From 83c3771469c45cc38e82ba2e74a118a701d34954 Mon Sep 17 00:00:00 2001 From: Gabriel Dutra <57206480+Dutra-Apex@users.noreply.github.com> Date: Thu, 23 Mar 2023 10:12:21 -0400 Subject: [PATCH 1/6] Update environment.yml Removed build strings from dependencies, as well as libraries that could not be found. This allowed me to create a venv from the environment.yml file. Code ran fine despite changes. --- environment.yml | 84 ++++++++++++++++++++++--------------------------- 1 file changed, 38 insertions(+), 46 deletions(-) diff --git a/environment.yml b/environment.yml index edeab58..deb0393 100644 --- a/environment.yml +++ b/environment.yml @@ -5,55 +5,47 @@ channels: dependencies: - _libgcc_mutex=0.1=main - blas=1.0=mkl - - bzip2=1.0.8=h7b6447c_0 - - ca-certificates=2021.1.19=h06a4308_1 - - certifi=2020.12.5=py38h06a4308_0 - - cudatoolkit=10.2.89=hfd86e86_1 - - ffmpeg=4.3=hf484d3e_0 - - freetype=2.10.4=h5ab3b9f_0 - - gmp=6.2.1=h2531618_2 - - gnutls=3.6.5=h71b1129_1002 + - bzip2=1.0.8 + - ca-certificates=2021.1.19 + - certifi=2020.12.5 + - cudatoolkit=10.2.89 + - ffmpeg=4.3 + - freetype=2.10.4 - intel-openmp=2020.2=254 - - jpeg=9b=h024ee3a_2 - - lame=3.100=h7b6447c_0 - - lcms2=2.11=h396b838_0 - - ld_impl_linux-64=2.33.1=h53a641e_7 - - libffi=3.3=he6710b0_2 - - libgcc-ng=9.1.0=hdf63c60_0 - - libiconv=1.15=h63c8f33_5 - - libpng=1.6.37=hbc83047_0 - - libstdcxx-ng=9.1.0=hdf63c60_0 - - libtiff=4.1.0=h2733197_1 - - libuv=1.40.0=h7b6447c_0 - - lz4-c=1.9.3=h2531618_0 + - jpeg=9b + - lame=3.100 + - lcms2=2.11 + - libffi=3.3 + - libiconv=1.15 + - libpng=1.6.37 + - libtiff=4.1.0 + - libuv=1.40.0 + - lz4-c=1.9.3 - mkl=2020.2=256 - - mkl-service=2.3.0=py38he904b0f_0 - - mkl_fft=1.3.0=py38h54f3939_0 - - mkl_random=1.1.1=py38h0573a6f_0 - - ncurses=6.2=he6710b0_1 - - nettle=3.4.1=hbb512f6_0 - - ninja=1.10.2=py38hff7bd54_0 - - numpy=1.19.2=py38h54aff64_0 - - numpy-base=1.19.2=py38hfa32c7d_0 - - olefile=0.46=py_0 - - openh264=2.1.0=hd408876_0 - - openssl=1.1.1k=h27cfd23_0 - - pillow=8.1.2=py38he98fc37_0 - - pip=21.0.1=py38h06a4308_0 - - python=3.8.8=hdb3f193_4 - - pytorch=1.8.1=py3.8_cuda10.2_cudnn7.6.5_0 - - readline=8.1=h27cfd23_0 - - setuptools=52.0.0=py38h06a4308_0 - - six=1.15.0=py38h06a4308_0 - - sqlite=3.35.3=hdfb4753_0 - - tk=8.6.10=hbc83047_0 + - mkl-service=2.3.0 + - mkl_fft=1.3.0 + - mkl_random=1.1.1 + - ninja=1.10.2 + - numpy=1.19.2 + - numpy-base=1.19.2 + - olefile=0.46=py_0 + - openssl=1.1.1k + - pillow=8.1.2 + - pip=21.0.1 + - python=3.8.8 + - pytorch=1.8.1 + - setuptools=52.0.0 + - six=1.15.0 + - sqlite=3.35.3 + - tk=8.6.10 - torchaudio=0.8.1=py38 - torchvision=0.9.1=py38_cu102 - - typing_extensions=3.7.4.3=pyha847dfd_0 - - wheel=0.36.2=pyhd3eb1b0_0 - - xz=5.2.5=h7b6447c_0 - - zlib=1.2.11=h7b6447c_3 - - zstd=1.4.9=haebb681_0 + - typing_extensions=3.7.4.3 + - wheel=0.36.2 + - xz=5.2.5 + - zlib=1.2.11 + - zstd=1.4.9 + - pip: - absl-py==0.12.0 - attrdict==2.0.1 @@ -88,4 +80,4 @@ dependencies: - tifffile==2021.3.31 - tqdm==4.59.0 - urllib3==1.26.4 - - werkzeug==1.0.1 + - werkzeug==1.0.1 From 5d87c01c236330a90121b4daffcbdffbe4f83014 Mon Sep 17 00:00:00 2001 From: Gabriel Dutra <57206480+Dutra-Apex@users.noreply.github.com> Date: Thu, 23 Mar 2023 10:17:29 -0400 Subject: [PATCH 2/6] Update dataset.py Fixed 'num_samples = 0' error by changing how image_path and mask_path lists are created --- src/data/dataset.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/data/dataset.py b/src/data/dataset.py index 3874640..654883a 100644 --- a/src/data/dataset.py +++ b/src/data/dataset.py @@ -18,10 +18,10 @@ def __init__(self, args): self.mask_type = args.mask_type # image and mask - self.image_path = [] - for ext in ['*.jpg', '*.png']: - self.image_path.extend(glob(os.path.join(args.dir_image, args.data_train, ext))) - self.mask_path = glob(os.path.join(args.dir_mask, args.mask_type, '*.png')) + self.image_path, self.mask_path = [], [] + for ext in ['*.jpg', '*.png', '*.jpeg']: + self.image_path.extend(glob(f'{args.dir_image}*{ext})) + self.mask_path.extend(glob(f'{args.dir_image}*{ext})) # augmentation self.img_trans = transforms.Compose([ @@ -77,4 +77,4 @@ def __getitem__(self, index): data = InpaintingData(args) print(len(data), len(data.mask_path)) img, mask, filename = data[0] - print(img.size(), mask.size(), filename) \ No newline at end of file + print(img.size(), mask.size(), filename) From e158bed78f53d92fc854b6c1fc05c623b3bfd9f5 Mon Sep 17 00:00:00 2001 From: Gabriel Dutra <57206480+Dutra-Apex@users.noreply.github.com> Date: Thu, 9 Nov 2023 22:03:09 -0500 Subject: [PATCH 3/6] Created using Colaboratory --- Code/DS_Session_11_09_23.ipynb | 4604 ++++++++++++++++++++++++++++++++ 1 file changed, 4604 insertions(+) create mode 100644 Code/DS_Session_11_09_23.ipynb diff --git a/Code/DS_Session_11_09_23.ipynb b/Code/DS_Session_11_09_23.ipynb new file mode 100644 index 0000000..343bf57 --- /dev/null +++ b/Code/DS_Session_11_09_23.ipynb @@ -0,0 +1,4604 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Setup" + ], + "metadata": { + "id": "ROzgioymSolq" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eRlUMwij4PWg" + }, + "outputs": [], + "source": [ + "# Importing all the necessary libraries\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "source": [ + "# Mounting drive to colab, for detailed instructions, see: https://colab.research.google.com/notebooks/snippets/drive.ipynb\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "nBWEQMlun-Jl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c0e820fc-2d7f-47cf-aac9-0cdf66086bc9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Loads the data based on where it is stored on your personal drive\n", + "# https://www.kaggle.com/datasets/ahmedshahriarsakib/usa-real-estate-dataset/data\n", + "ds_path = \"/content/drive/My Drive/JoC_DS_Sessions/Datasets/\"\n", + "ds_original = pd.read_csv(ds_path+'us_real_estate_data.csv')" + ], + "metadata": { + "id": "zxSpyKAB73OL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Take a look at the 1st 10 rows in the data\n", + "ds_original.head(10)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "G0l-L_zQ9Thh", + "outputId": "5c783152-b05b-4473-a1ba-5fee8b657ec7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " status bed bath acre_lot city state zip_code \\\n", + "0 for_sale 3.0 2.0 0.12 Adjuntas Puerto Rico 601.0 \n", + "1 for_sale 4.0 2.0 0.08 Adjuntas Puerto Rico 601.0 \n", + "2 for_sale 2.0 1.0 0.15 Juana Diaz Puerto Rico 795.0 \n", + "3 for_sale 4.0 2.0 0.10 Ponce Puerto Rico 731.0 \n", + "4 for_sale 6.0 2.0 0.05 Mayaguez Puerto Rico 680.0 \n", + "5 for_sale 4.0 3.0 0.46 San Sebastian Puerto Rico 612.0 \n", + "6 for_sale 3.0 1.0 0.20 Ciales Puerto Rico 639.0 \n", + "7 for_sale 3.0 2.0 0.08 Ponce Puerto Rico 731.0 \n", + "8 for_sale 2.0 1.0 0.09 Ponce Puerto Rico 730.0 \n", + "9 for_sale 5.0 3.0 7.46 Las Marias Puerto Rico 670.0 \n", + "\n", + " house_size prev_sold_date price \n", + "0 920.0 NaN 105000.0 \n", + "1 1527.0 NaN 80000.0 \n", + "2 748.0 NaN 67000.0 \n", + "3 1800.0 NaN 145000.0 \n", + "4 NaN NaN 65000.0 \n", + "5 2520.0 NaN 179000.0 \n", + "6 2040.0 NaN 50000.0 \n", + "7 1050.0 NaN 71600.0 \n", + "8 1092.0 NaN 100000.0 \n", + "9 5403.0 NaN 300000.0 " + ], + "text/html": [ + "\n", + "
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statusbedbathacre_lotcitystatezip_codehouse_sizeprev_sold_dateprice
0for_sale3.02.00.12AdjuntasPuerto Rico601.0920.0NaN105000.0
1for_sale4.02.00.08AdjuntasPuerto Rico601.01527.0NaN80000.0
2for_sale2.01.00.15Juana DiazPuerto Rico795.0748.0NaN67000.0
3for_sale4.02.00.10PoncePuerto Rico731.01800.0NaN145000.0
4for_sale6.02.00.05MayaguezPuerto Rico680.0NaNNaN65000.0
5for_sale4.03.00.46San SebastianPuerto Rico612.02520.0NaN179000.0
6for_sale3.01.00.20CialesPuerto Rico639.02040.0NaN50000.0
7for_sale3.02.00.08PoncePuerto Rico731.01050.0NaN71600.0
8for_sale2.01.00.09PoncePuerto Rico730.01092.0NaN100000.0
9for_sale5.03.07.46Las MariasPuerto Rico670.05403.0NaN300000.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
0for_sale3.02.00.12AdjuntasPuerto Rico601.0920.0105000.0
1for_sale4.02.00.08AdjuntasPuerto Rico601.01527.080000.0
2for_sale2.01.00.15Juana DiazPuerto Rico795.0748.067000.0
3for_sale4.02.00.10PoncePuerto Rico731.01800.0145000.0
5for_sale4.03.00.46San SebastianPuerto Rico612.02520.0179000.0
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\n" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# However, notice that we lost over half of our data\n", + "print(\"Total number of rows in the dataset:\", len(ds_cleaned))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NVyqH-sZYRr6", + "outputId": "481f199c-fcda-401b-d597-3ca24d55f6fd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total number of rows in the dataset: 413083\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We still have 400k rows, so we will be able to perform our analysis." + ], + "metadata": { + "id": "DpRuDtrDpWwI" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Q1) How expensive is a typical house in New Jersey?\n", + "\n", + "A common way of thinking about the typical price of something is to think of the mean (or average) of that. So let's calculate it.\n" + ], + "metadata": { + "id": "f18wWMUCYmwN" + } + }, + { + "cell_type": "code", + "source": [ + "# First, we need to isolate the NJ data:\n", + "nj_data = ds_cleaned[ds_cleaned['state'] == \"New Jersey\"]\n", + "nj_data.head(5)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "cLplorfnY9FC", + "outputId": "159121c7-ae9b-48ec-a263-8b0b34f56ab2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " status bed bath acre_lot city state zip_code \\\n", + "30126 for_sale 3.0 3.0 0.07 Burlington New Jersey 8016.0 \n", + "45320 for_sale 3.0 3.0 0.07 Burlington New Jersey 8016.0 \n", + "385082 for_sale 3.0 3.0 0.07 Burlington New Jersey 8016.0 \n", + "388797 for_sale 3.0 3.0 0.07 Burlington New Jersey 8016.0 \n", + "389915 for_sale 3.0 3.0 0.07 Burlington New Jersey 8016.0 \n", + "\n", + " house_size price \n", + "30126 1500.0 333490.0 \n", + "45320 1500.0 333490.0 \n", + "385082 1500.0 333490.0 \n", + "388797 1500.0 333490.0 \n", + "389915 1500.0 333490.0 " + ], + "text/html": [ + "\n", + "
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
30126for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
45320for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
385082for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
388797for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
389915for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
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\n" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Now, we can easily get the average prices\n", + "print(\"Average house price in NJ: $\", round(nj_data['price'].mean(), 2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "voBsIZb2Z4Py", + "outputId": "e2c94a3f-ba1e-4112-def1-412d9b4d7c33" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Average house price in NJ: $ 575461.92\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "However, in this case, is the mean the best way to answer the question? Does it really represent the typical price of a house?\n", + "\n", + "To answer that, let us look at the distribution of our data. To visualize a distribution, we can plot a histogram of our values" + ], + "metadata": { + "id": "5B81W_Nw-nD6" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Plot example: Histogram" + ], + "metadata": { + "id": "zYCppEh0-6jx" + } + }, + { + "cell_type": "code", + "source": [ + "values = [1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5]\n", + "plt.hist(values)\n", + "plt.xlabel(\"Value\")\n", + "plt.ylabel(\"Quantity\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 466 + }, + "id": "kXEyaHNQUoCv", + "outputId": "b160f35a-a836-4d4f-a38c-83684a3f7537" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Quantity')" + ] + }, + "metadata": {}, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "A histogram allows us to see how many items of a given value there are. In the example above, we initialize a sample array 'values' and plot its histogram. On the plot, we can see that there are 3 items with value of 1, 4 items with value of 2, and so on.\n", + "\n", + "The histogram above has 5 bars (also called bins). Each bar groups together values that are similar, or identical in value.\n", + "For a more in-depth overview of histograms, check: https://chartio.com/learn/charts/histogram-complete-guide/\n" + ], + "metadata": { + "id": "h2gIaCXe_EHq" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Continuing the question\n", + "\n", + "Now, we may plot the histogram of the price data" + ], + "metadata": { + "id": "FNXe-9g8AJc-" + } + }, + { + "cell_type": "code", + "source": [ + "# Plot a histogram of the NJ house price data\n", + "plt.title(\"Histogram of House prices in NJ\")\n", + "plt.hist(nj_data['price'], bins=100)\n", + "plt.xlabel(\"Price\")\n", + "plt.ylabel(\"Number of houses\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "ZDsXoykTbSU2", + "outputId": "c6cba0e5-52a5-4aa8-bab3-23a08efb5677" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As you can see, while a large amount of data lies within the 0 to 5 million range (expressed in the x axis as the (0.0, 0.5) interval). However, we do have houses with much higher prices, going up to 25 million. Those houses are called *outliers*. They are extreme points that do not represent the overrall data.\n", + "\n", + "When we calculate the mean, we add all the price values in our nj_data (including the outliers) and divide by how many houses we have. Because we include the outliers in this calculation, the value of the mean becomes *skewed*. Hence, the mean of the data does not represent the typical house price.\n", + "\n", + "Here's an example that illustrates this:" + ], + "metadata": { + "id": "hg_tTCqWAa-L" + } + }, + { + "cell_type": "code", + "source": [ + "sample_values = np.array([1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 180])\n", + "print(\"Mean of values: \", round(sample_values.mean(), 2))\n", + "print(\"Median of prices: \", np.median(sample_values))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OOa7yi7peG19", + "outputId": "2024ab90-348c-4dee-90db-a6e31cde714b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean of values: 16.62\n", + "Median of prices: 3.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The mean is **much** higher than all the values of the data, except for one. This clearely is not an accurate representation of the typical value.\n", + "When the data has many outliers, it is common practice to use the *median* value of the data to represent its typical value. In our example, the median is 3, which is a much more accurate representation of a typical value." + ], + "metadata": { + "id": "Z-KVuw-VC2JQ" + } + }, + { + "cell_type": "markdown", + "source": [ + "We can get the median of our nj_data as follows:" + ], + "metadata": { + "id": "6QBgxrh9CcDj" + } + }, + { + "cell_type": "code", + "source": [ + "# Now, we can easily get the average prices\n", + "print(\"Median house price in NJ: $\", round(nj_data['price'].median(), 2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "My29VRoffbq0", + "outputId": "087bdda7-5eec-4d50-ab88-424734e4623d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Median house price in NJ: $ 407500.