trash_datathon_2021.ipynb

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  "metadata": {
    "colab": {
      "name": "trash_datathon_2021",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFVWJ7bMCBLH"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "### Trash Datathon 2021\n",
        "### Artificial Intelligence (AI) and Modeling\n",
        "\n",
        "Welcome to the 2021 Trash Datathon! Purpose of this notebook will be to provide an example application of machine learning for trash level score classification using the City of LA [CleanStat][01.00] dataset from their open data portal. CleanStat is part of a larger city initiative to address litter, and additional info is provided in this [factsheet][01.01].\n",
        "\n",
        "This notebook is based an introductory template from Google Colab, so listed below are instructions for those who are new to Colab, Jupyter or Python followed by the example application under its own section. All sections can be navigated within the notebook.\n",
        "\n",
        "Please note that this notebook is intended as a brief introduction to AI/ML modeling and examples are provided to encourage additional development/collaboration.\n",
        "\n",
        "[01.00]: https://geohub.lacity.org/datasets/clean-streets-index-2016-q1?geometry=-120.483%2C33.623%2C-116.341%2C34.420\n",
        "[01.01]: https://results4america.org/wp-content/uploads/2018/12/LosAngelesCaseStudy_Final.pdf\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fCEDCU_qrC0"
      },
      "source": [
        "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"/img/colab_favicon.ico\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "<h1>What is Colaboratory?</h1>\n",
        "\n",
        "Colaboratory, or \"Colab\" for short, allows you to write and execute Python in your browser, with \n",
        "- Zero configuration required\n",
        "- Free access to GPUs\n",
        "- Easy sharing\n",
        "\n",
        "Whether you're a **student**, a **data scientist** or an **AI researcher**, Colab can make your work easier. Watch [Introduction to Colab](https://www.youtube.com/watch?v=inN8seMm7UI) to learn more, or just get started below!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJBs_flRovLc"
      },
      "source": [
        "## **Getting started**\n",
        "\n",
        "The document you are reading is not a static web page, but an interactive environment called a **Colab notebook** that lets you write and execute code.\n",
        "\n",
        "For example, here is a **code cell** with a short Python script that computes a value, stores it in a variable, and prints the result:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJr_9dXGpJ05",
        "outputId": "0935e71a-dfa7-4ed0-8d49-c3c157926ca3"
      },
      "source": [
        "seconds_in_a_day = 24 * 60 * 60\n",
        "seconds_in_a_day\n"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZtuBW2GdDXO",
        "outputId": "aa7c53e2-ef4d-4515-d553-24bacce93ea4"
      },
      "source": [
        "seconds_new = seconds_in_a_day + 1\n",
        "seconds_new"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86401"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fhs6GZ4qFMx"
      },
      "source": [
        "To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut \"Command/Ctrl+Enter\". To edit the code, just click the cell and start editing.\n",
        "\n",
        "Variables that you define in one cell can later be used in other cells:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gE-Ez1qtyIA",
        "outputId": "7f58dd80-0b6d-454c-a8e4-c8fc2e4847e6"
      },
      "source": [
        "seconds_in_a_week = 7 * seconds_in_a_day\n",
        "seconds_in_a_week"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "604800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSrWNr3MuFUS"
      },
      "source": [
        "Colab notebooks allow you to combine **executable code** and **rich text** in a single document, along with **images**, **HTML**, **LaTeX** and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see [Overview of Colab](/notebooks/basic_features_overview.ipynb). To create a new Colab notebook you can use the File menu above, or use the following link: [create a new Colab notebook](http://colab.research.google.com#create=true).\n",
        "\n",
        "Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see [jupyter.org](https://www.jupyter.org)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdRyKR44dcNI"
      },
      "source": [
        "## Data science\n",
        "\n",
