Basic_Temporal_Supervised_Machine_Learning_MLP.ipynb

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  "metadata": {
    "colab": {
      "name": "Basic Temporal Supervised Machine Learning - MLP.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/todnewman/coe_training/blob/master/Basic_Temporal_Supervised_Machine_Learning_MLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO7hIpIYm72S",
        "colab_type": "text"
      },
      "source": [
        "# Supervised Machine Learning for Time Series Data\n",
        "**Author**: W. Tod Newman\n",
        "\n",
        "**Updates**: New Version\n",
        "\n",
        "## Problem\n",
        "\n",
        "Determine the probability of rain some number of hours in the future based on the last X hours of collected data across six features.\n",
        "\n",
        "This notebook shows how to take advantage of the information contained in sequences from time series data.  It shows how to convert time series data into dataframes useful for supervised machine learning. \n",
        "\n",
        "Additionally, this notebook shows how to:\n",
        "1.  Follow a managed workflow from Data to Verification.  This uses GitHub, Google Drive, and Tensorboard to demonstrate what a production workflow might look like.\n",
        "2.  Connect a Keras/Tensorflow model with SKLearn and Pandas tools\n",
        "3.  Use Tensorboard for Evaluating models\n",
        "\n",
        "## Learning Objectives\n",
        "\n",
        "\n",
        "* Evaluate why time series data is interesting and useful for prediction.\n",
        "* Learn techniques for unrolling sequences and building a supervised learning dataset from time series data.\n",
        "* Learn techniques for shifting labels to enable predictions of future states using current data.\n",
        "* Demonstrate real-time inference using new data.\n",
        "\n",
        "## Dataset\n",
        "\n",
        "This dataset is the famous \"Tod Newman Back Porch\" Dataset.  See http://www.todnewman.com for details on the data collection system.\n",
        "\n",
        "Features include:\n",
        "* Distance of Lightning Strike (km)\n",
        "* Energy of Lightning Strike (unitless)\n",
        "* Visible Light Luminosity (lux)\n",
        "* Humidity (%)\n",
        "* Pressure (HPa)\n",
        "* Temperature (deg F)\n",
        "\n",
        "The Label is:\n",
        "* Rain: Was it raining during the collection interval or not.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skj9F394nV6k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os, sys \n",
        "sys.path.insert(0, os.getcwd()) \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import matplotlib\n",
        "import sklearn as sk\n",
        "from sklearn import preprocessing\n",
        "from sklearn import metrics\n",
        "#from sklearn.grid_search import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn import linear_model\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
        "tensorboard = False\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBg-8Bzm63d_",
        "colab_type": "text"
      },
      "source": [
        "# Data\n",
        "\n",
        "Below I demonstrate a number of different approaches to getting data into our processing pipeline.  I mostly load data and common python modules from my GitHub account (https://github.com/todnewman).  I tend to store models and other artifacts of this workflow in my Google Drive.  When using this workflow, the developer will of course read from and save to their own Google Drive.  You'll need a Google account (which you probably need anyway to use this Notebook) and would have to authenticate.  That is all handled in number 2 below.\n",
        "\n",
        "Right now this workflow is automated and the products are very available.  This is very useful for using as training modules.\n",
        "\n",
        "##Methods of Data Insertion:\n",
        "1. Direct import from your PC local storage\n",
        "2. Load from Google Drive \n",
        "3. Load from GitHub\n",
        "4. Direct acquisition from repositories (like UC Irvine's Open Data Repository).  Use wget or import a URL directly in to pandas read_csv()."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKRtNbcXhSzh",
        "colab_type": "text"
      },
      "source": [
        "### Here's how you can load files to and from YOUR Google Drive.  \n",
        "\n",
        "It will give you a link to follow which will ask you to log in to your Google account.  Then your Google drive will show up as /content/drive/'My Drive'\n",
