diff --git a/hw/hw8/eva-congrats.png b/hw/hw8/eva-congrats.png new file mode 100644 index 0000000..ecae959 Binary files /dev/null and b/hw/hw8/eva-congrats.png differ diff --git a/hw/hw8/hw8.ipynb b/hw/hw8/hw8.ipynb new file mode 100644 index 0000000..d8066c1 --- /dev/null +++ b/hw/hw8/hw8.ipynb @@ -0,0 +1,1889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e33c5b0c-d0f1-40c0-b794-3b3471ac73d2", + "metadata": {}, + "source": [ + "# CPSC 330 - Applied Machine Learning \n", + "\n", + "## Homework 8: Word embeddings, time series, and communication\n", + "### Associated lectures: Lectures 17, 19, 20, and ML communication \n", + "\n", + "**Due date: April 12, 2022 at 11:59pm**" + ] + }, + { + "cell_type": "markdown", + "id": "e851ce52-f981-4c4e-9e9e-a740edd12e41", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "- [Submission instructions](#sg) (4%)\n", + "- [Exercise 1 - Exploring pre-trained word embeddings](#1) (24%)\n", + "- [Exercise 2 - Exploring time series data](#2) (16%)\n", + "- [Exercise 3 - Short answer questions](#4) (10%)\n", + "- [Exercise 4 - Communication](#4) (46%)\n", + "- (Optional)[Exercise 5 - Course take away](#5)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a4651888-484b-42a0-95e1-d273e5069205", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.cluster import DBSCAN, KMeans\n", + "from sklearn.compose import ColumnTransformer, make_column_transformer\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.model_selection import (\n", + " GridSearchCV,\n", + " RandomizedSearchCV,\n", + " cross_validate,\n", + " train_test_split,\n", + ")\n", + "from sklearn.pipeline import Pipeline, make_pipeline\n", + "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n", + "\n", + "pd.set_option(\"display.max_colwidth\", 0)" + ] + }, + { + "cell_type": "markdown", + "id": "086914c2-5de1-414a-8770-23bef9f312d0", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "afe22cb5-f825-4dba-b5e3-3538f4afe703", + "metadata": {}, + "source": [ + "## Instructions \n", + "
\n", + "rubric={points:4}\n", + "\n", + "Follow the [homework submission instructions](https://github.com/UBC-CS/cpsc330/blob/master/docs/homework_instructions.md). \n", + "\n", + "**You may work on this homework in a group and submit your assignment as a group.** Below are some instructions on working as a group. \n", + "- The maximum group size is 2. \n", + "- Use group work as an opportunity to collaborate and learn new things from each other. \n", + "- Be respectful to each other and make sure you understand all the concepts in the assignment well. \n", + "- It's your responsibility to make sure that the assignment is submitted by one of the group members before the deadline. \n", + "- You can find the instructions on how to do group submission on Gradescope [here](https://help.gradescope.com/article/m5qz2xsnjy-student-add-group-members)." + ] + }, + { + "cell_type": "markdown", + "id": "69be5b2d-1854-4c63-bcc6-9b6258b7293a", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "859b3f00-a3e5-45d8-b504-22a84ace38cd", + "metadata": {}, + "source": [ + "## Exercise 1: Exploring pre-trained word embeddings \n", + "
\n", + "\n", + "In lecture 17, we talked about natural language processing (NLP). Using pre-trained word embeddings is very common in NLP. It has been shown that pre-trained word embeddings [work well on a variety of text classification tasks](http://www.lrec-conf.org/proceedings/lrec2018/pdf/721.pdf). These embeddings are created by training a model like Word2Vec on a huge corpus of text such as a dump of Wikipedia or a dump of the web crawl. \n", + "\n", + "A number of pre-trained word embeddings are available out there. Some popular ones are: \n", + "\n", + "- [GloVe](https://nlp.stanford.edu/projects/glove/)\n", + " * trained using [the GloVe algorithm](https://nlp.stanford.edu/pubs/glove.pdf) \n", + " * published by Stanford University \n", + "- [fastText pre-trained embeddings for 294 languages](https://fasttext.cc/docs/en/pretrained-vectors.html) \n", + " * trained using the fastText algorithm\n", + " * published by Facebook\n", + " \n", + "In this exercise, you will be exploring GloVe Wikipedia pre-trained embeddings. The code below loads the word vectors trained on Wikipedia using an algorithm called Glove. You'll need `gensim` package for that in your cpsc330 conda environment. \n", + "\n", + "```\n", + "> conda activate cpsc330\n", + "> conda install -c anaconda gensim\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b4823523-ca44-48a3-94bb-f6e453d27f1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n" + ] + } + ], + "source": [ + "import gensim\n", + "import gensim.downloader\n", + "\n", + "print(list(gensim.downloader.info()[\"models\"].keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "83e4717e-215b-4c1b-b08a-9f5adbb52467", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[==================================================] 100.0% 128.1/128.1MB downloaded\n" + ] + } + ], + "source": [ + "# This will take a while to run when you run it for the first time.