{"cells":[{"cell_type":"markdown","id":"0904d215","metadata":{"id":"0904d215"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/ai2es/WAF_ML_Tutorial_Part1/blob/main/colab_notebooks/Notebook05_SimpleMLRegression.ipynb)\n","\n","# Notebook 05: Simple ML Regression [Colab Version]\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 regression problems is basically the same as the classification. We will still use the same steps as the previous notebook, just with a small changed to the labels ```y```. \n","\n","\n","#### Step 0: Get the github repo (we need some of the functions there)\n","\n","The first step with all of these Google Colab notebooks will be to grab the github repo and cd into the notebooks directory. \n","\n","To run things from the command line, put a ```!``` before your code"]},{"cell_type":"code","source":["#get the github repo \n","!git clone https://github.com/ai2es/WAF_ML_Tutorial_Part1.git \n","\n","#cd into the repo so the paths work \n","import os \n","os.chdir('/content/WAF_ML_Tutorial_Part1/jupyter_notebooks/')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fuI8BW5lYqGp","executionInfo":{"status":"ok","timestamp":1648653199125,"user_tz":300,"elapsed":22510,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"7f4f7821-e88c-4c54-8b45-46d80c243555"},"id":"fuI8BW5lYqGp","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'WAF_ML_Tutorial_Part1'...\n","remote: Enumerating objects: 301, done.\u001b[K\n","remote: Counting objects: 100% (301/301), done.\u001b[K\n","remote: Compressing objects: 100% (197/197), done.\u001b[K\n","remote: Total 301 (delta 139), reused 236 (delta 96), pack-reused 0\u001b[K\n","Receiving objects: 100% (301/301), 195.77 MiB | 26.08 MiB/s, done.\n","Resolving deltas: 100% (139/139), done.\n","Checking out files: 100% (100/100), done.\n"]}]},{"cell_type":"markdown","source":["#### Step 1 & 2: Import packages and load data for Regression \n","We only want 1 feature again to make things simple, which is feature 0. We also will need to change ```class_labels``` to false."],"metadata":{"id":"_CBMTtVUYmbO"},"id":"_CBMTtVUYmbO"},{"cell_type":"code","execution_count":2,"id":"831307b8","metadata":{"id":"831307b8","executionInfo":{"status":"ok","timestamp":1648653206519,"user_tz":300,"elapsed":7399,"user":{"displayName":"Randy C","userId":"12301342565919601334"}}},"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=False)"]},{"cell_type":"markdown","id":"763030a8","metadata":{"id":"763030a8"},"source":["Let's check to make sure the labels are indeed decimal numbers instead of 0's and 1's. "]},{"cell_type":"code","execution_count":3,"id":"8c5fa1db","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":305},"id":"8c5fa1db","executionInfo":{"status":"ok","timestamp":1648653215933,"user_tz":300,"elapsed":866,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"642a77e3-7af7-4013-987b-9d78a21131d7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'number of flahses')"]},"metadata":{},"execution_count":3},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"image/png":{"width":402,"height":270},"needs_background":"light"}}],"source":["plt.hist(y_train,bins=100)\n","plt.xlabel('number of flahses')"]},{"cell_type":"markdown","id":"fd7e3009","metadata":{"id":"fd7e3009"},"source":["Great, it is indeed more than just 0's and 1's, but something you'll notice right here, there are ALOT of no flash images. You will see if we plot the number of flashes as a function of the minimum brightness temperature it might be very difficult to fit a linear method (i.e., Linear regression) to the data"]},{"cell_type":"code","execution_count":4,"id":"f9943cc1","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360},"id":"f9943cc1","executionInfo":{"status":"ok","timestamp":1648653218595,"user_tz":300,"elapsed":981,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"5c95bd64-2681-4ca2-898d-bb5f53acd757"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Number of flashes')"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"image/png":{"width":349,"height":325}}}],"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","#color I like. The order of ratios is [Red,Green,Blue]\n","r = [255/255,127/255,127/255]\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","#plot data \n","ax.scatter(X_train[:,0],y_train,color=r,s=1,marker='+')\n","\n","#set limits \n","ax.set_xlim([-95,30])\n","#label axes\n","ax.set_xlabel('Minimum Brightness Temperature, [$\\degree$C]')\n","ax.set_ylabel('Number of flashes')"]},{"cell_type":"markdown","id":"b3d575fd","metadata":{"id":"b3d575fd"},"source":["For now, lets try it anyway \n","\n","#### Step 3: Initialize model\n","\n","Same as with classification, we can use the ```()``` after the model name to initialize a ML model."]