stock-price-prediction-gru.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":11883,"sourceType":"datasetVersion","datasetId":8388},{"sourceType":"datasetVersion","sourceId":7737114,"datasetId":4521864}],"dockerImageVersionId":22557,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Reference and complete code adopted by:\n\nhttps://www.kaggle.com/thebrownviking20/intro-to-recurrent-neural-networks-lstm-gru/notebook","metadata":{"_uuid":"9879e943d9e17c16a41965a81ee2fe204197bcda"}},{"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 in \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 \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-03-01T14:04:46.905971Z","iopub.execute_input":"2024-03-01T14:04:46.906515Z","iopub.status.idle":"2024-03-01T14:04:46.912882Z","shell.execute_reply.started":"2024-03-01T14:04:46.906466Z","shell.execute_reply":"2024-03-01T14:04:46.912081Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"['adaniportshg', 'stock-time-series-20050101-to-20171231']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"In a recurrent neural network we store the output activations from one or more of the layers of the network. Often these are hidden later activations. Then, the next time we feed an input example to the network, we include the previously-stored outputs as additional inputs. You can think of the additional inputs as being concatenated to the end of the “normal” inputs to the previous layer. For example, if a hidden layer had 10 regular input nodes and 128 hidden nodes in the layer, then it would actually have 138 total inputs (assuming you are feeding the layer’s outputs into itself à la Elman) rather than into another layer). Of course, the very first time you try to compute the output of the network you’ll need to fill in those extra 128 inputs with 0s or something.","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"}},{"cell_type":"markdown","source":"Now, even though RNNs are quite powerful, they suffer from Vanishing gradient problem which hinders them from using long term information, like they are good for storing memory 3-4 instances of past iterations but larger number of instances don't provide good results so we don't just use regular RNNs. Instead, we use a better variation of RNNs: Long Short Term Networks(LSTM).","metadata":{"_uuid":"c0b24127844720f3dd6da92c4c7648eb241e461b"}},{"cell_type":"markdown","source":"Vanishing Gradient problem:\n\nVanishing gradient problem is a difficulty found in training artificial neural networks with gradient-based learning methods and backpropagation. In such methods, each of the neural network's weights receives an update proportional to the partial derivative of the error function with respect to the current weight in each iteration of training. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value. In the worst case, this may completely stop the neural network from further training. As one example of the problem cause, traditional activation functions such as the hyperbolic tangent function have gradients in the range (0, 1), and backpropagation computes gradients by the chain rule. This has the effect of multiplying n of these small numbers to compute gradients of the \"front\" layers in an n-layer network, meaning that the gradient (error signal) decreases exponentially with n while the front layers train very slowly.","metadata":{"_uuid":"60e7de9523afb8ed7848b0ad25d6fb40764dcae8"}},{"cell_type":"markdown","source":"Long Short Term Memory(LSTM)\n\nLong short-term memory (LSTM) units (or blocks) are a building unit for layers of a recurrent neural network (RNN). A RNN composed of LSTM units is often called an LSTM network. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell is responsible for \"remembering\" values over arbitrary time intervals; hence the word \"memory\" in LSTM. Each of the three gates can be thought of as a \"conventional\" artificial neuron, as in a multi-layer (or feedforward) neural network: that is, they compute an activation (using an activation function) of a weighted sum. Intuitively, they can be thought as regulators of the flow of values that goes through the connections of the LSTM; hence the denotation \"gate\". There are connections between these gates and the cell.","metadata":{"_uuid":"a368149e0642bedd25875bf45efebb3de5b6b742"}},{"cell_type":"markdown","source":"The expression long short-term refers to the fact that LSTM is a model for the short-term memory which can last for a long period of time. An LSTM is well-suited to classify, process and predict time series given time lags of unknown size and duration between important events. LSTMs were developed to deal with the exploding and vanishing gradient problem when training traditional RNNs.","metadata":{"_uuid":"6cdf59bc1a01b32e86597ee564e0b15d9604d86f"}},{"cell_type":"markdown","source":"Components of LSTMs:\n\nForget Gate “f” ( a neural network with sigmoid)\n\nCandidate layer “C\"(a NN with Tanh)\n\nInput Gate “I” ( a NN with sigmoid )\n\nOutput Gate “O”( a NN with sigmoid)\n\nHidden state “H” ( a vector )\n\nMemory state “C” ( a vector)\n\nInputs to the LSTM cell at any step are Xt (current input) , Ht-1 (previous hidden state ) and Ct-1 (previous memory state).\n\nOutputs from the LSTM cell are Ht (current hidden state ) and Ct (current memory state)","metadata":{"_uuid":"6d6c8afe3c82f2ff48a076ecc19c6c6d700b4553"}},{"cell_type":"markdown","source":"I will use LSTMs for predicting the price of stocks of IBM for the year 2017","metadata":{"_uuid":"609da3c94a8b5aa668cf1b0ea0eb7f0366b0d13a"}},{"cell_type":"code","source":"# importing libraries\n\nimport matplotlib.pyplot as plt\nplt.style.use(\"fivethirtyeight\")\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, LSTM, GRU, Bidirectional\nfrom keras.optimizers import SGD\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n\nimport math\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"_uuid":"2545b193ed4ff111eef238d84452280d8fe7630d","execution":{"iopub.status.busy":"2024-03-01T14:04:46.918764Z","iopub.execute_input":"2024-03-01T14:04:46.919256Z","iopub.status.idle":"2024-03-01T14:04:46.926717Z","shell.execute_reply.started":"2024-03-01T14:04:46.919210Z","shell.execute_reply":"2024-03-01T14:04:46.925736Z"},"trusted":true},"execution_count":76,"outputs":[]},{"cell_type":"code","source":"# function which plots ibm stock prices: real and predicted both\n\ndef plot_predictions(test, predicted):\n    plt.plot(test, color=\"red\", label=\"real  stock price\")\n    plt.plot(predicted, color=\"blue\", label=\"predicted stock price\")\n    plt.title(\"stock price prediction\")\n    plt.xlabel(\"time\")\n    plt.ylabel(\" stock price\")\n    plt.legend()\n    plt.show()","metadata":{"_uuid":"517e677ade18c27821c140575d46d675f347f18f","execution":{"iopub.status.busy":"2024-03-01T14:04:46.930736Z","iopub.execute_input":"2024-03-01T14:04:46.931361Z","iopub.status.idle":"2024-03-01T14:04:46.945433Z","shell.execute_reply.started":"2024-03-01T14:04:46.931140Z","shell.execute_reply":"2024-03-01T14:04:46.944010Z"},"trusted":true},"execution_count":77,"outputs":[]},{"cell_type":"code","source":"# function which calculates root mean squared error\n\ndef return_rmse(test, predicted):\n    rmse = math.sqrt(mean_squared_error(test, predicted))\n    print(\"the root mean squared error is : {}.