        "\n",
        "This is a good way to get larger datafiles into your workflows.  They'll take up space on your Google Drive, but you have 15GB for free.\n",
        "\n",
        "If you want to grab tod's lightning_sensor_data.csv, it is public and accessible at this link:  https://drive.google.com/file/d/12rb8lkz16iTWuvB2OVXwZ0SJvE8AYSps/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7joL7CqgJjQu",
        "colab_type": "code",
        "outputId": "db4ab338-ed13-43f1-92cf-5335863bb10a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/'My Drive'/models\n",
        "%ls\n",
        "%cp rain_model_mlp /content/rain_model_mlp"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "/content/drive/My Drive/models\n",
            "cnn_fashion_mnist          model_n17.hdf5\n",
            "id_model                   ngrok\n",
            "lanl_lstm_model            ngrok-stable-linux-amd64.zip\n",
            "LANL_new_model             ngrok-stable-linux-amd64.zip.1\n",
            "LANL_new_model.05          quake_model_mlp\n",
            "lanl_quake_lstm            quake_model_mlp.41\n",
            "lanl_quake_lstm.1          quake_model_mlp.75\n",
            "lanl_quake_lstm150         quake_model_mlp.bak\n",
            "lanl_quake_lstm.2          quake_model_mlp_done\n",
            "\u001b[0m\u001b[01;34mlog\u001b[0m/                       rain_model\n",
            "lunarlander                rain_model_10k_dropout.2\n",
            "model_150_1000_30_36.hdf5  rain_model_5k_dropout.3\n",
            "model_150_1000_64_44.hdf5  rain_model_5k_dropout.4\n",
            "model_300_500_30_33        rain_model_mlp\n",
            "model_5_2.hdf5             rain_model_mlp_bce\n",
            "model.hdf5                 rain_seq_model\n",
            "model_lanl.hdf5            submission.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LSY7qL2nhWs",
        "colab_type": "text"
      },
      "source": [
        "### Loading data from GitHub\n",
        "\n",
        "Here's another really good way to get example data in.  I have a data project in GitHub that is associated with the coe_training project.  We can download the whole folder with the git clone command or just one file with the wget command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaNoF346iPSq",
        "colab_type": "code",
        "outputId": "9a2f9e77-c5a5-401c-db4d-046439479fa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "%cd /content\n",
        "# Below is how you clone the whole data project from Tod's GitHub\n",
        "#!git clone https://github.com/todnewman/data\n",
        "# Below is how you can grab just the single .csv file for this project.  You can get this address by clicking on a file, then clicking 'RAW'\n",
        "#  in GitHub.  Then copy the link address from the browser.\n",
        "!wget -O /content/lightning_sensor_data.csv 'https://raw.githubusercontent.com/todnewman/data/master/lightning_sensor_data.csv'\n",
        "!wget -O /content/lightning_sensor_data_predict.csv 'https://raw.githubusercontent.com/todnewman/data/master/lightning_sensor_data_predict.csv'\n",
        "!wget -O /content/pytools.py 'https://raw.githubusercontent.com/todnewman/coe_training/master/pytools.py'\n",
        "import pytools\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "--2019-06-11 01:03:27--  https://raw.githubusercontent.com/todnewman/data/master/lightning_sensor_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 430262 (420K) [text/plain]\n",
            "Saving to: ‘/content/lightning_sensor_data.csv’\n",
            "\n",
            "/content/lightning_ 100%[===================>] 420.18K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-06-11 01:03:27 (9.64 MB/s) - ‘/content/lightning_sensor_data.csv’ saved [430262/430262]\n",
            "\n",
            "--2019-06-11 01:03:28--  https://raw.githubusercontent.com/todnewman/data/master/lightning_sensor_data_predict.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 17320 (17K) [text/plain]\n",
            "Saving to: ‘/content/lightning_sensor_data_predict.csv’\n",
            "\n",
            "/content/lightning_ 100%[===================>]  16.91K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-06-11 01:03:28 (1.66 MB/s) - ‘/content/lightning_sensor_data_predict.csv’ saved [17320/17320]\n",
            "\n",
            "--2019-06-11 01:03:30--  https://raw.githubusercontent.com/todnewman/coe_training/master/pytools.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8563 (8.4K) [text/plain]\n",