        "With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses **numpy** to generate some random data, and uses **matplotlib** to visualize it. To edit the code, just click the cell and start editing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "C4HZx7Gndbrh",
        "outputId": "98a57729-db7d-48d3-a62b-e6885dcb63f6"
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "ys = 200 + np.random.randn(100)\n",
        "x = [x for x in range(len(ys))]\n",
        "\n",
        "plt.plot(x, ys, '-')\n",
        "plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n",
        "\n",
        "plt.title(\"Sample Visualization\")\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_kCnsPUqS6o"
      },
      "source": [
        "You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under [Working with Data](#working-with-data)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwuxHmxllTwN"
      },
      "source": [
        "## Machine learning\n",
        "\n",
        "With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just [a few lines of code](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb). Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including [GPUs and TPUs](#using-accelerated-hardware), regardless of the power of your machine. All you need is a browser."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufxBm1yRnruN"
      },
      "source": [
        "Colab is used extensively in the machine learning community with applications including:\n",
        "- Getting started with TensorFlow\n",
        "- Developing and training neural networks\n",
        "- Experimenting with TPUs\n",
        "- Disseminating AI research\n",
        "- Creating tutorials\n",
        "\n",
        "To see sample Colab notebooks that demonstrate machine learning applications, see the [machine learning examples](#machine-learning-examples) below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Rh3-Vt9Nev9"
      },
      "source": [
        "## More Resources\n",
        "\n",
        "### Working with Notebooks in Colab\n",
        "- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)\n",
        "- [Guide to Markdown](/notebooks/markdown_guide.ipynb)\n",
        "- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n",
        "- [Saving and loading notebooks in GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)\n",
        "- [Interactive forms](/notebooks/forms.ipynb)\n",
        "- [Interactive widgets](/notebooks/widgets.ipynb)\n",
        "- <img src=\"/img/new.png\" height=\"20px\" align=\"left\" hspace=\"4px\" alt=\"New\"></img>\n",
        " [TensorFlow 2 in Colab](/notebooks/tensorflow_version.ipynb)\n",
        "\n",
        "<a name=\"working-with-data\"></a>\n",
        "### Working with Data\n",
        "- [Loading data: Drive, Sheets, and Google Cloud Storage](/notebooks/io.ipynb) \n",
        "- [Charts: visualizing data](/notebooks/charts.ipynb)\n",
        "- [Getting started with BigQuery](/notebooks/bigquery.ipynb)\n",
        "\n",
        "### Machine Learning Crash Course\n",
        "These are a few of the notebooks from Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n",
        "- [Intro to Pandas](/notebooks/mlcc/intro_to_pandas.ipynb)\n",
        "- [Tensorflow concepts](/notebooks/mlcc/tensorflow_programming_concepts.ipynb)\n",
        "- [First steps with TensorFlow](/notebooks/mlcc/first_steps_with_tensor_flow.ipynb)\n",
        "- [Intro to neural nets](/notebooks/mlcc/intro_to_neural_nets.ipynb)\n",
        "- [Intro to sparse data and embeddings](/notebooks/mlcc/intro_to_sparse_data_and_embeddings.ipynb)\n",
        "\n",
        "<a name=\"using-accelerated-hardware\"></a>\n",
        "### Using Accelerated Hardware\n",
        "- [TensorFlow with GPUs](/notebooks/gpu.ipynb)\n",
        "- [TensorFlow with TPUs](/notebooks/tpu.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2UTV1JBD4L2"
      },
      "source": [
        "# Trash Level Classification\n",
        "\n",
        "### Exploratory Data Analysis and Modeling\n",
        "\n",
        "This section provides code for downloading the CleanStat dataset from the City of LA open data portal and complete exploratory data analysis, aka [EDA][02.00]. EDA includes reviewing the dataset dimensions, distribution and feature correlation.\n",
        "\n",
        "After EDA, the modeling is completed using the python [scikit-learn][02.01] library to train/test a random forest classifier. Results are evaluated using a confusion matrix, and code is available to encourage additional development/collaboration.\n",
        "\n",
        "[02.00]: https://en.wikipedia.org/wiki/Exploratory_data_analysis \n",
