From d3c33f7ee57fd952b3c09166509d6005fed2e3b7 Mon Sep 17 00:00:00 2001
From: Ulrich <ulrich.kerzel@rwth-aachen.de>
Date: Wed, 18 Oct 2023 07:34:05 +0200
Subject: [PATCH] simple timeseries forecasting with LSTM

---
 .../PyTorch_TimeSeries_LSTM.ipynb             | 574 ++++++++++++++++++
 1 file changed, 574 insertions(+)
 create mode 100644 datascienceintro/PyTorch_TimeSeries_LSTM.ipynb

diff --git a/datascienceintro/PyTorch_TimeSeries_LSTM.ipynb b/datascienceintro/PyTorch_TimeSeries_LSTM.ipynb
new file mode 100644
index 0000000..2f73f0c
--- /dev/null
+++ b/datascienceintro/PyTorch_TimeSeries_LSTM.ipynb
@@ -0,0 +1,574 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import torch.utils.data as data"
+      ],
+      "metadata": {
+        "id": "kml4fU_Wl9kQ"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get cpu or gpu device for training.\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "print(f\"Using {device} device\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iuy-JY9kC9Kt",
+        "outputId": "b4093340-9374-4e33-e60f-308e8c24d50c"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using cuda device\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Generate data\n",
+        "\n",
+        "We now create a small artificial dataset from two sine functions with an additional  noise term. The signal is periodic and therefore relatively simple to learn, however, the high frequency component and noise allow for some complexity."
+      ],
+      "metadata": {
+        "id": "8h7JY0qPv7VS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "##\n",
+        "## generate data - a simple sine\n",
+        "##\n",
+        "def generate_data(x_min, x_max, n_datapoints=300,\n",
+        "                  f1 = 10, f2=20, offset1 = 0, offset2=0.2, noise=0.1):\n",
+        "  x = np.linspace(x_min, x_max, n_datapoints)\n",
+        "  y = 0.5*np.sin(f1*(x-offset1))+0.2*np.sin(f2*(x-offset2))+noise*np.random.rand(n_datapoints)-0.5\n",
+        "  return x,y\n",
+        "\n",
+        "x_ticks,y = generate_data(x_min=0.0, x_max=10.0, n_datapoints=500,\n",
+        "                    f1=10, f2=17, offset1=0.1, offset2=0.3, noise=0.2)\n",
+        "plt.plot(x_ticks, y, label='Sine function')\n",
+        "plt.xlabel('x values', size=15)\n",
+        "plt.ylabel('y values', size=15)\n",
+        "plt.legend()\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "X3nlcI5vqzlv",
+        "outputId": "a4655fb4-dec2-4635-93f2-3712524cc152"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "However, for forecastig, we are only interested in the $y$ values as this is\n",
+        "our timeseries. Therefore, from here we refer to the $y$-values only.\n",
+        "\n",
+        "We would need  the $x$-values as indices for the $x$-axis in plotting - but they do not have any relevance for the timeseries forecasting as such.\n",
+        "\n",
+        "Therefore, for now, we will just focus on the $y$ values."