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Bonus question: Improving our nj_data price histogram\n", + "\n", + "We can improve our histogram by limiting the range of our data, as well as adding the median price to the graph:" + ], + "metadata": { + "id": "9wv3kadJDgaG" + } + }, + { + "cell_type": "code", + "source": [ + "# Determines the bin edges to be between 0 and 2 million, with one bin every 100,000 value\n", + "bin_edges = [i for i in range(0, 2*10**6, 10**5)]\n", + "\n", + "plt.title(\"Histogram of House prices in NJ\")\n", + "plt.hist(nj_data['price'], bins=bin_edges)\n", + "plt.xlabel(\"Price\")\n", + "plt.ylabel(\"Number of houses\")\n", + "\n", + "# Plots a dashed line at the median value, with color red.\n", + "plt.axvline(x=nj_data['price'].median(), color='r', label=\"Median Price\", linestyle='--')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "yHormgoH0M_g", + "outputId": "482517f9-fe77-46c2-f09b-b09f3df36c76" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Bonus question: What is the probability of finding a house in NJ that costs more than a million dollars?\n" + ], + "metadata": { + "id": "6UQjBvGBEOAi" + } + }, + { + "cell_type": "code", + "source": [ + "# Probability = Number of desired outcomes/Number of total possibilities\n", + "# Our total is the total number of houses in NJ\n", + "# Our desired is the number of house over 1 million dollars cost\n", + "\n", + "n_total = len(nj_data)\n", + "n_desired = len(nj_data[nj_data['price'] >= 10**6])\n", + "print(\"Probability is:\", round(n_desired/n_total, 5) * 100, \"%\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xjd7EthrfKN4", + "outputId": "dc07424a-5b9a-4623-aa04-c37eae1967f8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Probability is: 9.037 %\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Q2) What are the states/territories with the highest mean and median house prices?" + ], + "metadata": { + "id": "GFN4jSE3geIz" + } + }, + { + "cell_type": "code", + "source": [ + "# First we need to get a list of all states in our dataset\n", + "list_states = ds_cleaned['state'].unique()\n", + "print(list_states)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "imCCplrRgk7D", + "outputId": "d9ac67e9-645e-48a8-df24-d592d965926d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['Puerto Rico' 'Virgin Islands' 'Massachusetts' 'Connecticut' 'New Jersey'\n", + " 'New York' 'New Hampshire' 'Vermont' 'Rhode Island' 'Wyoming' 'Maine'\n", + " 'Pennsylvania' 'West Virginia' 'Delaware']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Then we can organize the median and mean house prices in each state as follows:\n", + "dict_means = {}\n", + "dict_medians = {}\n", + "\n", + "for state in list_states:\n", + " dict_means[state] = np.mean(ds_cleaned[ds_cleaned['state']==state]['price'])\n", + " dict_medians[state] = np.median(ds_cleaned[ds_cleaned['state']==state]['price'])" + ], + "metadata": { + "id": "GNk7giLThAe1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Finally, we sort the information to be able to visualize the states with highest mean/median values:\n", + "# For more information on sorting dictionaries, visit: https://www.geeksforgeeks.org/python-sort-a-dictionary/#\n", + "sorted_means = dict(sorted(dict_means.items(), key=lambda item: item[1], reverse = True))\n", + "sorted_medians = dict(sorted(dict_medians.items(), key=lambda item: item[1], reverse = True))" + ], + "metadata": { + "id": "rFgu-WdFh8xF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "sorted_medians" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1_Itw29Di-rx", + "outputId": "1efb20a2-cbb8-4f79-8b2c-0cd48858e901" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'Virgin Islands': 1295000.0,\n", + " 'New York': 895000.0,\n", + " 'Massachusetts': 649900.0,\n", + " 'Wyoming': 535000.0,\n", + " 'New Hampshire': 450000.0,\n", + " 'New Jersey': 407500.0,\n", + " 'Rhode Island': 389900.0,\n", + " 'Vermont': 375000.0,\n", + " 'Maine': 344900.0,\n", + " 'Connecticut': 319900.0,\n", + " 'Delaware': 295000.0,\n", + " 'Pennsylvania': 279900.0,\n", + " 'Puerto Rico': 145000.0,\n", + " 'West Virginia': 62500.0}" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ] + }, + { + "cell_type": "code", + "source": [ + "sorted_means" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ODnPNnRijbDJ", + "outputId": "548046e7-116d-4232-f7f0-15888aaecf1a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'Virgin Islands': 1953334.3070175438,\n", + " 'New York': 1532066.8280808714,\n", + " 'Massachusetts': 1056067.5250072274,\n", + " 'New Hampshire': 642339.2626155231,\n", + " 'New Jersey': 575461.915253776,\n", + " 'Vermont': 572109.4284170232,\n", + " 'Wyoming': 535000.0,\n", + " 'Maine': 529820.2938722295,\n", + " 'Rhode Island': 526744.7998375305,\n", + " 'Connecticut': 503652.60612973914,\n", + " 'Pennsylvania': 393789.6828546969,\n", + " 'Puerto Rico': 391127.3780376868,\n", + " 'Delaware': 341894.14762741653,\n", + " 'West Virginia': 62500.0}" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# We can also visualize our values through a bar plot:\n", + "plt.title(\"Mean price of houses\")\n", + "plt.bar(range(len(list_states)), sorted_means.values(), tick_label=list(sorted_means.keys()))\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + }, + "id": "17bI3t6ZjgRm", + "outputId": "9ec80ba8-45c3-4994-c158-be137a9d4036" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.title(\"Median price of houses\")\n", + "plt.bar(range(len(list_states)), sorted_medians.values(), tick_label=list(sorted_medians.keys()))\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + }, + "id": "r0j5osy3j4-c", + "outputId": "bcd9080f-64e7-4c50-8e3f-8242346b9010" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Bonus question: In what states is the median equal or higher to the mean?\n", + "\n", + "We saw in our nj_data histogram that the distribution of house prices was **right skewed**, which causes the mean of the data to be higher than the median. We also discussed that this behavior is expected when we are dealing with price data. Are there any States where this doesn't happen? Can we identify the cause for this unexpected behavior?" + ], + "metadata": { + "id": "mM77DUoJWOJJ" + } + }, + { + "cell_type": "code", + "source": [ + "# We can identify those states with a simple loop:\n", + "for state in list_states:\n", + " if dict_medians[state] >= dict_means[state]:\n", + " print(state)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jNZ6xRGalK7x", + "outputId": "9a65beb7-68fe-4467-8c01-40442dd59d0e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Wyoming\n", + "West Virginia\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Now we can isolate the data for those states:\n", + "wyoming_data = ds_cleaned[ds_cleaned['state'] == 'Wyoming']\n", + "west_virginia_data = ds_cleaned[ds_cleaned['state'] == 'West Virginia']" + ], + "metadata": { + "id": "dSJCE9z8lcsp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# When taking a peek at the data we can already identify the cause of this behavior\n", + "wyoming_data.head(5)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "GuYJrhCVlrRt", + "outputId": "1f9f1d05-6bbe-4cfe-ea19-1cb574379db4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " status bed bath acre_lot city state zip_code house_size \\\n", + "214489 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", + "234939 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", + "237632 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", + "\n", + " price \n", + "214489 535000.0 \n", + "234939 535000.0 \n", + "237632 535000.0 " + ], + "text/html": [ + "\n", + "
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\n" + ] + }, + "metadata": {}, + "execution_count": 52 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "After looking at the first entry of the data, we can see why those states have a median price equal to their mean price, as each State has only three entries, and those entries are duplicated." + ], + "metadata": { + "id": "0MzIj8l6n_K_" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 3) Based on the New York data, how much would a house with 3bd and 2bath cost? What about a house with 15 bd and 1bath?" + ], + "metadata": { + "id": "0OgG9gEbo5Sb" + } + }, + { + "cell_type": "code", + "source": [ + "# We start by isolating the New York data\n", + "ny_data = ds_cleaned[ds_cleaned['state'] == \"New York\"]" + ], + "metadata": { + "id": "aVVA9L9zpS0s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ny_data.head(5)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "mNqvzgzspaxy", + "outputId": "2c337e2c-bfb3-4718-f16a-5874062815ab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " status bed bath acre_lot city state zip_code \\\n", + "30149 for_sale 3.0 1.0 60.00 Berlin New York 12022.0 \n", + "54248 for_sale 3.0 2.0 2.02 Claverack New York 12521.0 \n", + "54258 for_sale 4.0 2.0 0.24 Copake New York 12521.0 \n", + "54259 for_sale 3.0 3.0 1.90 Copake New York 12516.0 \n", + "54262 for_sale 3.0 2.0 2.00 Copake New York 12517.0 \n", + "\n", + " house_size price \n", + "30149 1176.0 175000.0 \n", + "54248 1600.0 425000.0 \n", + "54258 1239.0 225000.0 \n", + "54259 1800.0 419000.0 \n", + "54262 1482.0 365000.0 " + ], + "text/html": [ + "\n", + "
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
30149for_sale3.01.060.00BerlinNew York12022.01176.0175000.0
54248for_sale3.02.02.02ClaverackNew York12521.01600.0425000.0
54258for_sale4.02.00.24CopakeNew York12521.01239.0225000.0
54259for_sale3.03.01.90CopakeNew York12516.01800.0419000.0
54262for_sale3.02.02.00CopakeNew York12517.01482.0365000.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
54248for_sale3.02.02.02ClaverackNew York12521.01600.0425000.0
54262for_sale3.02.02.00CopakeNew York12517.01482.0365000.0
54268for_sale3.02.02.90HillsdaleNew York12529.01404.0374900.0
54278for_sale3.02.01.20MillertonNew York12546.01350.0375000.0
54446for_sale3.02.010.00AusterlitzNew York12017.01152.0489900.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Now we do the same for bathrooms vs price\n", + "plt.scatter(ny_data[ny_data['bath']<30]['bath'], ny_data[ny_data['bath']<30]['price'])\n", + "plt.xlabel(\"Number of bathrooms\")\n", + "plt.ylabel(\"Price\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465 + }, + "id": "PioXLg3zysaQ", + "outputId": "b9e039f4-305f-4e87-b966-853845125494" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From the data, we can see that we have a **positive relationship** between # of beds, # of baths, and house price. This means that as the number of bedrooms and bathrooms increase, so does the price of the house. However, the relationship is not necessarily strong, as the data contain many outliers (houses with a high number of bath or bed that are not very expensive). We will see how this affects the accuracy of our linear model later on." + ], + "metadata": { + "id": "oK--eXaz-C3G" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Selecting our data\n", + "\n", + "To use linear regression, we need to select our dependent (y) and independent (X) variables. In this this case, because we want to predict price from number of bedrooms and bathrooms, our y variable is price, and our x variable is # beds and # baths." + ], + "metadata": { + "id": "3poCur6gAenV" + } + }, + { + "cell_type": "code", + "source": [ + "X = ny_data[['bed', 'bath']]\n", + "y = ny_data['price']" + ], + "metadata": { + "id": "NkixK1lAzC8K" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "As seen before, LR works by solving the following equation: $$ \\hat{\\beta} = (X^TX)^{-1}X^TY$$\n", + "\n", + "Where:\n", + "\n", + "Beta: Coefficients of the equation\n", + "\n", + "X: Matrix with independent values\n", + "\n", + "Y: Matrix with dependent values\n", + "\n", + "The vector Beta contains the coefficients to our LR model, which can be written as follows:\n", + "\n", + "$$ \\hat{y} = a_1x_1 + a_2x_2 + b$$\n", + "\n", + "In the context of our problem, the model is:\n", + "\n", + "$$ HousePrice = a_1 \\times bed + a_2 \\times bath + b$$\n", + "\n", + "If we want Beta to contain the constant term **b**, we need to add a column of ones into our X data, which can be done as follows:\n", + "\n" + ], + "metadata": { + "id": "WJ526AY7BXLi" + } + }, + { + "cell_type": "code", + "source": [ + "X = sm.add_constant(X)\n", + "X" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "BVu4MwbvBMRH", + "outputId": "3b990fb0-2dc5-4c4a-d888-67df915ec082" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " const bed bath\n", + "30149 1.0 3.0 1.0\n", + "54248 1.0 3.0 2.0\n", + "54258 1.0 4.0 2.0\n", + "54259 1.0 3.0 3.0\n", + "54262 1.0 3.0 2.0\n", + "... ... ... ...\n", + "904934 1.0 4.0 8.0\n", + "904935 1.0 6.0 8.0\n", + "904936 1.0 4.0 5.0\n", + "904937 1.0 4.0 7.0\n", + "904938 1.0 6.0 7.0\n", + "\n", + "[44614 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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constbedbath
301491.03.01.0
542481.03.02.0
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\n" + ] + }, + "metadata": {}, + "execution_count": 61 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Train/Test split\n", + "\n", + "In almost any model, you need to split your data into training and testing. Most models utilize information from the data to learn. If you want to evaluate the performance of the model, you need to do so in a set of data that the model has not seen before, otherwise your evaluation will be biased!\n", + "\n", + "A typical train/test split is 80/20, meaning that we will use 80% of our data to train the model, and 20% to evaluate it.\n", + "\n", + "Selecting train/test sets should be done at random, and we can do so with the following function:" + ], + "metadata": { + "id": "g7An8GxiEU8c" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split" + ], + "metadata": { + "id": "8pY7Qv_7z3Rc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# This function splits our (X,y) data into training and testing sets\n", + "# The random_state=42 is used to guarantee replicability, anyone who uses 42 as their state will get the same train/test split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ], + "metadata": { + "id": "2w8dphXgz_h5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Training and evaluating the model\n", + "\n", + "We may now perform Linear Regression in our data" + ], + "metadata": { + "id": "K0O6RSBPFpdu" + } + }, + { + "cell_type": "code", + "source": [ + "model = sm.OLS(y_train, X_train).fit()\n", + "model.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "9auIHcs21DRM", + "outputId": "f0ca7cd1-a5bc-4656-e99c-16f5c851a355" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: price R-squared: 0.261\n", + "Model: OLS Adj. R-squared: 0.261\n", + "Method: Least Squares F-statistic: 6287.\n", + "Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00\n", + "Time: 02:42:40 Log-Likelihood: -5.7632e+05\n", + "No. Observations: 35691 AIC: 1.153e+06\n", + "Df Residuals: 35688 BIC: 1.153e+06\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const -4.845e+05 2.79e+04 -17.338 0.000 -5.39e+05 -4.3e+05\n", + "bed -2.634e+05 8117.430 -32.454 0.000 -2.79e+05 -2.48e+05\n", + "bath 1.048e+06 1.06e+04 98.731 0.000 1.03e+06 1.07e+06\n", + "==============================================================================\n", + "Omnibus: 69215.532 Durbin-Watson: 1.987\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 560100534.227\n", + "Skew: 14.776 Prob(JB): 0.00\n", + "Kurtosis: 615.993 Cond. No. 12.4\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ], + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: price R-squared: 0.261
Model: OLS Adj. R-squared: 0.261
Method: Least Squares F-statistic: 6287.
Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00
Time: 02:42:40 Log-Likelihood: -5.7632e+05
No. Observations: 35691 AIC: 1.153e+06
Df Residuals: 35688 BIC: 1.153e+06
Df Model: 2
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
const -4.845e+05 2.79e+04 -17.338 0.000 -5.39e+05 -4.3e+05
bed -2.634e+05 8117.430 -32.454 0.000 -2.79e+05 -2.48e+05
bath 1.048e+06 1.06e+04 98.731 0.000 1.03e+06 1.07e+06
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 69215.532 Durbin-Watson: 1.987
Prob(Omnibus): 0.000 Jarque-Bera (JB): 560100534.227
Skew: 14.776 Prob(JB): 0.00
Kurtosis: 615.993 Cond. No. 12.4


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} & price & \\textbf{ R-squared: } & 0.261 \\\\\n\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.261 \\\\\n\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 6287. \\\\\n\\textbf{Date:} & Fri, 10 Nov 2023 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n\\textbf{Time:} & 02:42:40 & \\textbf{ Log-Likelihood: } & -5.7632e+05 \\\\\n\\textbf{No. Observations:} & 35691 & \\textbf{ AIC: } & 1.153e+06 \\\\\n\\textbf{Df Residuals:} & 35688 & \\textbf{ BIC: } & 1.153e+06 \\\\\n\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n\\midrule\n\\textbf{const} & -4.845e+05 & 2.79e+04 & -17.338 & 0.000 & -5.39e+05 & -4.3e+05 \\\\\n\\textbf{bed} & -2.634e+05 & 8117.430 & -32.454 & 0.000 & -2.79e+05 & -2.48e+05 \\\\\n\\textbf{bath} & 1.048e+06 & 1.06e+04 & 98.731 & 0.000 & 1.03e+06 & 1.07e+06 \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:} & 69215.532 & \\textbf{ Durbin-Watson: } & 1.987 \\\\\n\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 560100534.227 \\\\\n\\textbf{Skew:} & 14.776 & \\textbf{ Prob(JB): } & 0.00 \\\\\n\\textbf{Kurtosis:} & 615.993 & \\textbf{ Cond. No. } & 12.4 \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + }, + "metadata": {}, + "execution_count": 65 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Based on our coefficients (located under 'coef' in our summary above), we have this equation as our model:\n", + "\n", + "$$House Price = −4.845×10^\n", + "5\n", + " −2.634×10^5\n", + " ×bed+1.048×10^\n", + "6\n", + " ×bath$$\n" + ], + "metadata": { + "id": "R1X0zAV61fj1" + } + }, + { + "cell_type": "code", + "source": [ + "# We can now use our model on our test set\n", + "predictions = model.predict(X_test)" + ], + "metadata": { + "id": "SFJv2mYi1dKp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# We can also visualize our predictions as follows:\n", + "plt.scatter(X_test['bed'], y_test, label=\"Actual\")\n", + "plt.scatter(X_test['bed'], predictions, label=\"Predicted\")\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "5KRvIR2k1zUh", + "outputId": "a49f5592-3503-4256-bf57-2d660df56e4e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test['bath'], y_test, label=\"Actual\")\n", + "plt.scatter(X_test['bath'], predictions, label=\"Predicted\")\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "mYSQOrOD2RdD", + "outputId": "9c2f6dd8-247a-4c9d-dac6-52c18b0ed6dd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note that we are visualizing 'bath' and 'bed' separetly, this is done to give us a sense of performance. The model was trained on both bed and bath variables, but we may isolate one variable to visualize it. Alternatively, we may also get a 3D plot of our model:" + ], + "metadata": { + "id": "rsUfNxACGrxj" + } + }, + { + "cell_type": "code", + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "# Scatter plot of the data points\n", + "ax.scatter(X_test['bed'], X_test['bath'], y_test, c='b', marker='o', label='Actual Data')\n", + "\n", + "# Create a mesh grid for the predicted values\n", + "x1_range = np.linspace(X_test['bed'].min(), X_test['bath'].max(), 100)\n", + "x2_range = np.linspace(X_test['bed'].min(), X_test['bath'].max(), 100)\n", + "X1_grid, X2_grid = np.meshgrid(x1_range, x2_range)\n", + "y_pred_grid = model.predict(sm.add_constant(np.column_stack((X1_grid.ravel(), X2_grid.ravel()))))\n", + "y_pred_grid = np.array(y_pred_grid).reshape(X1_grid.shape)\n", + "\n", + "# Plot the regression surface\n", + "ax.plot_surface(X1_grid, X2_grid, y_pred_grid, color='r', alpha=0.5, label='Regression Surface')\n", + "ax.set_xlabel('Number of bedrooms')\n", + "ax.set_ylabel('Number of bathrooms')\n", + "ax.set_zlabel('Price')\n", + "plt.title('Linear Regression - 3D')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "t_YUWpdk2hp0", + "outputId": "c28739af-c4a6-45ae-d2f5-649ccb480db7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Measuring performance\n", + "\n", + "The most common way to measure the perfoamnce of a LR model is by using R^2, which is defined as follows:\n", + "\n", + "$$ R^2 = 1 - \\frac{\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n} (y_i - \\bar{y})^2} $$\n", + "\n", + "The R^2 value typically ranges from 0 to 1. A value of 0 means that the model is just as good as using the mean of the data as its prediction. A value of 1 means that the model predicts the exact values of the data. Hence, the higher R^2, the better the model is. \n", + "\n", + "We can convert this formula into code with just a few lines:" + ], + "metadata": { + "id": "0NNXD0TfHUIL" + } + }, + { + "cell_type": "code", + "source": [ + "def get_r2(y_actual, y_pred):\n", + "\n", + " n = len(y_actual)\n", + " y_mean = sum(y_actual) / n\n", + "\n", + " ssr = 0 # (Sum of Squared Residuals) Top of the fraction\n", + " sst = 0 # (Sum of Squared Total) Bottom of the fraction\n", + " for y_actual_i, y_pred_i in zip(y_actual, y_pred):\n", + " ssr += (y_actual_i - y_pred_i) ** 2\n", + " sst += (y_actual_i - y_mean) ** 2\n", + "\n", + " return 1 - (ssr / sst)" + ], + "metadata": { + "id": "ern-k_KE5Vju" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# We may now get the R^2 of our model base on our test data\n", + "r2 = get_r2(y_test, predictions)\n", + "print(\"R^2 of our model is:\", r2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_s9UYPvH7S3r", + "outputId": "75840826-3104-44e5-8de1-567dd7ba80bd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "R^2 of our model is: 0.28918126581445036\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# We can also use our model to predict how much a house with 3 bedrooms and 2 bathrooms would cost:\n", + "final_answer = model.predict([1, 3, 2])\n", + "print(\"A house in NY with 3bd and 2bath woud cost: $\", round(final_answer[0],3), \"according to our model\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nfueZA1g7tD-", + "outputId": "1271b39e-06b6-4f3d-fc4c-c3bb5bd27f2f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A house in NY with 3bd and 2bath woud cost: $ 820312.099 according to our model\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Our R^2 value seems relatively low, but we can improve it by adding more independent variables, as follows:" + ], + "metadata": { + "id": "vpoc3Vn2KVZI" + } + }, + { + "cell_type": "code", + "source": [ + "X_new = sm.add_constant(ny_data[['bed', 'bath', 'house_size']])\n", + "y = ny_data['price']" + ], + "metadata": { + "id": "y9o67SMT8M2p" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=42)" + ], + "metadata": { + "id": "y3vY241e8M2q" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model_new = sm.OLS(y_train, X_train).fit()\n", + "model_new.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 525 + }, + "outputId": "1ff268c7-a733-48df-e46d-97b37d93ab02", + "id": "Kb2SMhZL8M2q" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: price R-squared: 0.317\n", + "Model: OLS Adj. R-squared: 0.317\n", + "Method: Least Squares F-statistic: 5513.\n", + "Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00\n", + "Time: 02:58:27 Log-Likelihood: -5.7491e+05\n", + "No. Observations: 35691 AIC: 1.150e+06\n", + "Df Residuals: 35687 BIC: 1.150e+06\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const -4.794e+05 2.69e+04 -17.843 0.000 -5.32e+05 -4.27e+05\n", + "bed -3.129e+05 7856.708 -39.830 0.000 -3.28e+05 -2.98e+05\n", + "bath 7.237e+05 1.18e+04 61.203 0.000 7.01e+05 7.47e+05\n", + "house_size 500.4400 9.243 54.144 0.000 482.324 518.556\n", + "==============================================================================\n", + "Omnibus: 69530.113 Durbin-Watson: 1.989\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 692959703.729\n", + "Skew: 14.842 Prob(JB): 0.00\n", + "Kurtosis: 684.976 Cond. No. 6.40e+03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 6.4e+03. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\"\"\"" + ], + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: price R-squared: 0.317
Model: OLS Adj. R-squared: 0.317
Method: Least Squares F-statistic: 5513.
Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00
Time: 02:58:27 Log-Likelihood: -5.7491e+05
No. Observations: 35691 AIC: 1.150e+06
Df Residuals: 35687 BIC: 1.150e+06
Df Model: 3
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
const -4.794e+05 2.69e+04 -17.843 0.000 -5.32e+05 -4.27e+05
bed -3.129e+05 7856.708 -39.830 0.000 -3.28e+05 -2.98e+05
bath 7.237e+05 1.18e+04 61.203 0.000 7.01e+05 7.47e+05
house_size 500.4400 9.243 54.144 0.000 482.324 518.556
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Omnibus: 69530.113 Durbin-Watson: 1.989
Prob(Omnibus): 0.000 Jarque-Bera (JB): 692959703.729
Skew: 14.842 Prob(JB): 0.00
Kurtosis: 684.976 Cond. No. 6.40e+03


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.4e+03. This might indicate that there are
strong multicollinearity or other numerical problems." + ], + "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} & price & \\textbf{ R-squared: } & 0.317 \\\\\n\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.317 \\\\\n\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 5513. \\\\\n\\textbf{Date:} & Fri, 10 Nov 2023 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n\\textbf{Time:} & 02:58:27 & \\textbf{ Log-Likelihood: } & -5.7491e+05 \\\\\n\\textbf{No. Observations:} & 35691 & \\textbf{ AIC: } & 1.150e+06 \\\\\n\\textbf{Df Residuals:} & 35687 & \\textbf{ BIC: } & 1.150e+06 \\\\\n\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n\\midrule\n\\textbf{const} & -4.794e+05 & 2.69e+04 & -17.843 & 0.000 & -5.32e+05 & -4.27e+05 \\\\\n\\textbf{bed} & -3.129e+05 & 7856.708 & -39.830 & 0.000 & -3.28e+05 & -2.98e+05 \\\\\n\\textbf{bath} & 7.237e+05 & 1.18e+04 & 61.203 & 0.000 & 7.01e+05 & 7.47e+05 \\\\\n\\textbf{house\\_size} & 500.4400 & 9.243 & 54.144 & 0.000 & 482.324 & 518.556 \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:} & 69530.113 & \\textbf{ Durbin-Watson: } & 1.989 \\\\\n\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 692959703.729 \\\\\n\\textbf{Skew:} & 14.842 & \\textbf{ Prob(JB): } & 0.00 \\\\\n\\textbf{Kurtosis:} & 684.976 & \\textbf{ Cond. No. } & 6.40e+03 \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n [2] The condition number is large, 6.4e+03. This might indicate that there are \\newline\n strong multicollinearity or other numerical problems." + }, + "metadata": {}, + "execution_count": 85 + } + ] + }, + { + "cell_type": "code", + "source": [ + "predictions = model_new.predict(X_test)\n", + "r2 = get_r2(y_test, predictions)\n", + "r2" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QJdoG3SO8gLu", + "outputId": "e0f8b3da-3237-4c5c-de7e-d307578e7604" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.3558532984754764" + ] + }, + "metadata": {}, + "execution_count": 87 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Bonus question:\n", + "\n", + "Can you improve the model even further?" + ], + "metadata": { + "id": "fj7r5O8NLnlL" + } + } + ] +} \ No newline at end of file From 97833241efacc8ef44d2d6a7b536091b85908ab5 Mon Sep 17 00:00:00 2001 From: Gabriel Dutra <57206480+Dutra-Apex@users.noreply.github.com> Date: Thu, 9 Nov 2023 22:03:42 -0500 Subject: [PATCH 4/6] Delete Code/DS_Session_11_09_23.ipynb --- Code/DS_Session_11_09_23.ipynb | 4604 -------------------------------- 1 file changed, 4604 deletions(-) delete mode 100644 Code/DS_Session_11_09_23.ipynb diff --git a/Code/DS_Session_11_09_23.ipynb b/Code/DS_Session_11_09_23.ipynb deleted file mode 100644 index 343bf57..0000000 --- a/Code/DS_Session_11_09_23.ipynb +++ /dev/null @@ -1,4604 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Setup" - ], - "metadata": { - "id": "ROzgioymSolq" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eRlUMwij4PWg" - }, - "outputs": [], - "source": [ - "# Importing all the necessary libraries\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "source": [ - "# Mounting drive to colab, for detailed instructions, see: https://colab.research.google.com/notebooks/snippets/drive.ipynb\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "id": "nBWEQMlun-Jl", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c0e820fc-2d7f-47cf-aac9-0cdf66086bc9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Loads the data based on where it is stored on your personal drive\n", - "# https://www.kaggle.com/datasets/ahmedshahriarsakib/usa-real-estate-dataset/data\n", - "ds_path = \"/content/drive/My Drive/JoC_DS_Sessions/Datasets/\"\n", - "ds_original = pd.read_csv(ds_path+'us_real_estate_data.csv')" - ], - "metadata": { - "id": "zxSpyKAB73OL" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Take a look at the 1st 10 rows in the data\n", - "ds_original.head(10)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "G0l-L_zQ9Thh", - "outputId": "5c783152-b05b-4473-a1ba-5fee8b657ec7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " status bed bath acre_lot city state zip_code \\\n", - "0 for_sale 3.0 2.0 0.12 Adjuntas Puerto Rico 601.0 \n", - "1 for_sale 4.0 2.0 0.08 Adjuntas Puerto Rico 601.0 \n", - "2 for_sale 2.0 1.0 0.15 Juana Diaz Puerto Rico 795.0 \n", - "3 for_sale 4.0 2.0 0.10 Ponce Puerto Rico 731.0 \n", - "4 for_sale 6.0 2.0 0.05 Mayaguez Puerto Rico 680.0 \n", - "5 for_sale 4.0 3.0 0.46 San Sebastian Puerto Rico 612.0 \n", - "6 for_sale 3.0 1.0 0.20 Ciales Puerto Rico 639.0 \n", - "7 for_sale 3.0 2.0 0.08 Ponce Puerto Rico 731.0 \n", - "8 for_sale 2.0 1.0 0.09 Ponce Puerto Rico 730.0 \n", - "9 for_sale 5.0 3.0 7.46 Las Marias Puerto Rico 670.0 \n", - "\n", - " house_size prev_sold_date price \n", - "0 920.0 NaN 105000.0 \n", - "1 1527.0 NaN 80000.0 \n", - "2 748.0 NaN 67000.0 \n", - "3 1800.0 NaN 145000.0 \n", - "4 NaN NaN 65000.0 \n", - "5 2520.0 NaN 179000.0 \n", - "6 2040.0 NaN 50000.0 \n", - "7 1050.0 NaN 71600.0 \n", - "8 1092.0 NaN 100000.0 \n", - "9 5403.0 NaN 300000.0 " - ], - "text/html": [ - "\n", - "
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1for_sale4.02.00.08AdjuntasPuerto Rico601.01527.0NaN80000.0