            "Saving to: ‘/content/pytools.py’\n",
            "\n",
            "/content/pytools.py 100%[===================>]   8.36K  --.-KB/s    in 0s      \n",
            "\n",
            "2019-06-11 01:03:30 (84.8 MB/s) - ‘/content/pytools.py’ saved [8563/8563]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2V6oOFPGxJWI",
        "colab_type": "text"
      },
      "source": [
        "## Connect Tensorboard\n",
        "\n",
        "Note that this gives a link to the Tensorboard URL at the end.  Train the model first and then click the  link below (will have ngrok.io at the end) to see results in Tensorboard.\n",
        "\n",
        "Here's alink with basic info on Tensorboard:  https://www.tensorflow.org/guide/summaries_and_tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TE_ENXwa22pK",
        "colab_type": "code",
        "outputId": "20d5f6c3-4091-4b33-c61c-e6c73f5b5cfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "if not tensorboard:\n",
        "    !wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "    !unzip ngrok-stable-linux-amd64.zip\n",
        "LOG_DIR = './log'\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "\"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n",
        "\n",
        "    \n",
        "tensorboard = True"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-06-11 00:58:12--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.22.145.207, 34.206.253.53, 52.204.136.9, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.22.145.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16648024 (16M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip.1’\n",
            "\n",
            "\r          ngrok-sta   0%[                    ]       0  --.-KB/s               \r         ngrok-stab  10%[=>                  ]   1.70M  8.32MB/s               \rngrok-stable-linux- 100%[===================>]  15.88M  44.6MB/s    in 0.4s    \n",
            "\n",
            "2019-06-11 00:58:13 (44.6 MB/s) - ‘ngrok-stable-linux-amd64.zip.1’ saved [16648024/16648024]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "replace ngrok? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: ngrok                   \n",
            "https://a37c73d7.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-PvkeFqs0Ex",
        "colab_type": "text"
      },
      "source": [
        "## Split Data into Training and Test Sets\n",
        "This function takes a dataframe, the size of the test set in percentage of the whole, a randomizing seed, and the target column number as arguments.\n",
        "First we capture the target column to prevent it from being scaled by the scaler.\n",
        "Then, we convert the data (X) into numpy arrays and scale.  There are three scalers that are effective for this work.  MaxAbsScaler is better with sparse data.\n",
        "Finally, we use Python sklearn's train_test_split function to return X and y for training and X and y for test.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmffmRHDsrVm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_data(df, size, predict_flag, target):\n",
        "    ''' Split the data into training and testing sets.  Column headers below are custom for the \"Douglas data set\".\n",
        "    NaN's are filled with zero to maintain consistent data shapes.\n",
        "    '''\n",
        "    from sklearn.model_selection import train_test_split\n",
        "    from sklearn import preprocessing\n",
        "    from imblearn.combine import SMOTEENN\n",
        "    from imblearn.over_sampling import ADASYN\n",
        "    \n",
        "    df = df.convert_objects(convert_numeric=True).fillna(0)\n",
        "    \n",
        "    y = df.pop(target) # Remove the 'target' column that has the labels\n",
        "    y = np.array(y).astype(int) # in case the target is a float for some reason\n",
        "    X = df.fillna(0)    \n",
        "    \n",
        "    #  Scale the data.  This allows many of the algorithms to work more effectively\n",
        " \n",
        "    X = preprocessing.MinMaxScaler().fit_transform(X)\n",
        "    X = np.array(X)\n",
        "    \n",
        "    \n",
        "    \n",
        "    if predict_flag:\n",
        "        return X, y\n",
        "    else:\n",
        "        # Scale the data so it works better with the ML algorithms\n",
        "        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=size) # Keeping the test set small to evaluate timing\n",