+      ],
+      "metadata": {
+        "id": "owu57rZPv_e8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(y, label='timeseries')\n",
+        "plt.xlabel('steps', size=15)\n",
+        "plt.ylabel('y values', size=15)\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "TN5SntDUrFdH",
+        "outputId": "cb7f4e29-2897-435a-8a58-a51a67f63193"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In timeseries forecasting, we need to remember that the data-points are auto-correlated, i.e. previous data points (earlier in the series) carry the predictive power for the data points later in the series.\n",
+        "\n",
+        "This means that we cannot use a fuction like [test_train_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) from scikit-learn as these functions assume that\n",
+        "the data are i.i.d. distributed.\n",
+        "\n",
+        "Instead, we take the first 80% of the timeseries as training data, and the remainint 20% as independent test data.\n",
+        "Note that, for the later use with PyTorch, we also convert the data from ```float``` to ```double``` (i.e. ```float32```).\n",
+        "We also need to bring the timeseries in the format expected later on (```np.reshape(.)```)"
+      ],
+      "metadata": {
+        "id": "3jMF8Cx8yxsx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#convert from float to double\n",
+        "timeseries = y.astype('float32')\n",
+        "timeseries = np.reshape(timeseries, (len(timeseries),1))\n",
+        "\n",
+        "# define the size of the size of the training data as 80%\n",
+        "train_size = round(len(timeseries) * 0.8)\n",
+        "test_size  = len(timeseries) - train_size\n",
+        "\n",
+        "# the training data is then the timeseries from the beginning to 80%\n",
+        "train      = timeseries[:train_size]\n",
+        "\n",
+        "# the test data starts at the index of the last element of the train series\n",
+        "# until the end of the array\n",
+        "test       = timeseries[train_size:]"
+      ],
+      "metadata": {
+        "id": "NPrAKH1-sKH5"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate training features\n",
+        "\n",
+        "We now need to transform the data in to a sliding window of a fixed width.\n",
+        "One key feature of sequences is that the data points are auto-correlated which means that the sequence of the data points matters.\n",
+        "In \"normal\" supervised learning approaches we would shuffle the training data and reserve a fraction chosen at random as the test data.\n",
+        "In the case of time-series data, we cannot do this as the sequence of the data points is a key aspect of the structure of a time-series.\n",
+        "\n",
+        "LSTM networks are a supervised training network where we predict a label (y_train) from a number of training data (x_data). In our case we take a part of the original training data by considering only a number n points at the time and then predict the next element that would follow immediately after this sequence of n points.\n",
+        "Then we shift the window by one time-step (or data point), consider the next window of n data-points and predict the one following the the new sequence\n",
+        "and repeat the process until we have processed the complete time-series.\n",
+        "\n",
+        "Essentially, we give a sequence of time-series elements to the ML model and train the model on predicting the next point in the series"
+      ],
+      "metadata": {
+        "id": "mbyWDkua2YTs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_features_target(data, seq_length):\n",
+        "  X, y = [], []\n",
+        "\n",
+        "  for i in range(len(data) - seq_length):\n",
+        "    # a sequence of n points is taken as features for the trainnig\n",
+        "    x_value = data[i: i+seq_length]\n",
+        "    X.append(x_value)\n",
+        "\n",
+        "    # the prediction target is then the shifted sequence,\n",
+        "    # i.e. including the next data-point (the one we are interested in)\n",
+        "    y_value = data[i+1 : i+seq_length+1]\n",
+        "    y.append(y_value)\n",
+        "\n",
+        "  # convert to PyTorch data structures for later use\n",
+        "  return torch.tensor(np.array(X)), torch.tensor(np.array(y))"
+      ],
+      "metadata": {
+        "id": "lAjhrDYg2YCW"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# as this is a relatively simple time-series, we only look at a short series\n",
+        "sequence_length = 4\n",
+        "X_train, y_train = create_features_target(train, sequence_length)\n",
+        "X_test, y_test = create_features_target(test, sequence_length)"
+      ],
+      "metadata": {
+        "id": "JQrOeKI54Xtq"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# move to GPU, if available\n",
+        "X_train = X_train.to(device)\n",
+        "y_train = y_train.to(device)\n",
+        "X_test  = X_test.to(device)\n",
+        "y_test  = y_test.to(device)"
+      ],
+      "metadata": {
+        "id": "JCLdoXFGDbre"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X_train.shape, y_train.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2KaxTdvH-TC9",
+        "outputId": "4bfc85eb-746a-4aad-ce7e-573c3df32b07"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "torch.Size([396, 4, 1]) torch.Size([396, 4, 1])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# LSTM Model\n",
+        "\n",
+        "We now need to create our LSTM model using the [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html) from PyTorch.\n",
+        "In the following, we only need the output of the LSTM cell, but we do not need *h_n* and *c_n*. Therefore, we do not capture them (siginfied by the underscore).\n",
+        "\n",
+        "We start with a simple network that has only one LSTM layer, followed\n",
+        "by a linear layer to convert the output of the LSTM cell back into a single,\n",
+        "real-valued number."
