{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5a67ee6",
   "metadata": {},
   "source": [
    "# Notebook 04: Simple ML Classification \n",
    "\n",
    "### Goal: Basic training a ML using a single feature/predictor/input and a single ML model\n",
    "\n",
    "#### Reminder of Problem Statement\n",
    "\n",
    "Before we jump into the ML, I want to remind you of the ML task we want to accomplish in the paper. \n",
    "\n",
    "1. Does this image contain a thunderstorm? <-- Classification\n",
    "2. How many lightning flashes are in this image? <-- Regression\n",
    "\n",
    "#### Background\n",
    "\n",
    "For the training of machine learning we will use the [scikit-learn](https://scikit-learn.org/stable/) python package. Scikit-learn (also known as sklearn) has a wealth of resources for learning how to apply many ML methods. Furthermore, their documentation on any one method is extensive and very helpful. I encourage you to use their documentation if you want to use more that what we will show you in this tutorial. \n",
    "\n",
    "What is really nice about the sklearn package is that all of its models all work off the same syntax. In general this is how you use any of their models:\n",
    "\n",
    "1. Create or load your input data. It must be of shape ```[n_samples,n_features]```. It is commonly written as ```X``` in all of the sklearn documentation. \n",
    "2. Create or load your output data. It must be of shape ```[n_samples]```. It is commonly written as ```y``` in all of the sklearn documentation \n",
    "3. Initialize your model. To initialize a model in python usually you just need to add ```()``` at the end of the model name. \n",
    "4. Fit your model. To do this all we will need to do is apply the ```.fit(X,y)``` to your initialized model \n",
    "5. Evaluate your trained model. To get the model predictions for evaluation we will use the ```model.predict(X_val)``` or the ```model.predict_proba(X_val)```. \n",
    "\n",
    "Now that we have the basic work flow, lets actually do this with the simple example in the paper, 1 input predictor. We will start with the classification task first\n",
    "\n",
    "#### Step 1 & 2: Import packages and load data for Classification  \n",
    "Last notebook, we showed you how to do train/val and test data splits. We will do that all with our pre-made function now. But we only want 1 feature, which is feature 0. So all we need to change is the ```features_to_keep``` keyword to get just the first feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a90ffcc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#needed packages \n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#plot parameters that I personally like, feel free to make these your own.\n",
    "import matplotlib\n",
    "matplotlib.rcParams['axes.facecolor'] = [0.9,0.9,0.9] #makes a grey background to the axis face\n",
    "matplotlib.rcParams['axes.labelsize'] = 14 #fontsize in pts\n",
    "matplotlib.rcParams['axes.titlesize'] = 14 \n",
    "matplotlib.rcParams['xtick.labelsize'] = 12 \n",
    "matplotlib.rcParams['ytick.labelsize'] = 12 \n",
    "matplotlib.rcParams['legend.fontsize'] = 12 \n",
    "matplotlib.rcParams['legend.facecolor'] = 'w' \n",
    "matplotlib.rcParams['savefig.transparent'] = False\n",
    "\n",
    "#make default resolution of figures much higher (i.e., High definition)\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "#import some helper functions for our other directory.\n",
    "import sys\n",
    "sys.path.insert(1, '../scripts/')\n",
    "from aux_functions import load_n_combine_df\n",
    "(X_train,y_train),(X_validate,y_validate),(X_test,y_test) = load_n_combine_df(path_to_data='../datasets/sevir/',features_to_keep=np.arange(0,1,1),class_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd293191",
   "metadata": {},
   "source": [
    "Let's print out the shapes of things to make sure they match what we expected, ```[n_samples,n_features]``` for ```X``` and ```[n_samples]``` for ```y```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d32e5df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, y_train shapes: (446320, 1),(446320,)\n",
      "X_val, y_val shapes: (82871, 1),(82871,)\n",
      "X_test, y_test shapes: (89718, 1),(89718,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train, y_train shapes: {},{}'.format(X_train.shape,y_train.shape))\n",
    "print('X_val, y_val shapes: {},{}'.format(X_validate.shape,y_validate.shape))\n",
    "print('X_test, y_test shapes: {},{}'.format(X_test.shape,y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0fa5d2",
   "metadata": {},
   "source": [
