714498550631106\nEpoch  96 MSE:  0.00027075959951616824\nEpoch  97 MSE:  0.000270695862127468\nEpoch  98 MSE:  0.000275315745966509\nEpoch  99 MSE:  0.00027256901375949383\nTraining time: 25.459589958190918\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\noriginal = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nsns.set_style(\"darkgrid\")    \n\nfig = plt.figure()\nfig.subplots_adjust(hspace=0.2, wspace=0.2)\n\nplt.subplot(1, 2, 1)\nax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\nax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\nax.set_title('Stock price', size = 14, fontweight='bold')\nax.set_xlabel(\"Days\", size = 14)\nax.set_ylabel(\"Cost (USD)\", size = 14)\nax.set_xticklabels('', size=10)\n\n\nplt.subplot(1, 2, 2)\nax = sns.lineplot(data=hist, color='royalblue')\nax.set_xlabel(\"Epoch\", size = 14)\nax.set_ylabel(\"Loss\", size = 14)\nax.set_title(\"Training Loss\", size = 14, fontweight='bold')\nfig.set_figheight(6)\nfig.set_figwidth(16)","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import math, time\nfrom sklearn.metrics import mean_squared_error\n\n# make predictions\ny_test_pred = model(x_test)\n\n# invert predictions\ny_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\ny_train = scaler.inverse_transform(y_train_lstm.detach().numpy())\ny_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\ny_test = scaler.inverse_transform(y_test_lstm.detach().numpy())\n\n# calculate root mean squared error\ntrainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\nlstm.append(trainScore)\nlstm.append(testScore)\nlstm.append(training_time)","execution_count":15,"outputs":[{"output_type":"stream","text":"Train Score: 9.66 RMSE\nTest Score: 236.79 RMSE\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(price)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred\n\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(price)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred\n\noriginal = scaler.inverse_transform(price['Close'].values.reshape(-1,1))\n\npredictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\npredictions = np.append(predictions, original, axis=1)\nresult = pd.DataFrame(predictions)","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import plotly.express as px\nimport plotly.graph_objects as go\n\nfig = go.Figure()\nfig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n                    mode='lines',\n                    name='Train prediction')))\nfig.add_trace(go.Scatter(x=result.index, y=result[1],\n                    mode='lines',\n                    name='Test prediction'))\nfig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n                    mode='lines',\n                    name='Actual Value')))\nfig.update_layout(\n    xaxis=dict(\n        showline=True,\n        showgrid=True,\n        showticklabels=False,\n        linecolor='white',\n        linewidth=2\n    ),\n    yaxis=dict(\n        title_text='Close (USD)',\n        titlefont=dict(\n            family='Rockwell',\n            size=12,\n            color='white',\n        ),\n        showline=True,\n        showgrid=True,\n        showticklabels=True,\n        linecolor='white',\n        linewidth=2,\n        ticks='outside',\n        tickfont=dict(\n            family='Rockwell',\n            size=12,\n            color='white',\n        ),\n    ),\n    showlegend=True,\n    template = 'plotly_dark'\n\n)\n\n\n\nannotations = []\nannotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n                              xanchor='left', yanchor='bottom',\n                              text='Results (LSTM)',\n                              font=dict(family='Rockwell',\n                                        size=26,\n                                        color='white'),\n                              showarrow=False))\nfig.update_layout(annotations=annotations)\n\nfig.show()","execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"c32f3885-6fe6-4db7-8ffb-2cc5ba42c5a4\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"c32f3885-6fe6-4db7-8ffb-2cc5ba42c5a4\")) {\n                    Plotly.newPlot(\n                        'c32f3885-6fe6-4db7-8ffb-2cc5ba42c5a4',\n       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</div>"},"metadata":{}}]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"#!pip install chart-studio","execution_count":22,"outputs":[{"output_type":"stream","text":"Collecting chart-studio\n  Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\n\u001b[K     |████████████████████████████████| 64 kB 1.1 MB/s eta 0:00:011\n\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from chart-studio) (1.14.0)\nRequirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.7/site-packages (from chart-studio) (1.3.3)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from chart-studio) (2.23.0)\nRequirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from chart-studio) (4.7.1)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->chart-studio) (1.24.3)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->chart-studio) (2020.4.5.1)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->chart-studio) (3.0.4)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->chart-studio) (2.9)\nInstalling