{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Overview**","metadata":{"datalore":{"node_id":"YwLfZNO4ErjhQDcwwj8qMA","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"## ***\"Predicting in-race win probabilities for multi-class races\"*: MIT Advanced Analytics Project**","metadata":{"datalore":{"node_id":"kCa3CkRSdJ0UlBFTmrsJG2","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"### We are delighted to present our submission to the **Big Data Derby 2022**, hosted by Kaggle, the New York Thoroughbred Horsemen's Association, and the New York Racing Association. We worked on this fascinating challenge as a class project for [Advanced Analytics Edge (15.072)](https://ocw.mit.edu/courses/15-071-the-analytics-edge-spring-2017/pages/syllabus/) at MIT, a course dedicated to the application of machine learning and data science in real-world applications.\n\n### For our submission, we explored the topic of predicting live in-race win probabilities for multi-class races: that is, we created a prediction model that can predict the outcome of a horse race, in terms of finishing positions, at any time point (trakus index) during a race. Moreover, unlike traditional classifiers, we have built a single model that can predict the outcome of races with a differing number of horses.\n\n### By doing so, we also investigated the overall predictability of races at various points in time, and explored the factors that predict race outcomes under different tracks, conditions and race types. We also gained insights into different factors in horse racing that contribute to the physical strains placed on horses. We hope that some of our features that we created can be used to improve equine welfare by the NYTHA and NYRA.\n\n### Before you read through our notebook, we strongly recommend that you see the [Explainer Video](https://ocw.mit.edu/courses/15-071-the-analytics-edge-spring-2017/pages/syllabus/) we created, where we talk through the motivation of our project, our methodology and our results.\n\n### As a summary of the result of our work, please enjoy the animation below. Depicted is a typical example of a horse race from the data set, and the live changing win-probability predictions from our model as the race progresses.\n\nThis animation uses the inner tracks created in the [Inner Track Cell](attachment:./#inner_tracks), and probabilities output from our [model](attachment:./#model_output) created later in the notebook.","metadata":{"datalore":{"node_id":"pf8zHEqlwOu9eUTSkKDvXc","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# General packages\nimport seaborn, random\nimport swifter\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport datetime\nfrom scipy import spatial\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport alphashape\nfrom descartes import PolygonPatch\nfrom sklearn.cluster import KMeans\nimport math\nfrom scipy.interpolate import splprep, splev\nimport itertools\n%matplotlib inline\n\n# Seeding randomness\nrandom.seed(42)\nnp.random.seed(42)\n\n# Sklearn and scipy\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\n# Models\nfrom sklearn import tree # for decision tree models\nfrom sklearn.ensemble import RandomForestClassifier\nfrom xgboost import XGBClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\n\n# Performance Metrics\nfrom sklearn.metrics import precision_recall_fscore_support as score\nfrom sklearn.metrics import f1_score, accuracy_score, roc_auc_score, recall_score\nfrom sklearn.metrics import precision_score, accuracy_score, classification_report, confusion_matrix\nfrom sklearn.model_selection import GridSearchCV, StratifiedKFold\nfrom sklearn.preprocessing import StandardScaler\n\n# Extra Cosmetic Packages\nfrom scipy import stats\nfrom tqdm import tqdm\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nfrom fuzzywuzzy import process","metadata":{"datalore":{"node_id":"oPaLw8jL1GsWfP8Loc3lzm","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Reading dataset\ndf = pd.read_csv('small_kaggle_data.csv',index_col = \"Unnamed: 0\",dtype = {\"pg\":str})\ndf[df.select_dtypes(np.float64).columns] = df.select_dtypes(np.float64).astype(np.float32)\ndf = df.sort_values(['race_date','track_id', 'race_number','trakus_index','pg']).reset_index(drop=True)","metadata":{"datalore":{"node_id":"3Z4QphbCvgHF84lKJsjJTF","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### For ease of readability, we have pre-processed the dataset in-advance. Please see the  [Inner Tracks cell](attachment:./#inner_tracks)","metadata":{"datalore":{"node_id":"LF001XXh2IhAzoBgnoZx4I","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"![MIT-MBAn-Logo%20%281%29.png](attachment:./MIT-MBAn-Logo%20%281%29.png)","metadata":{"datalore":{"node_id":"51ftYmrYA7Fsf4sREOtrBH","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"attachments":{"./MIT-MBAn-Logo%20%281%29.png":{"image/png":"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Dhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDhw4cODAgQMHDmiHDmQAAAAABvlb3+MrhAwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYryY2yAAAA4tJREFUMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYeA4Ew5B7Wl1cSA4AAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"","metadata":{"datalore":{"node_id":"9LYgIYhHUw0I26rgrchKX4","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"","metadata":{"datalore":{"node_id":"GW0K5bPCzirPA4NlleMkcl","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"","metadata":{"datalore":{"node_id":"WCKfiviWoLq9WsHmgNfds2","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"animation_probs = pd.read_csv('sample_probs.csv')\nanimation_probs.set_index(['race_date', 'race_number', 'track_id'], inplace=True)\nanimation_probs = animation_probs.loc[animation_probs.index.unique()[0]]\nprob_names = animation_probs.jockey.unique()\n\nanimation_race = df[(df['track_id']=='Saratoga') & (df['course_type']=='turf')]\nanimation_race.set_index(['race_date', 'race_number', 'track_id'], inplace=True)\nanimation_race = animation_race.loc[animation_probs.index.unique()[0]]\njockeys = animation_race.jockey.unique()\n\ninner_track_sar = pd.read_csv('sar_inner_tracks_df_reduced.csv')\nouter_track_sar = pd.read_csv('sar_outer_track_df.csv')\n\n\nfinx = animation_race[animation_race['position_at_finish']==1].iloc[-1]['latitude']\nfiny = animation_race[animation_race['position_at_finish']==1].iloc[-1]['longitude']\ncoords_sar_T = np.array(inner_track_sar[['sar_T_lat', 'sar_T_lon']].values.tolist())\ntree_sar_T = spatial.KDTree(coords_sar_T)\nq1 = tree_sar_T.query(np.array([finx, finy]), 1)\n\ncoords_sar_T_out = np.array(outer_track_sar[['T_lat', 'T_lon']].values.tolist())\ntree_sar_T_out = spatial.KDTree(coords_sar_T_out)\nq2 = tree_sar_T_out.query(np.array([inner_track_sar['sar_T_lat'][q1[1]], inner_track_sar['sar_T_lon'][q1[1]]]),1)\n\nfin_line_x = np.array([inner_track_sar['sar_T_lat'][q1[1]], outer_track_sar['T_lat'][q2[1]]])\nfin_line_y = np.array([inner_track_sar['sar_T_lon'][q1[1]], outer_track_sar['T_lon'][q2[1]]])\n\n\nfig_an = make_subplots(rows=3, cols=1, specs=[[{\"rowspan\":2}],\n           [None],\n           [{}]],\nsubplot_titles = ('Race', 'In-race probabilities'))\n\n\nfor c, jockey in enumerate(animation_race.jockey.unique()):\n  fig_an.add_trace(go.Scatter(\n      x= fin_line_x,\n        y= fin_line_y,\n        mode = 'markers',\n        hoverinfo='name+x+y',\n        legendgroup= jockey,\n        name = jockey,\n        showlegend= True), row=1, col=1)\nfig_an.add_trace(go.Scatter(x=inner_track_sar['sar_T_lat'], y=inner_track_sar['sar_T_lon'], mode='lines',line=dict(color=\"#964B00\"),name='Inner Track'), row=1, col=1)\nfig_an.add_scatter(x=outer_track_sar['T_lat'], y=outer_track_sar['T_lon'], mode='lines',line=dict(color=\"#964B00\"), name='Outer Track', row=1, col=1)\nfig_an.add_scatter(x=fin_line_x, y=fin_line_y, mode='lines', line=dict(color=\"#000000\"), name='Finish Line', row=1, col=1)\n\nfor c, jockey in enumerate(animation_race.jockey.unique()):\n  fig_an.add_trace(go.Scatter(\n      x= [],\n        y= [],\n        mode = 'lines',\n        hoverinfo='name',\n        legendgroup= jockey,\n        name= jockey,\n        showlegend= True), row=3, col=1)\n\nfig_an.update_layout(xaxis2 = dict(range=[0, max(animation_race['trakus_index'])]))\nfig_an.update_layout(yaxis2 = dict(range=[0, 1]))\nfig_an.update_layout(width=1000, height=1000);\n\nfor k, jockey in enumerate(jockeys):\n    exec(f't_{k} = animation_race[animation_race[\"jockey\"]==jockey]')\n\nfor k, jockey in enumerate(prob_names):\n    exec(f'p_{k} = animation_probs[animation_probs[\"jockey\"]==jockey]')\n\nnumber_frames = max(animation_race['trakus_index'])\nframes = [dict(\n    name=k,\n    data=[go.Scatter(x=np.array(t_0['latitude'].iloc[k]), y=np.array(t_0['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_1['latitude'].iloc[k]), y=np.array(t_1['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_2['latitude'].iloc[k]), y=np.array(t_2['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_3['latitude'].iloc[k]), y=np.array(t_3['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_4['latitude'].iloc[k]), y=np.array(t_4['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_5['latitude'].iloc[k]), y=np.array(t_5['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_6['latitude'].iloc[k]), y=np.array(t_6['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_7['latitude'].iloc[k]), y=np.array(t_7['longitude'].iloc[k])),\n    go.Scatter(x=np.array(t_8['latitude'].iloc[k]), y=np.array(t_8['longitude'].iloc[k])),\n    go.Scatter(visible=True),\n    go.Scatter(visible=True),\n    go.Scatter(visible=True),\n    