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\n" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Data Cleaning\n", - "\n", - "Our goal is to clean the invalid values in the dataset\n", - "While there are many reliable methods of dealing with missing values, they do require more advanced techniques. For now, we will only remove the rows containing missing values." - ], - "metadata": { - "id": "KCw0WmZjS7OG" - } - }, - { - "cell_type": "code", - "source": [ - "# Make a copy of the original dataset\n", - "# All cleaning with be done in ds_cleaned only\n", - "ds_cleaned = ds_original.copy()" - ], - "metadata": { - "id": "ApUitLrlX2Vn" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Checking how many NaN values are there on each column\n", - "print(\"Column vs Percent of Values that are NaN \\n\")\n", - "for column in ds_original.columns:\n", - " nan_count = ds_original[column].isna().sum()\n", - " print(f\"{column}: {round(nan_count/len(ds_original)*100, 2)}%\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MscsGlycTQ39", - "outputId": "31acc331-9568-4ba1-b6fc-a481330fac6c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Column vs Percent of Values that are NaN \n", - "\n", - "status: 0.0%\n", - "bed: 14.35%\n", - "bath: 12.58%\n", - "acre_lot: 29.46%\n", - "city: 0.01%\n", - "state: 0.0%\n", - "zip_code: 0.02%\n", - "house_size: 32.36%\n", - "prev_sold_date: 50.73%\n", - "price: 0.01%\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Let's stick with the naive approach of dealing with missing values - simply remove them\n", - "# We can do this since we have a lot of data available\n", - "print(\"Total number of rows in the dataset:\", len(ds_original))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UHqFo3bwVu13", - "outputId": "f75bf3d1-7fb3-406b-f336-f517546eefbe" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Total number of rows in the dataset: 904966\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Delete the \"prev_sold_date\" as it has a lot of NaN values\n", - "del ds_cleaned['prev_sold_date']" - ], - "metadata": { - "id": "InOt5hftWTX1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Now, drop all rows where NaN values are present\n", - "ds_cleaned = ds_cleaned.dropna()" - ], - "metadata": { - "id": "EffNG8BOXPpU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Check for missing values again\n", - "print(\"Column vs Percent of Values that are NaN \\n\")\n", - "for column in ds_cleaned.columns:\n", - " nan_count = ds_cleaned[column].isna().sum()\n", - " print(f\"{column}: {round(nan_count/len(ds_cleaned)*100, 2)}%\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Fdu9LyZ6XZP8", - "outputId": "af5ac3b6-6032-4876-f16e-bf53afa705d4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Column vs Percent of Values that are NaN \n", - "\n", - "status: 0.0%\n", - "bed: 0.0%\n", - "bath: 0.0%\n", - "acre_lot: 0.0%\n", - "city: 0.0%\n", - "state: 0.0%\n", - "zip_code: 0.0%\n", - "house_size: 0.0%\n", - "price: 0.0%\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Now ds_cleaned is the fully cleaned data\n", - "ds_cleaned.head(5)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "uhlJjBoZYGwI", - "outputId": "a31de14d-e432-458d-d6f8-83583ccac068" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " status bed bath acre_lot city state zip_code \\\n", - "0 for_sale 3.0 2.0 0.12 Adjuntas Puerto Rico 601.0 \n", - "1 for_sale 4.0 2.0 0.08 Adjuntas Puerto Rico 601.0 \n", - "2 for_sale 2.0 1.0 0.15 Juana Diaz Puerto Rico 795.0 \n", - "3 for_sale 4.0 2.0 0.10 Ponce Puerto Rico 731.0 \n", - "5 for_sale 4.0 3.0 0.46 San Sebastian Puerto Rico 612.0 \n", - "\n", - " house_size price \n", - "0 920.0 105000.0 \n", - "1 1527.0 80000.0 \n", - "2 748.0 67000.0 \n", - "3 1800.0 145000.0 \n", - "5 2520.0 179000.0 " - ], - "text/html": [ - "\n", - "
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
0for_sale3.02.00.12AdjuntasPuerto Rico601.0920.0105000.0
1for_sale4.02.00.08AdjuntasPuerto Rico601.01527.080000.0
2for_sale2.01.00.15Juana DiazPuerto Rico795.0748.067000.0
3for_sale4.02.00.10PoncePuerto Rico731.01800.0145000.0
5for_sale4.03.00.46San SebastianPuerto Rico612.02520.0179000.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
30126for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
45320for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
385082for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
388797for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
389915for_sale3.03.00.07BurlingtonNew Jersey8016.01500.0333490.0
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\n" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Now, we can easily get the average prices\n", - "print(\"Average house price in NJ: $\", round(nj_data['price'].mean(), 2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "voBsIZb2Z4Py", - "outputId": "e2c94a3f-ba1e-4112-def1-412d9b4d7c33" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Average house price in NJ: $ 575461.92\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "However, in this case, is the mean the best way to answer the question? Does it really represent the typical price of a house?\n", - "\n", - "To answer that, let us look at the distribution of our data. To visualize a distribution, we can plot a histogram of our values" - ], - "metadata": { - "id": "5B81W_Nw-nD6" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Plot example: Histogram" - ], - "metadata": { - "id": "zYCppEh0-6jx" - } - }, - { - "cell_type": "code", - "source": [ - "values = [1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5]\n", - "plt.hist(values)\n", - "plt.xlabel(\"Value\")\n", - "plt.ylabel(\"Quantity\")\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 466 - }, - "id": "kXEyaHNQUoCv", - "outputId": "b160f35a-a836-4d4f-a38c-83684a3f7537" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'Quantity')" - ] - }, - "metadata": {}, - "execution_count": 12 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "A histogram allows us to see how many items of a given value there are. In the example above, we initialize a sample array 'values' and plot its histogram. On the plot, we can see that there are 3 items with value of 1, 4 items with value of 2, and so on.\n", - "\n", - "The histogram above has 5 bars (also called bins). Each bar groups together values that are similar, or identical in value.\n", - "For a more in-depth overview of histograms, check: https://chartio.com/learn/charts/histogram-complete-guide/\n" - ], - "metadata": { - "id": "h2gIaCXe_EHq" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Continuing the question\n", - "\n", - "Now, we may plot the histogram of the price data" - ], - "metadata": { - "id": "FNXe-9g8AJc-" - } - }, - { - "cell_type": "code", - "source": [ - "# Plot a histogram of the NJ house price data\n", - "plt.title(\"Histogram of House prices in NJ\")\n", - "plt.hist(nj_data['price'], bins=100)\n", - "plt.xlabel(\"Price\")\n", - "plt.ylabel(\"Number of houses\")\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "ZDsXoykTbSU2", - "outputId": "c6cba0e5-52a5-4aa8-bab3-23a08efb5677" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As you can see, while a large amount of data lies within the 0 to 5 million range (expressed in the x axis as the (0.0, 0.5) interval). However, we do have houses with much higher prices, going up to 25 million. Those houses are called *outliers*. They are extreme points that do not represent the overrall data.\n", - "\n", - "When we calculate the mean, we add all the price values in our nj_data (including the outliers) and divide by how many houses we have. Because we include the outliers in this calculation, the value of the mean becomes *skewed*. Hence, the mean of the data does not represent the typical house price.\n", - "\n", - "Here's an example that illustrates this:" - ], - "metadata": { - "id": "hg_tTCqWAa-L" - } - }, - { - "cell_type": "code", - "source": [ - "sample_values = np.array([1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 180])\n", - "print(\"Mean of values: \", round(sample_values.mean(), 2))\n", - "print(\"Median of prices: \", np.median(sample_values))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOa7yi7peG19", - "outputId": "2024ab90-348c-4dee-90db-a6e31cde714b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mean of values: 16.62\n", - "Median of prices: 3.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "The mean is **much** higher than all the values of the data, except for one. This clearely is not an accurate representation of the typical value.\n", - "When the data has many outliers, it is common practice to use the *median* value of the data to represent its typical value. In our example, the median is 3, which is a much more accurate representation of a typical value." - ], - "metadata": { - "id": "Z-KVuw-VC2JQ" - } - }, - { - "cell_type": "markdown", - "source": [ - "We can get the median of our nj_data as follows:" - ], - "metadata": { - "id": "6QBgxrh9CcDj" - } - }, - { - "cell_type": "code", - "source": [ - "# Now, we can easily get the average prices\n", - "print(\"Median house price in NJ: $\", round(nj_data['price'].median(), 2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "My29VRoffbq0", - "outputId": "087bdda7-5eec-4d50-ab88-424734e4623d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Median house price in NJ: $ 407500.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Bonus question: Improving our nj_data price histogram\n", - "\n", - "We can improve our histogram by limiting the range of our data, as well as adding the median price to the graph:" - ], - "metadata": { - "id": "9wv3kadJDgaG" - } - }, - { - "cell_type": "code", - "source": [ - "# Determines the bin edges to be between 0 and 2 million, with one bin every 100,000 value\n", - "bin_edges = [i for i in range(0, 2*10**6, 10**5)]\n", - "\n", - "plt.title(\"Histogram of House prices in NJ\")\n", - "plt.hist(nj_data['price'], bins=bin_edges)\n", - "plt.xlabel(\"Price\")\n", - "plt.ylabel(\"Number of houses\")\n", - "\n", - "# Plots a dashed line at the median value, with color red.\n", - "plt.axvline(x=nj_data['price'].median(), color='r', label=\"Median Price\", linestyle='--')\n", - "plt.legend()\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "yHormgoH0M_g", - "outputId": "482517f9-fe77-46c2-f09b-b09f3df36c76" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Bonus question: What is the probability of finding a house in NJ that costs more than a million dollars?\n" - ], - "metadata": { - "id": "6UQjBvGBEOAi" - } - }, - { - "cell_type": "code", - "source": [ - "# Probability = Number of desired outcomes/Number of total possibilities\n", - "# Our total is the total number of houses in NJ\n", - "# Our desired is the number of house over 1 million dollars cost\n", - "\n", - "n_total = len(nj_data)\n", - "n_desired = len(nj_data[nj_data['price'] >= 10**6])\n", - "print(\"Probability is:\", round(n_desired/n_total, 5) * 100, \"%\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Xjd7EthrfKN4", - "outputId": "dc07424a-5b9a-4623-aa04-c37eae1967f8" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Probability is: 9.037 %\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Q2) What are the states/territories with the highest mean and median house prices?" - ], - "metadata": { - "id": "GFN4jSE3geIz" - } - }, - { - "cell_type": "code", - "source": [ - "# First we need to get a list of all states in our dataset\n", - "list_states = ds_cleaned['state'].unique()\n", - "print(list_states)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "imCCplrRgk7D", - "outputId": "d9ac67e9-645e-48a8-df24-d592d965926d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['Puerto Rico' 'Virgin Islands' 'Massachusetts' 'Connecticut' 'New Jersey'\n", - " 'New York' 'New Hampshire' 'Vermont' 'Rhode Island' 'Wyoming' 'Maine'\n", - " 'Pennsylvania' 'West Virginia' 'Delaware']\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Then we can organize the median and mean house prices in each state as follows:\n", - "dict_means = {}\n", - "dict_medians = {}\n", - "\n", - "for state in list_states:\n", - " dict_means[state] = np.mean(ds_cleaned[ds_cleaned['state']==state]['price'])\n", - " dict_medians[state] = np.median(ds_cleaned[ds_cleaned['state']==state]['price'])" - ], - "metadata": { - "id": "GNk7giLThAe1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Finally, we sort the information to be able to visualize the states with highest mean/median values:\n", - "# For more information on sorting dictionaries, visit: https://www.geeksforgeeks.org/python-sort-a-dictionary/#\n", - "sorted_means = dict(sorted(dict_means.items(), key=lambda item: item[1], reverse = True))\n", - "sorted_medians = dict(sorted(dict_medians.items(), key=lambda item: item[1], reverse = True))" - ], - "metadata": { - "id": "rFgu-WdFh8xF" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "sorted_medians" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1_Itw29Di-rx", - "outputId": "1efb20a2-cbb8-4f79-8b2c-0cd48858e901" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'Virgin Islands': 1295000.0,\n", - " 'New York': 895000.0,\n", - " 'Massachusetts': 649900.0,\n", - " 'Wyoming': 535000.0,\n", - " 'New Hampshire': 450000.0,\n", - " 'New Jersey': 407500.0,\n", - " 'Rhode Island': 389900.0,\n", - " 'Vermont': 375000.0,\n", - " 'Maine': 344900.0,\n", - " 'Connecticut': 319900.0,\n", - " 'Delaware': 295000.0,\n", - " 'Pennsylvania': 279900.0,\n", - " 'Puerto Rico': 145000.0,\n", - " 'West Virginia': 62500.0}" - ] - }, - "metadata": {}, - "execution_count": 39 - } - ] - }, - { - "cell_type": "code", - "source": [ - "sorted_means" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ODnPNnRijbDJ", - "outputId": "548046e7-116d-4232-f7f0-15888aaecf1a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'Virgin Islands': 1953334.3070175438,\n", - " 'New York': 1532066.8280808714,\n", - " 'Massachusetts': 1056067.5250072274,\n", - " 'New Hampshire': 642339.2626155231,\n", - " 'New Jersey': 575461.915253776,\n", - " 'Vermont': 572109.4284170232,\n", - " 'Wyoming': 535000.0,\n", - " 'Maine': 529820.2938722295,\n", - " 'Rhode Island': 526744.7998375305,\n", - " 'Connecticut': 503652.60612973914,\n", - " 'Pennsylvania': 393789.6828546969,\n", - " 'Puerto Rico': 391127.3780376868,\n", - " 'Delaware': 341894.14762741653,\n", - " 'West Virginia': 62500.0}" - ] - }, - "metadata": {}, - "execution_count": 40 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# We can also visualize our values through a bar plot:\n", - "plt.title(\"Mean price of houses\")\n", - "plt.bar(range(len(list_states)), sorted_means.values(), tick_label=list(sorted_means.keys()))\n", - "plt.xticks(rotation=90)\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 548 - }, - "id": "17bI3t6ZjgRm", - "outputId": "9ec80ba8-45c3-4994-c158-be137a9d4036" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.title(\"Median price of houses\")\n", - "plt.bar(range(len(list_states)), sorted_medians.values(), tick_label=list(sorted_medians.keys()))\n", - "plt.xticks(rotation=90)\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 548 - }, - "id": "r0j5osy3j4-c", - "outputId": "bcd9080f-64e7-4c50-8e3f-8242346b9010" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Bonus question: In what states is the median equal or higher to the mean?\n", - "\n", - "We saw in our nj_data histogram that the distribution of house prices was **right skewed**, which causes the mean of the data to be higher than the median. We also discussed that this behavior is expected when we are dealing with price data. Are there any States where this doesn't happen? Can we identify the cause for this unexpected behavior?" - ], - "metadata": { - "id": "mM77DUoJWOJJ" - } - }, - { - "cell_type": "code", - "source": [ - "# We can identify those states with a simple loop:\n", - "for state in list_states:\n", - " if dict_medians[state] >= dict_means[state]:\n", - " print(state)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jNZ6xRGalK7x", - "outputId": "9a65beb7-68fe-4467-8c01-40442dd59d0e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Wyoming\n", - "West Virginia\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Now we can isolate the data for those states:\n", - "wyoming_data = ds_cleaned[ds_cleaned['state'] == 'Wyoming']\n", - "west_virginia_data = ds_cleaned[ds_cleaned['state'] == 'West Virginia']" - ], - "metadata": { - "id": "dSJCE9z8lcsp" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# When taking a peek at the data we can already identify the cause of this behavior\n", - "wyoming_data.head(5)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 143 - }, - "id": "GuYJrhCVlrRt", - "outputId": "1f9f1d05-6bbe-4cfe-ea19-1cb574379db4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " status bed bath acre_lot city state zip_code house_size \\\n", - "214489 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", - "234939 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", - "237632 for_sale 3.0 3.0 0.29 Cody Wyoming 82414.0 1935.0 \n", - "\n", - " price \n", - "214489 535000.0 \n", - "234939 535000.0 \n", - "237632 535000.0 " - ], - "text/html": [ - "\n", - "