\".format(rmse))","metadata":{"_uuid":"10308b7b6f41747a7c834481b50b431cd4e14568","execution":{"iopub.status.busy":"2024-03-01T14:04:46.946733Z","iopub.execute_input":"2024-03-01T14:04:46.947338Z","iopub.status.idle":"2024-03-01T14:04:46.958597Z","shell.execute_reply.started":"2024-03-01T14:04:46.947282Z","shell.execute_reply":"2024-03-01T14:04:46.957300Z"},"trusted":true},"execution_count":78,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(\"/kaggle/input/adaniportshg/ADANIPORTS.csv\", index_col='Date', parse_dates=[\"Date\"])\n\ndata.shape","metadata":{"_uuid":"d0be4371f7b67cda99e0d25788fe7134fadeca78","execution":{"iopub.status.busy":"2024-03-01T14:04:46.959986Z","iopub.execute_input":"2024-03-01T14:04:46.960670Z","iopub.status.idle":"2024-03-01T14:04:46.999799Z","shell.execute_reply.started":"2024-03-01T14:04:46.960244Z","shell.execute_reply":"2024-03-01T14:04:46.998982Z"},"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"(3322, 14)"},"metadata":{}}]},{"cell_type":"code","source":"# Check the columns in the DataFrame\nprint(data.columns)\n\n# Remove the 'EQ' column\ndata = data.drop('Series', axis=1)\n\n# Check the columns again\nprint(data.columns)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:04:47.000958Z","iopub.execute_input":"2024-03-01T14:04:47.001420Z","iopub.status.idle":"2024-03-01T14:04:47.010712Z","shell.execute_reply.started":"2024-03-01T14:04:47.001351Z","shell.execute_reply":"2024-03-01T14:04:47.009612Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"Index(['Symbol', 'Series', 'Prev Close', 'Open', 'High', 'Low', 'Last',\n       'Close', 'VWAP', 'Volume', 'Turnover', 'Trades', 'Deliverable Volume',\n       '%Deliverble'],\n      dtype='object')\nIndex(['Symbol', 'Prev Close', 'Open', 'High', 'Low', 'Last', 'Close', 'VWAP',\n       'Volume', 'Turnover', 'Trades', 'Deliverable Volume', '%Deliverble'],\n      dtype='object')\n","output_type":"stream"}]},{"cell_type":"code","source":"data.head(5)","metadata":{"_uuid":"6452a30f49d0dc061e3bd9253881a9cac2f84470","execution":{"iopub.status.busy":"2024-03-01T14:04:47.012653Z","iopub.execute_input":"2024-03-01T14:04:47.012996Z","iopub.status.idle":"2024-03-01T14:04:47.058611Z","shell.execute_reply.started":"2024-03-01T14:04:47.012932Z","shell.execute_reply":"2024-03-01T14:04:47.057181Z"},"trusted":true},"execution_count":81,"outputs":[{"execution_count":81,"output_type":"execute_result","data":{"text/plain":"                Symbol  Prev Close     ...       Deliverable Volume  %Deliverble\nDate                                   ...                                      \n2007-11-27  MUNDRAPORT      440.00     ...                  9859619       0.3612\n2007-11-28  MUNDRAPORT      962.90     ...                  1453278       0.3172\n2007-11-29  MUNDRAPORT      893.90     ...                  1069678       0.2088\n2007-11-30  MUNDRAPORT      884.20     ...                  1260913       0.2735\n2007-12-03  MUNDRAPORT      921.55     ...                   816123       0.2741\n\n[5 rows x 13 columns]","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>Symbol</th>\n      <th>Prev Close</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Last</th>\n      <th>Close</th>\n      <th>VWAP</th>\n      <th>Volume</th>\n      <th>Turnover</th>\n      <th>Trades</th>\n      <th>Deliverable Volume</th>\n      <th>%Deliverble</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2007-11-27</th>\n      <td>MUNDRAPORT</td>\n      <td>440.00</td>\n      <td>770.00</td>\n      <td>1050.00</td>\n      <td>770.0</td>\n      <td>959.0</td>\n      <td>962.90</td>\n      <td>984.72</td>\n      <td>27294366</td>\n      <td>2.687719e+15</td>\n      <td>NaN</td>\n      <td>9859619</td>\n      <td>0.3612</td>\n    </tr>\n    <tr>\n      <th>2007-11-28</th>\n      <td>MUNDRAPORT</td>\n      <td>962.90</td>\n      <td>984.00</td>\n      <td>990.00</td>\n      <td>874.0</td>\n      <td>885.0</td>\n      <td>893.90</td>\n      <td>941.38</td>\n      <td>4581338</td>\n      <td>4.312765e+14</td>\n      <td>NaN</td>\n      <td>1453278</td>\n      <td>0.3172</td>\n    </tr>\n    <tr>\n      <th>2007-11-29</th>\n      <td>MUNDRAPORT</td>\n      <td>893.90</td>\n      <td>909.00</td>\n      <td>914.75</td>\n      <td>841.0</td>\n      <td>887.0</td>\n      <td>884.20</td>\n      <td>888.09</td>\n      <td>5124121</td>\n      <td>4.550658e+14</td>\n      <td>NaN</td>\n      <td>1069678</td>\n      <td>0.2088</td>\n    </tr>\n    <tr>\n      <th>2007-11-30</th>\n      <td>MUNDRAPORT</td>\n      <td>884.20</td>\n      <td>890.00</td>\n      <td>958.00</td>\n      <td>890.0</td>\n      <td>929.0</td>\n      <td>921.55</td>\n      <td>929.17</td>\n      <td>4609762</td>\n      <td>4.283257e+14</td>\n      <td>NaN</td>\n      <td>1260913</td>\n      <td>0.2735</td>\n    </tr>\n    <tr>\n      <th>2007-12-03</th>\n      <td>MUNDRAPORT</td>\n      <td>921.55</td>\n      <td>939.75</td>\n      <td>995.00</td>\n      <td>922.0</td>\n      <td>980.0</td>\n      <td>969.30</td>\n      <td>965.65</td>\n      <td>2977470</td>\n      <td>2.875200e+14</td>\n      <td>NaN</td>\n      <td>816123</td>\n      <td>0.2741</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train = data[:'2016'].iloc[:,1:2].values\ntest = data['2017':].iloc[:,1:2].values","metadata":{"_uuid":"69f104f7dac7d03ff17fff84d0d3c253646cc590","execution":{"iopub.status.busy":"2024-03-01T14:04:47.060132Z","iopub.execute_input":"2024-03-01T14:04:47.060516Z","iopub.status.idle":"2024-03-01T14:04:47.070539Z","shell.execute_reply.started":"2024-03-01T14:04:47.060446Z","shell.execute_reply":"2024-03-01T14:04:47.069489Z"},"trusted":true},"execution_count":82,"outputs":[]},{"cell_type":"code","source":"# visualization of \"High\" attribute of the dataset\n\ndata[\"High\"][:'2016'].plot(figsize=(16,4), legend=True)\ndata[\"High\"][\"2017\":].plot(figsize=(16,4), legend=True)\nplt.legend([\"Training set (before 2017)\", \"Test set (from 2017)\"])\nplt.title(\" stock prices\")\nplt.show()","metadata":{"_uuid":"39b3e5c2fac4af5a4c07e993541f05f6a728fbbc","execution":{"iopub.status.busy":"2024-03-01T14:04:47.072353Z","iopub.execute_input":"2024-03-01T14:04:47.072648Z","iopub.status.idle":"2024-03-01T14:04:47.537488Z","shell.execute_reply.started":"2024-03-01T14:04:47.072606Z","shell.execute_reply":"2024-03-01T14:04:47.536211Z"},"trusted":true},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check the data types of the 'High' attribute\nprint(data['High'].dtype)\n\n# If it's not already numeric, convert it to numeric\ndata['High'] = pd.to_numeric(data['High'], errors='coerce')\n\n# Check the data types again\nprint(data['High'].dtype)\n\n# Now, you can proceed with the scaling\nsc = MinMaxScaler(feature_range=(0,1))\ntrain_scaled = sc.fit_transform(train)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:04:47.538804Z","iopub.execute_input":"2024-03-01T14:04:47.539259Z","iopub.status.idle":"2024-03-01T14:04:47.547947Z","shell.execute_reply.started":"2024-03-01T14:04:47.539212Z","shell.execute_reply":"2024-03-01T14:04:47.546734Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"float64\nfloat64\n","output_type":"stream"}]},{"cell_type":"code","source":"# scaling the training set\n\n#sc = MinMaxScaler(feature_range=(0,1))\n#train_scaled = sc.fit_transform(train)","metadata":{"_uuid":"76e27e5c228e5d36fc4f7b52434d10dd4355934f","_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-03-01T14:04:47.549206Z","iopub.execute_input":"2024-03-01T14:04:47.549530Z","iopub.status.idle":"2024-03-01T14:04:47.557145Z","shell.execute_reply.started":"2024-03-01T14:04:47.549480Z","shell.execute_reply":"2024-03-01T14:04:47.556048Z"},"trusted":true},"execution_count":85,"outputs":[]},{"cell_type":"code","source":"# Since LSTMs store long term memory state, we create a data structure with 60 timesteps and 1 output\n# So for each element of training set, we have 60 previous training