go.Scatter(x=np.array(p_0['trakus_index'].iloc[:k]), y=np.array(p_0['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_1['trakus_index'].iloc[:k]), y=np.array(p_1['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_2['trakus_index'].iloc[:k]), y=np.array(p_2['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_3['trakus_index'].iloc[:k]), y=np.array(p_3['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_4['trakus_index'].iloc[:k]), y=np.array(p_4['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_5['trakus_index'].iloc[:k]), y=np.array(p_5['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_6['trakus_index'].iloc[:k]), y=np.array(p_6['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_7['trakus_index'].iloc[:k]), y=np.array(p_7['race_prob'].iloc[:k])),\n    go.Scatter(x=np.array(p_8['trakus_index'].iloc[:k]), y=np.array(p_8['race_prob'].iloc[:k]))], \n    traces=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] \n) for k in range(number_frames)]\n\n\nupdatemenus = [dict(type='buttons',\n                    buttons=[dict(label='Play',\n                                  method='animate',\n                                  args=[[f'{k}' for k in range(number_frames)],\n                                        dict(frame=dict(duration=20, redraw=False),\n                                             transition=dict(duration=0),\n                                             easing='linear',\n                                             fromcurrent=True,\n                                             mode='immediate'\n                                             )]),\n                             dict(label='Pause',\n                                  method='animate',\n                                  args=[[None],\n                                        dict(frame=dict(duration=0, redraw=False),\n                                             transition=dict(duration=0),\n                                             mode='immediate'\n                                             )])\n                             ],\n                    direction='left',\n                    pad=dict(r=10, t=85),\n                    showactive=True, x=0.1, y=0, xanchor='right', yanchor='top')\n               ]\n\n\n# Slider\nsliders = [{'yanchor': 'top',\n            'xanchor': 'left',\n            'currentvalue': {'font': {'size': 16}, 'prefix': 'Frame: ', 'visible': True, 'xanchor': 'right'},\n            'transition': {'duration': 0, 'easing': 'linear'},\n            'pad': {'b': 10, 't': 50},\n            'len': 0.9, 'x': 0.1, 'y': 0,\n            'steps': [{'args': [[k], {'frame': {'duration': 0, 'easing': 'linear', 'redraw': False},\n                                      'transition': {'duration': 0, 'easing': 'linear'}}],\n                       'label': k, 'method': 'animate'} for k in range(number_frames)\n                      ]}]\n\nfig_an.update(frames=frames),\nfig_an.update_layout(updatemenus=updatemenus,\n                  sliders=sliders)\nfig_an.show()","metadata":{"datalore":{"node_id":"Lk6Xn3qEaPYEo5WYj2nTcD","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Exploratory Data Analysis**","metadata":{"datalore":{"node_id":"bBbBz94Z2xmqTlilylcQlu","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## General Data Engineering: Finding the Inner Track\n### The data provided for this challenge (in particular the horse coordinate data) has the potential to provide incredible insights into predictors for horse race outcomes, and the physical strain exerted on horses throughout the race. However, the path that competitors run in a race is not a naturally occuring phenomena. Horses run on a track. In particular, they run a set course where they must at all times remain on the outside of some inner boundary. Finding an accurate representation of this inner boundary therefore can unlock insights into the position, path efficiency, and direction of horses running in a race.\n\n### In the absence of precise data on the coordinates of the inner track, one may assume that an interpolated path through the closest horse coordinates to this inner path at every point over the course of the races provides a fairly accurate estimate.\n\n### As we don't have labels classifying where on the track the horse coordinates are, we must turn to unsupervised learning.\n\n### Firstly, we must find a hyperplane that partitions the track into two halves. We can then find the closest horse coordinates on either side of this line to find our inner track.\n\n### We now present an example of how the inner track of the Belmont track, course type \"turf\" was found. The full code for finding the inner and outer tracks can be found in the [Inner Tracks cell](attachment:./#inner_tracks) at the bottom of the notebook.\n\n### Firstly we find the centre of the track using K-Means clustering, and the convex hull of the horse coordinates from the dataset.","metadata":{"datalore":{"node_id":"9Nzv8p2BEmbMMxrzVXAYwb","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"data_for_tracks = pd.read_csv(\"nyra_2019_complete.csv\")\ndata_for_tracks.columns = [\n  \"track_id\",\n  \"race_date\",\n  \"race_number\",\n  \"program_number\",\n  \"trakus_index\",\n  \"latitude\",\n  \"longitude\",\n  \"distance_id\",\n  \"course_type\",\n  \"track_condition\",\n  \"run_up_distance\",\n  \"race_type\",\n  \"purse\",\n  \"post_time\",\n  \"weight_carried\",\n  \"jockey\",\n  \"odds\",\n  \"position_at_finish\"\n  ]\n\n# Firstly we rotate the coordinates for better accuracy in the algorithm for finding\n# the inner track\nbel = data_for_tracks[data_for_tracks['track_id']=='BEL']\ndel data_for_tracks\nbel_coords = np.array(bel[['latitude', 'longitude']].values.tolist())\nbel_I = bel[bel['course_type']=='I']\nbel_coords_I = np.array(bel_I[['latitude', 'longitude']].values.tolist())\n\n# Finding the centre of the track\nkmeans = KMeans(n_clusters=2)\nlabs_bel = kmeans.fit_predict(bel_coords_I)\nk2_centroids_bel = kmeans.cluster_centers_\ncentre_bel = [(k2_centroids_bel[0,0] + k2_centroids_bel[1,0])/2, (k2_centroids_bel[0,1] + k2_centroids_bel[1,1])/2]\nbel['rotated_lon'] = bel.apply(lambda x: rotate_y((centre_bel),(x.latitude, x.longitude),0.5*np.pi), axis=1)\nbel['rotated_lat'] = bel.apply(lambda x: rotate_x((centre_bel),(x.latitude, x.longitude),0.5*np.pi), axis=1)\n\n# Now we isolate the coordinates of horses running round the Belmont turf course\nbel_T = bel[bel['course_type']=='T']\nbel_coords_T = np.array(bel_T[['rotated_lat', 'rotated_lon']].values.tolist())\n\n# Now we find the convex hull of the belmont turf coordinates\nbel_hull_T = alphashape.alphashape(bel_coords_T, 0)\nbel_hull_T_pts = bel_hull_T.exterior.coords.xy\nbel_hull_T","metadata":{"datalore":{"node_id":"ejkz0lsAX7q2UuaLg5HlVR","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Inspection of the concave hull of the horse coordinates allows us to find the gradient of the track straights (given the outer and inner track straights run parallel).","metadata":{"datalore":{"node_id":"NV6vwr0USmm38sEbmiuAtS","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"plt.plot(bel_hull_T_pts[0][22:35], bel_hull_T_pts[1][22:35], 'r-')\nplt.plot(bel_hull_T_pts[0][0:22], bel_hull_T_pts[1][0:22], 'b-')\nplt.plot(bel_hull_T_pts[0][35:], bel_hull_T_pts[1][35:], 'g-')\nplt.show()\n\ngrad_bel_T = (bel_hull_T_pts[1][35] - bel_hull_T_pts[1][22]) / (bel_hull_T_pts[0][35] - bel_hull_T_pts[0][22])\n\ndef sepline_bel_T(x):\n    y = centre_bel[1] + grad_bel_T*(x-centre_bel[0])\n    return y\n\nyvals_bel_T = []\nfor x in bel_hull_T_pts[0]:\n    yvals_bel_T.append(centre_bel[1]+grad_bel_T*(x-centre_bel[0]))","metadata":{"datalore":{"node_id":"H2Io1zxNip0YZRDhqRKSHF","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"bel_coords_Tf_T = pd.DataFrame(bel_coords_T)\nbel_coordstrn_T = bel_coords_Tf_T.values.T\nbel_a_T = list(zip(bel_coordstrn_T[0], bel_coordstrn_T[1]))\nbel_coords_Tf_T['coords'] = bel_a_T\nbel_coords_Tf_T['line_y'] = bel_coords_Tf_T[0].apply(lambda x: sepline_bel_T(x))\nlower_bel_T = bel_coords_Tf_T[bel_coords_Tf_T[1]<bel_coords_Tf_T.line_y]\nupper_bel_T = bel_coords_Tf_T[bel_coords_Tf_T[1]>bel_coords_Tf_T.line_y]\nplt.scatter(lower_bel_T[0], lower_bel_T[1], color='pink', s=0.01)\nplt.scatter(upper_bel_T[0], upper_bel_T[1], color='green', s=0.01)\nplt.plot(bel_coords_T[:,0], sepline_bel_T(bel_coords_T[:,0]), 'r-')\nplt.show()","metadata":{"datalore":{"node_id":"IamxxRYDA01rNk1ckpBaT1","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Passing a line with the same gradient as the track straights through the centre point of the track provides a strong separating hyperplane. After this, we can classify horse coordinates into either half of the track, and then by splitting up the track into intervals, find the closest horse coordinates in each interval to the separating hyperplane. Running a B-spline through this set of closest points from the upper and lower track halves gives our estimate for the inner track.