+      ],
+      "metadata": {
+        "id": "AcvNy1VT42V7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_hidden_1 = 100\n",
+        "\n",
+        "class Simple_Model(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.lstm = nn.LSTM(input_size=1, hidden_size=n_hidden_1, num_layers=1, batch_first=True)\n",
+        "        self.linear = nn.Linear(n_hidden_1, 1)\n",
+        "    def forward(self, x):\n",
+        "        x, _ = self.lstm(x)\n",
+        "        x = self.linear(x)\n",
+        "        return x"
+      ],
+      "metadata": {
+        "id": "sEDBehJ947Gz"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Network training\n",
+        "\n",
+        "Now we need to train our (simple) network.\n",
+        "We start with the setups:\n",
+        "\n",
+        "First we need to create an instance of our model, then choose an optimiser\n",
+        "(using the [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) optimiser), and define a loss function.\n",
+        "Here, we use the mean squared error ([MSE](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html))as a regression loss.\n",
+        "\n",
+        "PyTorch offers a number of convencince functions for handling data.\n",
+        "Remember, up to this point, the training and test data are arrays of numbers. During trainign, we want to make use of batches, as well as not having to iterate over the data ourselves, including shuffling of the data between training epochs.\n",
+        "The main function that helps us here is [```DataLoader```](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), that, in turn, requires\n",
+        "PyTorch tensors which we can create using [```TensorDataset```](https://pytorch.org/docs/stable/data.html#torch.utils.data.TensorDataset)."
+      ],
+      "metadata": {
+        "id": "iGW8oF7r58qy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "85ytxE0qlHm0",
+        "outputId": "996168f1-0fcd-48f4-87af-116c59b40779"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 0: train MSE 0.1126, test MSE 0.1142\n",
+            "Epoch 10: train MSE 0.0128, test MSE 0.0135\n",
+            "Epoch 20: train MSE 0.0128, test MSE 0.0135\n",
+            "Epoch 30: train MSE 0.0119, test MSE 0.0125\n",
+            "Epoch 40: train MSE 0.0131, test MSE 0.0136\n",
+            "Epoch 50: train MSE 0.0115, test MSE 0.0120\n",
+            "Epoch 60: train MSE 0.0116, test MSE 0.0122\n",
+            "Epoch 70: train MSE 0.0115, test MSE 0.0120\n",
+            "Epoch 80: train MSE 0.0119, test MSE 0.0125\n",
+            "Epoch 90: train MSE 0.0115, test MSE 0.0121\n",
+            "Epoch 100: train MSE 0.0112, test MSE 0.0118\n",
+            "Epoch 110: train MSE 0.0113, test MSE 0.0118\n",
+            "Epoch 120: train MSE 0.0117, test MSE 0.0122\n",
+            "Epoch 130: train MSE 0.0112, test MSE 0.0118\n",
+            "Epoch 140: train MSE 0.0112, test MSE 0.0117\n",
+            "Epoch 150: train MSE 0.0112, test MSE 0.0117\n",
+            "Epoch 160: train MSE 0.0113, test MSE 0.0118\n",
+            "Epoch 170: train MSE 0.0112, test MSE 0.0117\n",
+            "Epoch 180: train MSE 0.0114, test MSE 0.0119\n",
+            "Epoch 190: train MSE 0.0113, test MSE 0.0119\n"
+          ]
+        }
+      ],
+      "source": [
+        "#\n",
+        "# setups\n",
+        "#\n",
+        "\n",
+        "# instantiate model and move to GPU (if available)\n",
+        "model = Simple_Model().to(device)\n",
+        "\n",
+        "# Optimizer\n",
+        "optimizer = optim.Adam(model.parameters())\n",
+        "\n",
+        "# Loss function\n",
+        "loss_fn = nn.MSELoss()\n",
+        "\n",
+        "# convenience function for handling training data\n",
+        "loader = data.DataLoader(data.TensorDataset(X_train, y_train), shuffle=True, batch_size=8)\n",
+        "size = len(loader.dataset)\n",
+        "\n",
+        "loss_values = []\n",
+        "\n",
+        "#\n",
+        "# training loop\n",
+        "#\n",
+        "n_epochs = 200\n",
+        "for epoch in range(n_epochs):\n",
+        "    running_loss = 0.0\n",
+        "    # make sure we're in training mode\n",