\n", + "import gensim.downloader as api\n", + "\n", + "glove_wiki_vectors = api.load(\"glove-wiki-gigaword-100\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76ec38c4-ce89-4372-b015-035f4d682132", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "400000" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(glove_wiki_vectors)" + ] + }, + { + "cell_type": "markdown", + "id": "8c78dafb-f712-447a-b870-1fac6c249e5f", + "metadata": {}, + "source": [ + "There are 400,000 word vectors in these pre-trained model. " + ] + }, + { + "cell_type": "markdown", + "id": "ce2a75ac-fd18-4a53-89d3-26f1051c4ef3", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "8119fb78-d2be-4ccf-8c8d-31026563e072", + "metadata": {}, + "source": [ + "### 1.1 Word similarity using pre-trained embeddings\n", + "rubric={points:4}\n", + "\n", + "Now that we have GloVe Wiki vectors (`glove_wiki_vectors`) loaded, let's explore the embeddings. \n", + "\n", + "**Your tasks:**\n", + "\n", + "1. Calculate the cosine similarity for the following word pairs (`word_pairs`) using the [`similarity`](https://radimrehurek.com/gensim/models/keyedvectors.html?highlight=similarity#gensim.models.keyedvectors.KeyedVectors.similarity) method of the model.\n", + "2. Do the similarities make sense? " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "aadd1aa6-6bb8-48d7-a959-691e19d411ec", + "metadata": {}, + "outputs": [], + "source": [ + "word_pairs = [\n", + " (\"coast\", \"shore\"),\n", + " (\"clothes\", \"closet\"),\n", + " (\"old\", \"new\"),\n", + " (\"smart\", \"intelligent\"),\n", + " (\"dog\", \"cat\"),\n", + " (\"tree\", \"lawyer\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "b0331ffe-bf58-4198-bd1e-3b62a5d34319", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "4d528120-c1ff-4203-82b8-404b62ba6bb0", + "metadata": {}, + "source": [ + "### 1.2 Bias in embeddings\n", + "rubric={points:10}\n", + "\n", + "**Your tasks:**\n", + "1. In Lecture 17 we saw that our pre-trained word embedding model output an analogy that reinforced a gender stereotype. Give an example of how using such a model could cause harm in the real world.\n", + "2. Here we are using pre-trained embeddings which are built using a dump of Wikipedia data. Explore whether there are any worrisome biases present in these embeddings or not by trying out some examples. You can use the following two methods or other methods of your choice to explore what kind of stereotypes and biases are encoded in these embeddings. \n", + " - You can use the `analogy` function below which gives words analogies. \n", + " - You can also use [similarity](https://radimrehurek.com/gensim/models/keyedvectors.html?highlight=similarity#gensim.models.keyedvectors.KeyedVectors.similarity) or [distance](https://radimrehurek.com/gensim/models/keyedvectors.html?highlight=distance#gensim.models.keyedvectors.KeyedVectors.distances) methods. (An example is shown below.) \n", + "3. Discuss your observations. Do you observe the gender stereotype we observed in class with GloVe Wikipedia embeddings?\n", + "\n", + "> Note that most of the recent embeddings are de-biased. But you might still observe some biases in the embeddings. Also, not all stereotypes present in pre-trained embeddings are necessarily bad. But you should be aware of them when you use embeddings in your models. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "59f7ef2b-d6b4-4338-a153-77fd4fc9847e", + "metadata": {}, + "outputs": [], + "source": [ + "def analogy(word1, word2, word3, model=glove_wiki_vectors):\n", + " \"\"\"\n", + " Returns analogy word using the given model.\n", + "\n", + " Parameters\n", + " --------------\n", + " word1 : (str)\n", + " word1 in the analogy relation\n", + " word2 : (str)\n", + " word2 in the analogy relation\n", + " word3 : (str)\n", + " word3 in the analogy relation\n", + " model :\n", + " word embedding model\n", + "\n", + " Returns\n", + " ---------------\n", + " pd.dataframe\n", + " \"\"\"\n", + " print(\"%s : %s :: %s : ?\" % (word1, word2, word3))\n", + " sim_words = model.most_similar(positive=[word3, word2], negative=[word1])\n", + " return pd.DataFrame(sim_words, columns=[\"Analogy word\", \"Score\"])" + ] + }, + { + "cell_type": "markdown", + "id": "a0d900c0-3027-4b9f-a750-bca946cf7bdb", + "metadata": {}, + "source": [ + "An example of using similarity between words to explore biases and stereotypes. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "13f0bacc-ba70-43e3-a7be-07c263f48048", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.447236" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glove_wiki_vectors.similarity(\"white\", \"rich\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2d2e6671-c6db-4cc9-9652-faba038e8e50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.51745194" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glove_wiki_vectors.similarity(\"black\", \"rich\")" + ] + }, + { + "cell_type": "markdown", + "id": "19f04b87-5fa0-4eb4-bb50-cb21aa7ffd1c", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "9e6ccd3e-25c1-413d-b079-043fb2949090", + "metadata": {}, + "source": [ + "### 1.3 Representation of all words in English\n", + "rubric={reasoning:2}\n", + "\n", + "**Your tasks:**\n", + "1. The vocabulary size of Wikipedia embeddings is quite