        "        #X_train = tf.convert_to_tensor(X_train)\n",
        "        print(\"X_train size: {}\".format(X_train.shape))\n",
        "        print(\"y_train size: {}\".format(y_train.shape))\n",
        "        print(\"X_test size: {}\".format(X_test.shape))\n",
        "        print(\"y_test size: {}\".format(y_test.shape))\n",
        "        \n",
        "        #sm = ADASYN(random_state=41)\n",
        "        sm = SMOTEENN(random_state=41)\n",
        "        X_train, y_train = sm.fit_sample(X_train, y_train) # This just oversamples the training set.    \n",
        "        return X_train, X_test, y_train, y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cX_2N05eCWDA",
        "colab_type": "text"
      },
      "source": [
        "## Create Multi-Layer Perceptron network for this problem\n",
        "\n",
        "We're designing this MLP network to work with our data shape.  Adding a Leaky RELU activation function and Dropout to control for overfitting.  Classification layer is a dense layer size one since we're only doing binary classification.\n",
        "\n",
        "* It may be interesting to play with the dropout parameters.  Sometimes I see better performance when dropout is higher at lower layer numbers and vice versa.\n",
        "* The Leaky Relu does really seem to be advantageous.  What is the optimal alpha parameter?  See here:  https://keras.io/layers/advanced-activations/\n",
        "* There are lots and lots of metrics to choose.  Maybe accuracy isn't always the best.  What other metrics (and loss functions) might be useful?  https://keras.io/metrics/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJHGOJiIeFHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def nn_mlp(input_shape, learning_rate):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(64, input_dim=input_shape[1]))\n",
        "    model.add(LeakyReLU(alpha=0.05))\n",
        "    model.add(Dropout(0.4))\n",
        "    model.add(Dense(32))\n",
        "    model.add(LeakyReLU(alpha=0.05))\n",
        "    model.add(Dropout(0.4))\n",
        "    model.add(Dense(16))\n",
        "    model.add(LeakyReLU(alpha=0.05))\n",
        "    model.add(Dropout(0.4))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    optimizer = optimizers.Adam(lr=learning_rate)\n",
        "    model.compile(loss='mse',\n",
        "              optimizer=optimizer,\n",
        "              metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLxcqNaRfzsv",
        "colab_type": "text"
      },
      "source": [
        "## Process Data and execute shift_time function in order to create temporal supervised ML model.\n",
        "\n",
        "Shift_time essentially takes the 'shift_depth' number of previous sequences for each feature and makes new features out of them.  This flattens the record with new_features = orig_number_of_features * shift_depth\n",
        "\n",
        "Then shift_time takes the target vector and shifts the future target 'y_shift' sequences away into the record.  \n",
        "\n",
        "What this allows is one large record with the current samples of each feature, some number of previous samples for each feature (shift_depth number) and the target value from the future.\n",
        "\n",
        "In the case of this example, this allows us to use the shift_depth previous sequences of environmental data to predict whether it will be raining y_shift hours in the future."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFQlVueibb-z",
        "colab_type": "code",
        "outputId": "812bfbf7-5b32-45ad-f7c5-87f4bd868231",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3306
        }
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.layers import LSTM, Flatten, Dense, Activation, Dropout, MaxPooling1D\n",
        "from keras.models import Sequential\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras import optimizers\n",
        "from numpy import concatenate\n",
        "from math import sqrt\n",
        "from keras.callbacks import TensorBoard\n",
        "import pytools\n",
        "\n",
        "target = 'Rain'\n",
        "num_epochs = 500\n",
        "batch_size = 64\n",
        "shift_depth = 8 # The number of previous observations to use as features\n",
        "learning_rate = .001\n",
        "y_shift = -12  # Shift the target so we're predicting events in the future.  In this case, 12 hours.\n",
        "\n",
        "weights_file = 'rain_model_mlp'\n",
        "reuse_trained_model = False\n",