\n\n### To find an estimate for the outer track (although less important), one can utilise the concave hull of the set of horse coordinates.","metadata":{"datalore":{"node_id":"8cYhs862IoeSfSeFsRYgoU","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"min_x_upr_bel_T = min(upper_bel_T[0])\nmax_x_upr_bel_T = max(upper_bel_T[0])\nxvals_upr_bel_T = np.linspace(min_x_upr_bel_T, max_x_upr_bel_T, 200)\nxvals_upr_use_bel_T = []\nyvals_upr_bel_T = []\nfor i in range(1,199,2):\n    subset = upper_bel_T[upper_bel_T[0].between(xvals_upr_bel_T[i-1], xvals_upr_bel_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_T(xvals_upr_bel_T[i]))\n        arg = series.argmin()\n        yvals_upr_bel_T.append(subset[1].iloc[arg])\n        xvals_upr_use_bel_T.append(xvals_upr_bel_T[i])\n\nmin_x_lwr_bel_T = min(lower_bel_T[0])\nmax_x_lwr_bel_T = max(upper_bel_T[0])\nxvals_lwr_bel_T = np.linspace(min_x_lwr_bel_T, max_x_lwr_bel_T, 200)\nxvals_lwr_use_bel_T = []\nyvals_lwr_bel_T = []\nfor i in range(1,199,2):\n    subset = lower_bel_T[lower_bel_T[0].between(xvals_lwr_bel_T[i-1], xvals_lwr_bel_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_T(xvals_lwr_bel_T[i]))\n        arg = series.argmin()\n        yvals_lwr_bel_T.append(subset[1].iloc[arg])\n        xvals_lwr_use_bel_T.append(xvals_lwr_bel_T[i])\n    \ntrack_vals_bel_T1 = np.array([(xvals_upr_use_bel_T[i], yvals_upr_bel_T[i]) for i in range(8,len(xvals_upr_use_bel_T)-1)])\ntrack_vals_bel_T2 = np.array([(xvals_lwr_use_bel_T[i], yvals_lwr_bel_T[i]) for i in range(len(xvals_lwr_use_bel_T)-3,3,-1)])\ntrack_vals_bel_T = np.concatenate((track_vals_bel_T1, track_vals_bel_T2))\n\ntck_bel_T, u_bel_T = splprep(track_vals_bel_T.T, u=None, s=0.0, per=1) \nu_new_bel_T = np.linspace(u_bel_T.min(), u_bel_T.max(), 100000)\nx_new_bel_T, y_new_bel_T = splev(u_new_bel_T, tck_bel_T, der=0)\n\nfinal_y_new_bel_T = rotate_y((centre_bel),(x_new_bel_T, y_new_bel_T),-0.5*np.pi)\nfinal_x_new_bel_T = rotate_x((centre_bel),(x_new_bel_T, y_new_bel_T),-0.5*np.pi)\n\nplt.scatter(bel_T.latitude, bel_T.longitude, color='blue',s=0.01)\nplt.plot(final_x_new_bel_T, final_y_new_bel_T, 'r-')\nplt.show()","metadata":{"datalore":{"node_id":"IaT1oIJbLBIQKNHtjpQwMD","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## General Predictive Indicators\n\n### In our exploratory data analysis, we sought to investigate several aspects of race predictability. Firstly, we are looking at General Predictive Indicators that could help predict the finishing position of each horse/jockey team.\n\n### More specifically, we asked ourselves:\n- ### Are race outcomes related to **odds**? If yes, are odds biased to any degree? For example, if certain horses or jockeys are systematically under- or over-rated. Can we correct for these biases?\n- ### Are race outcomes related to historical statistics that could be incorporated prior to the race? Here we sought to investigate jockey and horse experience, win rates, as well as performance trends.","metadata":{"datalore":{"node_id":"0x8YFA3Yqdq50213gQuB0h","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## General Predictive Indicators: Odds","metadata":{"datalore":{"node_id":"x4et5QodnLR8ecNXM4j05Q","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"### As can be seen from the graph below, There is a generally positive relationship between odds and final position.","metadata":{"datalore":{"node_id":"n4sjQC1trxqHJQVsnkJSuz","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"odds_by_pos = {}\n\nfor i in range(1, 7):\n    odds_by_pos[i] = df[df['position_at_finish'] == i]['odds'].reset_index(drop = True)\n\nfig, ax = plt.subplots()\nfig.set_size_inches(20, 10)\nseaborn.boxplot(data = pd.DataFrame(odds_by_pos), ax = ax)\nax.set_title(\"Odds By Final Finishing Position\", fontsize = 15)\nax.set_ylabel(\"Odds\", fontsize = 15)\nax.set_xlabel(\"Final Finishing Position\", fontsize = 15)","metadata":{"datalore":{"node_id":"YreCrYvsC0lxXfNTL1eaYC","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# generic function to plot relationship between two variables\n\nimport matplotlib.pyplot as plt\nimport scipy\nfrom scipy.stats import t\n\ndef plot_relationship(x, y, xlabel, ylabel, title, axis):\n\n    slope, intercept = np.polyfit(x, y, 1)  # linear model adjustment\n\n    y_model = np.polyval([slope, intercept], x)   # modeling...\n\n    x_mean = np.mean(x)\n    y_mean = np.mean(y)\n    n = x.size                        # number of samples\n    m = 2                             # number of parameters\n    dof = n - m                       # degrees of freedom\n    t = scipy.stats.t.ppf(0.90, dof)       # Students statistic of interval confidence\n\n    residual = y - y_model\n\n    std_error = (np.sum(residual**2) / dof)**.5   # Standard deviation of the error\n\n    numerator = np.sum((x - x_mean)*(y - y_mean))\n    denominator = ( np.sum((x - x_mean)**2) * np.sum((y - y_mean)**2) )**.5\n    correlation_coef = numerator / denominator\n    correlation_coef**2\n\n    # mean squared error\n    MSE = 1/n * np.sum( (y - y_model)**2 )\n\n    # to plot the adjusted model\n    x_line = np.linspace(np.min(x), np.max(x), 100)\n    y_line = np.polyval([slope, intercept], x_line)\n\n    # confidence interval\n    ci = t * std_error * (1/n + (x_line - x_mean)**2 / np.sum((x - x_mean)**2))**.5\n    # predicting interval\n    pi = t * std_error * (1 + 1/n + (x_line - x_mean)**2 / np.sum((x - x_mean)**2))**.5  \n\n    from matplotlib.offsetbox import AnchoredText\n    act = AnchoredText(r\"$\\rho$ = \" + f\"{round(correlation_coef,3)}\", loc=2,borderpad=0.3,frameon=True, pad = 0.15)\n    axis.add_artist(act)\n\n    ############### Ploting\n    plt.rcParams.update({'font.size': 14})\n    fig = plt.figure()\n    axis.plot(x, y, 'o', color = 'royalblue')\n    axis.plot(x_line, y_line, color = 'royalblue', linewidth = 2, alpha=0.5)\n    axis.fill_between(x_line, y_line + pi, y_line - pi, color = 'lightcyan', label = '95% prediction interval')\n    axis.fill_between(x_line, y_line + ci, y_line - ci, color = 'lightcyan', label = '95% confidence interval')\n\n    axis.grid('on', alpha = 0.3)\n    axis.set_title(title)\n    axis.set_ylabel(ylabel, fontsize = 13)\n    axis.set_xlabel(xlabel, fontsize = 13)\n    # ax.set_ylim(-1000, 6500)","metadata":{"datalore":{"node_id":"0ef7LJbnJqhGYJY5pzb4ux","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We also tested whether there is a statistically significant difference in odds across all positions.\n\n### We conduct multiple t-tests for group mean differences in odds by final position. That is, we sought to measure whether horses who finished in one position had higher or lower odds than horses who finished in other positions. \n\n### Take for example, the difference in group means for odds between horses who finished 1st and those who finished 2nd, as shown in the formula below. In the test of the difference of two means, we expect that $\\bar{x}_1 – \\bar{x}_2$ would be close to $\\mu_1 – \\mu_2$. Therefore, the null hypothesis (which tests the status quo of no difference), is simply $H_0: \\mu_1 = \\mu_2$. The alternative hypothesis is that group means are different: $H_0: \\mu_1 \\neq \\mu_2$ (a two-tail test).","metadata":{"datalore":{"node_id":"ah9MPGFuh8L1BUeyKLDHrG","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"\\begin{equation}\nt_0=\\frac{\\left(\\bar{x}_1-\\bar{x}_2\\right)-\\left(\\mu_1-\\mu_2\\right)}{\\sqrt{\\frac{s_1^2}{n_1}+\\frac{s_2^2}{n_2}}}\n\\end{equation}","metadata":{"datalore":{"node_id":"X9W7Wdgcw51gnRhPzNiqHN","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"t_test_results = {}\nfor i in range(1, 7):\n    if i != 6:\n        ttest = stats.ttest_ind(odds_by_pos[i], odds_by_pos[i+1], equal_var = False)\n        t = round(ttest[0], 2)\n        p = round(ttest[1], 4)\n        t_test_results[i] = (t, p)","metadata":{"datalore":{"node_id":"FGSEZaxDQHokFfqCELbyqP","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"t_tests = pd.DataFrame(t_test_results).T.reset_index()\nt_tests.rename(columns = {0: \"difference in mean odds\", 1: \"p-value\"}, inplace = True)\nt_tests['group 1'] = [f\"position = {i}\" for i in range(1, 6)]\nt_tests['group 2'] = [f\"position = {i}\" for i in range(2, 7)]\nt_tests = t_tests[['group 1', 'group 2', 'difference in mean odds', 'p-value']]","metadata":{"datalore":{"node_id":"J8reXuTBD1zX79tAZvwwf4","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(t_tests)","metadata":{"datalore":{"node_id":"LzZTJV8Fc7388oUPfLvmGd","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Hence, there appear to be group differences in odds between finishing positions. In other words, horses with lower odds actually do perform better.","metadata":{"datalore":{"node_id":"EComfbclVaUZZzVh2u2XBS","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"## General Predictive Indicators: Experience and past win-rate\n### To incorporate more historic data on horses as well as jockeys, we included data on horse races from 2018 that was kindly shared by Kaggle user Fabrice Mulumba [here](https://www.kaggle.com/competitions/big-data-derby-2022/discussion/364175). Similarly, we are very thankful to Mark Green.\n\n### We cleaned this data and merged it with the race data for 2019 to calculate features based on past races.\nThe code to read in the data and clean it can be found in the [Experience/Win-Rate Cell](attachment:./#exp_win_rate_code) at the bottom of the notebook.\n\n### At first, we explored if the experience and win-rate of jockeys could help us predict their finishing position of any particular race. A first hypothesis would be that both a more experienced jockey and a jockey with a higher past win-rate would have a higher chance of winning the race in question.