large. Do you think it contains **all** words in English language? What would happen if you try to get a word vector that's not in the vocabulary (e.g., \"cpsc330\"). " + ] + }, + { + "cell_type": "markdown", + "id": "464a0fbf-3a9c-42b3-b9cc-5dc474bc2804", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "f1d3cd04-30c9-43f4-9b07-6443ab4ecd7d", + "metadata": {}, + "source": [ + "### 1.4 Classification with pre-trained embeddings\n", + "rubric={points:8}\n", + "\n", + "In lecture 16, we saw that you can conveniently get word vectors with `spaCy` with `en_core_web_md` model. In this exercise, you'll use word embeddings in multi-class text classification task. We will use [HappyDB](https://www.kaggle.com/ritresearch/happydb) corpus which contains about 100,000 happy moments classified into 7 categories: *affection, exercise, bonding, nature, leisure, achievement, enjoy_the_moment*. The data was crowd-sourced via [Amazon Mechanical Turk](https://www.mturk.com/). The ground truth label is not available for all examples, and in this lab, we'll only use the examples where ground truth is available (~15,000 examples). \n", + "\n", + "- Download the data from [here](https://www.kaggle.com/ritresearch/happydb).\n", + "- Unzip the file and copy it in the lab directory.\n", + "\n", + "The code below reads the data CSV (assuming that it's present in the current directory as *cleaned_hm.csv*), cleans it up a bit, and splits it into train and test splits. \n", + "\n", + "**Your tasks:**\n", + "\n", + "1. Train a logistic regression with bag-of-words features and show the classification report on the test set. \n", + "2. Train logistic regression with average embedding representation extracted using spaCy and classification report on the test set. (You can refer to lecture 17 notes for this. Hint: you may want to consider using different [solvers](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) if you see convergence issues). \n", + "3. Discuss your results. Which model would be more interpretable? \n", + "4. Are you observing any benefits of transfer learning here? Briefly discuss. " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "b7b35845-7976-4cda-b798-0c0700868fea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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widreflection_periodoriginal_hmcleaned_hmmodifiednum_sentenceground_truth_categorypredicted_category
hmid
2767620624hWe had a serious talk with some friends of ours who have been flaky lately. They understood and we had a good evening hanging out.We had a serious talk with some friends of ours who have been flaky lately. They understood and we had a good evening hanging out.True2bondingbonding
276784524hI meditated last night.I meditated last night.True1leisureleisure
2769749824hMy grandmother start to walk from the bed after a long time.My grandmother start to walk from the bed after a long time.True1affectionaffection
27705573224hI picked my daughter up from the airport and we have a fun and good conversation on the way home.I picked my daughter up from the airport and we have a fun and good conversation on the way home.True1bondingaffection
27715227224hwhen i received flowers from my best friendwhen i received flowers from my best friendTrue1bondingbonding
\n", + "
" + ], + "text/plain": [ + " wid reflection_period \\\n", + "hmid \n", + "27676 206 24h \n", + "27678 45 24h \n", + "27697 498 24h \n", + "27705 5732 24h \n", + "27715 2272 24h \n", + "\n", + " original_hm \\\n", + "hmid \n", + "27676 We had a serious talk with some friends of ours who have been flaky lately. They understood and we had a good evening hanging out. \n", + "27678 I meditated last night. \n", + "27697 My grandmother start to walk from the bed after a long time. \n", + "27705 I picked my daughter up from the airport and we have a fun and good conversation on the way home. \n", + "27715 when i received flowers from my best friend \n", + "\n", + " cleaned_hm \\\n", + "hmid \n", + "27676 We had a serious talk with some friends of ours who have been flaky lately. They understood and we had a good evening hanging out. \n", + "27678 I meditated last night. \n", + "27697 My grandmother start to walk from the bed after a long time. \n", + "27705 I picked my daughter up from the airport and we have a fun and good conversation on the way home. \n", + "27715 when i received flowers from my best friend \n", + "\n", + " modified num_sentence ground_truth_category predicted_category \n", + "hmid \n", + "27676 True 2 bonding bonding \n", + "27678 True 1 leisure leisure \n", + "27697 True 1 affection affection \n", + "27705 True 1 bonding affection \n", + "27715 True 1 bonding bonding " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"cleaned_hm.csv\", index_col=0)\n", + "sample_df = df.dropna()\n", + "sample_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "384fa13b-83a5-4e23-9280-c4e529937143", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yXKa7qfQXYPD", + "outputId": "8bbf5eeb-0151-4853-a49c-3876279bbeb7" + }, + "outputs": [], + "source": [ + "sample_df = sample_df.rename(\n", + " columns={\"cleaned_hm\": \"moment\", \"ground_truth_category\": \"target\"}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "de04d594-7174-409d-a9fa-c2fd738e2208", + "metadata": {}, + "outputs": [], + "source": [ + "train_df, test_df = train_test_split(sample_df, test_size=0.3, random_state=123)\n", + "X_train, y_train = train_df[\"moment\"], train_df[\"target\"]\n", + "X_test, y_test = test_df[\"moment\"], test_df[\"target\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "73f63a73-0d13-4276-9e79-7ad60575a198", + "metadata": {}, + "outputs": [], + "source": [ + "import spacy\n", + "\n", + "nlp = spacy.load(\"en_core_web_md\")" + ] + }, + { + "cell_type": "markdown", + "id": "161a6ab6-62ef-4fdd-ba0d-5e7e920154a3", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "ec620e19-016a-4476-bb7e-de0c402078d2", + "metadata": {}, + "source": [ + "## Exercise 2: Exploring time series data \n", + "
\n", + "\n", + "In this exercise we'll be looking at a [dataset of avocado prices](https://www.kaggle.com/neuromusic/avocado-prices). You should start by downloading the dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "b22471fe-942e-49aa-8fb8-9d4fbf6b00b3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
02015-12-271.3364236.621036.7454454.8548.168696.878603.6293.250.0conventional2015Albany
12015-12-201.3554876.98674.2844638.8158.339505.569408.0797.490.0conventional2015Albany
22015-12-130.93118220.22794.70109149.67130.508145.358042.21103.140.0conventional2015Albany
32015-12-061.0878992.151132.0071976.4172.585811.165677.40133.760.0conventional2015Albany
42015-11-291.2851039.60941.4843838.3975.786183.955986.26197.690.0conventional2015Albany
\n", + "
" + ], + "text/plain": [ + " Date AveragePrice Total Volume 4046 4225 4770 \\\n", + "0 2015-12-27 1.33 64236.62 1036.74 54454.85 48.16 \n", + "1 2015-12-20 1.35 54876.98 674.28 44638.81 58.33 \n", + "2 2015-12-13 0.93 118220.22 794.70 109149.67 130.50 \n", + "3 2015-12-06 1.08 78992.15 1132.00 71976.41 72.58 \n", + "4 2015-11-29 1.28 51039.60 941.48 43838.39 75.78 \n", + "\n", + " Total Bags Small Bags Large Bags XLarge Bags type year region \n", + "0 8696.87 8603.62 93.25 0.0 conventional 2015 Albany \n", + "1 9505.56 9408.07 97.49 0.0 conventional 2015 Albany \n", + "2 8145.35 8042.21 103.14 0.0 conventional 2015 Albany \n", + "3 5811.16 5677.40 133.76 0.0 conventional 2015 Albany \n", + "4 6183.95 5986.26 197.69 0.0 conventional 2015 Albany " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"avocado.csv\", parse_dates=[\"Date\"], index_col=0)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "3425e59d-9580-4512-8bd2-c8c9b836df8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18249, 13)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "6d0aa8d9-34b6-4401-8b91-77e1342510ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2015-01-04 00:00:00')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Date\"].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "3b64f1d1-9614-44df-b625-52a77c00af9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2018-03-25 00:00:00')" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Date\"].max()" + ] + }, + { + "cell_type": "markdown", + "id": "9dae5238-ac94-4b1b-b368-d972b6582d8a", + "metadata": {}, + "source": [ + "It looks like the data ranges from the start of 2015 to March 2018 (~5 years ago), for a total of 3.25 years or so. Let's split the data so that we have a 6 months of test data." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "5d4d6fb5-1cbb-47e0-a5c4-34b66d026d1a", + "metadata": {}, + "outputs": [], + "source": [ + "split_date = \"20170925\"\n", + "train_df = df[df[\"Date\"] <= split_date]\n", + "test_df = df[df[\"Date\"] > split_date]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "26a3b6ae-1406-48c3-8b5c-e7b65a041852", + "metadata": {}, + "outputs": [], + "source": [ + "assert len(train_df) + len(test_df) == len(df)" + ] + }, + { + "cell_type": "markdown", + "id": "2f5ed401-224b-42d3-a4bd-f67b60c3625b", + "metadata": {}, + "source": [ + "### 2.1\n", + "rubric={points:4}\n", + "\n", + "In the Rain in Australia dataset from lecture, we had different measurements for each Location. What about this dataset: for which categorical feature(s), if any, do we have separate measurements? Justify your answer by referencing the dataset." + ] + }, + { + "cell_type": "markdown", + "id": "ac17a187-bf66-4339-b5f4-3f79cb6948cc", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "a2b56b13-e2ff-45d9-b800-fb7006f92653", + "metadata": {}, + "source": [ + "### 2.2\n", + "rubric={points:4}\n", + "\n", + "In the Rain in Australia dataset, the measurements were generally equally spaced but with some exceptions. How about with this dataset? Justify your answer by referencing the dataset." + ] + }, + { + "cell_type": "markdown", + "id": "16dc1348-c2c6-46f6-bbdf-a77810930ac1", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "d3ab120c-6b2b-4dfb-8765-7367a9577482", + "metadata": {}, + "source": [ + "### 2.3\n", + "rubric={points:4}\n", + "\n", + "In the Rain in Australia dataset, each location was a different place in Australia. For this dataset, look at the names of the regions. Do you think the regions are all distinct, or are there overlapping regions? Justify your answer by referencing the data." + ] + }, + { + "cell_type": "markdown", + "id": "331ec42a-3093-46c5-b708-ca7716f939dc", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "49c9e680-d2b3-432b-884f-f05c3ed5e761", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "eeb32828-c8f6-4344-90bc-7ec214e448e7", + "metadata": {}, + "source": [ + "## Preparation for models\n", + "\n", + "We will use the entire dataset despite any location-based weirdness uncovered in the previous part.