collected packages: chart-studio\nSuccessfully installed chart-studio-1.1.0\n\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import chart_studio.plotly as py\nimport chart_studio\n\nchart_studio.tools.set_credentials_file(username='rodolfo_saldanha', api_key='zWJIVWJs23wfiAp516Mh')\npy.iplot(fig, filename='stock_prediction_lstm')","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"<IPython.lib.display.IFrame at 0x7f77f4089c50>","text/html":"\n        <iframe\n            width=\"100%\"\n            height=\"525px\"\n            src=\"https://plotly.com/~rodolfo_saldanha/11.embed\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        "},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"class GRU(nn.Module):\n    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n        super(GRU, self).__init__()\n        self.hidden_dim = hidden_dim\n        self.num_layers = num_layers\n        \n        self.gru = nn.GRU(input_dim, hidden_dim, num_layers, batch_first=True)\n        self.fc = nn.Linear(hidden_dim, output_dim)\n\n    def forward(self, x):\n        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n        out, (hn) = self.gru(x, (h0.detach()))\n        out = self.fc(out[:, -1, :]) \n        return out","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = GRU(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\ncriterion = torch.nn.MSELoss(reduction='mean')\noptimiser = torch.optim.Adam(model.parameters(), lr=0.01)","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist = np.zeros(num_epochs)\nstart_time = time.time()\ngru = []\n\nfor t in range(num_epochs):\n    y_train_pred = model(x_train)\n\n    loss = criterion(y_train_pred, y_train_gru)\n    print(\"Epoch \", t, \"MSE: \", loss.item())\n    hist[t] = loss.item()\n\n    optimiser.zero_grad()\n    loss.backward()\n    optimiser.step()\n\ntraining_time = time.time()-start_time    \nprint(\"Training time: {}\".format(training_time))","execution_count":28,"outputs":[{"output_type":"stream","text":"Epoch  0 MSE:  0.5241500735282898\nEpoch  1 MSE:  0.19016532599925995\nEpoch  2 MSE:  0.038654737174510956\nEpoch  3 MSE:  0.12166500836610794\nEpoch  4 MSE:  0.08947960287332535\nEpoch  5 MSE:  0.0395880788564682\nEpoch  6 MSE:  0.03717290610074997\nEpoch  7 MSE:  0.053974345326423645\nEpoch  8 MSE:  0.06350700557231903\nEpoch  9 MSE:  0.05972089618444443\nEpoch  10 MSE:  0.047462549060583115\nEpoch  11 MSE:  0.034788258373737335\nEpoch  12 MSE:  0.028943601995706558\nEpoch  13 MSE:  0.032459139823913574\nEpoch  14 MSE:  0.039932068437337875\nEpoch  15 MSE:  0.04223037138581276\nEpoch  16 MSE:  0.0369555801153183\nEpoch  17 MSE:  0.029436394572257996\nEpoch  18 MSE:  0.025257505476474762\nEpoch  19 MSE:  0.025664296001195908\nEpoch  20 MSE:  0.028345054015517235\nEpoch  21 MSE:  0.03021487034857273\nEpoch  22 MSE:  0.029449021443724632\nEpoch  23 MSE:  0.026161659508943558\nEpoch  24 MSE:  0.022061189636588097\nEpoch  25 MSE:  0.019374389201402664\nEpoch  26 MSE:  0.019257118925452232\nEpoch  27 MSE:  0.020522745326161385\nEpoch  28 MSE:  0.02042214572429657\nEpoch  29 MSE:  0.017647024244070053\nEpoch  30 MSE:  0.013924382627010345\nEpoch  31 MSE:  0.011867030523717403\nEpoch  32 MSE:  0.011914635077118874\nEpoch  33 MSE:  0.01184922456741333\nEpoch  34 MSE:  0.0094533646479249\nEpoch  35 MSE:  0.005647517740726471\nEpoch  36 MSE:  0.0038612501230090857\nEpoch  37 MSE:  0.004409337881952524\nEpoch  38 MSE:  0.002688415814191103\nEpoch  39 MSE:  0.00022042851196601987\nEpoch  40 MSE:  0.0028318925760686398\nEpoch  41 MSE:  0.003189019626006484\nEpoch  42 MSE:  0.0022669341415166855\nEpoch  43 MSE:  0.004435678943991661\nEpoch  44 MSE:  0.0020905761048197746\nEpoch  45 MSE:  0.001299767754971981\nEpoch  46 MSE:  0.0018879696726799011\nEpoch  47 MSE:  0.0006724561098963022\nEpoch  48 MSE:  0.00027449309709481895\nEpoch  49 MSE:  0.0010855024447664618\nEpoch  50 MSE:  0.001123631140217185\nEpoch  51 MSE:  0.0007027375977486372\nEpoch  52 MSE:  0.0009535611025057733\nEpoch  53 MSE:  0.0013590060407295823\nEpoch  54 MSE:  0.0011500116670504212\nEpoch  55 MSE:  0.0007823844789527357\nEpoch  56 MSE:  0.0008582908776588738\nEpoch  57 MSE:  0.0009445042232982814\nEpoch  58 MSE:  0.0006006640614941716\nEpoch  59 MSE:  0.0003390561032574624\nEpoch  60 MSE:  0.0004501468501985073\nEpoch  61 MSE:  0.0004209101025480777\nEpoch  62 MSE:  0.00018294209439773113\nEpoch  63 MSE:  0.0002393182658124715\nEpoch  64 MSE:  0.0004062674124725163\nEpoch  65 MSE:  0.0003030775405932218\nEpoch  66 MSE:  0.0002984996244776994\nEpoch  67 MSE:  0.00044176954543218017\nEpoch  68 MSE:  0.00036298102349974215\nEpoch  69 MSE:  0.0002690771361812949\nEpoch  70 MSE:  0.0003264618571847677\nEpoch  71 MSE:  0.000267159310169518\nEpoch  72 MSE:  0.00016746562323532999\nEpoch  73 MSE:  0.00020333417342044413\nEpoch  74 MSE:  0.0002088381297653541\nEpoch  75 MSE:  0.00015223764057736844\nEpoch  76 MSE:  0.00017756513261701912\nEpoch  77 MSE:  