},{"cell_type":"code","execution_count":5,"id":"aefd44a9","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aefd44a9","executionInfo":{"status":"ok","timestamp":1648653224432,"user_tz":300,"elapsed":883,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"162ad7bb-84c6-409d-bd33-e6c1e6c92d2e"},"outputs":[{"output_type":"stream","name":"stdout","text":["LinearRegression()\n"]}],"source":["#load model from sklearn\n","from sklearn.linear_model import LinearRegression\n","\n","#initialize\n","model = LinearRegression()\n","\n","print(model)"]},{"cell_type":"markdown","id":"2507a32d","metadata":{"id":"2507a32d"},"source":["#### Step 4: Train your ML model! "]},{"cell_type":"code","execution_count":6,"id":"4340ae46","metadata":{"id":"4340ae46","executionInfo":{"status":"ok","timestamp":1648653227939,"user_tz":300,"elapsed":269,"user":{"displayName":"Randy C","userId":"12301342565919601334"}}},"outputs":[],"source":["model = model.fit(X_train,y_train)"]},{"cell_type":"markdown","id":"3a8c08c9","metadata":{"id":"3a8c08c9"},"source":["#### Step 5: Evaluate your ML model\n","\n","As a sanity check, we will first look at the *one-to-one* plot where the x-axis is the predicted number of flashes, and the y-axis is the true number of flashes. A perfect prediction will be directly along the diagonal. "]},{"cell_type":"code","execution_count":7,"id":"bd387cce","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359},"id":"bd387cce","executionInfo":{"status":"ok","timestamp":1648653230688,"user_tz":300,"elapsed":774,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"165d5330-697c-4c68-9b9d-6593d05d025e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'GLM measurement, [$number of flashes$]')"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"image/png":{"width":331,"height":324}}}],"source":["#get predictions \n","yhat = model.predict(X_validate)\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","#plot data \n","ax.scatter(yhat,y_validate,color=r,s=1,marker='+')\n","ax.plot([0,3500],[0,3500],'-k')\n","ax.set_xlabel('ML Prediction, [$number of flashes$]')\n","ax.set_xlabel('GLM measurement, [$number of flashes$]')"]},{"cell_type":"markdown","id":"bef1b90e","metadata":{"id":"bef1b90e"},"source":["As you can see, there is not a great correspondence between the ML model predicted flashes and the true number of flashes. One work around of this issue is to train on instances where there is already more than 1 lightning flash. While this might seem like cheating, based on our > 90% accurate classification model, we could use the two ML models in tandem. In other words, we could use the regression model only on images classified to contain flashes from the classification model. So let's drop the zeros out of the dataset and give it a try."]},{"cell_type":"code","execution_count":8,"id":"de7dd49f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360},"id":"de7dd49f","executionInfo":{"status":"ok","timestamp":1648653239678,"user_tz":300,"elapsed":6498,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"6cb9da67-0b87-440a-9efa-45fbe10253dc"},"outputs":[{"output_type":"stream","name":"stdout","text":["1\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"image/png":{"width":349,"height":325}}}],"source":["(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=False,dropzeros=True)\n","\n","#remake scatter plot from Step 2\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","#plot data \n","ax.scatter(X_train[:,0],y_train,color=r,s=1,marker='+')\n","\n","#set limits \n","ax.set_xlim([-95,30])\n","#label axes\n","ax.set_xlabel('Minimum Brightness Temperature, [$\\degree$C]')\n","ax.set_ylabel('Number of flashes')\n","\n","\n","print(np.min(y_train))"]},{"cell_type":"markdown","id":"64d80579","metadata":{"id":"64d80579"},"source":["Good, there are no 0s in the label vector (```y```). Now let's re-train the model"]},{"cell_type":"code","execution_count":9,"id":"9e354516","metadata":{"id":"9e354516","executionInfo":{"status":"ok","timestamp":1648653243683,"user_tz":300,"elapsed":302,"user":{"displayName":"Randy C","userId":"12301342565919601334"}}},"outputs":[],"source":["model = model.