\n", + "\n", + "We would like to forecast the avocado price, which is the `AveragePrice` column. The function below is adapted from Lecture 19, with some improvements." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "87ef9e53-9170-4a0c-a249-7cf0715496b8", + "metadata": {}, + "outputs": [], + "source": [ + "def create_lag_feature(\n", + " df, orig_feature, lag, groupby, new_feature_name=None, clip=False\n", + "):\n", + " \"\"\"\n", + " Creates a new feature that's a lagged version of an existing one.\n", + "\n", + " NOTE: assumes df is already sorted by the time columns and has unique indices.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : pandas.core.frame.DataFrame\n", + " The dataset.\n", + " orig_feature : str\n", + " The column name of the feature we're copying\n", + " lag : int\n", + " The lag; negative lag means values from the past, positive lag means values from the future\n", + " groupby : list\n", + " Column(s) to group by in case df contains multiple time series\n", + " new_feature_name : str\n", + " Override the default name of the newly created column\n", + " clip : bool\n", + " If True, remove rows with a NaN values for the new feature\n", + "\n", + " Returns\n", + " -------\n", + " pandas.core.frame.DataFrame\n", + " A new dataframe with the additional column added.\n", + "\n", + " TODO: could/should simplify this function by using `df.shift()`\n", + " \"\"\"\n", + "\n", + " if new_feature_name is None:\n", + " if lag < 0:\n", + " new_feature_name = \"%s_lag%d\" % (orig_feature, -lag)\n", + " else:\n", + " new_feature_name = \"%s_ahead%d\" % (orig_feature, lag)\n", + "\n", + " new_df = df.assign(**{new_feature_name: np.nan})\n", + " for name, group in new_df.groupby(groupby):\n", + " if lag < 0: # take values from the past\n", + " new_df.loc[group.index[-lag:], new_feature_name] = group.iloc[:lag][\n", + " orig_feature\n", + " ].values\n", + " else: # take values from the future\n", + " new_df.loc[group.index[:-lag], new_feature_name] = group.iloc[lag:][\n", + " orig_feature\n", + " ].values\n", + "\n", + " if clip:\n", + " new_df = new_df.dropna(subset=[new_feature_name])\n", + "\n", + " return new_df" + ] + }, + { + "cell_type": "markdown", + "id": "89cbe62a-05a6-4bed-8ae9-b1bb7299c16a", + "metadata": {}, + "source": [ + "We first sort our dataframe properly:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "c0049d46-d7e2-40a4-9d41-74ce2e875a05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
02015-01-041.2240873.282819.5028287.4249.909716.469186.93529.530.0conventional2015Albany
12015-01-111.2441195.081002.8531640.34127.128424.778036.04388.730.0conventional2015Albany
22015-01-181.1744511.28914.1431540.32135.7711921.0511651.09269.960.0conventional2015Albany
32015-01-251.0645147.50941.3833196.16164.1410845.8210103.35742.470.0conventional2015Albany
42015-02-010.9970873.601353.9060017.20179.329323.189170.82152.360.0conventional2015Albany
..........................................
182442018-02-251.5718421.241974.262482.650.0013964.3313698.27266.060.0organic2018WestTexNewMexico
182452018-03-041.5417393.301832.241905.570.0013655.4913401.93253.560.0organic2018WestTexNewMexico
182462018-03-111.5622128.422162.673194.258.9316762.5716510.32252.250.0organic2018WestTexNewMexico
182472018-03-181.5615896.382055.351499.550.0012341.4812114.81226.670.0organic2018WestTexNewMexico
182482018-03-251.6215303.402325.302171.660.0010806.4410569.80236.640.0organic2018WestTexNewMexico
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18249 rows × 13 columns

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" + ], + "text/plain": [ + " Date AveragePrice Total Volume 4046 4225 4770 \\\n", + "0 2015-01-04 1.22 40873.28 2819.50 28287.42 49.90 \n", + "1 2015-01-11 1.24 41195.08 1002.85 31640.34 127.12 \n", + "2 2015-01-18 1.17 44511.28 914.14 31540.32 135.77 \n", + "3 2015-01-25 1.06 45147.50 941.38 33196.16 164.14 \n", + "4 2015-02-01 0.99 70873.60 1353.90 60017.20 179.32 \n", + "... ... ... ... ... ... ... \n", + "18244 2018-02-25 1.57 18421.24 1974.26 2482.65 0.00 \n", + "18245 2018-03-04 1.54 17393.30 1832.24 1905.57 0.00 \n", + "18246 2018-03-11 1.56 22128.42 2162.67 3194.25 8.93 \n", + "18247 2018-03-18 1.56 15896.38 2055.35 1499.55 0.00 \n", + "18248 2018-03-25 1.62 15303.40 2325.30 2171.66 0.00 \n", + "\n", + " Total Bags Small Bags Large Bags XLarge Bags type year \\\n", + "0 9716.46 9186.93 529.53 0.0 conventional 2015 \n", + "1 8424.77 8036.04 388.73 0.0 conventional 2015 \n", + "2 11921.05 11651.09 269.96 0.0 conventional 2015 \n", + "3 10845.82 10103.35 742.47 0.0 conventional 2015 \n", + "4 9323.18 9170.82 152.36 0.0 conventional 2015 \n", + "... ... ... ... ... ... ... \n", + "18244 