0.00021814745559822768\nEpoch  78 MSE:  0.00018916043336503208\nEpoch  79 MSE:  0.0001856303570093587\nEpoch  80 MSE:  0.00021595980797428638\nEpoch  81 MSE:  0.00019633195188362151\nEpoch  82 MSE:  0.00016825107741169631\nEpoch  83 MSE:  0.00017949826724361628\nEpoch  84 MSE:  0.00016969791613519192\nEpoch  85 MSE:  0.0001413116551702842\nEpoch  86 MSE:  0.0001473581069149077\nEpoch  87 MSE:  0.00015139115566853434\nEpoch  88 MSE:  0.0001353555708192289\nEpoch  89 MSE:  0.00014240665768738836\nEpoch  90 MSE:  0.00015248058480210602\nEpoch  91 MSE:  0.00014229434600565583\nEpoch  92 MSE:  0.00014501942496281117\nEpoch  93 MSE:  0.00015052917296998203\nEpoch  94 MSE:  0.00013940945791546255\nEpoch  95 MSE:  0.00013702358410228044\nEpoch  96 MSE:  0.00013890401169192046\nEpoch  97 MSE:  0.00012982473708689213\nEpoch  98 MSE:  0.00012810403131879866\nEpoch  99 MSE:  0.00013105844845995307\nTraining time: 19.279309511184692\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\noriginal = pd.DataFrame(scaler.inverse_transform(y_train_gru.detach().numpy()))","execution_count":29,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nsns.set_style(\"darkgrid\")    \n\nfig = plt.figure()\nfig.subplots_adjust(hspace=0.2, wspace=0.2)\n\nplt.subplot(1, 2, 1)\nax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\nax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (GRU)\", color='tomato')\nax.set_title('Stock price', size = 14, fontweight='bold')\nax.set_xlabel(\"Days\", size = 14)\nax.set_ylabel(\"Cost (USD)\", size = 14)\nax.set_xticklabels('', size=10)\n\n\nplt.subplot(1, 2, 2)\nax = sns.lineplot(data=hist, color='royalblue')\nax.set_xlabel(\"Epoch\", size = 14)\nax.set_ylabel(\"Loss\", size = 14)\nax.set_title(\"Training Loss\", size = 14, fontweight='bold')\nfig.set_figheight(6)\nfig.set_figwidth(16)","execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import math, time\nfrom sklearn.metrics import mean_squared_error\n\n# make predictions\ny_test_pred = model(x_test)\n\n# invert predictions\ny_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\ny_train = scaler.inverse_transform(y_train_gru.detach().numpy())\ny_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\ny_test = scaler.inverse_transform(y_test_gru.detach().numpy())\n\n# calculate root mean squared error\ntrainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\ngru.append(trainScore)\ngru.append(testScore)\ngru.append(training_time)","execution_count":31,"outputs":[{"output_type":"stream","text":"Train Score: 6.70 RMSE\nTest Score: 30.50 RMSE\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(price)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred\n\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(price)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred\n\noriginal = scaler.inverse_transform(price['Close'].values.reshape(-1,1))\n\npredictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\npredictions = np.append(predictions, original, axis=1)\nresult = pd.DataFrame(predictions)","execution_count":32,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import plotly.express as px\nimport plotly.graph_objects as go\n\nfig = go.Figure()\nfig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n                    mode='lines',\n                    name='Train prediction')))\nfig.add_trace(go.Scatter(x=result.index, y=result[1],\n                    mode='lines',\n                    name='Test prediction'))\nfig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n                    mode='lines',\n                    name='Actual Value')))\nfig.update_layout(\n    xaxis=dict(\n        showline=True,\n        showgrid=True,\n        showticklabels=False,\n        linecolor='white',\n        linewidth=2\n    ),\n    yaxis=dict(\n        title_text='Close (USD)',\n        titlefont=dict(\n            family='Rockwell',\n            size=12,\n            color='white',\n        ),\n        showline=True,\n        showgrid=True,\n        showticklabels=True,\n        linecolor='white',\n        linewidth=2,\n        ticks='outside',\n        tickfont=dict(\n            family='Rockwell',\n            size=12,\n            color='white',\n        ),\n    ),\n    showlegend=True,\n    template = 'plotly_dark'\n\n)\n\n\n\nannotations = []\nannotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n                              xanchor='left', yanchor='bottom',\n                              text='Results (GRU)',\n                              font=dict(family='Rockwell',\n                                        size=26,\n                                        color='white'),\n                              showarrow=False))\nfig.update_layout(annotations=annotations)\n\nfig.show()","execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"b9c00539-625e-4ce0-8cd1-573947371e4d\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"b9c00539-625e-4ce0-8cd1-573947371e4d\")) {\n                    Plotly.newPlot(\n                        'b9c00539-625e-4ce0-8cd1-573947371e4d',\n        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