Neural_Prediction_Uncertainty_Young.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Neural-Prediction-Uncertainty-Young.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPut9gpNu1IfuT/ExLSnK2u"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Mounting Google Drive"
      ],
      "metadata": {
        "id": "ESNkUUOEoS8j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GWpiHXuGn6Oy"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd 'drive/MyDrive/IIT J Summer Internship 2022/Code/Neural-Uncertainty-Representation/'"
      ],
      "metadata": {
        "id": "bpxEP-yeoWPX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing Necessary Libraries\n"
      ],
      "metadata": {
        "id": "Hq9mNRMMomT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, TimeDistributed\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ],
      "metadata": {
        "id": "SRXBkwcIogPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing Data\n",
        "We import the data of a young subject's 3D-Tensor Data of dimensions (TR, Voxel, Number of Subjects) for the 5 different ROIs as follows:\n",
        "- **Prefrontal areas:** dlPFC, vlPFC, lOFC, dmPFC, mPFC, mOFC\n",
        "\n"
      ],
      "metadata": {
        "id": "_R2-Cetmxned"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_names = [name for name in os.listdir('/content/drive/MyDrive/IIT J Summer Internship 2022/Hitchcock/Data/YOUNG/Voxel_BOLD/Numpy') if name.endswith(\"npy\") and (\"(1)\" not in name) and (\"PFC\" in name) or (\"OFC\" in name)]\n",
        "file_names = [name for name in file_names if (\"537\" not in name)]"
      ],
      "metadata": {
        "id": "XUJPFBg1898z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ROI_names = [file_name.replace(\"data_\",\"\").replace(\".npy\",\"\") for file_name in file_names]"
      ],
      "metadata": {
        "id": "5lVnzf5N_Ufy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_ori = list()\n",
        "for file_name in file_names:\n",
        "  data_ori.append(np.load('/content/drive/MyDrive/IIT J Summer Internship 2022/Hitchcock/Data/YOUNG/Voxel_BOLD/Numpy/'+file_name))"
      ],
      "metadata": {
        "id": "Kg22rkIGpfP2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preparation"
      ],
      "metadata": {
        "id": "Kcaxzm8WDjgO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, here we prepare the data to be fed to the Neural Network Architechtures. We can't predict the time ahead upto which the subjects are predicting correctly so, we will take a bit round about way to predict the BOLD values ahead in time(for several choices of time points e.g. 1 time point ahead, 2 time point ahead, etc) and will compare the results for young and old subjects."
      ],
      "metadata": {
        "id": "K9rJ8CAb97K2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will start a basic data preparation where we will average across all the voxels for a ROI and fit the model which predicts this average BOLD value. Later we will avoid doing an average since, all voxels in a ROI is not equally important and hence we will want to predict the BOLD values of several voxels simultaneously."
      ],
      "metadata": {
        "id": "TYuu8ZM2-u9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = [np.mean(dat, axis=1, keepdims=True) for dat in data_ori]"
      ],
      "metadata": {
        "id": "pFvaFL69geft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's Start the Data Preparation for lOFC later we will iterate the same procedure for the other ROIs."
      ],
      "metadata": {
        "id": "mY3usMssn3T5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[-1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71dv3Zkcgu3N",
        "outputId": "0fad6727-4d9f-4c91-ff3e-c9bb44fc3e83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(189, 1, 111)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(data[-1][0,0,:])\n",
        "plt.title(\"Average BOLD for \"+ROI_names[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "AseI4ZNeg26E",
        "outputId": "23649e6d-6542-4fcb-e6f1-514188bc05aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average BOLD for lOFC')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have changed the data in the form (Number of Subjects, TR, Voxels) below."
      ],
      "metadata": {
        "id": "-wIOccLpE0d-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = [np.transpose(dat, (2,0,1)) for dat in data]"
      ],
      "metadata": {
        "id": "0pHK54KjDnah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[-1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRXHJ1cwgrzp",
        "outputId": "2e29eb42-f8e8-4eb2-b029-d36a29cd2bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(111, 189, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We split the dataset of 111 young subjects into 110 subjects in the training data and 1 subject in the test data."
      ],
      "metadata": {
        "id": "gg50q17pp5xQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_unscaled = data[-1][:-1]\n",
        "test_data_unscaled = np.expand_dims(data[-1][-1], axis=0)  "
      ],
      "metadata": {
        "id": "hrldyz8XnDlv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We scaled the BOLD values of each voxel in the train data between 0 and 1 for each voxel. We use the same MinMaxScaler fit on the train data on the test data."