13964.33 13698.27 266.06 0.0 organic 2018 \n", + "18245 13655.49 13401.93 253.56 0.0 organic 2018 \n", + "18246 16762.57 16510.32 252.25 0.0 organic 2018 \n", + "18247 12341.48 12114.81 226.67 0.0 organic 2018 \n", + "18248 10806.44 10569.80 236.64 0.0 organic 2018 \n", + "\n", + " region \n", + "0 Albany \n", + "1 Albany \n", + "2 Albany \n", + "3 Albany \n", + "4 Albany \n", + "... ... \n", + "18244 WestTexNewMexico \n", + "18245 WestTexNewMexico \n", + "18246 WestTexNewMexico \n", + "18247 WestTexNewMexico \n", + "18248 WestTexNewMexico \n", + "\n", + "[18249 rows x 13 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sort = df.sort_values(by=[\"region\", \"type\", \"Date\"]).reset_index(drop=True)\n", + "df_sort" + ] + }, + { + "cell_type": "markdown", + "id": "fbaee71e-d45c-48dc-81a3-ebf7195cbea4", + "metadata": {}, + "source": [ + "We then call `create_lag_feature`. This creates a new column in the dataset `AveragePriceNextWeek`, which is the following week's `AveragePrice`. We have set `clip=True` which means it will remove rows where the target would be missing." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "90736df7-04b7-40d4-a835-e8eb899da7f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge BagstypeyearregionAveragePriceNextWeek
02015-01-041.2240873.282819.5028287.4249.909716.469186.93529.530.0conventional2015Albany1.24
12015-01-111.2441195.081002.8531640.34127.128424.778036.04388.730.0conventional2015Albany1.17
22015-01-181.1744511.28914.1431540.32135.7711921.0511651.09269.960.0conventional2015Albany1.06
32015-01-251.0645147.50941.3833196.16164.1410845.8210103.35742.470.0conventional2015Albany0.99
42015-02-010.9970873.601353.9060017.20179.329323.189170.82152.360.0conventional2015Albany0.99
.............................................
182432018-02-181.5617597.121892.051928.360.0013776.7113553.53223.180.0organic2018WestTexNewMexico1.57
182442018-02-251.5718421.241974.262482.650.0013964.3313698.27266.060.0organic2018WestTexNewMexico1.54
182452018-03-041.5417393.301832.241905.570.0013655.4913401.93253.560.0organic2018WestTexNewMexico1.56
182462018-03-111.5622128.422162.673194.258.9316762.5716510.32252.250.0organic2018WestTexNewMexico1.56
182472018-03-181.5615896.382055.351499.550.0012341.4812114.81226.670.0organic2018WestTexNewMexico1.62
\n", + "

18141 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Date AveragePrice Total Volume 4046 4225 4770 \\\n", + "0 2015-01-04 1.22 40873.28 2819.50 28287.42 49.90 \n", + "1 2015-01-11 1.24 41195.08 1002.85 31640.34 127.12 \n", + "2 2015-01-18 1.17 44511.28 914.14 31540.32 135.77 \n", + "3 2015-01-25 1.06 45147.50 941.38 33196.16 164.14 \n", + "4 2015-02-01 0.99 70873.60 1353.90 60017.20 179.32 \n", + "... ... ... ... ... ... ... \n", + "18243 2018-02-18 1.56 17597.12 1892.05 1928.36 0.00 \n", + "18244 2018-02-25 1.57 18421.24 1974.26 2482.65 0.00 \n", + "18245 2018-03-04 1.54 17393.30 1832.24 1905.57 0.00 \n", + "18246 2018-03-11 1.56 22128.42 2162.67 3194.25 8.93 \n", + "18247 2018-03-18 1.56 15896.38 2055.35 1499.55 0.00 \n", + "\n", + " Total Bags Small Bags Large Bags XLarge Bags type year \\\n", + "0 9716.46 9186.93 529.53 0.0 conventional 2015 \n", + "1 8424.77 8036.04 388.73 0.0 conventional 2015 \n", + "2 11921.05 11651.09 269.96 0.0 conventional 2015 \n", + "3 10845.82 10103.35 742.47 0.0 conventional 2015 \n", + "4 9323.18 9170.82 152.36 0.0 conventional 2015 \n", + "... ... ... ... ... ... ... \n", + "18243 13776.71 13553.53 223.18 0.0 organic 2018 \n", + "18244 13964.33 13698.27 266.06 0.0 organic 2018 \n", + "18245 13655.49 13401.93 253.56 0.0 organic 2018 \n", + "18246 16762.57 16510.32 252.25 0.0 organic 2018 \n", + "18247 12341.48 12114.81 226.67 0.0 organic 2018 \n", + "\n", + " region AveragePriceNextWeek \n", + "0 Albany 1.24 \n", + "1 Albany 1.17 \n", + "2 Albany 1.06 \n", + "3 Albany 0.99 \n", + "4 Albany 0.99 \n", + "... ... ... \n", + "18243 WestTexNewMexico 1.57 \n", + "18244 WestTexNewMexico 1.54 \n", + "18245 WestTexNewMexico 1.56 \n", + "18246 WestTexNewMexico 1.56 \n", + "18247 WestTexNewMexico 1.62 \n", + "\n", + "[18141 rows x 14 columns]" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_hastarget = create_lag_feature(\n", + " df_sort, \"AveragePrice\", +1, [\"region\", \"type\"], \"AveragePriceNextWeek\", clip=True\n", + ")\n", + "df_hastarget" + ] + }, + { + "cell_type": "markdown", + "id": "5dbfa73e-48d7-49be-af1c-dba397d9f946", + "metadata": {}, + "source": [ + "I will now split the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "8e952769-dbd1-4052-855f-2d2f2b4101d0", + "metadata": {}, + "outputs": [], + "source": [ + "train_df = df_hastarget[df_hastarget[\"Date\"] <= split_date]\n", + "test_df = df_hastarget[df_hastarget[\"Date\"] > split_date]" + ] + }, + { + "cell_type": "markdown", + "id": "65fb19b2-0a17-4133-bf78-3dea9aa61526", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "848e951f-8dde-4a34-bf4e-b2c3a6270bb0", + "metadata": {}, + "source": [ + "### 2.4 Baseline\n", + "rubric={points:4}\n", + "\n", + "Let's try a baseline. Previously we used `DummyClassifier` or `DummyRegressor` as a baseline. This time, we'll do something else as a