    "Okay, things look correct. Our ```n_samples``` match of each dataset, and ```X``` is indeed ```[n_samples,n_features]```. Let us reproduce Figure 8 to *gauge* the predictive power of the minimum brightness temperature from the clean infrared channel. To do this, we need to find which samples have lightning flashes and which do not. We will use the ```np.where``` to find the indicies of ```X``` and ```y``` where the label (i.e., ```y```) is equal to 1. When you set ```class_labels=True``` in the ```load_n_combine_df``` it will already label ```y``` as either 0 where there are no flashes, and 1 where there is greater than or equal to 1 flash in the image.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "732c01b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find samples with more at least 1 flash \n",
    "idx_flash = np.where(y_train == 1)[0]\n",
    "#find samples with no flashes \n",
    "idx_noflash = np.where(y_train == 0)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d24361",
   "metadata": {},
   "source": [
    "For those who have never used the ```np.where``` function before, we can double check it worked by looking at a few histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60466166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([224262.,      0.,      0.]),\n",
       " array([-0.01,  0.01,  0.99,  1.01]),\n",
       " <BarContainer object of 3 artists>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show us the whole dataset\n",
    "plt.hist(y_train,bins=[-0.01,0.01,0.99,1.01],edgecolor='k')\n",
    "\n",
    "#show us the flash data\n",
    "plt.figure()\n",
    "plt.hist(y_train[idx_flash],bins=[-0.01,0.01,0.99,1.01],edgecolor='k')\n",
    "\n",
    "#show us the no flash data\n",
    "plt.figure()\n",
    "plt.hist(y_train[idx_noflash],bins=[-0.01,0.01,0.99,1.01],edgecolor='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5cef69",
   "metadata": {},
   "source": [
    "Now that things look to be working properly, lets make the histogram of brightness temperatures. The expectation is that images with flashes in them will have generally colder minimum brightness temperatures since they will likely have stronger storms (on average). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e3bd13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 350,
       "width": 352
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this is something to help make the ticks show up where I want them \n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "\n",
    "#this is an array of bins I wish to do the histogram with, in degC\n",
    "xbins = np.arange(-100,50,2)\n",
    "#some colors I like. The order of ratios is [Red,Green,Blue]\n",
    "r = [255/255,127/255,127/255]\n",
    "b = [126/255,131/255,248/255]\n",
    "\n",
    "#make figure  \n",
    "fig = plt.figure(figsize=(5,5))\n",
    "#set background color to white so we can copy paste out of the notebook if we want \n",
    "fig.set_facecolor('w')\n",
    "\n",
    "#get axis for drawing\n",
    "ax = plt.gca()\n",
    "\n",
    "#use matplotlib histogram function \n",
    "ax.hist(X_train[idx_flash,0],density=True,bins=xbins,color=b,alpha=0.5,zorder=0,label='T-storm',edgecolor=b)\n",
    "ax.hist(X_train[idx_noflash,0],density=True,bins=xbins,color=r,alpha=0.5,zorder=0,label='No T-Storm',edgecolor=r)\n",
    "\n",
    "#add title and axis labels \n",
    "ax.set_title('Minimum IR')\n",
    "ax.set_ylabel(r'Normalized count')\n",
    "ax.set_xlabel(\"$T_{b}$, [$\\degree$C]\")\n",
    "#set tick locations \n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.001))\n",
    "#force xlimit\n",
    "ax.set_xlim([-100,50])\n",
    "#turn grid on \n",
    "ax.grid('on')\n",
    "#draw legend\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334acc45",
   "metadata": {},
   "source": [
    "Great! It follows our expectation. While there is considerable overlap between the two classes, there does seem to be a good decision boundary to separate the two. Let's start out by training a logistic regression first. \n",
    "\n",
    "#### Step 3: Initialize model\n",
    "\n",
    "Like I mentioned in the background, to initialize we can use the ```()``` after the model name. This is also where you can choose various hyperparameters. For now, just leave it empty, it will use the default parameters, which is a good place to start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e27560b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()\n"
     ]
    }
   ],
   "source": [
    "#load model from sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#initialize\n",
    "model = LogisticRegression()\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e43577bf",
   "metadata": {},
   "source": [
    "#### Step 4: Train your ML model! \n",
    "\n",
    "This is it, we have finally made it to actually doing some machine learning, are you ready? I am! So let's go!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0e12ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2bd057",
   "metadata": {},
   "source": [