\n\n### To check this, we are going calculate experience and win-rate features and plot these against finishing position.","metadata":{"datalore":{"node_id":"eolQgtuuzqN4B2ijUrx2CA","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"### We define experience as the number of races a jockey has participated in. Similarly, we count the total number of wins for each jockey since the start of 2018 to then determine their overall win-rate.","metadata":{"datalore":{"node_id":"U7oN4jk2m4cYjUIlTrMHtG","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"\\begin{equation}\n    \\text{win-rate} =  \\frac{\\text{total number of wins}}{\\text{total number of races participated in}}\n\\end{equation}","metadata":{"datalore":{"node_id":"jyaBMB2IPeMdV4SmGrIlKr","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"race_level_data = df[df[\"trakus_index\"] == 3]\nfig, ax = plt.subplots(nrows = 2, gridspec_kw={'hspace': 0.3})\nfig.set_size_inches(15, 12)\n\ntitle= \"Relationship between jockey experience and finishing position\"\nylabel= \"Number of past races of jockey\"\nxlabel= \"Finishing Position\"\n\nx = race_level_data['position_at_finish']\ny = race_level_data['experience_jockey']\n\nplot_relationship(x,y,xlabel,ylabel,title, axis = ax[0])\n\nx = race_level_data['position_at_finish']\ny = race_level_data['win_rate_jockey']\n\ntitle= \"Relationship between past win rate of jockey and finishing position\"\nylabel= \"Jockey past win rate\"\nxlabel= \"Finishing Position\"\n\nplot_relationship(x,y,xlabel,ylabel,title, axis = ax[1])\n\nfig.suptitle(\"Jockeys with high win rate and high past experience finish stronger on average\")","metadata":{"datalore":{"node_id":"biCJvUBFGKFRMzVIIu6F28","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# title= \"Relationship between horse experience and finishing position\"\n# ylabel= \"Number of past races of horse\"\n# xlabel= \"Finishing Position\"\n\n# x = race_level_data[[\"experience_horse\",'position_at_finish']].dropna()['position_at_finish']\n# y = race_level_data[[\"experience_horse\",'position_at_finish']].dropna()['experience_horse']\n\n# plot_relationship(x,y,xlabel,ylabel,title)\n# data_df.set_index(['race', \"track_id\", 'race_date', 'race_number', 'horse_id'])","metadata":{"datalore":{"node_id":"H7gOD5oo3Rjx96hBqpPySI","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### As we can see, both experience and past win rate of jockeys are correlated with actual finishing position. Thus, generally speaking, the more experienced/the higher the total win rate from past races the lower (better) their finishing position for the race in question. This indicates that experience and past jockey performance might be a good predictor for future jockey performance.","metadata":{"datalore":{"node_id":"GuGRjngIG3bxkiWHsQHXP9","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## General Predictive Indicators: Performance trends\n### Next we will look at performance trends of jockeys and horses. A performance trend might indicate that a particular horse/jockey currently has some form of momentum. Momentum is a phenomenom often observed in sports where certain actors are temporarily said to be \"on a winning streak\" or \"hard to be stopped\". Thus, a good performance over the last few races might indicate momentum, i.e., that this trend is likely to continue. \n\n### Therefore, we calculate the performance trend or \"momentum\" of a horse/jockey by weighting the finishing positions of recent races. Using a smoothing factor $\\alpha$, more recent races are weighted higher than races further in the past. With $finpos_{t-1}$ as the finishing position in the last race we get the following formula for the exponentially weighted mean:","metadata":{"datalore":{"node_id":"9tnnIKeWJ0TpLDJQkotvEV","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"\\begin{equation}\ntrend_t = \\frac{finpos_{t-1} + (1-\\alpha)* finpos_{t-2}+(1-\\alpha)^2* finpos_{t-3}+...+(1-\\alpha)^t*finpos_0}{1+(1-\\alpha)+(1-\\alpha)^2+...+(1-\\alpha)^t} \n\\end{equation}","metadata":{"datalore":{"node_id":"KeJlq4uifVDV9anGvSXqqx","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"title= \"Relationship between performance trend of horse and finishing position\"\nylabel= \"Trend of horse\"\nxlabel= \"Finishing Position\"\n\nx = race_level_data['position_at_finish']\ny = race_level_data['trend_horse']\n\nfig, ax = plt.subplots()\nfig.set_size_inches(20, 10)\nplot_relationship(x,y,xlabel,ylabel,title, axis=ax)","metadata":{"datalore":{"node_id":"z6gN05YKdgprLcJvf9GcQh","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### In the plot above, a relationship between the performance trend measure and the finishing position of a horse becomes apparent. However, looking at the number of races each horse completed, we can see that the majority of horses only participated in a small number of races. When using horse performance trend as a feature as part of our model, this might lead to problems as the trend is heavily dependent on a few or even just one race.","metadata":{"datalore":{"node_id":"8FTW7gSwXRkOpIjWIicK2Y","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"fig, ax = plt.subplots()\nfig.set_size_inches(20, 3)\nseaborn.boxplot(data = pd.DataFrame(race_level_data[[\"horse_id\"]].value_counts()), ax = ax, orient= \"h\")\nax.set_title(\"Number of races by horse\", fontsize = 15)\nax.set_xlabel(\"Number of races\", fontsize = 15)","metadata":{"datalore":{"node_id":"GeNJv9Bgc9vWBNppvzkYYW","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Similarly, we look at the momentum measure for jockeys which is calculated in the same way.","metadata":{"datalore":{"node_id":"MgURHrPtc7pBlC0DMbHjE5","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"title= \"Relationship between performance trend of jockey and finishing position\"\nylabel= \"Trend of jockey\"\nxlabel= \"Finishing Position\"\n\nx = race_level_data['position_at_finish']\ny = race_level_data['trend_jockey']\n\nfig, ax = plt.subplots()\nfig.set_size_inches(20, 8)\nplot_relationship(x,y,xlabel,ylabel,title, axis=ax)","metadata":{"datalore":{"node_id":"w3vUUc6yCKT10fPolY8VWi","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We observe that jockey performance trend might be a good predictor for their finishing positions. In addition, we can see that individual jockeys participated in much more races than horses, so that using this trend measure might be more robust.","metadata":{"datalore":{"node_id":"B42WjWR4aGGpTuc1QUHCZ8","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"Code for feature engineering regarding historical performance data can be found in the [Performance Trend Cell](attachment:./#performance_trend_code) and [Historical Performance Cell](attachment:./#historical_info_code) at the bottom of the notebook.","metadata":{"datalore":{"node_id":"TUonPEXVWqn4Xfy6rZGrpX","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"## In-Race features\n### Next, we will investigate in-race features, i.e., distinctive horse/jockey attributes that can be observed during the race. While General Predictive Indicators only relied on data from before the race has started, in-race features make use of the vast amount of data generated during the race. This is quite exciting because, using this data, the predicted finishing positions of horses and riders can be updated in real-time as the race evolves.\n\n### We asked ourselves:\n- ### Are there typical race paths and strategies that predict race outcomes? Can we devise archetypal winning paths for races of various types?\n- ### Do drafting strategies influence horse race outcomes? Do riders who stay in the slipstream perform better across the race or are front-runners more likely to stay ahead?","metadata":{"datalore":{"node_id":"42iDUxUAdAr3QVGMo9XtfK","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## In-Race features: Speed","metadata":{"datalore":{"node_id":"5bNlrvLvqXgN2j8i2dXY61","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"### At first, we are going to examine the difference in speed trajectories between the horses with best and worst finishing positions. We do this by comparing the average speed trajectory of the horses finishing in the top 30% of each race (\"strong finishers\") to the ones finishing in the bottom 30% in each race (\"weak finishers\").\n### To account for the unique characteristics of each track, we examine the horse speed trajectories for each of the three tracks independently. ","metadata":{"datalore":{"node_id":"EDqp8U58YLAM2xRWuLK6yz","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"def add_average_position_by_characteristic(df, characteristic, plot):\n\n    characteristic = 'track_id'\n\n    grouped = df.groupby([characteristic, 'trakus_index', 'standardized_position_at_finish'])\n    grouped_mean = grouped.mean()\n    grouped_mean = grouped_mean.reset_index()\n    mean_strong_finisher = grouped_mean[grouped_mean['standardized_position_at_finish'] <= 0.3]\n    mean_weak_finisher = grouped_mean[grouped_mean['standardized_position_at_finish'] >= 0.7]\n\n    mean_strong_finisher = mean_strong_finisher.reset_index().groupby(['trakus_index', characteristic]).mean()\n    mean_weak_finisher = mean_weak_finisher.reset_index().groupby(['trakus_index', characteristic]).mean()\n\n    mean_strong_finisher_plot = mean_strong_finisher.reset_index()\n    mean_weak_finisher_plot = mean_weak_finisher.reset_index()\n\n    categories = list(mean_strong_finisher_plot[characteristic].unique())\n\n    i = 0\n\n    if plot:\n\n        import matplotlib.pyplot as plt\n        fig, ax= plt.subplots(nrows = 3)\n\n        fig.set_size_inches(30, 20)\n\n        for category in categories:\n            category_strong_finisher = mean_strong_finisher_plot[mean_strong_finisher_plot[characteristic] == category]\n            category_weak_finisher = mean_weak_finisher_plot[mean_weak_finisher_plot[characteristic] == category]\n\n            category_strong_finisher['speed'].ewm(alpha = 0.2).mean().plot(ax = ax[i], color = 'blue', label = \"strong\")\n            category_weak_finisher['speed'].ewm(alpha = 0.2).mean().plot(ax = ax[i], color = 'lightcoral', label = \"weak\")\n\n            ax[i].grid('on')\n            ax[i].set_ylim([0, 25])\n            ax[i].legend()\n            i += 1\n\nadd_average_position_by_characteristic(df, characteristic, plot = True)","metadata":{"datalore":{"node_id":"2hq4z0nU7eAN65a3VOL0Hv","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## In-Race Features: Variation of Speed\n#### It is well known when driving a car, that sudden acceleration and deceleration is inefficient and leads to higher fuel consumption. Similarly, one can imagine that sudden changes in speed are more tiring for a horse than maintaining a consistent velocity. On the other hand, high variations in speed may indicate a horse with impressive acceleration which is obviously a benefit in the case of a sprint at the end of the race. Thus, we decided to investigate how the variation in a horse's speed impacts their likelihood of winning. We calculated an expanding standard deviation of horse speed at each time point as a race progresses (i.e. at each time point, record the standard deviation of the horse's speed up to that point in the race).