baseline: we'll assume the price stays the same from this week to next week. So, we'll set our prediction of \"AveragePriceNextWeek\" exactly equal to \"AveragePrice\", assuming no change. That is kind of like saying, \"If it's raining today then I'm guessing it will be raining tomorrow\". This simplistic approach will not get a great score but it's a good starting point for reference. If our model does worse than this, it must not be very good. \n", + "\n", + "Using this baseline approach, what $R^2$ do you get?" + ] + }, + { + "cell_type": "markdown", + "id": "a0d241cd-4669-46d9-bb21-1fd8006a39e8", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "6d0615dc-63c2-458f-844b-f17d30758ab1", + "metadata": {}, + "source": [ + "### (Optional) 2.5 Modeling\n", + "rubric={points:2}\n", + "\n", + "Now that the baseline is done, let's build some models to forecast the average avocado price a week later. Experiment with a few approachs for encoding the date. Justify the decisions you make. Which approach worked best? Report your test score and briefly discuss your results.\n", + "\n", + "> because we only have 2 splits here, we need to be a bit wary of overfitting on the test set. Try not to test on it a ridiculous number of times. If you are interested in some proper ways of dealing with this, see for example sklearn's [TimeSeriesSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html), which is like cross-validation for time series data." + ] + }, + { + "cell_type": "markdown", + "id": "51fe2285-38d1-409a-9f4c-3f5dfd50622a", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "15ad9e61-e5f6-4025-8356-f48f651a8e1e", + "metadata": {}, + "source": [ + "## Exercise 3: Short answer questions \n", + "\n", + "Each question is worth 2 points." + ] + }, + { + "cell_type": "markdown", + "id": "972060d9-742d-47ae-82c5-74b258de16e7", + "metadata": {}, + "source": [ + "### 3.1\n", + "rubric={points:4}\n", + "\n", + "The following questions pertain to Lecture 18 on time series data:\n", + "\n", + "1. Sometimes a time series has missing time points or, worse, time points that are unequally spaced in general. Give an example of a real world situation where the time series data would have unequally spaced time points.\n", + "2. In class we discussed two approaches to using temporal information: encoding the date as one or more features, and creating lagged versions of features. Which of these (one/other/both/neither) two approaches would struggle with unequally spaced time points? Briefly justify your answer." + ] + }, + { + "cell_type": "markdown", + "id": "ef053d93-f20e-417f-81ae-35edb701020c", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "bc3703ae-7d56-4987-b70b-4e222f1d8b15", + "metadata": {}, + "source": [ + "### 3.2\n", + "rubric={points:6}\n", + "\n", + "The following questions pertain to Lecture 19 on survival analysis. We'll consider the use case of customer churn analysis.\n", + "\n", + "1. What is the problem with simply labeling customers are \"churned\" or \"not churned\" and using standard supervised learning techniques, as we did in hw4?\n", + "2. Consider customer A who just joined last week vs. customer B who has been with the service for a year. Who do you expect will leave the service first: probably customer A, probably customer B, or we don't have enough information to answer?\n", + "3. If a customer's survival function is almost flat during a certain period, how do we interpret that?" + ] + }, + { + "cell_type": "markdown", + "id": "9d8ffdd0-8c1e-4042-8414-ac8e57caf980", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "ed594c68-91c3-45d9-baec-6ac38a02c971", + "metadata": {}, + "source": [ + "## Exercise 4: Communication \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5767926d-c17d-4a93-b59a-7242a4c76ff0", + "metadata": {}, + "source": [ + "### Exercise 4.1 Blog post \n", + "rubric={points:40}\n", + "\n", + "Write up your analysis from hw6 or any other assignment or your side machine learning related project in a \"blog post\" or report format. **You can write the post in Markdown in the notebook**, no need to write a real blog post (though you can if you want too!).\n", + "\n", + "The target audience for your blog post is someone like yourself right before you took this course. They don't necessarily have ML knowledge, but they have a solid foundation in technical matters. The post should focus on explaining **your results and what you did** in a way that's understandable to such a person, **not** a lesson trying to teach someone about machine learning. Again: focus on the results and why they are interesting; avoid pedagogical content.\n", + "\n", + "Your post must include the following elements (not necessarily in this order):\n", + "\n", + "- Description of the problem/decision.\n", + "- Description of the dataset (the raw data and/or some EDA).\n", + "- Description of the model.\n", + "- Description your results, both quantitatively and qualitatively. Make sure to refer to the original problem/decision.