    "<img src=\"../images/congrats.gif\" width=\"400\" height=\"200\" class=\"center\" />\n",
    "\n",
    "Thats it! Congrats, you have officially trained your first ML model. I know, it seems a bit underwhelming because there is no fanfare from python, but to make you feel better enjoy the gif. After you are done celebrating, time to move on to evaluating how well it did!\n",
    "\n",
    "#### Step 5: Evaluate your ML model\n",
    "\n",
    "In order to evaluate your model, we will need the predictions of the model on the validation dataset. We will use the ```model.predict()``` method to get a prediction value for each sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83f7b0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708e3dbc",
   "metadata": {},
   "source": [
    "I name the prediction vector yhat (i.e., $\\hat{y}$) to match usual math notation. But you can name it ypred if you want. This vector is a label for each sample, so we could look at a histogram of the results, or we can calculate some more informative metrics. \n",
    "\n",
    "##### a). Contingency Table: Accuracy \n",
    "\n",
    "To get those more informative metrics, lets get the contingency table which is:\n",
    "\n",
    "\n",
    "|             | Yes | No|\n",
    "| ----------- | :----: | :----: |\n",
    "|    **Yes**      | Hits       | False Alarms|\n",
    "|    **No**      | Misses        | Correct Nulls |\n",
    "\n",
    "The left column is the model predictions, while the top row is the observations (true labels). Each spot in the table is filled out based on:\n",
    "- if both the model and observation predict **Yes** (both predict a label of 1; which is a hit or *True Positive*)\n",
    "- if the model outputs a **Yes** but the observation is **No** (model=1,obs=0; False Alarm or *False Positive*)\n",
    "- if the model says **No** but the observation says **Yes** (model=0,obs=1; Miss or *False Positive*)\n",
    "- if the model and the observation say **No** (model=0,obs=0; Correct Null or *True Negative*)\n",
    "\n",
    "We have coded this up for you, so all we need to use is the function called ```get_contingency_table``` from ```gewitter_functions.py```. These functions were original written by Dr. Lagerquist during his PhD studies, but have been adapted by me to be able to run out of the box with just the ```gewitter_functions.py``` script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "daf40aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'num_true_positives': 36370, 'num_false_positives': 9695, 'num_false_negatives': 6353, 'num_true_negatives': 30453}\n"
     ]
    }
   ],
   "source": [
    "from gewitter_functions import get_contingency_table\n",
    "\n",
    "#the contingency table calculator expects y_true,y_pred\n",
    "cont_table = get_contingency_table(y_validate,yhat)\n",
    "\n",
    "\n",
    "#the function returns a dictionary (thats what the curly brackets mean) of each entry in the table. \n",
    "print(cont_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8aa8bde",
   "metadata": {},
   "source": [
    "Now that we have the contingency table, we can calculate a simple metric, accuracy. Which is:\n",
    "\n",
    "$$ Accuracy = \\frac{TruePositives + TrueNegatives}{SumOfAllCategories} \\times{100}$$\n",
    "\n",
    "for conciseness, we have made a function to do this named ```get_accuracy```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2358bc77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 81.0%\n"
     ]
    }
   ],
   "source": [
    "from gewitter_functions import get_acc\n",
    "accuracy = get_acc(cont_table)\n",
    "print('Accuracy = {}%'.format(np.round(accuracy,0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c1c27d",
   "metadata": {},
   "source": [
    "81% accuracy is good! Given we only use 1 input! But as pointed out in the paper, it is good to always use more than 1 evaluation metric. \n",
    "\n",
    "##### b). Contingency Table: Performance Diagram\n",
    "\n",
    "<img src=\"../images/Blank_Performance_Diagram.png\" width=\"400\" height=\"200\" class=\"center\" />\n",
    "\n",
    "One of my favorite diagrams to gauge model skill is a performance diagram (shown above). I have added some hopefully helpful annotations that show you where better performing models (top-right) and worse performing models (bottom left, top left or bottom right) show up on the diagram. \n",
    "\n",
    "To make this diagram we need to calculate 2 variables, the Probability of Detection (POD; y-axis) and Success Ratio (SR; x-axis), both that are calculated from the contingency table. \n",
    "\n",
    "$$ \\mathrm{POD} = \\frac{TruePositives}{TruePositives + FalseNegatives} $$\n",
    "\n",
    "$$ \\mathrm{SR} = \\frac{TruePositives}{TruePositives + FalsePositives} $$\n",
    "\n",