\n\n\n#### What we found was interesting. In general, the horses with the largest standard deviation of speed, and the horses with the lowest standard deviation of speed consistently finished in the worst positions of the race. What we can interpret from this is that a winning horse is likely to not showcase the lowest variation in speed, as they will have a significant ability to accelerate at their disposal both at the start and end of the race, but also they will not have the highest variation in speed as they are better able to maintain their top speed over the course of the race.\n\nCode creating the feature of variation in speed can be found in the [Variation in Speed Cell](attachment:./#std_speed_code) later in the notebook.","metadata":{"datalore":{"node_id":"7dRpEYMmImAO9v28o26W4I","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"## In-Race Features: Drafting\n### A fast moving large object can create a partial vacuum with reduced air and wind resistance in the space behind it. Slipstreaming or drifting is a key tactic used by jockeys in races where one rides close behind another horse so as to reduce aerodynamic drag. Indeed, a study carried out by the Royal Melbourne Institute of Technology showed that effective slipstreaming tactics can reduce the drag on a horse by up to 66 percent. The outcome of this is to drastically reduce the physical exertion required by the horse to move at quick speed, and when used effectively, can allow riders to preserve extra energy in their steeds for a final push of acceleration at the end of the race.\n\n### Important factors to consider when trying to give our model information about the impact of slipstreaming are the horse's speed and direction of travel. We considered for every horse, an area of intersecting ellipses trailing behind with major axes centred on the direction vector that the horse was travelling in:","metadata":{"datalore":{"node_id":"s9rcBueTLpn3XMgPCgljyN","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"from IPython.display import Image\nImage(filename='Slipstream_diagram.png')","metadata":{"datalore":{"node_id":"z5khs6vzDrVWIBreC4t7Lr","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We then computed at each time point in the race whether each horse was in the slipstream of another horse and gave assigned a weighted 'drafting' factor depending on the speed of the horse travelling in front. The code for forming the slipstream features can be found in the [Slipstream cell](attachment:./#Slipstream_code) at the bottom of the notebook. \n\n\n## In-Race Features: Taking the Inner Line\n\n### One factor we thought important to investigate was how closely jockey-horse pairs followed the inner line of the track. Evidently, the horse closest to the inner track has a clear advantage when traversing the curves due to the reduced distance it is required to cover. However, often it will be required for a horse to overtake a leading opponent on the outside. \n\n\n### Total distance covered to a point in a race alone can't fully indicate whether a horse-jockey pair have been running an efficient path as we also need to understand the distance ran relative to the full distance covered in the race. A feature with average distance from the inner curve to that point in the race therefore gives a clearer indication and can help to predict which horses will have more energy remaining as the race progresses.\n\n### Having constructed an accurate representation of the inner track for each race track and course type, one can use a k-d tree to find for every horse, at every point in the race, how far the horse is away from the inner track. An expanding average as the race progresses then provides the required feature. Code to calculate the distance from the inner track at every time point for every horse can be found in the [Distance from Inner Track cell](attachment:./#dist_to_inner_code) at the bottom of the notebook","metadata":{"datalore":{"node_id":"gaJUuCFhIDccSf76Y2rNWA","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"# Modelling","metadata":{"datalore":{"node_id":"BnmbyHIzzqtYwHTBBuhuBt","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## A generic model that applies to all time points and all horses\n\n#### Our end goal is to estimate, at any time point in a race, the probability of each any winning the race. More formally, if $K$ is the number of horse jockey pairs in the race, then for each horse $k \\in \\{1,..., K\\}$ we wish to predict:\n\n$$\\text{Pr}(Y_k = 1| X_{k,t}), \\quad \\forall \\; k \\in K, \\forall \\; t \\in T$$\n\n#### where $X_{k, t}$ are the features we have available about the horse and jockey pair $k$ at time point $t$. $X_{k, 0}$, for example represents the odds and historical statistics of the horse and jockey, and all other information available at time point $t=0$. As the race progresses and $t$ becomes large, $X_{k,t}$ includes the speed of the horse, its distance from other riders and its current position.\n\n#### Note that here we predict $\\text{Pr}(Y_k = 1)$, which implicity models the probability that horse $k$ wins overall and therefore that all other horses lose: $\\text{Pr}(Y_k = 1 \\; \\text{AND} \\; Y_h = 0 \\; \\forall \\; h \\in K \\setminus k)$. This gives us, in the end, our desired in-race probability for each horse.","metadata":{"datalore":{"node_id":"mKu4yhpru989AC8ELKLRjJ","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"## One vs one (OVO) modelling approach\n\n#### Notice that the above formulation faces an issue when being used in multi-class races: for different categories of races that have a different number of horses, the base-rate win probability will naturally differ. Hence the model would not account for the fact that 6 horse races are naturally less competitive than for example 12 horse races. We circumvent this problem using a OneVsOne (OVO) modeling approach. We reformulate our prediction model to always predict binary wins and losses. In doing so, we focus on predicting:\n\n$$\\text{Pr}(Y_k = 1, Y_h = 0| X_{k,t}, X_{h,t}), \\quad \\forall \\; (k, h) \\in K \\times H, \\forall \\; t \\in T$$\n\n#### where $K \\times H$ is the cartesian product representing all unique permutations of matchups between two horses $k$ and $h$. We obtain $\\text{Pr}(Y_k = 1, Y_h = 0)$ for each battle of two horses using a set of probabilistic binary classifiers. In other terms, $\\text{Pr}(Y_k = 1, Y_h = 0| X_{k,t}, X_{h,t}) = f(X_{k,t}, X_{h,t})$, where we chose $f$ based on cross-validation from Support Vector Machines, XGBoost, Random Forest, Naive Bayes, K-Nearest Neigbhours and Logistic Regression. \n\n#### The features $(X_{k, t}, X_{h, t})$ are specifically adjusted for this modeling approach. In most cases, we do not consider individual statistics such as the position of the jockey at a specific time but the difference in position between the two competing jockeys $k$ and $h$. This allows us to reduce the number of features in our model, while still indirectly accounting for within-group effects.\n\n\n#### We then use the individual winning probabilities of the jockeys to estimate the overall probability of them being first, second, third, or fourth in the race. We do this by calculating a score for each horse $k$, which is the sum of all winning probabilities from the binary matchups the horse participated in:\n\n$$S_k = \\sum_{h \\in H} \\text{Pr}(Y_k = 1, Y_h = 0| X_{k,t}, X_{h,t})$$\n\n#### When we come to predict the probability that the horse wins the entire race, we use an aggregation scheme based on a softmax weighting function:\n\n#### $$\\text{Pr}(Y_k = 1 \\; \\text{AND} \\; Y_h = 0 \\; \\forall \\; h \\in K \\setminus k) = \\frac{e^{S_k}}{\\underset{h \\in H}{\\sum} e^{S_h}}$$\n\n#### This softmax function weights exponentially towards horses with high scores from the binary battles, which is more reflective of competitive dynamics.\n\n#### Our main goal is to predict the expected success of each horse and jockey pairing at any point in time during the race. By extending to pre-race odds which currently are used to reflect the outcome probabilities of horse races, interesting insights for the sport of horse-racing can potentially be derived. These include:\n\n#### - Which strategies are promising and for which distances/race types do these strategies work?\n#### - At what point in time can we predict the outcome of a horse race on average?","metadata":{"datalore":{"node_id":"CpkUhgrXogYOCcdWWm2oKU","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"markdown","source":"## Time-varying feature importance\n\n### In creating a generic model for the entire race, we consider how the importance of features varies as the race progresses.