set elements\n\nx_train = []\ny_train = []\n\nfor i in range(60, len(train_scaled)):\n    x_train.append(train_scaled[i-60:i, 0])\n    y_train.append(train_scaled[i, 0])\n\nx_train, y_train = np.array(x_train), np.array(y_train)","metadata":{"_uuid":"9ff51bcbc8e2ff75bc998ad1ebc90df08af74b8d","execution":{"iopub.status.busy":"2024-03-01T14:04:47.558573Z","iopub.execute_input":"2024-03-01T14:04:47.558848Z","iopub.status.idle":"2024-03-01T14:04:47.577135Z","shell.execute_reply.started":"2024-03-01T14:04:47.558802Z","shell.execute_reply":"2024-03-01T14:04:47.576042Z"},"trusted":true},"execution_count":86,"outputs":[]},{"cell_type":"code","source":"x_train[0]","metadata":{"_uuid":"3ff1fb2983bce02934f606a96d6d04fd5ab4778b","execution":{"iopub.status.busy":"2024-03-01T14:04:47.579880Z","iopub.execute_input":"2024-03-01T14:04:47.580176Z","iopub.status.idle":"2024-03-01T14:04:47.588865Z","shell.execute_reply.started":"2024-03-01T14:04:47.580133Z","shell.execute_reply":"2024-03-01T14:04:47.587857Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"array([0.27679353, 0.71274334, 0.65521697, 0.64712993, 0.67826921,\n       0.71807912, 0.77823169, 0.81241402, 0.81145525, 0.82904665,\n       0.80653633, 0.78340073, 0.77435491, 0.85201551, 0.83579974,\n       0.78056609, 0.80616116, 0.79944975, 0.79386385, 0.87440077,\n       0.9103339 , 0.92013006, 0.9513527 , 0.9677769 , 0.99116262,\n       1.        , 0.97361291, 0.94405769, 0.93313602, 0.91441911,\n       0.89395139, 0.8450123 , 0.81524866, 0.77297928, 0.78498478,\n       0.76901913, 0.76109884, 0.73917212, 0.59781567, 0.52319813,\n       0.62445287, 0.58872817, 0.6317062 , 0.59427237, 0.610905  ,\n       0.60231773, 0.60832048, 0.59806578, 0.62370253, 0.60552753,\n       0.58318396, 0.57380466, 0.56363333, 0.50289716, 0.47796907,\n       0.46934011, 0.50172996, 0.51752887, 0.55325357, 0.54683397])"},"metadata":{}}]},{"cell_type":"code","source":"y_train[0]","metadata":{"_uuid":"3ed21c8133e4096caed6e286c88889271d0f03f8","execution":{"iopub.status.busy":"2024-03-01T14:04:47.589957Z","iopub.execute_input":"2024-03-01T14:04:47.590229Z","iopub.status.idle":"2024-03-01T14:04:47.599982Z","shell.execute_reply.started":"2024-03-01T14:04:47.590183Z","shell.execute_reply":"2024-03-01T14:04:47.598852Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"0.5203218141648255"},"metadata":{}}]},{"cell_type":"code","source":"len(x_train)","metadata":{"_uuid":"bb698ff1d566c8a01af0d1e25970bcb0e166ee79","execution":{"iopub.status.busy":"2024-03-01T14:04:47.601150Z","iopub.execute_input":"2024-03-01T14:04:47.601506Z","iopub.status.idle":"2024-03-01T14:04:47.610655Z","shell.execute_reply.started":"2024-03-01T14:04:47.601390Z","shell.execute_reply":"2024-03-01T14:04:47.609563Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"2191"},"metadata":{}}]},{"cell_type":"code","source":"len(y_train)","metadata":{"_uuid":"53b8e2e42c4e492dca18f153dc7b82eae0cc9f94","execution":{"iopub.status.busy":"2024-03-01T14:04:47.612273Z","iopub.execute_input":"2024-03-01T14:04:47.612643Z","iopub.status.idle":"2024-03-01T14:04:47.623657Z","shell.execute_reply.started":"2024-03-01T14:04:47.612578Z","shell.execute_reply":"2024-03-01T14:04:47.622727Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"2191"},"metadata":{}}]},{"cell_type":"code","source":"x_train.shape","metadata":{"_uuid":"d9f57a2e556633dfef966879b7ea0545d1f7799a","execution":{"iopub.status.busy":"2024-03-01T14:04:47.624931Z","iopub.execute_input":"2024-03-01T14:04:47.625228Z","iopub.status.idle":"2024-03-01T14:04:47.638070Z","shell.execute_reply.started":"2024-03-01T14:04:47.625164Z","shell.execute_reply":"2024-03-01T14:04:47.636790Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":"(2191, 