        "\n",
        "df = pd.read_csv('/content/lightning_sensor_data.csv', error_bad_lines=False)\n",
        "\n",
        "# convert series to supervised learning.  This is a sneaky trick that simplifies\n",
        "# data preparation by creating new features comprised of a sequence of features.\n",
        "# The number of features goes up when you do that by a factor of shift_depth.\n",
        "# We pull the label (df_y) out beforehand though, so past sequences of the label\n",
        "# don't get pulled in.\n",
        "\n",
        "def shift_time(data, df_y,  n_in=1,  y_shift=6, dropnan=True):\n",
        "    \"\"\"\n",
        "\t  Frame a time series as a supervised learning dataset.\n",
        "\t  Arguments:\n",
        "\t\t  data: Sequence of observations as a list or NumPy array.\n",
        "\t\t  n_in: Number of lag observations as input (X).\n",
        "\t\t  dropnan: Boolean whether or not to drop rows with NaN values.\n",
        "\t  Returns:\n",
        "\t\t  Pandas DataFrame of series framed for supervised learning.\n",
        "\t  \"\"\"\n",
        "    df_y_copy = df_y\n",
        "    n_vars = 1 if type(data) is list else data.shape[1]\n",
        "    df = pd.DataFrame(data)\n",
        "    cols, names = list(), list()\n",
        "    \n",
        "    # input sequence (t-n, ... t-1)\n",
        "    # This is where we select a sequence of data as features.\n",
        "    \n",
        "    for i in range(n_in, 0, -1):\n",
        "        cols.append(df.shift(i))\n",
        "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "        \n",
        "    #  Here's where we shift our label.  My goal is to predict rain 12 hours in\n",
        "    #  the future using the last 8 values in the sequence for each of the X features.\n",
        "    y_len = df_y.shape[0]\n",
        "    df_y = df_y.shift(periods=y_shift).fillna(0)\n",
        "    df_y = df_y.drop(df_y.index[y_len-y_shift:y_len])\n",
        "    \n",
        "              \n",
        "    # put it all together\n",
        "    agg = pd.concat(cols, axis=1)\n",
        "    agg_len = agg.shape[0]\n",
        "    agg = agg.drop(agg.index[agg_len-y_shift:agg_len])\n",
        "    agg.columns = names\n",
        "    \n",
        "    # drop rows with NaN values\n",
        "    \n",
        "    if dropnan:\n",
        "        agg.dropna(inplace=True)\n",
        "        \n",
        "    agg = agg.reset_index(drop=True)\n",
        "    agg['Rain'] = df_y\n",
        "    return (agg)\n",
        "      \n",
        "#selecting only the column that we are going to use in the prediction process\n",
        "data_to_use=df[['Full Light Spectrum', 'Humidity (%)', 'IR Spectrum', 'Luminosity (lux)', 'Pressure (HP-900)', 'Temperature (deg F)', 'Rain']]\n",
        "\n",
        "# load dataset\n",
        "y = data_to_use.pop('Rain')\n",
        "dataset = data_to_use\n",
        "values = dataset.values\n",
        "\n",
        "# frame as supervised learning\n",
        "reframed = shift_time(values, y, shift_depth, y_shift)\n",
        "reframed['Rain'] = y\n",
        "\n",
        "reframed.to_csv(\"/content/reframed.csv\")\n",
        "\n",
        "# split into train and test sets\n",
        "\n",
        "train_X, test_X, train_y, test_y = split_data(reframed, 0.40, False, target) # Predict_flag = False\n",
        "\n",
        "# design network\n",
        "\n",
        "model = nn_mlp(train_X.shape, learning_rate)\n",
        "if reuse_trained_model:\n",
        "    model.load_weights(weights_file) \n",
        "# fit network\n",
        "print()\n",
        "print(\"Fitting the Model.  See time below:\")\n",
        "\n",
        "# Call TensorBoard for visual metrics\n",
        "if tensorboard:\n",
        "    tbCallBack = TensorBoard(log_dir='./log', histogram_freq=1,\n",
        "                             write_graph=True,\n",
        "                             write_grads=True,\n",
        "                             batch_size=batch_size,\n",
        "                             write_images=True)\n",
        "\n",
        "# Fit the model based on everything we've defined to date.  Write metrics to \n",
        "# TensorBoard\n",
        "if tensorboard:\n",
        "    history = model.fit(train_X, train_y,            \n",
        "                              batch_size=batch_size,\n",
        "                              epochs=num_epochs,\n",
        "                              verbose=1,\n",