\n\n### For example, odds are very important at the beginning of the race, when little in-race information is known. However, odds quickly diminish in performance as we acquire in-race data on speed, position, etc. On the contrary, variables like distance should receive an increasing weight as the race progresses, reflecting that distance margins become more important towards the end of the race.\n\n### To do this, we use time decay parameters. These parameters, which we call $\\lambda$, are scalars between 0 and 1 when a feature decreases in importance as the race progresses and a value larger than 1 when the feature increases in importance. Mathematically, given a feature $x_j$, we define the time-decaying variable as:\n\n$$x^D_{j, t} = x_{j, t} \\times \\lambda^t$$\n\n### Examples of these parameters $\\lambda$ are shown below. ","metadata":{"datalore":{"node_id":"OzEXoAjGT4CvQabRTbrhOy","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"fig, ax= plt.subplots(ncols = 2)\nfig.set_size_inches(20, 10)\n\nfor lamb in [0.95,0.955,0.96,0.965,0.97,0.975,0.98,0.985,0.99, 0.995]:\n    ax[0].plot([lamb**i for i in range(400)], label = r\"$\\lambda =$\" + f\"{lamb}\")\n    ax[0].legend(fontsize = 15)\n\nfor lamb2 in [1.001, 1.002, 1.003, 1.003, 1.004, 1.005, 1.006, 1.007, 1.008, 1.01]:\n    ax[1].plot([lamb2**i for i in range(400)], label = r\"$\\lambda =$\" + f\"{lamb2}\")\n    ax[1].legend(fontsize = 15)\n\nax[0].set_ylabel(\"Feature importance\", fontsize = 15)\nax[0].grid(True)\nax[1].grid(True)\nax[0].set_title(\"Features whose importance decays through the race ($\\lambda < 1$)\")\nax[1].set_title(\"Features whose importance increases through the race ($\\lambda > 1$)\")\nax[0].set_xlabel(\"Trakus index in the race\")","metadata":{"datalore":{"node_id":"8LLbRGutTd8XRWTWYamxHS","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{"datalore":{"node_id":"BiqeOGcZSsQ8Q6QI3mIuWo","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"#### We also use 3 different $\\lambda$ values for short, medium and long races. Typically, $\\lambda$ will reflect faster decay or exponentiation for short races. This is to reflect the fact that, for short races, in-race factors become important earlier than in medium or long races. We use cross-validation on a sub-sampled set of 100k rows to obtain these values.","metadata":{"datalore":{"node_id":"YT7a4lAxNj23XF4gQRrAbe","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"#  Bar Plot dirstribution  \ndf_one = df[df['trakus_index'] == 1]\nfreq = pd.DataFrame(df_one.groupby(['track_id', 'race_date', 'race_number']).size(),columns = ['no_of_horses']).value_counts().sort_index()","metadata":{"datalore":{"node_id":"8nmlUScndh6OaiIMSQ1Jeg","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create one vs. one dataframe for all races for all jockey and horse pairs\ndf2 = pd.merge(df,\n    df[['track_id','race_date','race_number','trakus_index', 'pg', 'position_at_finish','position', 'distance', \n        'speed', 'odds', 'finished','weight_carried', 'jockey_standardised_position_by_race_condition', \n        'speed_diff_from_typical_winning_rider', 'std_speed', 'experience_jockey', 'experience_horse',\n        'win_rate_jockey', 'trend_jockey', 'trend_horse']],\n    on=['track_id','race_date','race_number','trakus_index'],\n    how='left')","metadata":{"datalore":{"node_id":"8UNTE0WKQFrtScDFRmjZAI","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Selecting only unique one vs one combination \ndf2 = df2[df2['pg_x'] != df2['pg_y']]\ndf2 = df2[~df2[[\"pg_x\", \"pg_y\", 'track_id', 'race_date', 'race_number', 'trakus_index']].swifter.apply(frozenset, axis=1).duplicated()]\ndf2 = df2.sort_values(['track_id', 'race_date', 'race_number', 'pg_x','pg_y']).reset_index(drop=True)\n\n# Winning variable\ndf2['winning'] = (df2['position_at_finish_x'] < df2['position_at_finish_y']).astype(int)\ndf2 = df2.reset_index(drop=True)\ndf2 = df2[df2['trakus_index'] != 1]\ndf2 = df2[~df2['position_x'].isna()]\ndf2 = df2[~df2['position_y'].isna()]","metadata":{"datalore":{"node_id":"zEd23PMDVoACtiwo9CiJVk","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Function for splitting data into test and training set. Split is alwazs performed without shuffle - we, consequently, always predicted races that lay ahead in time.\ndef train_test_split(df2,train_ratio, features):\n    df2 = df2.sort_values([ 'race_date','track_id', 'race_number', 'trakus_index',]).reset_index(drop=True)\n    df2 = df2.set_index(['race_date', 'race_number','track_id'])\n    train_idx = int(train_ratio*len(df2.index.unique()))\n    index_train = df2.index.unique()[:train_idx]\n\n    test_idx = int(train_ratio*len(df2.index.unique()))+1\n    index_test = df2.index.unique()[test_idx:]\n    train = df2.loc[index_train]\n    test = df2.loc[index_test]\n    X_train_ns = train[features]\n    X_test_ns = test[features]\n    y_train = train['winning']\n    y_test = test['winning']\n\n    # Standerdize the data\n    sc = StandardScaler()\n    X_train = sc.fit_transform(X_train_ns)\n    X_test = sc.transform(X_test_ns)\n\n    return train, test, X_train, y_train, X_test, y_test","metadata":{"datalore":{"node_id":"4r7wME2ab9HmQRogv3ZdgT","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def modelling(X_train,y_train,SVM=False,GB=False,RF=False,DT=False,NB=False,KNN=False,LR=False):\n\n    params_SVM = {'C': [0.1, 1],\n              'kernel': ['rbf', 'sigmoid']}\n    \n    params_GB = {'eta':[0.001,0.01],\n       'gamma': [0.5, 1.5]\n        }\n\n    params_RF = {\n     'max_depth': [7, 10]\n    }\n\n    params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n\n    params_KNN = {'n_neighbors': [1,2,3,5,7,10,13,15], 'weights': ['uniform','distance'], 'metric':['euclidean','manhattan']}\n\n    params_DT = {'max_depth': [2, 3, 4, 5]}\n\n    params_LR = {'C': [0.01,0.1,1]}\n\n    models = []\n    kfold = StratifiedKFold(n_splits=2, shuffle=False)\n\n    if RF:\n        RF_CL = RandomForestClassifier()\n        RF = GridSearchCV(estimator = RF_CL, param_grid = params_RF, cv = kfold, n_jobs = -1, scoring='roc_auc')\n        RF.fit(X_train, y_train)\n        models.append(RF)\n\n    if DT:\n        # CART\n        DT_CL = tree.DecisionTreeClassifier()\n        DT = GridSearchCV(estimator = DT_CL, param_grid = params_DT, cv = kfold, n_jobs = -1, scoring='roc_auc')\n        DT.fit(X_train, y_train)\n        models.append(DT)\n\n    if NB:\n        # Naive Bayes\n        NB_CL = GaussianNB()\n        NB = GridSearchCV(NB_CL, params_NB, cv=kfold , n_jobs=-1, scoring='roc_auc')\n        NB.fit(X_train, y_train)\n        models.append(NB)\n\n    if KNN:\n        # KNN\n        KNN_CL = KNeighborsClassifier()\n        KNN = GridSearchCV(KNN_CL, params_KNN, cv=kfold , n_jobs=-1, scoring='roc_auc')\n        KNN.fit(X_train, y_train)\n        models.append(KNN)\n\n    if LR:\n        # LR\n        LR_CL = LogisticRegression(solver = 'sag')\n        LR = GridSearchCV(LR_CL, params_LR, cv=kfold , n_jobs=-1, scoring='roc_auc')\n        LR.fit(X_train, y_train)\n        models.append(LR)\n\n    if GB:\n        # XGBoosting\n        GB_CL = XGBClassifier()\n        GB = GridSearchCV(estimator = GB_CL, param_grid = params_GB, cv=kfold, n_jobs=-1, scoring='roc_auc')\n        GB.fit(X_train, y_train)\n        models.append(GB)\n\n    return(models)\n\n    ","metadata":{"datalore":{"node_id":"OC3XnwHbFjG2oBp1KM8mDI","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def BinaryCrossEntropy(y_true, y_pred):\n    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n    term_1 = y_true * np.log(y_pred + 1e-7)\n    return -np.mean(term_0+term_1, axis=0), -1*(term_0+term_1)","metadata":{"datalore":{"node_id":"05yeovonlCvjTBOwZyALTM","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def model_eval(model, X_test, y_test):\n    results = []\n    preds = {}\n    names = []\n    for model in models:\n        y_pred = model.predict(X_test)\n        preds[model.estimator] = model.predict_proba(X_test)[:,1]\n        accuracy = accuracy_score(y_test,y_pred)\n        roc_auc = roc_auc_score(y_test,y_pred)\n        precision,recall,fscore,support=score(y_test,y_pred,average='binary')\n        cross_entropy = BinaryCrossEntropy(y_test, model.predict_proba(X_test)[:, 1])\n        results.append([model.estimator,accuracy,precision,recall,fscore,roc_auc,cross_entropy])\n        names.append(model.estimator)\n    results_df = pd.DataFrame(results, columns = [\"name\",\"accuracy\",\"precision\",\"recall\",\"fscore\",\"AUC\",\"cross_entropy\"])\n    return results_df , preds, names","metadata":{"datalore":{"node_id":"u7gZ9N4WWcnBcm9gwckpqh","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#def grid_search_time_param(df,itterations)\n#grid = [0.955,0.96,0.965,0.97,0.975,0.98,0.985,0.99,0.992,0.994,0.996,0.998,0.999,0.9999,0.99999]\n\nfrom tqdm import tqdm\n\ngrid = [0.985,0.99,0.995]\n\ndf_head = df2.head(100000)\n\ndf_head['lambd'] = np.nan\n\nlambda_perf_dict = {}\n\nfeatures = ['distance_diff', 'speed_diff', 'odds_decay',\n            'diff_in_standardised_position', 'diff_speed_from_typical_winning_rider',\n            'win_rate_jockey','experience_jockey','experience_horse', 'std_speed_diff'\n            ]\n\nfor lambd1 in grid:\n    for lambd2 in tqdm(grid, position = 0, leave = True):\n        for lambd3 in grid:\n            \n            # Define different scaling factors for time - what aspect is important at which time in the race \n            lambda_short = lambd1\n            lambda_medium = lambd2\n            lambda_long = lambd3\n            df_head['lambd'].mask(df_head['dist_type'] == 'Short' ,lambda_short** df_head['trakus_index'] , inplace=True)\n            df_head['lambd'].mask(df_head['dist_type'] == 'Medium' ,lambda_medium** df_head['trakus_index'] , inplace=True)\n            df_head['lambd'].mask(df_head['dist_type'] == 'Long' , lambda_long** df_head['trakus_index'] , inplace=True)\n\n            df_head['decay_trakus_position'] = ((1/df_head['lambd'])*(df_head['position_x'] - df_head['position_y'])).round(5)\n            df_head['speed_diff'] = ((1/df_head['lambd'])*(df_head['speed_x']  - df_head['speed_y'])).round(5)\n            df_head['odds_decay'] = (((df_head['odds_x']  - df_head['odds_y'])*df_head['lambd'])).round(5)\n\n            df_head['trend_jockey'] = ((df_head['trend_jockey_x']  - df_head['trend_jockey_y']*df_head['lambd'])).round(5)\n            df_head['trend_horse'] = ((df_head['trend_horse_x']  - df_head['trend_horse_y']*df_head['lambd'])).round(5)\n            df_head['win_rate_jockey'] = ((df_head['win_rate_jockey_x']  - df_head['win_rate_jockey_y']*df_head['lambd'])).round(5)\n            df_head['experience_jockey'] =( (df_head['experience_jockey_x']  - df_head['experience_jockey_y'])*df_head['lambd']).round(5)\n            df_head['experience_horse'] = ((df_head['experience_horse_x']  - df_head['experience_horse_y'])*df_head['lambd']).round(5)\n            # Diff in stand. position\n            df_head['diff_in_standardised_position'] = ((df_head['jockey_standardised_position_by_race_condition_x'] - df_head['jockey_standardised_position_by_race_condition_y']*df_head['lambd'])).round(5)\n            # Diff in stand. speed\n            df_head['std_speed_diff'] = (((df_head['std_speed_x'] - df_head['std_speed_y'])*df_head['lambd'])).round(5)\n            # Closeness to optimal speed curve \n            df_head['diff_speed_from_typical_winning_rider'] = (((df_head['speed_diff_from_typical_winning_rider_x'] - df_head['speed_diff_from_typical_winning_rider_y'])*df_head['lambd'])).round(5)\n            df_head['distance_diff'] = (((df_head['distance_x'] - df_head['distance_y'])*df_head['lambd'])).round(5)\n\n\n\n            \n            \n\n            train_val, test, X_train_val, y_train_val, X_test, y_test = train_test_split(df_head, 0.8, features)\n\n            train_val.reset_index(inplace = True)\n            train, val, X_train, y_train, X_val, y_val = train_test_split(train_val, 0.8, features)\n            \n            models = modelling(X_train,y_train,SVM=False,GB=False,RF=False,DT=False,NB=False,KNN=False,LR=True)\n    \n            result, preds, names = model_eval(models,X_val,y_val)\n\n            lambda_perf_dict[lambd1,lambd2,lambd3] = result.loc[:,'AUC'].values\n            # return lambda_perf_dict","metadata":{"datalore":{"node_id":"YCf9id2mg0qxx9giVlxGZ1","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# These are the best lambda vaues for long, medium, and short races - we select the combination of lambda that maximizes the overall AUC on the validation set \nbest_time_vac = pd.DataFrame(lambda_perf_dict.items())\nbest_time_vac.sort_values([1],ascending=True).iloc[0].to_csv(\"best_lambdas.csv\")\nbest_time_vac.head(5).rename(columns = {0: r\"$\\lambda_1, \\lambda_2, \\lambda_3$\", 1: \"AUC\"})","metadata":{"datalore":{"node_id":"b7aiRFBz4SQO2sJink7Ak6","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Adding the tuned lambda to the variables that we want to include in the model \nlambda_short, lambda_medium, lambda_long = best_time_vac.iloc[0,0]\ndf_head['lambd'].mask(df_head['dist_type'] == 'Short' ,lambda_short** df_head['trakus_index'] , inplace=True)\ndf_head['lambd'].mask(df_head['dist_type'] == 'Medium' ,lambda_medium** df_head['trakus_index'] , inplace=True)\ndf_head['lambd'].mask(df_head['dist_type'] == 'Long' ,lambda_long** df_head['trakus_index'] , inplace=True)","metadata":{"datalore":{"node_id":"vrfJCRwybwrJvCUY4ysENT","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# In-race features\ndf_head['decay_trakus_position'] = ((1/df_head['lambd'])*(df_head['position_x'] - df_head['position_y'])).round(5)\n# Diff in stand. speed\ndf_head['speed_diff'] = ((1/df_head['lambd'])*(df_head['speed_x']  - df_head['speed_y'])).round(5)\ndf_head['odds_decay'] = (((df_head['odds_x']  - df_head['odds_y'])*df_head['lambd'])).round(5)\ndf_head['std_speed_diff'] = (((df_head['std_speed_x'] - df_head['std_speed_y'])*df_head['lambd'])).round(5)\n# Closeness to optimal speed curve\ndf_head['diff_speed_from_typical_winning_rider'] = (((df_head['speed_diff_from_typical_winning_rider_x'] - df_head['speed_diff_from_typical_winning_rider_y'])*df_head['lambd'])).round(5)\ndf_head['distance_diff'] = (((df_head['distance_x'] - df_head['distance_y'])*df_head['lambd'])).round(5)\n\ndf_head['trend_jockey'] = ((df_head['trend_jockey_x']  - df_head['trend_jockey_y']*df_head['lambd'])).round(5)\ndf_head['trend_horse'] = ((df_head['trend_horse_x']  - df_head['trend_horse_y']*df_head['lambd'])).round(5)\ndf_head['win_rate_jockey'] = ((df_head['win_rate_jockey_x']  - df_head['win_rate_jockey_y']*df_head['lambd'])).round(5)\ndf_head['experience_jockey'] =( (df_head['experience_jockey_x']  - df_head['experience_jockey_y'])*df_head['lambd']).round(5)\ndf_head['experience_horse'] = ((df_head['experience_horse_x']  - df_head['experience_horse_y'])*df_head['lambd']).round(5)\n# Diff in stand. positiondf_head\ndf_head['diff_in_standardised_position'] = ((df_head['jockey_standardised_position_by_race_condition_x'] - df_head['jockey_standardised_position_by_race_condition_y']*df_head['lambd'])).round(5)","metadata":{"datalore":{"node_id":"XfuFONSet0jcXBg9oZaX1U","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Grid-search and model selection\n### For us, both maximum performance and interpretability are of interest. While the former allows us to make predictions as accurately as possible, the latter can help us gain further insights into the strategies and characteristics of different jockeys, horses, and race types. For all models under consideration, we GridSearch the best possible set of hyperparameters. Depending on what model is selected and the size of the dataset, significant training time may be required. The best-performing model on the complete dataset is XGBoost. The trained algorithm including correct hyperparameter settings can be loaded from the file .... This model does not require further training and can be directly applied to new data.","metadata":{"datalore":{"node_id":"BWla5XftNsAaXpfxuyp2u1","type":"MD","hide_input_from_viewers":false,"hide_output_from_viewers":false,"report_properties":{}}}},{"cell_type":"code","source":"# Finding the best possible model \n# train_val.reset_index(inplace = True)\n\ntrain_val, test, X_train_val, y_train_val, X_test, y_test = train_test_split(df_head, 0.8, features)\n\ntrain_val.reset_index(inplace = True)\n\ntrain, val, X_train, y_train, X_val, y_val = train_test_split(train_val, 0.8, features)\n\nmodels = modelling(X_train,y_train,SVM=True,GB=True,RF=True,DT=True,NB=True,KNN=True,LR=True)\n    \nresult, preds, names = model_eval(models,X_val,y_val)\nresult.to_csv('model_selection_result.csv')","metadata":{"datalore":{"node_id":"Lo66K5utwjAYObVzUpdE3L","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"best_model = result['name'].loc[result['AUC'].idxmax()]\nbest_model","metadata":{"datalore":{"node_id":"CejIv1aUc6POErWUDrUQ4j","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Final Model ","metadata":{"datalore":{"node_id":"1raPO61iLSLgy6z1raQqWl","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}}},{"cell_type":"code","source":"lambda_short, lambda_medium,lambda_long = best_time_vac.iloc[0,0]\ndf2['lambd'] = 0\ndf2['lambd'].mask(df2['dist_type'] == 'Short' ,lambda_short** df2['trakus_index'] , inplace=True)\ndf2['lambd'].mask(df2['dist_type'] == 'Medium' ,lambda_medium** df2['trakus_index'] , inplace=True)\ndf2['lambd'].mask(df2['dist_type'] == 'Long' ,lambda_long** df2['trakus_index'] , inplace=True)","metadata":{"datalore":{"node_id":"A3ccddXQJp29AMUd3FyUkD","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# In-race features\ndf2['decay_trakus_position'] = ((1/df2['lambd'])*(df2['position_x'] - df2['position_y'])).round(5)\n# Diff in stand. speed\ndf2['speed_diff'] = ((1/df2['lambd'])*(df2['speed_x']  - df2['speed_y'])).round(5)\ndf2['odds_decay'] = (((df2['odds_x']  - df2['odds_y'])*df2['lambd'])).round(5)\ndf2['std_speed_diff'] = (((df2['std_speed_x'] - df2['std_speed_y'])*df2['lambd'])).round(5)\n# Closeness to optimal speed curve\ndf2['diff_speed_from_typical_winning_rider'] = (((df2['speed_diff_from_typical_winning_rider_x'] - df2['speed_diff_from_typical_winning_rider_y'])*df2['lambd'])).round(5)\ndf2['distance_diff'] = (((df2['distance_x'] - df2['distance_y'])*df2['lambd'])).round(5)\n\n# df2['trend_jockey'] = ((df2['trend_jockey_x']  - df2['trend_jockey_y']*df2['lambd'])).round(5)\n# df2['trend_horse'] = ((df2['trend_horse_x']  - df2['trend_horse_y']*df2['lambd'])).round(5)\ndf2['win_rate_jockey'] = ((df2['win_rate_jockey_x']  - df2['win_rate_jockey_y']*df2['lambd'])).round(5)\ndf2['experience_jockey'] =( (df2['experience_jockey_x']  - df2['experience_jockey_y'])*df2['lambd']).round(5)\ndf2['experience_horse'] = ((df2['experience_horse_x']  - df2['experience_horse_y'])*df2['lambd']).round(5)\n# Diff in stand. position\ndf2['diff_in_standardised_position'] = ((df2['jockey_standardised_position_by_race_condition_x'] - df2['jockey_standardised_position_by_race_condition_y']*df2['lambd'])).round(5)","metadata":{"datalore":{"node_id":"Zodape7OSUHsiVYgFLa0at","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Here