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\n" - ] - }, - "metadata": {}, - "execution_count": 52 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "After looking at the first entry of the data, we can see why those states have a median price equal to their mean price, as each State has only three entries, and those entries are duplicated." - ], - "metadata": { - "id": "0MzIj8l6n_K_" - } - }, - { - "cell_type": "markdown", - "source": [ - "# 3) Based on the New York data, how much would a house with 3bd and 2bath cost? What about a house with 15 bd and 1bath?" - ], - "metadata": { - "id": "0OgG9gEbo5Sb" - } - }, - { - "cell_type": "code", - "source": [ - "# We start by isolating the New York data\n", - "ny_data = ds_cleaned[ds_cleaned['state'] == \"New York\"]" - ], - "metadata": { - "id": "aVVA9L9zpS0s" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "ny_data.head(5)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "mNqvzgzspaxy", - "outputId": "2c337e2c-bfb3-4718-f16a-5874062815ab" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " status bed bath acre_lot city state zip_code \\\n", - "30149 for_sale 3.0 1.0 60.00 Berlin New York 12022.0 \n", - "54248 for_sale 3.0 2.0 2.02 Claverack New York 12521.0 \n", - "54258 for_sale 4.0 2.0 0.24 Copake New York 12521.0 \n", - "54259 for_sale 3.0 3.0 1.90 Copake New York 12516.0 \n", - "54262 for_sale 3.0 2.0 2.00 Copake New York 12517.0 \n", - "\n", - " house_size price \n", - "30149 1176.0 175000.0 \n", - "54248 1600.0 425000.0 \n", - "54258 1239.0 225000.0 \n", - "54259 1800.0 419000.0 \n", - "54262 1482.0 365000.0 " - ], - "text/html": [ - "\n", - "
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
30149for_sale3.01.060.00BerlinNew York12022.01176.0175000.0
54248for_sale3.02.02.02ClaverackNew York12521.01600.0425000.0
54258for_sale4.02.00.24CopakeNew York12521.01239.0225000.0
54259for_sale3.03.01.90CopakeNew York12516.01800.0419000.0
54262for_sale3.02.02.00CopakeNew York12517.01482.0365000.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
54248for_sale3.02.02.02ClaverackNew York12521.01600.0425000.0
54262for_sale3.02.02.00CopakeNew York12517.01482.0365000.0
54268for_sale3.02.02.90HillsdaleNew York12529.01404.0374900.0
54278for_sale3.02.01.20MillertonNew York12546.01350.0375000.0
54446for_sale3.02.010.00AusterlitzNew York12017.01152.0489900.0
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statusbedbathacre_lotcitystatezip_codehouse_sizeprice
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Now we do the same for bathrooms vs price\n", - "plt.scatter(ny_data[ny_data['bath']<30]['bath'], ny_data[ny_data['bath']<30]['price'])\n", - "plt.xlabel(\"Number of bathrooms\")\n", - "plt.ylabel(\"Price\")\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 465 - }, - "id": "PioXLg3zysaQ", - "outputId": "b9e039f4-305f-4e87-b966-853845125494" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "From the data, we can see that we have a **positive relationship** between # of beds, # of baths, and house price. This means that as the number of bedrooms and bathrooms increase, so does the price of the house. However, the relationship is not necessarily strong, as the data contain many outliers (houses with a high number of bath or bed that are not very expensive). We will see how this affects the accuracy of our linear model later on." - ], - "metadata": { - "id": "oK--eXaz-C3G" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Selecting our data\n", - "\n", - "To use linear regression, we need to select our dependent (y) and independent (X) variables. In this this case, because we want to predict price from number of bedrooms and bathrooms, our y variable is price, and our x variable is # beds and # baths." - ], - "metadata": { - "id": "3poCur6gAenV" - } - }, - { - "cell_type": "code", - "source": [ - "X = ny_data[['bed', 'bath']]\n", - "y = ny_data['price']" - ], - "metadata": { - "id": "NkixK1lAzC8K" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "As seen before, LR works by solving the following equation: $$ \\hat{\\beta} = (X^TX)^{-1}X^TY$$\n", - "\n", - "Where:\n", - "\n", - "Beta: Coefficients of the equation\n", - "\n", - "X: Matrix with independent values\n", - "\n", - "Y: Matrix with dependent values\n", - "\n", - "The vector Beta contains the coefficients to our LR model, which can be written as follows:\n", - "\n", - "$$ \\hat{y} = a_1x_1 + a_2x_2 + b$$\n", - "\n", - "In the context of our problem, the model is:\n", - "\n", - "$$ HousePrice = a_1 \\times bed + a_2 \\times bath + b$$\n", - "\n", - "If we want Beta to contain the constant term **b**, we need to add a column of ones into our X data, which can be done as follows:\n", - "\n" - ], - "metadata": { - "id": "WJ526AY7BXLi" - } - }, - { - "cell_type": "code", - "source": [ - "X = sm.add_constant(X)\n", - "X" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423 - }, - "id": "BVu4MwbvBMRH", - "outputId": "3b990fb0-2dc5-4c4a-d888-67df915ec082" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " const bed bath\n", - "30149 1.0 3.0 1.0\n", - "54248 1.0 3.0 2.0\n", - "54258 1.0 4.0 2.0\n", - "54259 1.0 3.0 3.0\n", - "54262 1.0 3.0 2.0\n", - "... ... ... ...\n", - "904934 1.0 4.0 8.0\n", - "904935 1.0 6.0 8.0\n", - "904936 1.0 4.0 5.0\n", - "904937 1.0 4.0 7.0\n", - "904938 1.0 6.0 7.0\n", - "\n", - "[44614 rows x 3 columns]" - ], - "text/html": [ - "\n", - "
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constbedbath
301491.03.01.0
542481.03.02.0
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\n" - ] - }, - "metadata": {}, - "execution_count": 61 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Train/Test split\n", - "\n", - "In almost any model, you need to split your data into training and testing. Most models utilize information from the data to learn. If you want to evaluate the performance of the model, you need to do so in a set of data that the model has not seen before, otherwise your evaluation will be biased!\n", - "\n", - "A typical train/test split is 80/20, meaning that we will use 80% of our data to train the model, and 20% to evaluate it.\n", - "\n", - "Selecting train/test sets should be done at random, and we can do so with the following function:" - ], - "metadata": { - "id": "g7An8GxiEU8c" - } - }, - { - "cell_type": "code", - "source": [ - "from sklearn.model_selection import train_test_split" - ], - "metadata": { - "id": "8pY7Qv_7z3Rc" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# This function splits our (X,y) data into training and testing sets\n", - "# The random_state=42 is used to guarantee replicability, anyone who uses 42 as their state will get the same train/test split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" - ], - "metadata": { - "id": "2w8dphXgz_h5" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Training and evaluating the model\n", - "\n", - "We may now perform Linear Regression in our data" - ], - "metadata": { - "id": "K0O6RSBPFpdu" - } - }, - { - "cell_type": "code", - "source": [ - "model = sm.OLS(y_train, X_train).fit()\n", - "model.summary()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 469 - }, - "id": "9auIHcs21DRM", - "outputId": "f0ca7cd1-a5bc-4656-e99c-16f5c851a355" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: price R-squared: 0.261\n", - "Model: OLS Adj. R-squared: 0.261\n", - "Method: Least Squares F-statistic: 6287.\n", - "Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00\n", - "Time: 02:42:40 Log-Likelihood: -5.7632e+05\n", - "No. Observations: 35691 AIC: 1.153e+06\n", - "Df Residuals: 35688 BIC: 1.153e+06\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "const -4.845e+05 2.79e+04 -17.338 0.000 -5.39e+05 -4.3e+05\n", - "bed -2.634e+05 8117.430 -32.454 0.000 -2.79e+05 -2.48e+05\n", - "bath 1.048e+06 1.06e+04 98.731 0.000 1.03e+06 1.07e+06\n", - "==============================================================================\n", - "Omnibus: 69215.532 Durbin-Watson: 1.987\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 560100534.227\n", - "Skew: 14.776 Prob(JB): 0.00\n", - "Kurtosis: 615.993 Cond. No. 12.4\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: price R-squared: 0.261
Model: OLS Adj. R-squared: 0.261
Method: Least Squares F-statistic: 6287.
Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00
Time: 02:42:40 Log-Likelihood: -5.7632e+05
No. Observations: 35691 AIC: 1.153e+06
Df Residuals: 35688 BIC: 1.153e+06
Df Model: 2
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
const -4.845e+05 2.79e+04 -17.338 0.000 -5.39e+05 -4.3e+05
bed -2.634e+05 8117.430 -32.454 0.000 -2.79e+05 -2.48e+05
bath 1.048e+06 1.06e+04 98.731 0.000 1.03e+06 1.07e+06
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 69215.532 Durbin-Watson: 1.987
Prob(Omnibus): 0.000 Jarque-Bera (JB): 560100534.227
Skew: 14.776 Prob(JB): 0.00
Kurtosis: 615.993 Cond. No. 12.4


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} & price & \\textbf{ R-squared: } & 0.261 \\\\\n\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.261 \\\\\n\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 6287. \\\\\n\\textbf{Date:} & Fri, 10 Nov 2023 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n\\textbf{Time:} & 02:42:40 & \\textbf{ Log-Likelihood: } & -5.7632e+05 \\\\\n\\textbf{No. Observations:} & 35691 & \\textbf{ AIC: } & 1.153e+06 \\\\\n\\textbf{Df Residuals:} & 35688 & \\textbf{ BIC: } & 1.153e+06 \\\\\n\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n\\midrule\n\\textbf{const} & -4.845e+05 & 2.79e+04 & -17.338 & 0.000 & -5.39e+05 & -4.3e+05 \\\\\n\\textbf{bed} & -2.634e+05 & 8117.430 & -32.454 & 0.000 & -2.79e+05 & -2.48e+05 \\\\\n\\textbf{bath} & 1.048e+06 & 1.06e+04 & 98.731 & 0.000 & 1.03e+06 & 1.07e+06 \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:} & 69215.532 & \\textbf{ Durbin-Watson: } & 1.987 \\\\\n\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 560100534.227 \\\\\n\\textbf{Skew:} & 14.776 & \\textbf{ Prob(JB): } & 0.00 \\\\\n\\textbf{Kurtosis:} & 615.993 & \\textbf{ Cond. No. } & 12.4 \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - }, - "metadata": {}, - "execution_count": 65 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Based on our coefficients (located under 'coef' in our summary above), we have this equation as our model:\n", - "\n", - "$$House Price = −4.845×10^\n", - "5\n", - " −2.634×10^5\n", - " ×bed+1.048×10^\n", - "6\n", - " ×bath$$\n" - ], - "metadata": { - "id": "R1X0zAV61fj1" - } - }, - { - "cell_type": "code", - "source": [ - "# We can now use our model on our test set\n", - "predictions = model.predict(X_test)" - ], - "metadata": { - "id": "SFJv2mYi1dKp" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# We can also visualize our predictions as follows:\n", - "plt.scatter(X_test['bed'], y_test, label=\"Actual\")\n", - "plt.scatter(X_test['bed'], predictions, label=\"Predicted\")\n", - "plt.legend()\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 445 - }, - "id": "5KRvIR2k1zUh", - "outputId": "a49f5592-3503-4256-bf57-2d660df56e4e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test['bath'], y_test, label=\"Actual\")\n", - "plt.scatter(X_test['bath'], predictions, label=\"Predicted\")\n", - "plt.legend()\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 445 - }, - "id": "mYSQOrOD2RdD", - "outputId": "9c2f6dd8-247a-4c9d-dac6-52c18b0ed6dd" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Note that we are visualizing 'bath' and 'bed' separetly, this is done to give us a sense of performance. The model was trained on both bed and bath variables, but we may isolate one variable to visualize it. Alternatively, we may also get a 3D plot of our model:" - ], - "metadata": { - "id": "rsUfNxACGrxj" - } - }, - { - "cell_type": "code", - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "# Scatter plot of the data points\n", - "ax.scatter(X_test['bed'], X_test['bath'], y_test, c='b', marker='o', label='Actual Data')\n", - "\n", - "# Create a mesh grid for the predicted values\n", - "x1_range = np.linspace(X_test['bed'].min(), X_test['bath'].max(), 100)\n", - "x2_range = np.linspace(X_test['bed'].min(), X_test['bath'].max(), 100)\n", - "X1_grid, X2_grid = np.meshgrid(x1_range, x2_range)\n", - "y_pred_grid = model.predict(sm.add_constant(np.column_stack((X1_grid.ravel(), X2_grid.ravel()))))\n", - "y_pred_grid = np.array(y_pred_grid).reshape(X1_grid.shape)\n", - "\n", - "# Plot the regression surface\n", - "ax.plot_surface(X1_grid, X2_grid, y_pred_grid, color='r', alpha=0.5, label='Regression Surface')\n", - "ax.set_xlabel('Number of bedrooms')\n", - "ax.set_ylabel('Number of bathrooms')\n", - "ax.set_zlabel('Price')\n", - "plt.title('Linear Regression - 3D')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 435 - }, - "id": "t_YUWpdk2hp0", - "outputId": "c28739af-c4a6-45ae-d2f5-649ccb480db7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Measuring performance\n", - "\n", - "The most common way to measure the perfoamnce of a LR model is by using R^2, which is defined as follows:\n", - "\n", - "$$ R^2 = 1 - \\frac{\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n} (y_i - \\bar{y})^2} $$\n", - "\n", - "The R^2 value typically ranges from 0 to 1. A value of 0 means that the model is just as good as using the mean of the data as its prediction. A value of 1 means that the model predicts the exact values of the data. Hence, the higher R^2, the better the model is. \n", - "\n", - "We can convert this formula into code with just a few lines:" - ], - "metadata": { - "id": "0NNXD0TfHUIL" - } - }, - { - "cell_type": "code", - "source": [ - "def get_r2(y_actual, y_pred):\n", - "\n", - " n = len(y_actual)\n", - " y_mean = sum(y_actual) / n\n", - "\n", - " ssr = 0 # (Sum of Squared Residuals) Top of the fraction\n", - " sst = 0 # (Sum of Squared Total) Bottom of the fraction\n", - " for y_actual_i, y_pred_i in zip(y_actual, y_pred):\n", - " ssr += (y_actual_i - y_pred_i) ** 2\n", - " sst += (y_actual_i - y_mean) ** 2\n", - "\n", - " return 1 - (ssr / sst)" - ], - "metadata": { - "id": "ern-k_KE5Vju" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# We may now get the R^2 of our model base on our test data\n", - "r2 = get_r2(y_test, predictions)\n", - "print(\"R^2 of our model is:\", r2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_s9UYPvH7S3r", - "outputId": "75840826-3104-44e5-8de1-567dd7ba80bd" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "R^2 of our model is: 0.28918126581445036\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# We can also use our model to predict how much a house with 3 bedrooms and 2 bathrooms would cost:\n", - "final_answer = model.predict([1, 3, 2])\n", - "print(\"A house in NY with 3bd and 2bath woud cost: $\", round(final_answer[0],3), \"according to our model\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nfueZA1g7tD-", - "outputId": "1271b39e-06b6-4f3d-fc4c-c3bb5bd27f2f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "A house in NY with 3bd and 2bath woud cost: $ 820312.099 according to our model\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Our R^2 value seems relatively low, but we can improve it by adding more independent variables, as follows:" - ], - "metadata": { - "id": "vpoc3Vn2KVZI" - } - }, - { - "cell_type": "code", - "source": [ - "X_new = sm.add_constant(ny_data[['bed', 'bath', 'house_size']])\n", - "y = ny_data['price']" - ], - "metadata": { - "id": "y9o67SMT8M2p" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=42)" - ], - "metadata": { - "id": "y3vY241e8M2q" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model_new = sm.OLS(y_train, X_train).fit()\n", - "model_new.summary()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 525 - }, - "outputId": "1ff268c7-a733-48df-e46d-97b37d93ab02", - "id": "Kb2SMhZL8M2q" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: price R-squared: 0.317\n", - "Model: OLS Adj. R-squared: 0.317\n", - "Method: Least Squares F-statistic: 5513.\n", - "Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00\n", - "Time: 02:58:27 Log-Likelihood: -5.7491e+05\n", - "No. Observations: 35691 AIC: 1.150e+06\n", - "Df Residuals: 35687 BIC: 1.150e+06\n", - "Df Model: 3 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "const -4.794e+05 2.69e+04 -17.843 0.000 -5.32e+05 -4.27e+05\n", - "bed -3.129e+05 7856.708 -39.830 0.000 -3.28e+05 -2.98e+05\n", - "bath 7.237e+05 1.18e+04 61.203 0.000 7.01e+05 7.47e+05\n", - "house_size 500.4400 9.243 54.144 0.000 482.324 518.556\n", - "==============================================================================\n", - "Omnibus: 69530.113 Durbin-Watson: 1.989\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 692959703.729\n", - "Skew: 14.842 Prob(JB): 0.00\n", - "Kurtosis: 684.976 Cond. No. 6.40e+03\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "[2] The condition number is large, 6.4e+03. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: price R-squared: 0.317
Model: OLS Adj. R-squared: 0.317
Method: Least Squares F-statistic: 5513.