60)"},"metadata":{}}]},{"cell_type":"code","source":"y_train.shape","metadata":{"_uuid":"1c7be616e2000311d0fb7d8e8d042409d17a28a5","execution":{"iopub.status.busy":"2024-03-01T14:04:47.639607Z","iopub.execute_input":"2024-03-01T14:04:47.639917Z","iopub.status.idle":"2024-03-01T14:04:47.649172Z","shell.execute_reply.started":"2024-03-01T14:04:47.639864Z","shell.execute_reply":"2024-03-01T14:04:47.647937Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"(2191,)"},"metadata":{}}]},{"cell_type":"code","source":"# reshaping x_train for efficient modelling\n\nx_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))","metadata":{"_uuid":"1838955cecc7ae8176c549db364e8766bbd385da","execution":{"iopub.status.busy":"2024-03-01T14:04:47.651163Z","iopub.execute_input":"2024-03-01T14:04:47.651622Z","iopub.status.idle":"2024-03-01T14:04:47.658987Z","shell.execute_reply.started":"2024-03-01T14:04:47.651549Z","shell.execute_reply":"2024-03-01T14:04:47.657954Z"},"trusted":true},"execution_count":93,"outputs":[]},{"cell_type":"code","source":"x_train.shape","metadata":{"_uuid":"33a7b19afde00beb7eaa5af9f54bf2b576e1c9d4","execution":{"iopub.status.busy":"2024-03-01T14:04:47.660232Z","iopub.execute_input":"2024-03-01T14:04:47.660649Z","iopub.status.idle":"2024-03-01T14:04:47.672082Z","shell.execute_reply.started":"2024-03-01T14:04:47.660578Z","shell.execute_reply":"2024-03-01T14:04:47.670822Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"(2191, 60, 1)"},"metadata":{}}]},{"cell_type":"code","source":"dataset_total = pd.concat((data['High'][:'2016'], data['High']['2017':]), axis=0)\ninputs = dataset_total[len(dataset_total)-len(test)-60 : ].values\ninputs = inputs.reshape(-1,1)\ninputs = sc.transform(inputs)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:04:47.673420Z","iopub.execute_input":"2024-03-01T14:04:47.673825Z","iopub.status.idle":"2024-03-01T14:04:47.688407Z","shell.execute_reply.started":"2024-03-01T14:04:47.673748Z","shell.execute_reply":"2024-03-01T14:04:47.687485Z"},"trusted":true},"execution_count":95,"outputs":[]},{"cell_type":"code","source":"# preparing x_test\n\nx_test = []\nfor i in range(60,311):\n    x_test.append(inputs[i-60:i, 0])\n    \nx_test = np.array(x_test)\nx_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))","metadata":{"_uuid":"44aaf9d2328e56756a6359f13119920960a9d74e","execution":{"iopub.status.busy":"2024-03-01T14:04:47.690024Z","iopub.execute_input":"2024-03-01T14:04:47.690428Z","iopub.status.idle":"2024-03-01T14:04:47.702341Z","shell.execute_reply.started":"2024-03-01T14:04:47.690334Z","shell.execute_reply":"2024-03-01T14:04:47.701022Z"},"trusted":true},"execution_count":96,"outputs":[]},{"cell_type":"markdown","source":"Gated Recurrent Units\n\nIn simple words, the GRU unit does not have to use a memory unit to control the flow of information like the LSTM unit. It can directly makes use of the all hidden states without any control. GRUs have fewer parameters and thus may train a bit faster or need less data to generalize. But, with large data, the LSTMs with higher expressiveness may lead to better results.\n\nThey are almost similar to LSTMs except that they have two gates: reset gate and update gate. Reset gate determines how to combine new input to previous memory and update gate determines how much of the previous state to keep. Update gate in GRU is what input gate and forget gate were in LSTM. We don't have the second non linearity in GRU before calculating the output, .neither they have the output gate.","metadata":{"_uuid":"89bcb2f3641ab26954a9f3abe2328c796a5aa7c0"}},{"cell_type":"code","source":"# The GRU architecture\nregressorGRU = Sequential()\n# First GRU layer with Dropout regularisation\nregressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\nregressorGRU.add(Dropout(0.2))\n# Second GRU layer\nregressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\nregressorGRU.add(Dropout(0.2))\n# Third