        "                              validation_data=(test_X, test_y), \n",
        "                              callbacks=[tbCallBack],\n",
        "                              shuffle = False) \n",
        "else:\n",
        "    history = model.fit(train_X, train_y,            \n",
        "                              batch_size=batch_size,\n",
        "                              epochs=num_epochs,\n",
        "                              verbose=False,\n",
        "                              validation_data=(test_X, test_y),                               \n",
        "                              shuffle = False) \n",
        "\n",
        "\n",
        "score = model.evaluate(test_X, test_y, batch_size=batch_size)\n",
        "print(score)\n",
        "print(history)\n",
        "print(model.summary())\n",
        "\n",
        "model.save_weights(weights_file, overwrite=True)\n",
        "\n",
        "# plot history\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "df_out = pd.DataFrame()\n",
        "# make a prediction\n",
        "yhat = model.predict(test_X)\n",
        "df_out['yhat'] = pd.Series(yhat.flatten())\n",
        "df_out['y'] = test_y\n",
        "\n",
        "print(\"Report that scores the ability of the model to predict the target on the test data:\\n\")\n",
        "\n",
        "yhat_int = df_out['yhat'].astype(int)\n",
        "print(metrics.classification_report(test_y, yhat_int))\n",
        "\n",
        "# The Kappa statistic (or value) is a metric that compares an Observed Accuracy with an \n",
        "# Expected Accuracy (random chance).  The kappa statistic can range between -1 and 1. \n",
        "# The maximum value means complete agreement; zero or lower means chance agreement.\n",
        "\n",
        "ck_score = cohen_kappa_score(test_y, yhat_int)\n",
        "print(\"Cohen-Kappa Score: %7.2f\" % ck_score)\n",
        "\n",
        "# Confusion Matrix\n",
        "\n",
        "class_names = [ 'Not Rain', 'Rain']\n",
        "pytools.call_confusion_matrix(test_y,yhat_int, class_names, 'rain_confusion_matrix.png', 5)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
            "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_train size: (2774, 48)\n",
            "y_train size: (2774,)\n",
            "X_test size: (1850, 48)\n",
            "y_test size: (1850,)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "\n",
            "Fitting the Model.  See time below:\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "1850/1850 [==============================] - 0s 11us/step\n",
            "[0.028342434278304462, 0.9691891886092521]\n",
            "<keras.callbacks.History object at 0x7f3b1605f5c0>\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 64)                3136      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 16)                528       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 5,761\n",
            "Trainable params: 5,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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MMeUicj0wG/AC04wxK0XkfiDDGDMTeARIBt4SEYAtxpjJwBDgWRHxYTuYh0KifhoUr302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\nd/7YqM1VGplW5G9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g1o87Ks9l5Rby2apdlJRHaYfgqebPyB8p4hZ9jxeung2/+Tp4PGCk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\nUwKCB/cbCbX0o4xdBcXEx3honxTHMQ9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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Report that scores the ability of the model to predict the target on the test data:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1792\n",
            "           1       0.84      0.79      0.81        58\n",
            "\n",
            "    accuracy                           0.99      1850\n",
            "   macro avg       0.91      0.89      0.90      1850\n",
            "weighted avg       0.99      0.99      0.99      1850\n",
            "\n",
            "Cohen-Kappa Score:    0.81\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n",
            "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