\n", + "- A section on caveats, describing at least 3 reasons why your results might be incorrect, misleading, overconfident, or otherwise problematic. Make reference to your specific dataset, model, approach, etc. To check that your reasons are specific enough, make sure they would not make sense, if left unchanged, to most students' submissions; for example, do not just say \"overfitting\" without explaining why you might be worried about overfitting in your specific case.\n", + "- At least 3 visualizations. These visualizations must be embedded/interwoven into the text, not pasted at the end. The text must refer directly to each visualization. For example \"as shown below\" or \"the figure demonstrates\" or \"take a look at Figure 1\", etc. It is **not** sufficient to put a visualization in without referring to it directly.\n", + "\n", + "A reasonable length for your entire post would be **800 words**. The maximum allowed is **1000 words**." + ] + }, + { + "cell_type": "markdown", + "id": "6169eefb-18a8-4e13-b7ca-1b3539a79215", + "metadata": {}, + "source": [ + "#### Example blog posts\n", + "\n", + "Here are some examples of applied ML blog posts that you may find useful as inspiration. The target audiences of these posts aren't necessarily the same as yours, and these posts are longer than yours, but they are well-structured and engaging. You are **not required to read these** posts as part of this assignment - they are here only as examples if you'd find that useful.\n", + "\n", + "From the UBC Master of Data Science blog, written by a past student:\n", + "\n", + "- https://ubc-mds.github.io/2019-07-26-predicting-customer-probabilities/\n", + "\n", + "This next one uses R instead of Python, but that might be good in a way, as you can see what it's like for a reader that doesn't understand the code itself (the target audience for your post here):\n", + "\n", + "- https://rpubs.com/RosieB/taylorswiftlyricanalysis\n", + "\n", + "Finally, here are a couple interviews with winners from Kaggle competitions. The format isn't quite the same as a blog post, but you might find them interesting/relevant:\n", + "\n", + "- https://medium.com/kaggle-blog/instacart-market-basket-analysis-feda2700cded\n", + "- https://medium.com/kaggle-blog/winner-interview-with-shivam-bansal-data-science-for-good-challenge-city-of-los-angeles-3294c0ed1fb2\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfdd094-eeb6-4f00-a3bc-5a6105eedb12", + "metadata": {}, + "source": [ + "#### A note on plagiarism\n", + "\n", + "You may **NOT** include text or visualizations that were not written/created by you. If you are in any doubt as to what constitutes plagiarism, please just ask. For more information see the [UBC Academic Misconduct policies](http://www.calendar.ubc.ca/vancouver/index.cfm?tree=3,54,111,959). Please don't copy this from somewhere 🙏. If you can't do it." + ] + }, + { + "cell_type": "markdown", + "id": "4052395d-a695-4063-97b6-46c4e13016d8", + "metadata": {}, + "source": [ + "

" + ] + }, + { + "cell_type": "markdown", + "id": "d59667c9-db6a-4c12-a556-5b9815ef3564", + "metadata": {}, + "source": [ + "### Exercise 4.2\n", + "rubric={points:6}\n", + "\n", + "Describe one effective communication technique that you used in your post, or an aspect of the post that you are particularly satisfied with.\n", + "\n", + "Max 3 sentences" + ] + }, + { + "cell_type": "markdown", + "id": "87ea9c37-34c9-4b3e-a2df-e00cedd3e8ae", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "04cefc8e-cf76-4c27-9aa6-c560cbc0fc2b", + "metadata": {}, + "source": [ + "### (Optional) Exercise 5 \n", + "rubric={points:1}\n", + "\n", + "**Your tasks:**\n", + "\n", + "What is your biggest takeaway from this course? \n", + "\n", + "> I'm looking forward to read your answers. " + ] + }, + { + "cell_type": "markdown", + "id": "a2fb9e2f-a2d2-4f56-9fb4-c91492e4801b", + "metadata": {}, + "source": [ + "



" + ] + }, + { + "cell_type": "markdown", + "id": "ab723dc5-4ea6-4c44-ace9-bf345bf8c120", + "metadata": {}, + "source": [ + "## Submission instructions \n", + "\n", + "**PLEASE READ:** When you are ready to submit your assignment do the following:\n", + "\n", + "1. Run all cells in your notebook to make sure there are no errors by doing `Kernel -> Restart Kernel and Clear All Outputs` and then `Run -> Run All Cells`. \n", + "2. Notebooks with cell execution numbers out of order or not starting from “1” will have marks deducted. Notebooks without the output displayed may not be graded at all (because we need to see the output in order to grade your work).\n", + "3. Upload the assignment using Gradescope's drag and drop tool. Check out this [Gradescope Student Guide](https://lthub.ubc.ca/guides/gradescope-student-guide/) if you need help with Gradescope submission. " + ] + }, + { + "cell_type": "markdown", + "id": "1b4e160c-d947-4123-8e67-fa3c89c9aa8f", + "metadata": {}, + "source": [ + "### Congratulations on finishing all homework assignments! :clap: :clap: " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "15f3bee4-0171-4465-838f-e5ac8a943e10", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "Image(\"eva-congrats.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cpsc330]", + "language": "python", + "name": "conda-env-cpsc330-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}