GRU layer\nregressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\nregressorGRU.add(Dropout(0.2))\n# Fourth GRU layer\nregressorGRU.add(GRU(units=50, activation='tanh'))\nregressorGRU.add(Dropout(0.2))\n# The output layer\nregressorGRU.add(Dense(units=1))","metadata":{"_uuid":"a5aa8587cc5b6bce6b75d8d9ffb7e8649f3bacde","execution":{"iopub.status.busy":"2024-03-01T14:04:47.704045Z","iopub.execute_input":"2024-03-01T14:04:47.704424Z","iopub.status.idle":"2024-03-01T14:04:48.818746Z","shell.execute_reply.started":"2024-03-01T14:04:47.704352Z","shell.execute_reply":"2024-03-01T14:04:48.817698Z"},"trusted":true},"execution_count":97,"outputs":[]},{"cell_type":"code","source":"# compiling the model\n\nregressorGRU.compile(optimizer=SGD(lr=0.01, decay=1e-7, momentum=0.9, nesterov=False), loss='mean_squared_error')\n\n# fitting the model\n\nregressorGRU.fit(x_train, y_train, epochs=5, batch_size=150)","metadata":{"_uuid":"8a8fc071fea6be62a872f82682526410dde9affa","execution":{"iopub.status.busy":"2024-03-01T14:04:48.823196Z","iopub.execute_input":"2024-03-01T14:04:48.823571Z","iopub.status.idle":"2024-03-01T14:05:20.994166Z","shell.execute_reply.started":"2024-03-01T14:04:48.823494Z","shell.execute_reply":"2024-03-01T14:05:20.993259Z"},"trusted":true},"execution_count":98,"outputs":[{"name":"stdout","text":"Epoch 1/5\n2191/2191 [==============================] - 8s 4ms/step - loss: 0.0420\nEpoch 2/5\n2191/2191 [==============================] - 5s 2ms/step - loss: 0.0275\nEpoch 3/5\n2191/2191 [==============================] - 5s 2ms/step - loss: 0.0208\nEpoch 4/5\n2191/2191 [==============================] - 5s 2ms/step - loss: 0.0146\nEpoch 5/5\n2191/2191 [==============================] - 5s 2ms/step - loss: 0.0090\n","output_type":"stream"},{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7b7c393aeb70>"},"metadata":{}}]},{"cell_type":"code","source":"# Assuming len(test) is the correct length of your test set\n# Adjust the range accordingly to match the length of your test set\nx_test = np.array([inputs[i-60:i, 0] for i in range(60, len(test) + 60)])\n\nx_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n\n# predicting the stock prices for the test set\npredicted_with_gru = regressorGRU.predict(x_test)\npredicted_with_gru = sc.inverse_transform(predicted_with_gru)\n\n# visualizing the results: predicted vs test\nplot_predictions(test, predicted_with_gru)\n\n# evaluating the model performance\nreturn_rmse(test, predicted_with_gru)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:05:20.995317Z","iopub.execute_input":"2024-03-01T14:05:20.995758Z","iopub.status.idle":"2024-03-01T14:05:23.285289Z","shell.execute_reply.started":"2024-03-01T14:05:20.995711Z","shell.execute_reply":"2024-03-01T14:05:23.284421Z"},"trusted":true},"execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"the root mean squared error is : 62.65847331590434.\n","output_type":"stream"}]},{"cell_type":"code","source":"# evaluating the model performance\n\nreturn_rmse(test, predicted_with_gru)","metadata":{"_uuid":"f89d027315299d71067bad7537d577fe5451a337","execution":{"iopub.status.busy":"2024-03-01T14:05:23.286489Z","iopub.execute_input":"2024-03-01T14:05:23.287077Z","iopub.status.idle":"2024-03-01T14:05:23.292462Z","shell.execute_reply.started":"2024-03-01T14:05:23.287014Z","shell.execute_reply":"2024-03-01T14:05:23.291514Z"},"trusted":true},"execution_count":100,"outputs":[{"name":"stdout","text":"the root mean squared error is : 62.65847331590434.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Thanks if you find it useful! :)","metadata":{"_uuid":"9cb818bc89b8a4d248ed4f1d151ce700c2ef5f7a"}},{"cell_type":"code","source":"","metadata":{"_uuid":"582cb4a4b06dd42697de8f96826c32f73fc868f6","trusted":true},"execution_count":null,"outputs":[]}]}