Date: Fri, 10 Nov 2023 Prob (F-statistic): 0.00
Time: 02:58:27 Log-Likelihood: -5.7491e+05
No. Observations: 35691 AIC: 1.150e+06
Df Residuals: 35687 BIC: 1.150e+06
Df Model: 3
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
const -4.794e+05 2.69e+04 -17.843 0.000 -5.32e+05 -4.27e+05
bed -3.129e+05 7856.708 -39.830 0.000 -3.28e+05 -2.98e+05
bath 7.237e+05 1.18e+04 61.203 0.000 7.01e+05 7.47e+05
house_size 500.4400 9.243 54.144 0.000 482.324 518.556
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 69530.113 Durbin-Watson: 1.989
Prob(Omnibus): 0.000 Jarque-Bera (JB): 692959703.729
Skew: 14.842 Prob(JB): 0.00
Kurtosis: 684.976 Cond. No. 6.40e+03


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.4e+03. This might indicate that there are
strong multicollinearity or other numerical problems." - ], - "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} & price & \\textbf{ R-squared: } & 0.317 \\\\\n\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.317 \\\\\n\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 5513. \\\\\n\\textbf{Date:} & Fri, 10 Nov 2023 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n\\textbf{Time:} & 02:58:27 & \\textbf{ Log-Likelihood: } & -5.7491e+05 \\\\\n\\textbf{No. Observations:} & 35691 & \\textbf{ AIC: } & 1.150e+06 \\\\\n\\textbf{Df Residuals:} & 35687 & \\textbf{ BIC: } & 1.150e+06 \\\\\n\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n\\midrule\n\\textbf{const} & -4.794e+05 & 2.69e+04 & -17.843 & 0.000 & -5.32e+05 & -4.27e+05 \\\\\n\\textbf{bed} & -3.129e+05 & 7856.708 & -39.830 & 0.000 & -3.28e+05 & -2.98e+05 \\\\\n\\textbf{bath} & 7.237e+05 & 1.18e+04 & 61.203 & 0.000 & 7.01e+05 & 7.47e+05 \\\\\n\\textbf{house\\_size} & 500.4400 & 9.243 & 54.144 & 0.000 & 482.324 & 518.556 \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:} & 69530.113 & \\textbf{ Durbin-Watson: } & 1.989 \\\\\n\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 692959703.729 \\\\\n\\textbf{Skew:} & 14.842 & \\textbf{ Prob(JB): } & 0.00 \\\\\n\\textbf{Kurtosis:} & 684.976 & \\textbf{ Cond. No. } & 6.40e+03 \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n [2] The condition number is large, 6.4e+03. This might indicate that there are \\newline\n strong multicollinearity or other numerical problems." - }, - "metadata": {}, - "execution_count": 85 - } - ] - }, - { - "cell_type": "code", - "source": [ - "predictions = model_new.predict(X_test)\n", - "r2 = get_r2(y_test, predictions)\n", - "r2" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QJdoG3SO8gLu", - "outputId": "e0f8b3da-3237-4c5c-de7e-d307578e7604" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.3558532984754764" - ] - }, - "metadata": {}, - "execution_count": 87 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Bonus question:\n", - "\n", - "Can you improve the model even further?" - ], - "metadata": { - "id": "fj7r5O8NLnlL" - } - } - ] -} \ No newline at end of file From df9f1fa84d32cc6f9dda819da29eeb7db919fe2f Mon Sep 17 00:00:00 2001 From: Gabriel Dutra <57206480+Dutra-Apex@users.noreply.github.com> Date: Sat, 27 Jan 2024 22:47:41 -0500 Subject: [PATCH 5/6] Created using Colaboratory --- mistral-model/Mistral_with_RAG_colab.ipynb | 10335 +++++++++++++++++++ 1 file changed, 10335 insertions(+) create mode 100644 mistral-model/Mistral_with_RAG_colab.ipynb diff --git a/mistral-model/Mistral_with_RAG_colab.ipynb b/mistral-model/Mistral_with_RAG_colab.ipynb new file mode 100644 index 0000000..b0d77f9 --- /dev/null +++ b/mistral-model/Mistral_with_RAG_colab.ipynb @@ -0,0 +1,10335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uuwYnVLqGfC-" + }, + "source": [ + "# Notebook config\n", + "Create script so output text wraps" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "Ytf3v5nvGbBx" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML, display\n", + "\n", + "def set_css():\n", + " display(HTML('''\n", + " \n", + " '''))\n", + "\n", + "get_ipython().events.register('pre_run_cell', set_css)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "Q0mmVu_OA4Zw", + "outputId": "70edf3bd-24b1-4718-d4c9-68833f3743f5" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('en_US', 'UTF-8')" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "import locale\n", + "\n", + "def getpreferredencoding(do_setlocale = True):\n", + " return \"UTF-8\"\n", + "\n", + "locale.getpreferredencoding = getpreferredencoding\n", + "locale.getdefaultlocale()\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Setting up Mistral\n" + ], + "metadata": { + "id": "KVwgpO_sFc69" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3HsYv_NlXblE" + }, + "source": [ + "## Download and import Libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V26fHL2X7-Aw" + }, + "source": [ + "LLM and LangChain libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675 + }, + "id": "ZxnmLAoJxrI4", + "outputId": "06fcda78-9ca7-48f7-9036-c85cd566bfed" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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AutoTokenizer\n", + "import transformers\n", + "import tensorflow\n", + "import torch\n", + "from langchain.llms import HuggingFacePipeline\n", + "from langchain.chains import LLMChain\n", + "from langchain.prompts import PromptTemplate\n", + "\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tauIFVkSXn7L" + }, + "source": [ + "## Download the Mistral 7B Instruct Model and Tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510, + "referenced_widgets": [ + "7d541b3c7c944fc7ac7ee30fb3e46fe3", + "b3c398a89a9d421b9b7b05e4342cd342", + "5cd58e3470e64939b597631feb61bf69", + "109ba143839d499c8ca85da6d97a5362", + "57e5d52d40fd4751b9ca636f750996d8", + "ceb49f5a2ad84fa88c652cfc140f9545", + "6ff86b64380447f886be420362a09240", + "6c7ee7e30e7e4597b50d1df7c1a10054", + "1380dea45e8649e086020d5f5b8d9358", + 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"stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/596 [00:00" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:226: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n", + " warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.')\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " ### Instruction: Act as physicist.\n", + "Explain what the three body problem is.\n", + " ### Answer:\n", + " The three-body problem is a complex problem in physics which deals with the motion of three celestial bodies, interacting with each other via gravitational forces. Unlike the two-body problem, where the motion of two objects can be described using the laws of physics, the three-body problem does not have a general, exact solution.\n", + "\n", + " In the context of celestial mechanics, the three-body problem arises when considering three astronomical objects: a triple star system, a star with a planet in close proximity, or two objects in close proximity to each other, where the gravitational interaction between the three cannot be assumed to be negligible.\n", + "\n", + " Although it does not have an exact solution, there are several methods available to approximate the solutions of the three-body problem. These include numerical methods such as the Runge-Kutta algorithm, perturbation methods, and Poincaré's limiting solutions. Despite these advances, the three-body problem continues to be a topic of ongoing research, as it provides insights into complex systems with multiple degrees of freedom, non-linear dynamics, and chaos.\n", + "\n", + " Under certain conditions, it may be possible to approximate the three-body problem as two separate, simpler two-body problems. However, these approximations are not always valid, as the interaction between the three bodies can lead to significant deviations from the predicted behavior.\n", + "\n", + " The three-body problem has important applications in various fields of physics and astronomy, including the study of binary stars, planetary systems, celestial mechanics, and plasma physics. Understanding the behavior of three-body systems can also be important for the analysis of technological systems, such as satellite constellations and space debris.\n" + ] + } + ], + "source": [ + "prompt = \"\"\"### Instruction: Act as physicist.\n", + "Explain what the three body problem is.\n", + " ### Answer:\n", + " \"\"\"\n", + "\n", + "encoded_instruction = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n", + "model_inputs = encoded_instruction.to(device)\n", + "\n", + "generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n", + "decoded = tokenizer.batch_decode(generated_ids)\n", + "print(decoded[0])" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Test Mistral without RAG" + ], + "metadata": { + "id": "uqvJXXlbJAn-" + } + }, + { + "cell_type": "code", + "source": [ + "prompt = \"\"\"### Instruction: Act as ML engineer.\n", + "Explain what the Mamba model is in Machine Learning.\n", + " ### Answer:\n", + " \"\"\"\n", + "\n", + "encoded_instruction = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n", + "model_inputs = encoded_instruction.to(device)\n", + "\n", + "generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n", + "decoded = tokenizer.batch_decode(generated_ids)\n", + "print(decoded[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + }, + "id": "3PbJcObDJLjC", + "outputId": "457a78a1-dae0-4df6-fa05-56e0507ad418" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " ### Instruction: Act as ML engineer.\n", + "Explain what the Mamba model is in Machine Learning.\n", + " ### Answer:\n", + " The Mamba model is not a specific machine learning model with a well-defined meaning in the field of machine learning. Instead, it's a software implementation for the gradient boosting algorithm, particularly for scikit-learn community. Scikit-learn is a popular open-source library for machine learning in Python.\n", + "\n", + " Mamba is a gradient boosting implementation in scikit-learn that includes Multi-Output, MAnymeaR-Based, parallel, and adaptive Boosting methods. This means it allows handling multiple output variables, can make use of parallel processing, incorporates an adaptive learning process, and builds upon the R `gbm` library for gradient boosting.\n", + "\n", + " It provides several regression and classification gradient boosting models, including MGBClassifier for classification problems and MGBRegressor for regression problems. You can find more details about Mamba (Gradient Boosting) and its different variants in Scikit-learn documentation: [Official Scikit-learn Mamba Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.MGBClassifier.html) and [MGBRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.MGBRegressor.html).\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model halucinates and gives wrong answer to the question. This is expected since the Mamba model being referred is newer tha the Mistral Model, so the LLM has no knowledge of it." + ], + "metadata": { + "id": "G1Hu2g7UKudR" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EESXS39mfH0v" + }, + "source": [ + "# Integrate LangChain with Mistral" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I4QL5Gw1fRjT" + }, + "source": [ + "## Create Text Generation Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "cC736zU7Y2H2", + "outputId": "b149beef-98f5-414b-f884-86dbef0ab6e7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "text_generation_pipeline = transformers.pipeline(\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " task=\"text-generation\",\n", + " temperature=0.2, # The higher the temperature, the more 'creative' the model gets\n", + " repetition_penalty=1.1,\n", + " return_full_text=True,\n", + " max_new_tokens=1000,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "sp3QXWhe4_DJ", + "outputId": "2bbb7031-7ff3-452f-8c41-499d3a4ccbb6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "# Create an instance of Mistral\n", + "mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Nrt5qSi-gl2" + }, + "source": [ + "### Create an LLM chain\n", + "\n", + "Source: https://blog.gopenai.com/rag-pipeline-with-mistral-7b-instruct-model-a-step-by-step-guide-138df378a0c2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "3mQFVABKCBsZ", + "outputId": "f4530d10-a77b-4ebf-8150-2ab80fc9eb94" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "from langchain.docstore.document import Document\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.vectorstores import Chroma\n", + "from langchain.chains import RetrievalQA\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Source for Mamba article: https://www.unite.ai/mamba-redefining-sequence-modeling-and-outforming-transformers-architecture/" + ], + "metadata": { + "id": "PSQbGzOwJrlJ" + } + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "sXcwgU_ACYQt", + "outputId": "c11119f7-af46-4e24-df85-51d976d597f2" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "prompt = \"\"\"\n", + "In this article on Mamba, we'll explore how this innovative state-space model (SSM) revolutionizes sequence modeling. Developed by Albert Gu and Tri Dao, Mamba is distinguished for its efficiency in processing complex sequences in fields like language processing, genomics, and audio analysis. Its linear-time sequence modeling with selective state spaces ensures exceptional performance across these diverse modalities.\n", + "\n", + "We'll delve into Mamba's ability to overcome computational challenges faced by traditional Transformers, especially with long sequences. Its selective approach in state space models allows for faster inference and linear scaling with sequence length, significantly improving throughput.\n", + "\n", + "Mamba's uniqueness lies in its rapid processing capability, selective SSM layer, and hardware-friendly design inspired by FlashAttention. These features enable Mamba to outperform many existing models, including those based on the transformer approach, making it a noteworthy advancement in machine learning.\n", + "\n", + "Transformers vs Mamba\n", + "Transformers, like GPT-4, have set benchmarks in natural language processing. However, their efficiency dips with longer sequences. Here's where Mamba leaps ahead, with its ability to process long sequences more efficiently and its unique architecture that simplifies the entire process.\n", + "\n", + "Transformers adept at handling sequences of data, such as text for language models. Unlike previous models that processed data sequentially, Transformers process entire sequences simultaneously, enabling them to capture complex relationships within the data.\n", + "\n", + "They use attention mechanism, which allows the model to focus on different parts of the sequence when making predictions.\n", + "\n", + "This attention is computed using three sets of weights: queries, keys, and values, derived from the input data. Each element in a sequence is compared to every other element, providing a weight that signifies the importance, or ‘attention', that each element should receive when predicting the next element in the sequence.\n", + "\n", + "Transformers maintain two main blocks: the encoder, which processes the input data, and the decoder, which generates the output. The encoder consists of multiple layers, each containing two sub-layers: a multi-head self-attention mechanism and a simple, position-wise fully connected feed-forward network. Normalization and residual connections are used at each sub-layer to help in training deep networks.\n", + "\n", + "The decoder also has layers with two sub-layers similar to the encoder but adds a third sub-layer that performs multi-head attention over the encoder's output. The sequential nature of the decoder ensures that predictions for a position can only consider earlier positions, preserving the autoregressive property.\n", + "\n", + "In contrast to Transformers, the Mamba model takes a different approach. While Transformers deal with the issue of long sequences by using more complex attention mechanisms, Mamba uses selective state spaces, providing a more comput\n", + "\n", + "Here's a high-level overview of how a transformer functions:\n", + "\n", + "Input Processing: Transformers first encode input data into a format that the model can understand, often using embeddings that also incorporate the position of each element in the sequence.\n", + "Attention Mechanism: At its core, the attention mechanism computes a score that represents how much focus to put on other parts of the input sequence when understanding a current element.\n", + "Encoder-Decoder Architecture: The transformer model is composed of an encoder to process the input and a decoder to generate the output. Each consists of multiple layers that refine the model's understanding of the input.\n", + "Multi-Head Attention: Within both the encoder and decoder, multi-head attention allows the model to simultaneously attend to different parts of the sequence from different representational spaces, improving its ability to learn from diverse contexts.\n", + "Position-wise Feed-Forward Networks: After attention, a simple neural network processes the output of each position separately and identically. This is combined with the input through a residual connection and followed by layer normalization.\n", + "Output Generation: The decoder then predicts an output sequence, influenced by the encoder's context and what it has generated so far.\n", + "The transformer’s ability to handle sequences in parallel and its robust attention mechanism make it powerful for tasks like translation and text generation.\n", + "\n", + "In contrast, the Mamba model operates differently by using selective state spaces to process sequences. This approach addresses the computational inefficiency in Transformers when dealing with lengthy sequences. Mamba's design enables faster inference and scales linearly with sequence length, setting a new paradigm for sequence modeling that could be more efficient, especially as sequences become increasingly lengthy.\n", + "\n", + "Mamba\n", + "What makes Mamba truly unique is its departure from traditional attention and MLP blocks. This simplification leads to a lighter, faster model that scales linearly with the sequence length – a feat unmatched by its predecessors.\n", + "\n", + "Key features of Mamba include:\n", + "\n", + "Selective SSMs: These allow Mamba to filter irrelevant information and focus on relevant data, enhancing its handling of sequences. This selectivity is crucial for efficient content-based reasoning.\n", + "Hardware-aware Algorithm: Mamba uses a parallel algorithm that's optimized for modern hardware, especially GPUs. This design enables faster computation and reduces the memory requirements compared to traditional models.\n", + "Simplified Architecture: By integrating selective SSMs and eliminating attention and MLP blocks, Mamba offers a simpler, more homogeneous structure. This leads to better scalability and performance.\n", + "Mamba has demonstrated superior performance in various domains, including language, audio, and genomics, excelling in both pretraining and domain-specific tasks. For instance, in language modeling, Mamba matches or exceeds the performance of larger Transformer models.\n", + "\n", + "Mamba's code and pre-trained models are openly available for community use at GitHub.\n", + "\n", + "Standard Copying tasks are simple for linear models. Selective Copying and Induction Heads require dynamic, content-aware memory for LLMs.\n", + "Standard Copying tasks are simple for linear models. Selective Copying and Induction Heads require dynamic, content-aware memory for LLMs.\n", + "\n", + "Structured State Space (S4) models have recently emerged as a promising class of sequence models, encompassing traits from RNNs, CNNs, and classical state space models. S4 models derive inspiration from continuous systems, specifically a type of system that maps one-dimensional functions or sequences through an implicit latent state. In the context of deep learning, they represent a significant innovation, providing a new methodology for designing sequence models that are efficient and highly adaptable.\n", + "\n", + "The Dynamics of S4 Models\n", + "SSM (S4) This is the basic structured state space model. It takes a sequence x and produces an output y using learned parameters A, B, C, and a delay parameter Δ. The transformation involves discretizing the parameters (turning continuous functions into discrete ones) and applying the SSM operation, which is time-invariant—meaning it doesn't change over different time steps.