    "One perk of this diagram is that we can glean the *Critical Success Index* (CSI) from the same diagram. CSI is formulated as: \n",
    "\n",
    "$$ \\mathrm{CSI} = \\frac{TruePositives}{TruePositives + FalsePositives + FalseNegatives} $$ \n",
    "\n",
    "What is great about the performance diagram is that it helps us evaluate ML tasks that might have unbalanced data. By unbalanced data, I mean if the scenario we are trying to predict is rare (e.g., hail, tornadoes). What happens in rare scenarios is that the data naturally have a ton of Null cases, so some metrics that consider TrueNegatives in the numerator (e.g., Accuracy) might be inflated. \n",
    "\n",
    "We have included the calculation of these parameters as functions as well ```get_pod```, ```get_sr```, ```csi_from_sr_and_pod```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b73677cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POD:0.85, SR:0.79, CSI:0.69\n"
     ]
    }
   ],
   "source": [
    "from gewitter_functions import get_pod,get_sr,csi_from_sr_and_pod\n",
    "pod = get_pod(cont_table)\n",
    "sr = get_sr(cont_table)\n",
    "csi = csi_from_sr_and_pod(sr,pod)\n",
    "\n",
    "print('POD:{}, SR:{}, CSI:{}'.format(np.round(pod,2),np.round(sr,2),np.round(csi,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9487fb90",
   "metadata": {},
   "source": [
    "Now that we have our calculated values, you could put these in a table, or you could put it on the performance diagram. We made a function to make the performance diagram axis for us, named ```make_performance_diagram```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a1fbd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 327,
       "width": 353
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gewitter_functions import make_performance_diagram_axis\n",
    "ax = make_performance_diagram_axis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c07c80a",
   "metadata": {},
   "source": [
    "the function returns the axis (```ax```) so we can plot on it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "844c1e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 327,
       "width": 353
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = make_performance_diagram_axis()\n",
    "ax.plot(sr,pod,'o',color='dodgerblue',markerfacecolor='w');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a739b4d",
   "metadata": {},
   "source": [
    "and BAM. You have plotted your model's performance on the performance diagram. Turns out this model is doing very well, with a large POD and SR and is near the center line. \n",
    "\n",
    "#### Step 6: Save your trained model\n",
    "\n",
    "Sometimes loading the training dataset and re-training the model each time can be cumbersome. There is a way to save the trained models. We will use the python ```joblib``` package to do this. \n",
    "\n",
    "**Notes:** \n",
    "\n",
    "1. Storage efficiency: While most of the models in sklearn can be stored in a decent file size, your random forest model could become large (on the order of 1 GB). I know a GB is not alot of space nowadays, but it is important to note. This is also why the trained random forest from the paper is not found in the ```datasets``` folder.\n",
    "2. Keep note of your ```sklearn``` version: ```joblib``` will load the model to the syntax of the ```sklearn``` that it is saved with. So if you are getting some odd error with ```sklearn``` while loading the saved model, check to make sure the version hasn’t changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "868e1bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "name = 'LogisticRegression.pkl'\n",
    "start_path = '../datasets/sklearnmodels/classification/onefeature/'\n",
    "savefile = open(start_path + name,'wb')\n",
    "joblib.dump(model, savefile, 9) #the 9 here is the highest compression. Slows the time of saving/loading but saves disk space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61254a51",
   "metadata": {},
   "source": [
    "#### Step 7: Load a saved model\n",
    "\n",
    "Now that you have it saved, if you need to load it do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "298d94ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "name = 'LogisticRegression.pkl'\n",
    "start_path = '../datasets/sklearnmodels/classification/onefeature/'\n",
    "#notice the change from wb to rb \n",
    "savefile = open(start_path + name,'rb')\n",
    "#notice the change from dump to load \n",
    "model = joblib.load(savefile)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d0f087",
   "metadata": {},
   "source": [
    "In the next notebook we will look at the Regression problem. If you want to continue on with the classification, check out Notebook 6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f6659f2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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