        "[02.01]: https://scikit-learn.org/stable/\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UI-rUZN5I0U4",
        "outputId": "0128d8ee-5ad2-4d96-b5cc-2c1d13a30dcd"
      },
      "source": [
        "# 01.00 setup module\n",
        "\n",
        "# data analysis modules\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# plot module and config settings\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# plot module and config settings\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "sns.set(color_codes=True)\n",
        "sns.set_palette(sns.color_palette(\"muted\"))\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "\n",
        "# ml module\n",
        "import sklearn as sk\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# read data and view contents\n",
        "# https://geohub.lacity.org/datasets/clean-streets-index-2016-q1\n",
        "df = pd.read_csv('https://opendata.arcgis.com/datasets/661fe82f121a4eb795a1d3884a06f1da_0.csv')\n",
        "print('Output first 5 rows:')\n",
        "print(df.head(), '\\n')\n",
        "print('Output dataset info:')\n",
        "print(df.info(), '\\n')\n",
        "print('Output unique values for segment name:')\n",
        "print(df['FullName'].unique(), '\\n')\n",
        "\n",
        "# filter data for model train/predict\n",
        "# https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html\n",
        "df_subset = df[[\n",
        "    'OBJECTID',\n",
        "    'SegmentID',\n",
        "    'Seg_ID_Sco',\n",
        "    'Seg_Bk_Sco',\n",
        "    'Seg_LL_Sco',\n",
        "    'Seg_Wd_Sco',\n",
        "    'SegScore'\n",
        "]]\n",
        "\n",
        "# split train and test data\n",
        "# https://stackoverflow.com/questions/24147278/how-do-i-create-test-and-train-samples-from-one-dataframe-with-pandas\n",
        "print('Output df shape:')\n",
        "print(df.shape, '\\n')\n",
        "train, test = train_test_split(df_subset, test_size=0.2)\n",
        "print('Output train df shape:')\n",
        "print(train.shape, '\\n')\n",
        "print('Output test df shape:')\n",
        "print(test.shape, '\\n')\n",
        "\n",
        "# train rf classifier\n",
        "# https://stackabuse.com/classification-in-python-with-scikit-learn-and-pandas/\n",
        "y_train = train.iloc[:,6]\n",
        "X_train = train.iloc[:,:6]\n",
        "y_test = test.iloc[:,6]\n",
        "X_test = test.iloc[:,:6]\n",
        "\n",
        "# encode labels as continuous\n",
        "# https://stackoverflow.com/questions/41925157/logisticregression-unknown-label-type-continuous-using-sklearn-in-python\n",
        "# lab_enc = preprocessing.LabelEncoder()\n",
        "# y_train = lab_enc.fit_transform(y_train)\n",
        "# y_test = lab_enc.fit_transform(y_test)\n",
        "y_train = y_train.astype('int64')\n",
        "y_test = y_test.astype('int64')\n",
        "print('Output X_train df info:')\n",
        "print(X_train.info(), '\\n')\n",
        "print('Output X_test df info:')\n",
        "print(X_test.info(), '\\n')\n",
        "print('Output y_train df info:')\n",
        "print(y_train.dtype, '\\n')\n",
        "print('Output y_test df info:')\n",
        "print(y_test.dtype, '\\n')\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Output first 5 rows:\n",
            "   OBJECTID FullName  MIN_From_L  ...  SHAPE_Leng  SegScore  DisplayGra\n",
            "0         1                    0  ...   42.521183       1.0           1\n",
            "1         2                    0  ...    2.307199       1.0           1\n",
            "2         3                    0  ...   84.871168       1.0           1\n",
            "3         4                    0  ...   19.283651       1.0           1\n",
            "4         5                    0  ...  366.016428       1.5           2\n",
            "\n",
            "[5 rows x 20 columns] \n",
            "\n",
            "Output dataset info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38343 entries, 0 to 38342\n",
            "Data columns (total 20 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   OBJECTID    38343 non-null  int64  \n",
            " 1   FullName    38343 non-null  object \n",
            " 2   MIN_From_L  38343 non-null  int64  \n",
            " 3   BulkyItems  38343 non-null  int64  \n",
            " 4   IllegalDum  38343 non-null  int64  \n",
            " 5   LooseLitte  38343 non-null  int64  \n",
            " 6   Weeds       38343 non-null  int64  \n",
            " 7   SW_Bulky_I  38343 non-null  int64  \n",
            " 8   SW_Illegal  38343 non-null  int64  \n",
            " 9   SW_LooseLi  38343 non-null  int64  \n",