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":10,"id":"a9e412dd","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359},"id":"a9e412dd","executionInfo":{"status":"ok","timestamp":1648653244997,"user_tz":300,"elapsed":325,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"377838cf-cd87-4395-e7f0-ccb7ae2453a6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'GLM measurement, [$number of flashes$]')"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"image/png":{"width":331,"height":324}}}],"source":["#get predictions \n","yhat = model.predict(X_validate)\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","#plot data \n","ax.scatter(yhat,y_validate,color=r,s=1,marker='+')\n","ax.plot([0,3500],[0,3500],'-k')\n","ax.set_xlabel('ML Prediction, [$number of flashes$]')\n","ax.set_xlabel('GLM measurement, [$number of flashes$]')"]},{"cell_type":"markdown","id":"52e8e150","metadata":{"id":"52e8e150"},"source":["It's better. but still doesn’t look great. But given the relatively non-linear relationship between the minimum brightness temperature and the number of flashes, this is probably to be expected. Let's calculate some metrics to quantitatively evaluate the trained ML model.\n","\n","The metrics for regression are a bit different than for classification. Common metrics are the mean bias, mean absolute error (MAE), Root Mean Square Error (RMSE) and the coefficient of determination (R^2). Mathematically, these metrics are defined: \n","\n","$$ \\mathrm{Bias} = \\frac{1}{N} \\sum_{j=1}^{N} (y_j - \\hat{y}_j) $$\n","\n","$$ \\mathrm{MAE} = \\frac{1}{N} \\sum_{j=1}^{N} |y_j - \\hat{y}_j| $$\n","\n","$$ \\mathrm{RMSE} = \\sqrt{\\frac{1}{N} \\sum_{j=1}^{N} (y_j - \\hat{y}_j)^{2}} $$ \n","\n","$$ \\mathrm{R^{2}} = 1 - \\frac{\\sum_{j=1}^{N} (y_j - \\hat{y}_j)^{2}}{\\sum_{j=1}^{N} (y_j - \\bar{y})^{2}} $$\n","\n","We have included all of these metrics again in the ```gewitter_functions.py``` script."]},{"cell_type":"code","execution_count":11,"id":"c8a0071d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c8a0071d","executionInfo":{"status":"ok","timestamp":1648653247207,"user_tz":300,"elapsed":2,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"24d10b5b-419e-4b3f-d875-818b34d1cf86"},"outputs":[{"output_type":"stream","name":"stdout","text":["MAE:146.68 flashes, RMSE:235.46 flashes, Bias:30.98 flashes, Rsquared:0.2\n"]}],"source":["from gewitter_functions import get_mae,get_rmse,get_bias,get_r2\n","\n","yhat = model.predict(X_validate)\n","mae = get_mae(y_validate,yhat)\n","rmse = get_rmse(y_validate,yhat)\n","bias = get_bias(y_validate,yhat)\n","r2 = get_r2(y_validate,yhat)\n","\n","#print them out so we can see them \n","print('MAE:{} flashes, RMSE:{} flashes, Bias:{} flashes, Rsquared:{}'.format(np.round(mae,2),np.round(rmse,2),np.round(bias,2),np.round(r2,2)))"]},{"cell_type":"markdown","id":"d1324e94","metadata":{"id":"d1324e94"},"source":["There we go! We have a simple linear regression predicting the number of flashes in an image. While these results don’t look great (missing on average by 30 flashes) we will show in the more advanced ML model (using more features) that we can get a more accurate model."]},{"cell_type":"markdown","id":"14bb77ce","metadata":{"id":"14bb77ce"},"source":["#### 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 ```pickle``` package, I know kinda an odd name, to do this."]},{"cell_type":"code","execution_count":12,"id":"a70fbfad","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":222},"id":"a70fbfad","executionInfo":{"status":"error","timestamp":1648653256433,"user_tz":300,"elapsed":569,"user":{"displayName":"Randy C","userId":"12301342565919601334"}},"outputId":"0851557f-da70-44af-f55d-3250973ac9df"},"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'LinearRegression.pkl'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mstart_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../datasets/sklearnmodels/regression/onefeature/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msavefile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msavefile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../datasets/sklearnmodels/regression/onefeature/LinearRegression.pkl'"]}],"source":["import pickle\n","name = 'LinearRegression.pkl'\n","start_path = '../datasets/sklearnmodels/regression/onefeature/'\n","savefile = open(start_path + name,'wb')\n","pickle.dump(model,savefile)"]},{"cell_type":"markdown","id":"633eeb38","metadata":{"id":"633eeb38"},"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":null,"id":"9409e50b","metadata":{"id":"9409e50b","outputId":"a52a9d55-b822-4f36-bb5c-f45a2a9ffb0e"},"outputs":[{"name":"stdout","output_type":"stream","text":["LinearRegression()\n"]}],"source":["import pickle\n","name = 'LinearRegression.pkl'\n","start_path = '../datasets/sklearnmodels/regression/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 = pickle.load(savefile)\n","\n","print(model)"]},{"cell_type":"markdown","id":"c8444c07","metadata":{"id":"c8444c07"},"source":["In the next notebook we will look at training a ML model with all 36 predictors. Notebook 6 is for classification, while Notebook 7 is for regression."]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.7"},"colab":{"name":"Notebook05_SimpleMLRegression.ipynb","provenance":[]}},"nbformat":4,"nbformat_minor":5}