\n", + "\n", + "The Significance of Discretization\n", + "Discretization is a key process that transforms the continuous parameters into discrete ones through fixed formulas, enabling the S4 models to maintain a connection with continuous-time systems. This endows the models with additional properties, such as resolution invariance, and ensures proper normalization, enhancing model stability and performance. Discretization also draws parallels to the gating mechanisms found in RNNs, which are critical for managing the flow of information through the network.\n", + "\n", + "Linear Time Invariance (LTI)\n", + "A core feature of the S4 models is their linear time invariance. This property implies that the model’s dynamics remain consistent over time, with the parameters fixed for all timesteps. LTI is a cornerstone of recurrence and convolutions, offering a simplified yet powerful framework for building sequence models.\n", + "\n", + "Overcoming Fundamental Limitations\n", + "The S4 framework has been traditionally limited by its LTI nature, which poses challenges in modeling data that require adaptive dynamics. The recent research paper presents a approach that overcomes these limitations by introducing time-varying parameters, thus removing the constraint of LTI. This allows the S4 models to handle a more diverse set of sequences and tasks, significantly expanding their applicability.\n", + "\n", + "The term ‘state space model' broadly covers any recurrent process involving a latent state and has been used to describe various concepts across multiple disciplines. In the context of deep learning, S4 models, or structured SSMs, refer to a specific class of models that have been optimized for efficient computation while retaining the ability to model complex sequences.\n", + "\n", + "S4 models can be integrated into end-to-end neural network architectures, functioning as standalone sequence transformations. They can be viewed as analogous to convolution layers in CNNs, providing the backbone for sequence modeling in a variety of neural network architectures.\n", + "\n", + "SSM vs SSM + Selection\n", + "SSM vs SSM + Selection\n", + "\n", + "Motivation for Selectivity in Sequence Modeling\n", + "Structured SSMs\n", + "Structured SSMs\n", + "\n", + "The paper argues that a fundamental aspect of sequence modeling is the compression of context into a manageable state. Models that can selectively focus on or filter inputs provide a more effective means of maintaining this compressed state, leading to more efficient and powerful sequence models. This selectivity is vital for models to adaptively control how information flows along the sequence dimension, an essential capability for handling complex tasks in language modeling and beyond.\n", + "\n", + "Selective SSMs enhance conventional SSMs by allowing their parameters to be input-dependent, which introduces a degree of adaptiveness previously unattainable with time-invariant models. This results in time-varying SSMs that can no longer use convolutions for efficient computation but instead rely on a linear recurrence mechanism, a significant deviation from traditional models.\n", + "\n", + "SSM + Selection (S6) This variant includes a selection mechanism, adding input-dependence to the parameters B and C, and a delay parameter Δ. This allows the model to selectively focus on certain parts of the input sequence x. The parameters are discretized taking into account the selection, and the SSM operation is applied in a time-varying manner using a scan operation, which processes elements sequentially, adjusting the focus dynamically over time.\n", + "\n", + "Performance Highlights of Mamba\n", + "Mamba is best-in-class on every single evaluation result\n", + "Mamba is best-in-class on every single evaluation result\n", + "\n", + "In terms of performance, Mamba excels in both inference speed and accuracy. It's design enables better utilization of longer contexts, which is demonstrated in both DNA and audio modeling, outperforming prior models on complex tasks requiring long-range dependencies. Its versatility is also highlighted in zero-shot evaluations across multiple tasks, setting a new standard for such models in terms of efficiency and scalability.\n", + "\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "__7a5_2jC4to" + }, + "source": [ + "### Chunk Documents" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "jELDnzDVC4Q0", + "outputId": "94336b4a-07c9-4ad9-8350-8b42cd3261da" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "# Create Document object from text documents\n", + "docs = [Document(page_content=post) for post in [prompt]]\n", + "\n", + "# Split documents into chunks\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=500, chunk_overlap=10, separators=['\\n\\n', '\\n', '.']\n", + ")\n", + "\n", + "document_chunks = text_splitter.split_documents(docs)" + ] + }, + { + "cell_type": "code", + "source": [ + "print(document_chunks[:2])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "id": "b2WSGXL8J4_r", + "outputId": "900030ba-b325-470a-db95-97c0c175d968" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Document(page_content=\"In this article on Mamba, we'll explore how this innovative state-space model (SSM) revolutionizes sequence modeling. Developed by Albert Gu and Tri Dao, Mamba is distinguished for its efficiency in processing complex sequences in fields like language processing, genomics, and audio analysis. Its linear-time sequence modeling with selective state spaces ensures exceptional performance across these diverse modalities.\"), Document(page_content=\"We'll delve into Mamba's ability to overcome computational challenges faced by traditional Transformers, especially with long sequences. Its selective approach in state space models allows for faster inference and linear scaling with sequence length, significantly improving throughput.\")]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HUtzLvJwDldV" + }, + "source": [ + "### Download an Embedding Model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "ef061840e27b4285af138f2bc0e4bf92", + "7951e782540e4991976a3cab72ac29ae", + "343862294d204fe2afb3f416855c72c1", + "dfe38827be0146c88cf1b2a22333f595", + "abfbea38a1014dd0979824d87bfbcc10", + "1fe73dcdc9124338a735f26250140a95", + "e83a64a24de840b2ae47b32d54b65b06", + "e9aeb9feae94462093095e4ffd77fcfa", + "9d938665f2044d54ace78ddb0ecdecea", + "e24a2c0493f64abd9d498da7e2fb112a", + "59dbf0c7cc3d435d90705bc334bddb02", + "210ecfd6e48448bba5f9411e4ea2ef33", + "f17c00d158ea4816817b167dae20a1c2", + "47d2dcf9a12b45ac9bb15a0a13e2724a", + "56e2c457a41945fd950ee7acb98285d4", + "0bc6e99c0e094fd5b080999f8c4d3a0e", + "a94c3d64b4254e4ab8e3ced854a68d20", + "e52ce8caae9841a8804df18b031516ec", + "9f2593d8c2f74ecaa1c16939a4212823", + "8afd0a43b0bb445eac6b403a4d74eb42", + "5f7df20ba8454690be75dbeff2b084b4", + "d450bc9a84154d96b7c7e2c001c1fb2a", + "1cafd2c0a74148f68c91899cb6dbdec3", + "0bb50651591547e7b0136191b484736d", + "d13869f59c4b426e9f47d05ff5e6a3e8", + "10447244fadd4f379167c53e74155883", + "075dc31bbe524b3aae1ce15ce2f1db8e", + "d3da1098f60d401d8a81b3b208dbe34e", + "47ff862a83234ea186d8d4c6824ec35d", + "730edfb6493046698188b71ca7656316", + "8c5b3b014d324b4ebcfda6c2b74b922a", + "033b4af233a3483aaec00ed7d280e380", + "52ae5911e3534f6dbf1cb6558d3f6c74", + "d6057d89c03b4ae390f7a0a235aedb02", + "42b089e6a1424eafa70d933b4448876e", + "a14c53b99e4d4cef8cd3e1e796b26365", + "ca4a1d55d92443eda70cd43307296133", + "06ddc7268bda4eecb45455027b34cca1", + "5da1f50558cc4b5388c7af40418c1b81", + 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"output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "# Prompt template\n", + "qa_template = \"\"\"[INST] You are a helpful assistant.\n", + "Use the following context to Answer the question below briefly:\n", + "\n", + "{context}\n", + "\n", + "{question} [/INST] \n", + "\"\"\"\n", + "\n", + "# Create a prompt instance\n", + "QA_PROMPT = PromptTemplate.from_template(qa_template)\n", + "\n", + "# Custom QA Chain\n", + "qa_chain = RetrievalQA.from_chain_type(\n", + " llm = mistral_llm,\n", + " retriever=retriever,\n", + " chain_type_kwargs={\"prompt\": QA_PROMPT}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9W5acyw1kOcI" + }, + "source": [ + "### Query Mistral 7B Instruct Model" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 200 + }, + "id": "c5LJBerGkfOa", + "outputId": "1719d1c8-fdfe-4703-c388-c92d02ed2794" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", + " warn_deprecated(\n", + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n", + "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The Mamba model is a state-of-the-art sequence modeling approach developed by Albert Gu and Tri Dao. It differentiates itself from traditional attention and Multi-Layer Perceptron (MLP) block models by adopting a simplified architecture based on selective State-Space Models (SSMs). This design results in improved scalability and performance, allowing Mamba to process complex sequences efficiently in various domains such as language, audio, and genomics. Additionally, Mamba demonstrates superior performance compared to larger Transformer models in language modeling tasks.\n" + ] + } + ], + "source": [ + "question = \"What is the Mamba model?\"\n", + "response = qa_chain({\"query\": question})\n", + "print(response['result'])" + ] + }, + { + "cell_type": "code", + "source": [ + "question = \"Explain what are S4 models and how they work.\"\n", + "response = qa_chain({\"query\": question})\n", + "print(response['result'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 254 + }, + "id": "iwIRwuzGKffl", + "outputId": "212e6107-b993-4d58-c452-c0ef626d1c66" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n", + "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "S4 models, also known as Structured State Space models, are a type of sequence model that transform continuous parameters into discrete ones through discretization. They maintain a connection with continuous-time systems, providing additional properties like resolution invariance and proper normalization, which enhance model stability and performance.\n", + "\n", + "At their core, S4 models use the Sequential State Space Model (SSM), which takes a sequence 'x' as input and generates an output 'y'. The model uses learned parameters A, B, C, and a delay parameter Δ. The discretization process converts continuous functions into discrete ones, allowing the application of the time-invariant SSM operation.\n", + "\n", + "Inspired by continuous systems, S4 models map one-dimensional functions or sequences through an implicit latent state. Traditionally, S4 models were limited due to their LTI (Linear Time Invariant) nature, making it challenging to model data requiring adaptive dynamics. However, recent research has introduced time-varying parameters, eliminating the LTI constraint, enabling S4 models to handle a broader range of sequences and tasks, significantly expanding their applicability.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model now gives correct answers based on the article" + ], + "metadata": { + "id": "Cn-L4R4QK-hY" + } + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "7d541b3c7c944fc7ac7ee30fb3e46fe3": { + "model_module": "@jupyter-widgets/controls", + "model_name": 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deletions(-) delete mode 100644 mistral-model/Mistral_with_RAG_colab.ipynb diff --git a/mistral-model/Mistral_with_RAG_colab.ipynb b/mistral-model/Mistral_with_RAG_colab.ipynb deleted file mode 100644 index b0d77f9..0000000 --- a/mistral-model/Mistral_with_RAG_colab.ipynb +++ /dev/null @@ -1,10335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uuwYnVLqGfC-" - }, - "source": [ - "# Notebook config\n", - "Create script so output text wraps" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "Ytf3v5nvGbBx" - }, - "outputs": [], - "source": [ - "from IPython.display import HTML, display\n", - "\n", - "def set_css():\n", - " display(HTML('''\n", - " \n", - " '''))\n", - "\n", - "get_ipython().events.register('pre_run_cell', set_css)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "Q0mmVu_OA4Zw", - "outputId": "70edf3bd-24b1-4718-d4c9-68833f3743f5" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "('en_US', 'UTF-8')" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ], - "source": [ - "import locale\n", - "\n", - "def getpreferredencoding(do_setlocale = True):\n", - " return \"UTF-8\"\n", - "\n", - "locale.getpreferredencoding = getpreferredencoding\n", - "locale.getdefaultlocale()\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Setting up Mistral\n" - ], - "metadata": { - "id": "KVwgpO_sFc69" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3HsYv_NlXblE" - }, - "source": [ - "## Download and import Libraries" - ] - }, - { - "cell_type": 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torch==2.1\n", - "!pip install -qU accelerate bitsandbytes langchain\n", - "!pip install -qU sentence-transformers chromadb==0.4.2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uVfkgpMJYP0n" - }, - "source": [ - "Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "kQNPqNtQYNN4", - "outputId": "92feb7cc-2fcd-4544-8252-660821e676a5" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "# Import libraries\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "import transformers\n", - "import tensorflow\n", - "import torch\n", - "from langchain.llms import HuggingFacePipeline\n", - "from langchain.chains import LLMChain\n", - "from langchain.prompts import PromptTemplate\n", - "\n", - "device = 'cuda' if torch.cuda.is_available() else 'cpu'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tauIFVkSXn7L" - }, - "source": [ - "## Download the Mistral 7B Instruct Model and Tokenizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 510, - "referenced_widgets": [ - "7d541b3c7c944fc7ac7ee30fb3e46fe3", - "b3c398a89a9d421b9b7b05e4342cd342", - "5cd58e3470e64939b597631feb61bf69", - "109ba143839d499c8ca85da6d97a5362", - "57e5d52d40fd4751b9ca636f750996d8", - "ceb49f5a2ad84fa88c652cfc140f9545", - "6ff86b64380447f886be420362a09240", - "6c7ee7e30e7e4597b50d1df7c1a10054", - "1380dea45e8649e086020d5f5b8d9358", - "e42d3ad3d31a479ebf6837710f959cf6", - "9289e1a4929742a5b13fa8bd2b1fcf2c", - "b74c48ae282f408596a59ed53651e191", - "13df4aa70ff14b2b9c5e2fe0362b2d1a", - "87bcb68ae6b44a6bb40130bf374c5594", - "c8788db29e2d4509a64ebc468de37a77", - "89c1325c7d304d489d0c2b902b620449", - 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"cb3d9448323c44d0b1ebbee4b8ef5436", - "c64d1758bbc846639c366dba4e5f0f5d", - "e8eb2affb1ef4a85bed091e00672b02f", - "78a47ea5dd5b418db49015be0e7b297f", - "3ec52048d1f7431481a97908dd2532d2", - "e6e3bdc7141a4215b642c2c72a749a1f", - "3b946489b07b4a8fa9de955070e828e8", - "a173bcb971084520acf2a0badd0e2f05", - "e322400daa5a46f6b7cbf8209c7a45ae", - "e1e6a27e970847dfa075fd383f3d665b", - "785f4e4c4ebd4fb9b6b148abdb43131f", - "8a3ad3d61990468ea2d63d24a425364f" - ] - }, - "id": "uI19EJ0bXuWP", - "outputId": "5eda2a9c-860e-4283-d503-ee5b35834d59" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/596 [00:00" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:226: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n", - " warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.')\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - " ### Instruction: Act as physicist.\n", - "Explain what the three body problem is.\n", - " ### Answer:\n", - " The three-body problem is a complex problem in physics which deals with the motion of three celestial bodies, interacting with each other via gravitational forces. Unlike the two-body problem, where the motion of two objects can be described using the laws of physics, the three-body problem does not have a general, exact solution.\n", - "\n", - " In the context of celestial mechanics, the three-body problem arises when considering three astronomical objects: a triple star system, a star with a planet in close proximity, or two objects in close proximity to each other, where the gravitational interaction between the three cannot be assumed to be negligible.\n", - "\n", - " Although it does not have an exact solution, there are several methods available to approximate the solutions of the three-body problem. These include numerical methods such as the Runge-Kutta algorithm, perturbation methods, and Poincaré's limiting solutions. Despite these advances, the three-body problem continues to be a topic of ongoing research, as it provides insights into complex systems with multiple degrees of freedom, non-linear dynamics, and chaos.\n", - "\n", - " Under certain conditions, it may be possible to approximate the three-body problem as two separate, simpler two-body problems. However, these approximations are not always valid, as the interaction between the three bodies can lead to significant deviations from the predicted behavior.\n", - "\n", - " The three-body problem has important applications in various fields of physics and astronomy, including the study of binary stars, planetary systems, celestial mechanics, and plasma physics. Understanding the behavior of three-body systems can also be important for the analysis of technological systems, such as satellite constellations and space debris.\n" - ] - } - ], - "source": [ - "prompt = \"\"\"### Instruction: Act as physicist.\n", - "Explain what the three body problem is.\n", - " ### Answer:\n", - " \"\"\"\n", - "\n", - "encoded_instruction = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n", - "model_inputs = encoded_instruction.to(device)\n", - "\n", - "generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n", - "decoded = tokenizer.batch_decode(generated_ids)\n", - "print(decoded[0])" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Test Mistral without RAG" - ], - "metadata": { - "id": "uqvJXXlbJAn-" - } - }, - { - "cell_type": "code", - "source": [ - "prompt = \"\"\"### Instruction: Act as ML engineer.\n", - "Explain what the Mamba model is in Machine Learning.\n", - " ### Answer:\n", - " \"\"\"\n", - "\n", - "encoded_instruction = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n", - "model_inputs = encoded_instruction.to(device)\n", - "\n", - "generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n", - "decoded = tokenizer.batch_decode(generated_ids)\n", - "print(decoded[0])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 236 - }, - "id": "3PbJcObDJLjC", - "outputId": "457a78a1-dae0-4df6-fa05-56e0507ad418" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - " ### Instruction: Act as ML engineer.\n", - "Explain what the Mamba model is in Machine Learning.\n", - " ### Answer:\n", - " The Mamba model is not a specific machine learning model with a well-defined meaning in the field of machine learning. Instead, it's a software implementation for the gradient boosting algorithm, particularly for scikit-learn community. Scikit-learn is a popular open-source library for machine learning in Python.\n", - "\n", - " Mamba is a gradient boosting implementation in scikit-learn that includes Multi-Output, MAnymeaR-Based, parallel, and adaptive Boosting methods. This means it allows handling multiple output variables, can make use of parallel processing, incorporates an adaptive learning process, and builds upon the R `gbm` library for gradient boosting.\n", - "\n", - " It provides several regression and classification gradient boosting models, including MGBClassifier for classification problems and MGBRegressor for regression problems. You can find more details about Mamba (Gradient Boosting) and its different variants in Scikit-learn documentation: [Official Scikit-learn Mamba Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.MGBClassifier.html) and [MGBRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.MGBRegressor.html).