            "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qerr7b8rM/jpJwxSa6r+icNLfRqEbQiYY2ERhGr9dc9b9QCHglqTNlT2oj5b0ak7a3MfN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\nkHI0+wcAAACApLWCqf5U6+V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OO27Xfvsf0CDdnXfcrnfffWfJ45kzqjfoKgAs7Wj2jxZj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JyZd2Tm/FZgn5kX1gBgAWVZv/dExF2ZeUAzXYf3uC8izgSWofQCOMTAX1p81YD+PRHxDcowohnA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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w7MLgTEl/N7MPFcbZV0n6q5k9njEpber/75ICylwc24/3aor/Dy8mn4J6RtUFwT+AknL3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JjS5X9tSuXkm4CPWEEpSYNn8C9J06hHBUBnEkCA7SLiQ81VAJqTATbyWNxrX5ImQ1cF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\n/wF0h4txRt+TrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LE9DILDd6rZY",
        "colab_type": "text"
      },
      "source": [
        "## Save trained Model for future use\n",
        "\n",
        "Need to have the Google Drive loaded (see above)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEaoRldOJy7d",
        "colab_type": "code",
        "outputId": "0b46b6bc-6664-4533-a129-fca0568fbd5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "%cd /content\n",
        "%ls\n",
        "#%cp reframed.csv /content/drive/'My Drive'/reframed.csv\n",
        "%cp rain_model_mlp /content/drive/'My Drive'/models/rain_model_mlp"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "1_rain_confusion_matrix.png        ngrok-stable-linux-amd64.zip\n",
            "2_rain_confusion_matrix.png        ngrok-stable-linux-amd64.zip.1\n",
            "\u001b[0m\u001b[01;34mdrive\u001b[0m/                             \u001b[01;34m__pycache__\u001b[0m/\n",
            "lightning_sensor_data.csv          pytools.py\n",
            "lightning_sensor_data_predict.csv  rain_model_mlp\n",
            "\u001b[01;34mlog\u001b[0m/                               reframed.csv\n",
            "\u001b[01;32mngrok\u001b[0m*                             \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3Uf96A5HHLm",
        "colab_type": "text"
      },
      "source": [
        "# Predict New Data\n",
        "\n",
        "We're going to load our saved model first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ne26SZIZi0TJ",
        "colab_type": "code",
        "outputId": "c3974901-c3ad-42d9-ba3f-689397c1f053",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        }
      },
      "source": [
        "weights_file = 'rain_model_mlp'\n",
        "model.load_weights(weights_file)\n",
        "df1 = pd.read_csv('/content/lightning_sensor_data_predict.csv', error_bad_lines=False)\n",
        "print(df1.shape)\n",
        "\n",
        "df_time = df1['Time']\n",
        "\n",
        "data_to_use1=df1[['Full Light Spectrum', 'Humidity (%)', 'IR Spectrum', 'Luminosity (lux)', 'Pressure (HP-900)', 'Temperature (deg F)']]\n",
        "\n",
        "dataset = data_to_use1\n",
        "dataset['Rain'] = 0\n",
        "y = dataset.pop('Rain')\n",
        "\n",
        "#print(dataset)\n",
        "values = dataset.values\n",
        "\n",
        "# frame as supervised learning\n",
        "\n",
        "reframed = shift_time(values, y, shift_depth, y_shift)\n",
        "\n",
        "\n",
        "\n",
        "#print(reframed)\n",
        "\n",
        "predict_X, y_bogus = split_data(reframed, 0.30, True, target) # Predict_flag = False\n",
        "\n",
        "# reshape input to be 3D [samples, timesteps, features]\n",
        "\n",
        "print(predict_X.shape)\n",
        "\n",
        "probs = model.predict_proba(predict_X)\n",
        "print (type(probs))\n",
        "df_t = pd.Series(df_time[0:predict_X.shape[0]])\n",
        "\n",
        "#df_pred = reframed.copy()\n",
        "df_pred = pd.DataFrame()\n",
        "df_pred['Time'] = df_t\n",
        "\n",
        "#df_pred['Predicted Value'] = (predict_val[0:])\n",
        "df_pred['Predicted Probability of Rain'] = probs\n",
        "\n",
        "#df_pred.to_csv(out_filename)\n",
        "k = df_pred.keys()\n",
        "\n",
        "#df_pred.sort_values(['Predicted Value'], ascending=[False])\n",
        "\n",
        "plt.figure( figsize=(30,10))\n",
        "plt.title(\"Probability of rain in 12 hours\")\n",
        "plt.ylabel(\"Probability of Rain\")\n",
        "plt.scatter(df_pred[k[0]], df_pred[k[1]], label=\"Probability of Rain in 12 hours\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(193, 10)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
            "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(185, 48)\n",
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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8AsKf+5EhszxmGCJ/4un0BhkmyvfDD8dl2Wx5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\n",
            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SHib6xxfR6T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}