      ],
      "metadata": {
        "id": "MDV7H0sYt6jd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "train_data = sc.fit_transform(train_data_unscaled[:,:,0])\n",
        "test_data = sc.transform(test_data_unscaled[:,:,0])"
      ],
      "metadata": {
        "id": "-xkJQLj3tORj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = np.expand_dims(train_data, axis = -1)\n",
        "test_data = np.expand_dims(test_data, axis = -1)"
      ],
      "metadata": {
        "id": "Ail126iVuR6s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train Data Shape: \", train_data.shape)\n",
        "print(\"Test Data Shape: \", test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cOAv_VrnbLU",
        "outputId": "45cee61a-054d-45c9-eb5d-98434b319e02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data Shape:  (110, 189, 1)\n",
            "Test Data Shape:  (1, 189, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now build a data generator which would produce mini-batches during the training of the neural network."
      ],
      "metadata": {
        "id": "0yjlAwWeiEru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class KerasBatchGenerator(object):\n",
        "    def __init__(self, data, num_TRs, batch_size, look_ahead=1):\n",
        "        self.data = data\n",
        "        self.num_TRs = num_TRs\n",
        "        self.batch_size = batch_size\n",
        "        self.current_idx = 0\n",
        "        self.look_ahead = look_ahead\n",
        "\n",
        "    def generate(self):\n",
        "        while True:\n",
        "            if self.current_idx + self.batch_size >= self.data.shape[0]:\n",
        "              self.current_idx = 0\n",
        "            x = self.data[self.current_idx:(self.current_idx+self.batch_size),:(-self.look_ahead),:]\n",
        "            y = self.data[self.current_idx:(self.current_idx+self.batch_size),self.look_ahead:,:]\n",
        "            self.current_idx = self.current_idx + self.batch_size\n",
        "            yield x, y "
      ],
      "metadata": {
        "id": "Blko7LXpgtwA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_generator = KerasBatchGenerator(train_data, train_data.shape[1], 16, 1)\n",
        "test_data_generator = KerasBatchGenerator(test_data, test_data.shape[1], 1, 1)"
      ],
      "metadata": {
        "id": "lZN56Eg3mrsc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_x, batch_y = next(train_data_generator.generate())\n",
        "print(\"Train Batch X Shape: \", batch_x.shape)\n",
        "print(\"Train Batch y Shape: \", batch_y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UPT6iRxpJ7c",
        "outputId": "162463c3-974d-4a01-c216-cb43159739b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Batch X Shape:  (16, 188, 1)\n",
            "Train Batch y Shape:  (16, 188, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_x, batch_y = next(test_data_generator.generate())\n",
        "print(\"Test Batch X Shape: \", batch_x.shape)\n",
        "print(\"Test Batch y Shape: \", batch_y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Clavfro-pSEV",
        "outputId": "fe357e08-35de-4e86-b746-334e9cd3a130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Batch X Shape:  (1, 188, 1)\n",
            "Test Batch y Shape:  (1, 188, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building Model"
      ],
      "metadata": {
        "id": "31NRewZjqoi1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will start off by building a Single Layer Deep LSTM Model."
      ],
      "metadata": {
        "id": "8Ry-jGmHqqx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(16, return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(TimeDistributed(Dense(1)))"
      ],
      "metadata": {
        "id": "fqoXwHTTqqCR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.build(input_shape=test_data.shape)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYHgnyz-v5nA",
        "outputId": "53cb2135-c81a-4a85-8bb7-869ff3365045"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (1, 189, 16)              1152      \n",
            "                                                                 \n",
            " dropout (Dropout)           (1, 189, 16)              0         \n",
            "                                                                 \n",
            " time_distributed (TimeDistr  (1, 189, 1)              17        \n",
            " ibuted)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,169\n",
            "Trainable params: 1,169\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# I am already trained the model hence I commented the following lines\n",
        "# model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "metadata": {
        "id": "kf-dFQmfzqTA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# num_epochs = 100\n",
        "# checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath=\"./Models/LSTM-H16-lOFC/\", verbose=1)"
      ],
      "metadata": {
        "id": "vcZsfhkz3lCH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with tf.device('/device:GPU:0'):\n",
        "#     model.fit_generator(train_data_generator.generate(), train_data.shape[0]//16, num_epochs,\n",
        "#                         validation_data=test_data_generator.generate(),\n",
        "#                         validation_steps=test_data.shape[0]//1, callbacks=[checkpointer])"
      ],
      "metadata": {
        "id": "AFb-kTLR21hl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model.predict(test_data)"
      ],
      "metadata": {
        "id": "7mpQ5Rap6kQ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(np.squeeze(test_data))\n",
        "plt.plot(np.squeeze(preds))\n",
        "plt.ylabel(\"Average BOLD\")\n",
        "plt.xlabel(\"TRs\")\n",
        "plt.title(\"Average BOLD for Last Subject's \"+ROI_names[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "WBLOHLixAWnk",
        "outputId": "4dfe1831-e70b-40dd-b54c-8bd9ad2adda3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Average BOLD for Last Subject's lOFC\")"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}