            " 10  SW_Weeds    38343 non-null  int64  \n",
            " 11  SegmentID   38343 non-null  int64  \n",
            " 12  Seg_ID_Sco  38343 non-null  int64  \n",
            " 13  Seg_Bk_Sco  38343 non-null  int64  \n",
            " 14  Seg_LL_Sco  38343 non-null  int64  \n",
            " 15  Seg_Wd_Sco  38343 non-null  int64  \n",
            " 16  NonGrade    38343 non-null  int64  \n",
            " 17  SHAPE_Leng  38343 non-null  float64\n",
            " 18  SegScore    38343 non-null  float64\n",
            " 19  DisplayGra  38343 non-null  int64  \n",
            "dtypes: float64(2), int64(17), object(1)\n",
            "memory usage: 5.9+ MB\n",
            "None \n",
            "\n",
            "Output unique values for segment name:\n",
            "[' ' '10th Ave' '10th Helena Dr' ... 'Will Rogers State Park Rd'\n",
            " 'Workman Rd' 'Zalvidea St'] \n",
            "\n",
            "Output df shape:\n",
            "(38343, 20) \n",
            "\n",
            "Output train df shape:\n",
            "(30674, 7) \n",
            "\n",
            "Output test df shape:\n",
            "(7669, 7) \n",
            "\n",
            "Output X_train df info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 30674 entries, 24174 to 34162\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype\n",
            "---  ------      --------------  -----\n",
            " 0   OBJECTID    30674 non-null  int64\n",
            " 1   SegmentID   30674 non-null  int64\n",
            " 2   Seg_ID_Sco  30674 non-null  int64\n",
            " 3   Seg_Bk_Sco  30674 non-null  int64\n",
            " 4   Seg_LL_Sco  30674 non-null  int64\n",
            " 5   Seg_Wd_Sco  30674 non-null  int64\n",
            "dtypes: int64(6)\n",
            "memory usage: 1.6 MB\n",
            "None \n",
            "\n",
            "Output X_test df info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7669 entries, 23883 to 33290\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype\n",
            "---  ------      --------------  -----\n",
            " 0   OBJECTID    7669 non-null   int64\n",
            " 1   SegmentID   7669 non-null   int64\n",
            " 2   Seg_ID_Sco  7669 non-null   int64\n",
            " 3   Seg_Bk_Sco  7669 non-null   int64\n",
            " 4   Seg_LL_Sco  7669 non-null   int64\n",
            " 5   Seg_Wd_Sco  7669 non-null   int64\n",
            "dtypes: int64(6)\n",
            "memory usage: 419.4 KB\n",
            "None \n",
            "\n",
            "Output y_train df info:\n",
            "int64 \n",
            "\n",
            "Output y_test df info:\n",
            "int64 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "tNc0pelN-2S3",
        "outputId": "c6eeea3f-92b9-4cae-8b02-3cb71ee9f69f"
      },
      "source": [
        "# 01.01 distribution plot\n",
        "\n",
        "# plot distribution\n",
        "# https://www.datacamp.com/community/tutorials/python-data-profiling\n",
        "sns.histplot(\n",
        "    df[\"SegScore\"].dropna(),\n",
        "    bins=10\n",
        ").set_title('Distribution of Segment Score Rating')\n",
        "# visualize histogram with log scale\n",
        "# https://stackoverflow.com/questions/23913151/log-log-lmplot-with-seaborn\n",
        "plt.yscale('log')\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vQV3hb6xHZFF",
        "outputId": "8a5a5ea7-cfb2-40c5-93af-e1a85ecb9314"
      },
      "source": [
        "# 01.02 correlation heatmap plot\n",
        "\n",
        "# remove non-numeric columns\n",
        "# https://stackoverflow.com/questions/13411544/delete-column-from-pandas-dataframe\n",
        "df_num = df.drop('FullName', 1)\n",
        "print('Output dataset info:')\n",
        "print(df_num.info(), '\\n')\n",
        "# calculate correlation and generate mask for upper triangle\n",
        "# https://seaborn.pydata.org/examples/many_pairwise_correlations.html\n",
        "corr = df_num.corr()\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "# create heatmap with color palette\n",
        "# https://seaborn.pydata.org/generated/seaborn.heatmap.html\n",
        "# https://heartbeat.fritz.ai/seaborn-heatmaps-13-ways-to-customize-correlation-matrix-visualizations-f1c49c816f07\n",
        "sns.heatmap(\n",
        "    corr,\n",
        "    mask=mask,\n",
        "    cmap='coolwarm'\n",
        ").set_title('Correlation Heatmap')\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Output dataset info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38343 entries, 0 to 38342\n",