\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Model halucinates and gives wrong answer to the question. This is expected since the Mamba model being referred is newer tha the Mistral Model, so the LLM has no knowledge of it." - ], - "metadata": { - "id": "G1Hu2g7UKudR" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EESXS39mfH0v" - }, - "source": [ - "# Integrate LangChain with Mistral" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I4QL5Gw1fRjT" - }, - "source": [ - "## Create Text Generation Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "cC736zU7Y2H2", - "outputId": "b149beef-98f5-414b-f884-86dbef0ab6e7" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "text_generation_pipeline = transformers.pipeline(\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " task=\"text-generation\",\n", - " temperature=0.2, # The higher the temperature, the more 'creative' the model gets\n", - " repetition_penalty=1.1,\n", - " return_full_text=True,\n", - " max_new_tokens=1000,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "sp3QXWhe4_DJ", - "outputId": "2bbb7031-7ff3-452f-8c41-499d3a4ccbb6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "# Create an instance of Mistral\n", - "mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9Nrt5qSi-gl2" - }, - "source": [ - "### Create an LLM chain\n", - "\n", - "Source: https://blog.gopenai.com/rag-pipeline-with-mistral-7b-instruct-model-a-step-by-step-guide-138df378a0c2" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "3mQFVABKCBsZ", - "outputId": "f4530d10-a77b-4ebf-8150-2ab80fc9eb94" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", - "from langchain.vectorstores import Chroma\n", - "from langchain.chains import RetrievalQA\n", - "from langchain.chat_models import ChatOpenAI\n", - "from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Source for Mamba article: https://www.unite.ai/mamba-redefining-sequence-modeling-and-outforming-transformers-architecture/" - ], - "metadata": { - "id": "PSQbGzOwJrlJ" - } - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "sXcwgU_ACYQt", - "outputId": "c11119f7-af46-4e24-df85-51d976d597f2" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "prompt = \"\"\"\n", - "In this article on Mamba, we'll explore how this innovative state-space model (SSM) revolutionizes sequence modeling. Developed by Albert Gu and Tri Dao, Mamba is distinguished for its efficiency in processing complex sequences in fields like language processing, genomics, and audio analysis. Its linear-time sequence modeling with selective state spaces ensures exceptional performance across these diverse modalities.\n", - "\n", - "We'll delve into Mamba's ability to overcome computational challenges faced by traditional Transformers, especially with long sequences. Its selective approach in state space models allows for faster inference and linear scaling with sequence length, significantly improving throughput.\n", - "\n", - "Mamba's uniqueness lies in its rapid processing capability, selective SSM layer, and hardware-friendly design inspired by FlashAttention. These features enable Mamba to outperform many existing models, including those based on the transformer approach, making it a noteworthy advancement in machine learning.\n", - "\n", - "Transformers vs Mamba\n", - "Transformers, like GPT-4, have set benchmarks in natural language processing. However, their efficiency dips with longer sequences. Here's where Mamba leaps ahead, with its ability to process long sequences more efficiently and its unique architecture that simplifies the entire process.\n", - "\n", - "Transformers adept at handling sequences of data, such as text for language models. Unlike previous models that processed data sequentially, Transformers process entire sequences simultaneously, enabling them to capture complex relationships within the data.\n", - "\n", - "They use attention mechanism, which allows the model to focus on different parts of the sequence when making predictions.\n", - "\n", - "This attention is computed using three sets of weights: queries, keys, and values, derived from the input data. Each element in a sequence is compared to every other element, providing a weight that signifies the importance, or ‘attention', that each element should receive when predicting the next element in the sequence.\n", - "\n", - "Transformers maintain two main blocks: the encoder, which processes the input data, and the decoder, which generates the output. The encoder consists of multiple layers, each containing two sub-layers: a multi-head self-attention mechanism and a simple, position-wise fully connected feed-forward network. Normalization and residual connections are used at each sub-layer to help in training deep networks.\n", - "\n", - "The decoder also has layers with two sub-layers similar to the encoder but adds a third sub-layer that performs multi-head attention over the encoder's output. The sequential nature of the decoder ensures that predictions for a position can only consider earlier positions, preserving the autoregressive property.\n", - "\n", - "In contrast to Transformers, the Mamba model takes a different approach. While Transformers deal with the issue of long sequences by using more complex attention mechanisms, Mamba uses selective state spaces, providing a more comput\n", - "\n", - "Here's a high-level overview of how a transformer functions:\n", - "\n", - "Input Processing: Transformers first encode input data into a format that the model can understand, often using embeddings that also incorporate the position of each element in the sequence.\n", - "Attention Mechanism: At its core, the attention mechanism computes a score that represents how much focus to put on other parts of the input sequence when understanding a current element.\n", - "Encoder-Decoder Architecture: The transformer model is composed of an encoder to process the input and a decoder to generate the output. Each consists of multiple layers that refine the model's understanding of the input.\n", - "Multi-Head Attention: Within both the encoder and decoder, multi-head attention allows the model to simultaneously attend to different parts of the sequence from different representational spaces, improving its ability to learn from diverse contexts.\n", - "Position-wise Feed-Forward Networks: After attention, a simple neural network processes the output of each position separately and identically. This is combined with the input through a residual connection and followed by layer normalization.\n", - "Output Generation: The decoder then predicts an output sequence, influenced by the encoder's context and what it has generated so far.\n", - "The transformer’s ability to handle sequences in parallel and its robust attention mechanism make it powerful for tasks like translation and text generation.\n", - "\n", - "In contrast, the Mamba model operates differently by using selective state spaces to process sequences. This approach addresses the computational inefficiency in Transformers when dealing with lengthy sequences. Mamba's design enables faster inference and scales linearly with sequence length, setting a new paradigm for sequence modeling that could be more efficient, especially as sequences become increasingly lengthy.\n", - "\n", - "Mamba\n", - "What makes Mamba truly unique is its departure from traditional attention and MLP blocks. This simplification leads to a lighter, faster model that scales linearly with the sequence length – a feat unmatched by its predecessors.\n", - "\n", - "Key features of Mamba include:\n", - "\n", - "Selective SSMs: These allow Mamba to filter irrelevant information and focus on relevant data, enhancing its handling of sequences. This selectivity is crucial for efficient content-based reasoning.\n", - "Hardware-aware Algorithm: Mamba uses a parallel algorithm that's optimized for modern hardware, especially GPUs. This design enables faster computation and reduces the memory requirements compared to traditional models.\n", - "Simplified Architecture: By integrating selective SSMs and eliminating attention and MLP blocks, Mamba offers a simpler, more homogeneous structure. This leads to better scalability and performance.\n", - "Mamba has demonstrated superior performance in various domains, including language, audio, and genomics, excelling in both pretraining and domain-specific tasks. For instance, in language modeling, Mamba matches or exceeds the performance of larger Transformer models.\n", - "\n", - "Mamba's code and pre-trained models are openly available for community use at GitHub.\n", - "\n", - "Standard Copying tasks are simple for linear models. Selective Copying and Induction Heads require dynamic, content-aware memory for LLMs.\n", - "Standard Copying tasks are simple for linear models. Selective Copying and Induction Heads require dynamic, content-aware memory for LLMs.\n", - "\n", - "Structured State Space (S4) models have recently emerged as a promising class of sequence models, encompassing traits from RNNs, CNNs, and classical state space models. S4 models derive inspiration from continuous systems, specifically a type of system that maps one-dimensional functions or sequences through an implicit latent state. In the context of deep learning, they represent a significant innovation, providing a new methodology for designing sequence models that are efficient and highly adaptable.\n", - "\n", - "The Dynamics of S4 Models\n", - "SSM (S4) This is the basic structured state space model. It takes a sequence x and produces an output y using learned parameters A, B, C, and a delay parameter Δ. The transformation involves discretizing the parameters (turning continuous functions into discrete ones) and applying the SSM operation, which is time-invariant—meaning it doesn't change over different time steps.\n", - "\n", - "The Significance of Discretization\n", - "Discretization is a key process that transforms the continuous parameters into discrete ones through fixed formulas, enabling the S4 models to maintain a connection with continuous-time systems. This endows the models with additional properties, such as resolution invariance, and ensures proper normalization, enhancing model stability and performance. Discretization also draws parallels to the gating mechanisms found in RNNs, which are critical for managing the flow of information through the network.\n", - "\n", - "Linear Time Invariance (LTI)\n", - "A core feature of the S4 models is their linear time invariance. This property implies that the model’s dynamics remain consistent over time, with the parameters fixed for all timesteps. LTI is a cornerstone of recurrence and convolutions, offering a simplified yet powerful framework for building sequence models.\n", - "\n", - "Overcoming Fundamental Limitations\n", - "The S4 framework has been traditionally limited by its LTI nature, which poses challenges in modeling data that require adaptive dynamics. The recent research paper presents a approach that overcomes these limitations by introducing time-varying parameters, thus removing the constraint of LTI. This allows the S4 models to handle a more diverse set of sequences and tasks, significantly expanding their applicability.\n", - "\n", - "The term ‘state space model' broadly covers any recurrent process involving a latent state and has been used to describe various concepts across multiple disciplines. In the context of deep learning, S4 models, or structured SSMs, refer to a specific class of models that have been optimized for efficient computation while retaining the ability to model complex sequences.\n", - "\n", - "S4 models can be integrated into end-to-end neural network architectures, functioning as standalone sequence transformations. They can be viewed as analogous to convolution layers in CNNs, providing the backbone for sequence modeling in a variety of neural network architectures.\n", - "\n", - "SSM vs SSM + Selection\n", - "SSM vs SSM + Selection\n", - "\n", - "Motivation for Selectivity in Sequence Modeling\n", - "Structured SSMs\n", - "Structured SSMs\n", - "\n", - "The paper argues that a fundamental aspect of sequence modeling is the compression of context into a manageable state. Models that can selectively focus on or filter inputs provide a more effective means of maintaining this compressed state, leading to more efficient and powerful sequence models. This selectivity is vital for models to adaptively control how information flows along the sequence dimension, an essential capability for handling complex tasks in language modeling and beyond.\n", - "\n", - "Selective SSMs enhance conventional SSMs by allowing their parameters to be input-dependent, which introduces a degree of adaptiveness previously unattainable with time-invariant models. This results in time-varying SSMs that can no longer use convolutions for efficient computation but instead rely on a linear recurrence mechanism, a significant deviation from traditional models.\n", - "\n", - "SSM + Selection (S6) This variant includes a selection mechanism, adding input-dependence to the parameters B and C, and a delay parameter Δ. This allows the model to selectively focus on certain parts of the input sequence x. The parameters are discretized taking into account the selection, and the SSM operation is applied in a time-varying manner using a scan operation, which processes elements sequentially, adjusting the focus dynamically over time.\n", - "\n", - "Performance Highlights of Mamba\n", - "Mamba is best-in-class on every single evaluation result\n", - "Mamba is best-in-class on every single evaluation result\n", - "\n", - "In terms of performance, Mamba excels in both inference speed and accuracy. It's design enables better utilization of longer contexts, which is demonstrated in both DNA and audio modeling, outperforming prior models on complex tasks requiring long-range dependencies. Its versatility is also highlighted in zero-shot evaluations across multiple tasks, setting a new standard for such models in terms of efficiency and scalability.\n", - "\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "__7a5_2jC4to" - }, - "source": [ - "### Chunk Documents" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "jELDnzDVC4Q0", - "outputId": "94336b4a-07c9-4ad9-8350-8b42cd3261da" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "# Create Document object from text documents\n", - "docs = [Document(page_content=post) for post in [prompt]]\n", - "\n", - "# Split documents into chunks\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " chunk_size=500, chunk_overlap=10, separators=['\\n\\n', '\\n', '.']\n", - ")\n", - "\n", - "document_chunks = text_splitter.split_documents(docs)" - ] - }, - { - "cell_type": "code", - "source": [ - "print(document_chunks[:2])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 90 - }, - "id": "b2WSGXL8J4_r", - "outputId": "900030ba-b325-470a-db95-97c0c175d968" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Document(page_content=\"In this article on Mamba, we'll explore how this innovative state-space model (SSM) revolutionizes sequence modeling. Developed by Albert Gu and Tri Dao, Mamba is distinguished for its efficiency in processing complex sequences in fields like language processing, genomics, and audio analysis. Its linear-time sequence modeling with selective state spaces ensures exceptional performance across these diverse modalities.\"), Document(page_content=\"We'll delve into Mamba's ability to overcome computational challenges faced by traditional Transformers, especially with long sequences. Its selective approach in state space models allows for faster inference and linear scaling with sequence length, significantly improving throughput.\")]\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HUtzLvJwDldV" - }, - "source": [ - "### Download an Embedding Model" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 433, - "referenced_widgets": [ - "ef061840e27b4285af138f2bc0e4bf92", - "7951e782540e4991976a3cab72ac29ae", - "343862294d204fe2afb3f416855c72c1", - "dfe38827be0146c88cf1b2a22333f595", - "abfbea38a1014dd0979824d87bfbcc10", - "1fe73dcdc9124338a735f26250140a95", - "e83a64a24de840b2ae47b32d54b65b06", - "e9aeb9feae94462093095e4ffd77fcfa", - "9d938665f2044d54ace78ddb0ecdecea", - "e24a2c0493f64abd9d498da7e2fb112a", - "59dbf0c7cc3d435d90705bc334bddb02", - "210ecfd6e48448bba5f9411e4ea2ef33", - "f17c00d158ea4816817b167dae20a1c2", - "47d2dcf9a12b45ac9bb15a0a13e2724a", - 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" \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - ".gitattributes: 0%| | 0.00/1.52k [00:00" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "# Initiate a chromadb instance\n", - "chroma_db = Chroma.from_documents(document_chunks, embedding_model)\n", - "retriever = chroma_db.as_retriever()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nbmBIjLbjtTL" - }, - "source": [ - "### Create Question Answering Chain" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "j5jAj4EDj68C", - "outputId": "8d09ac8c-deb7-4f41-9c6d-942ffa5ebc2f" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "# Prompt template\n", - "qa_template = \"\"\"[INST] You are a helpful assistant.\n", - "Use the following context to Answer the question below briefly:\n", - "\n", - "{context}\n", - "\n", - "{question} [/INST] \n", - "\"\"\"\n", - "\n", - "# Create a prompt instance\n", - "QA_PROMPT = PromptTemplate.from_template(qa_template)\n", - "\n", - "# Custom QA Chain\n", - "qa_chain = RetrievalQA.from_chain_type(\n", - " llm = mistral_llm,\n", - " retriever=retriever,\n", - " chain_type_kwargs={\"prompt\": QA_PROMPT}\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9W5acyw1kOcI" - }, - "source": [ - "### Query Mistral 7B Instruct Model" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 - }, - "id": "c5LJBerGkfOa", - "outputId": "1719d1c8-fdfe-4703-c388-c92d02ed2794" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", - " warn_deprecated(\n", - "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", - " warnings.warn(\n", - "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "The Mamba model is a state-of-the-art sequence modeling approach developed by Albert Gu and Tri Dao. It differentiates itself from traditional attention and Multi-Layer Perceptron (MLP) block models by adopting a simplified architecture based on selective State-Space Models (SSMs). This design results in improved scalability and performance, allowing Mamba to process complex sequences efficiently in various domains such as language, audio, and genomics. Additionally, Mamba demonstrates superior performance compared to larger Transformer models in language modeling tasks.\n" - ] - } - ], - "source": [ - "question = \"What is the Mamba model?\"\n", - "response = qa_chain({\"query\": question})\n", - "print(response['result'])" - ] - }, - { - "cell_type": "code", - "source": [ - "question = \"Explain what are S4 models and how they work.\"\n", - "response = qa_chain({\"query\": question})\n", - "print(response['result'])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 254 - }, - "id": "iwIRwuzGKffl", - "outputId": "212e6107-b993-4d58-c452-c0ef626d1c66" - }, - "execution_count": 24, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", - " warnings.warn(\n", - "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "S4 models, also known as Structured State Space models, are a type of sequence model that transform continuous parameters into discrete ones through discretization. They maintain a connection with continuous-time systems, providing additional properties like resolution invariance and proper normalization, which enhance model stability and performance.\n", - "\n", - "At their core, S4 models use the Sequential State Space Model (SSM), which takes a sequence 'x' as input and generates an output 'y'. The model uses learned parameters A, B, C, and a delay parameter Δ. The discretization process converts continuous functions into discrete ones, allowing the application of the time-invariant SSM operation.\n", - "\n", - "Inspired by continuous systems, S4 models map one-dimensional functions or sequences through an implicit latent state. Traditionally, S4 models were limited due to their LTI (Linear Time Invariant) nature, making it challenging to model data requiring adaptive dynamics. However, recent research has introduced time-varying parameters, eliminating the LTI constraint, enabling S4 models to handle a broader range of sequences and tasks, significantly expanding their applicability.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Model now gives correct answers based on the article" - ], - "metadata": { - "id": "Cn-L4R4QK-hY" - } - } - ], - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.10.13" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "7d541b3c7c944fc7ac7ee30fb3e46fe3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - 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