+        "    model.train()\n",
+        "\n",
+        "    #loop over all batches of training data\n",
+        "    for X_batch, y_batch in loader:\n",
+        "\n",
+        "        #move to GPU, if available\n",
+        "        X_batch = X_batch.to(device)\n",
+        "        y_batch = y_batch.to(device)\n",
+        "\n",
+        "        # prediction\n",
+        "        y_pred = model(X_batch)\n",
+        "\n",
+        "        # loss function\n",
+        "        loss = loss_fn(y_pred, y_batch)\n",
+        "        loss_item = loss.item()\n",
+        "        running_loss += loss_item\n",
+        "\n",
+        "        # network training step\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    loss_values.append(running_loss/size)\n",
+        "\n",
+        "    # every n epochs, print the current value of the MSE for the\n",
+        "    # training and test data\n",
+        "    if epoch % 10 == 0:\n",
+        "      model.eval()\n",
+        "      with torch.no_grad():\n",
+        "        y_pred = model(X_train)\n",
+        "        train_mse = loss_fn(y_pred, y_train)\n",
+        "\n",
+        "        y_pred = model(X_test)\n",
+        "        test_mse = loss_fn(y_pred, y_test)\n",
+        "        print('Epoch {}: train MSE {:.4f}, test MSE {:.4f}'.format(epoch, train_mse, test_mse))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Model evaluation\n",
+        "\n",
+        "In a first step, we look at the loss function."
+      ],
+      "metadata": {
+        "id": "KO8Kr_8mEz-t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.title('Loss function')\n",
+        "plt.plot(loss_values, label='loss')\n",
+        "plt.xlabel('epoch', size = 10)\n",
+        "plt.ylabel('loss', size = 10)\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "ZCA6jAAbD3VV",
+        "outputId": "548c8db4-41fe-41ff-b344-01a7a3bba82b"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we look at how the model performs.\n",
+        "To do this, we make predictions on the training data (these should be good, as\n",
+        "the network has seen all this data during training), as well as on the independent test data. The performance on this latter part will show us, how well the network has learned the pattersn in the data."
+      ],
+      "metadata": {
+        "id": "q3offP-ZFlOJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# first step: create some \"dummy\" arrays that will hold the\n",
+        "# the prediction on the training and test data.\n",
+        "train_plot = np.ones_like(timeseries) * np.nan\n",
+        "test_plot  = np.ones_like(timeseries) * np.nan\n",
+        "\n",
+        "with torch.no_grad():\n",
+        "    #\n",
+        "    # predictions on the training data\n",
+        "    #\n",
+        "    y_pred_train = model(X_train)\n",
+        "    # take the last number in the sequence (the actual prediction we are\n",
+        "    # interested in). We also need to move it back to the CPU if\n",
+        "    # trainingn on GPU\n",
+        "    y_pred_train = y_pred_train[:, -1, :].cpu()\n",
+        "    train_plot[sequence_length:train_size] = y_pred_train\n",
+        "\n",
+        "\n",
+        "    #\n",
+        "    # predictions on the training data\n",
+        "    #\n",
+        "    y_pred_test = model(X_test)\n",
+        "    y_pred_test = y_pred_test[:, -1, :].cpu()\n",
+        "    test_plot[train_size+sequence_length:len(timeseries)] = y_pred_test\n",
+        "\n",
+        "#\n",
+        "# now do the actual plot\n",
+        "#\n",
+        "plt.plot(x_ticks,timeseries, color='black', label='data')\n",
+        "plt.plot(x_ticks,train_plot, color='blue' , alpha=0.5, label='Pred. on train')\n",
+        "plt.plot(x_ticks,test_plot , color='red'  , alpha=0.5, label='Pred on test')\n",
+        "plt.legend()\n",
+        "plt.xlabel('x values' , size=10)\n",
+        "plt.ylabel('y values', size=10)\n",
+        "plt.title('Forecast')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "VKQA4YvhstFM",
+        "outputId": "48eac24e-0d04-4278-ede5-0c173d6481a3"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
-- 
GitLab