            "Data columns (total 19 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   OBJECTID    38343 non-null  int64  \n",
            " 1   MIN_From_L  38343 non-null  int64  \n",
            " 2   BulkyItems  38343 non-null  int64  \n",
            " 3   IllegalDum  38343 non-null  int64  \n",
            " 4   LooseLitte  38343 non-null  int64  \n",
            " 5   Weeds       38343 non-null  int64  \n",
            " 6   SW_Bulky_I  38343 non-null  int64  \n",
            " 7   SW_Illegal  38343 non-null  int64  \n",
            " 8   SW_LooseLi  38343 non-null  int64  \n",
            " 9   SW_Weeds    38343 non-null  int64  \n",
            " 10  SegmentID   38343 non-null  int64  \n",
            " 11  Seg_ID_Sco  38343 non-null  int64  \n",
            " 12  Seg_Bk_Sco  38343 non-null  int64  \n",
            " 13  Seg_LL_Sco  38343 non-null  int64  \n",
            " 14  Seg_Wd_Sco  38343 non-null  int64  \n",
            " 15  NonGrade    38343 non-null  int64  \n",
            " 16  SHAPE_Leng  38343 non-null  float64\n",
            " 17  SegScore    38343 non-null  float64\n",
            " 18  DisplayGra  38343 non-null  int64  \n",
            "dtypes: float64(2), int64(17)\n",
            "memory usage: 5.6 MB\n",
            "None \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation Heatmap')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjxPqep3DSk9",
        "outputId": "11efac57-a42b-4be7-ee7e-63e8c4772cb1"
      },
      "source": [
        "# 01.03 classifier model (train)\n",
        "\n",
        "# train initial classifier, then improve with grid search\n",
        "# https://stackoverflow.com/questions/30102973/how-to-get-best-estimator-on-gridsearchcv-random-forest-classifier-scikit\n",
        "rfc = RandomForestClassifier(\n",
        "    n_jobs=-1,\n",
        "    max_features='sqrt',\n",
        "    n_estimators=100,\n",
        "    oob_score=True,\n",
        "    random_state=12345\n",
        ")\n",
        "# run grid search to find best parameters\n",
        "# note: grid search takes ~5 min to run, so uncomment to run\n",
        "# param_grid = {\n",
        "#     'n_estimators': [200, 300, 400, 500],\n",
        "#     'max_features': ['auto', 'sqrt', 'log2']\n",
        "# }\n",
        "# rfc_cv = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=5)\n",
        "# rfc_cv.fit(X_train, y_train)\n",
        "# output best parameters, then refit model\n",
        "# https://www.kaggle.com/sociopath00/random-forest-using-gridsearchcv\n",
        "# print('Grid Search Results - Best Parameters:')\n",
        "# print(rfc_cv.best_params_)\n",
        "# output:\n",
        "# Grid Search Results - Best Parameters:\n",
        "# {'max_features': 'auto', 'n_estimators': 200}\n",
        "# refit model with best parameters from grid search\n",
        "rfc_params = RandomForestClassifier(\n",
        "    n_jobs=-1,\n",
        "    max_features='auto',\n",
        "    n_estimators=200,\n",
        "    oob_score=True,\n",
        "    random_state=12345\n",
        ")\n",
        "rfc_params.fit(X_train, y_train)\n"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=-1, oob_score=True, random_state=12345, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "YwuNbdhTNTNM",
        "outputId": "99cd9d77-8ec2-41fb-fdf5-2b738566a5ad"
      },
      "source": [
        "# 01.04 rf classifier model (test)\n",
        "\n",
        "# make predictions and calculate accuracy score\n",
        "# https://www.blopig.com/blog/2017/07/using-random-forests-in-python-with-scikit-learn/\n",
        "# rfc_params.predict(X_test)\n",
        "# print('Random Classifier Accuracy Score:')\n",
        "# round(rfc_params.score(X_test,y_test), 4)\n",
        "predicted = rfc_params.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predicted)\n",
        "print(f'Out-of-bag score estimate: {rfc_params.oob_score_:.4}')\n",
        "print(f'Mean accuracy score: {accuracy:.4}')\n",
        "\n",
        "# create/plot confusion matrix\n",
        "# https://www.blopig.com/blog/2017/07/using-random-forests-in-python-with-scikit-learn/\n",
        "cm = pd.DataFrame(\n",
        "    confusion_matrix(y_test, predicted),\n",
        "    # columns=df['SegScore'],\n",
        "    # index=df['SegScore']\n",
        ")\n",
        "sns.heatmap(cm,\n",
        "    annot=True,\n",
        "    cmap='coolwarm',\n",
        "    fmt='g'\n",
        ")\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Out-of-bag score estimate: 0.9921\n",
            "Mean accuracy score: 0.9919\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f523cad4610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}