we want to add the model that we selcted\ntrain, test, X_train, y_train, X_test, y_test = train_test_split(df2, 0.8, features)\nmodels = modelling(X_train,y_train,SVM=False,GB=False,RF=False,DT=True,NB=False,KNN=False,LR=False)\nresult, preds, names = model_eval(models,X_test,y_test)","metadata":{"datalore":{"node_id":"fJg00Xz8VwiQGpUOHFraTt","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"feature_names = ['position', 'distance', 'speed', 'odds', \n                 'std_position', 'sml_winner', 'win_rate_jockey', 'experience_jockey'\n                 'experience_horse', 'std_speed']","metadata":{"datalore":{"node_id":"9ZxaDyrSsZRR3ZeNlkmp0c","type":"CODE","hide_input_from_viewers":false,"hide_output_from_viewers":false,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"best_dt = models[0].best_estimator_","metadata":{"datalore":{"node_id":"z5UJfunnLLaUge8NG2DIaG","type":"CODE","hide_input_from_viewers":false,"hide_output_from_viewers":false,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.tree import plot_tree\n\nfig, ax = plt.subplots()\nfig.set_size_inches(20, 10)\n\nplot_tree(best_dt, filled = True, ax = ax, max_depth = 3, fontsize = 10.5, label= 'none', feature_names = feature_names, impurity= False)\n\nplt.show()","metadata":{"datalore":{"node_id":"NnebWIDhzYMP39IPdcH0h6","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result.to_csv(\"Final_model.csv\")","metadata":{"datalore":{"node_id":"fZFUEy6E2OWl4CnxskbnYJ","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def race_prob(preds,test,df):\n    probabilities = pd.DataFrame.from_dict(preds)\n    if test.index[0] != 0:\n        test.reset_index(inplace=True)\n    X_test_ns = test.set_index(['race_date', 'race_number', 'track_id', 'trakus_index', 'pg_x', 'pg_y'])\n    probabilities.index = X_test_ns.index\n    probabilities = pd.DataFrame(probabilities.mean(axis=1))\n    probabilities.reset_index(inplace=True)\n    probabilities = probabilities.rename({0: 'race_prob_x'}, axis=1)\n    probabilities['race_prob_y'] = 1 - probabilities['race_prob_x']\n    probabilities_x = probabilities[['track_id', 'race_date', 'race_number', 'trakus_index', 'pg_x', 'race_prob_x']].copy()\n    probabilities_y = probabilities[['track_id', 'race_date', 'race_number', 'trakus_index', 'pg_y', 'race_prob_y']].copy()\n    result = pd.DataFrame(\n        probabilities_x.groupby([ 'pg_x','track_id', 'race_date', 'race_number', 'trakus_index'])['race_prob_x'].sum())\n    result = result.append(pd.DataFrame(\n        probabilities_y.groupby(['pg_y','track_id', 'race_date', 'race_number', 'trakus_index'])[\n            'race_prob_y'].sum())).fillna(0)\n    result['race_prob'] = result['race_prob_x'] + result['race_prob_y']\n    final_result = pd.DataFrame(\n        result.groupby(['race_date', 'track_id', 'race_number', 'trakus_index', 'pg_x'])['race_prob'].sum())\n    final_result = final_result.sort_values(['race_date', 'track_id', 'race_number', 'trakus_index', 'race_prob'],\n                                            ascending=[True, False, True, True, False]).groupby(\n        ['race_date', 'track_id', 'race_number', 'trakus_index', 'pg_x'], as_index=False).head()  # strange interaction\n\n    lamb = 1.005\n\n    final_result.reset_index(inplace = True)\n    final_result['race_prob'] = final_result['race_prob']*lamb**final_result['trakus_index']\n\n    final_result['race_prob'] = final_result.groupby(\n        ['race_date', 'track_id', 'race_number', 'trakus_index']).race_prob.transform(lambda x: np.exp(x) / np.exp(x).sum())\n    final_result['predicted order'] = final_result.groupby(\n        ['race_date', 'track_id', 'race_number', 'trakus_index']).cumcount() + 1\n    final_result.reset_index(inplace=True)\n    result_team = pd.merge(final_result,\n                            df[['race_date', 'track_id', 'race_number', 'trakus_index', 'pg', 'horse_name', 'jockey',\n                                'position_at_finish', 'odds', 'max_trakus','course_type']],\n                            left_on=['track_id', 'race_date', 'race_number', 'trakus_index', 'pg_x'],\n                            right_on=['track_id', 'race_date', 'race_number', 'trakus_index', 'pg'],\n                            how='left', copy=False)\n    result_team['predicted_odds'] = result_team['race_prob'] / (1 - result_team['race_prob'])\n    order = ['race_date', 'track_id', 'race_number', 'trakus_index', 'max_trakus', 'pg_x',\n                'pg', 'horse_name', 'jockey', 'race_prob', 'predicted_odds', 'predicted order', 'odds', 'position_at_finish']\n    result_team = result_team[order]\n    result_team['frac_race_completed'] = result_team['trakus_index']/(result_team['max_trakus'])\n    result_team.drop(['pg_x'], axis=1, inplace=True)\n    return result_team","metadata":{"datalore":{"node_id":"fn6Y3wM2LsRkCB38j6r875","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team = race_prob(preds,test,df)","metadata":{"datalore":{"node_id":"ojHk2FZ18FnEl8dq7Ayo83","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def accuracy_team_pos(result_team):\n    t = pd.get_dummies(np.where(result_team['predicted order'] == result_team['position_at_finish'],'matched','unmatched')).sum(0)\n    overall_acc = t[0]/result_team.shape[0]\n\n    acc_by_time = []\n    unique_fractions = np.arange(0.02, 1.01, 0.01)\n    for i in unique_fractions:\n        trakus_index_20 = result_team[(result_team['frac_race_completed'] > i - 0.005) & (result_team['frac_race_completed'] < i + 0.005)]\n        trak_var = pd.get_dummies(np.where(trakus_index_20['predicted order'] == trakus_index_20['position_at_finish'],'matched','unmatched')).sum(0)\n        acc_by_time.append(trak_var[0]/trakus_index_20.shape[0])\n\n    return overall_acc, acc_by_time","metadata":{"datalore":{"node_id":"rjXOTEOW0KzHyqMiaQ8YvK","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"overall_acc, acc_by_time = accuracy_team_pos(result_team)","metadata":{"datalore":{"node_id":"j2eSSKZihwe34et0trU15J","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('The overall accuracy predicting all race positions is: ',round(100*overall_acc,2),\"%\")","metadata":{"datalore":{"node_id":"LpaZzipDnsdfjk22bOsXlM","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def accuray_winner(result_team):\n    winner = result_team[result_team['predicted order'] == 1]\n    t = pd.get_dummies(np.where(winner['predicted order'] == winner['position_at_finish'],'matched','unmatched')).sum(0)\n    overall_acc_winner = t[0]/winner.shape[0]\n\n    acc_winner_by_time = []\n    unique_fractions = np.arange(0.02, 1.01, 0.01)\n    for i in unique_fractions:\n        trakus_index_20 = winner[(winner['frac_race_completed'] > i - 0.005) & (winner['frac_race_completed'] < i + 0.005)]\n        trak_var = pd.get_dummies(np.where(trakus_index_20['predicted order'] == trakus_index_20['position_at_finish'],'matched','unmatched')).sum(0)\n        acc_winner_by_time.append(trak_var[0]/trakus_index_20.shape[0])\n\n    return overall_acc_winner, acc_winner_by_time","metadata":{"datalore":{"node_id":"yY9U1HWgCZWdIqh3OehJkT","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"overall_acc_winner, acc_winner_by_time = accuray_winner(result_team)","metadata":{"datalore":{"node_id":"Jc5UliAgQFlKbyq967LlSS","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('The overall accuracy predicting the winner of the race is: ',round(100*overall_acc_winner,2),\"%\")","metadata":{"datalore":{"node_id":"y6PDp8a3lFfcqcEB5QYDt7","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team['predicted order'] = result_team.groupby(\n    ['race_date', 'track_id', 'race_number', 'trakus_index']).cumcount() + 1","metadata":{"datalore":{"node_id":"mYhIt24BdoITvRUYutez4z","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team = result_team.sort_values(['race_date', 'track_id', 'race_number', 'trakus_index', 'odds'],\n                                            ascending=[True, False, True, True, True]).groupby(\n        ['race_date', 'track_id', 'race_number', 'trakus_index','pg'], as_index=False).head()","metadata":{"datalore":{"node_id":"t76yLNxzSYF0nP7WZHi6AW","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team['predicted order odds'] = result_team.groupby(\n        ['race_date', 'track_id', 'race_number', 'trakus_index']).cumcount() + 1","metadata":{"datalore":{"node_id":"0sM1kVKHTCuUBiz6rXLLR2","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team['odds_pred_correct'] = result_team['position_at_finish'] == result_team['predicted order odds']","metadata":{"datalore":{"node_id":"gPfs1YwmSzqD4PfrNWW1EN","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team.columns","metadata":{"datalore":{"node_id":"TFwW4m9AEvKKCt0qaLRSJW","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team.reset_index(inplace = True)","metadata":{"datalore":{"node_id":"7kt9lAv9xngqeSaEHpMFER","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pos_finish_1 = result_team[result_team['position_at_finish']== 1].mean()['odds_pred_correct']","metadata":{"datalore":{"node_id":"whAjExjCEkxs5JMq0xHChh","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"odds_pred_correct = result_team.mean()['odds_pred_correct']","metadata":{"datalore":{"node_id":"stEwLKF5bH1nJjAFJdXXKE","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"odds_pred_correct","metadata":{"datalore":{"node_id":"BFT5P6omUxtRQZeLsZAkXP","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Benchmark model - ryan u get a dataframe out with probabilities\ndf2['diff_position'] = (df2['position_x'] - df2['position_y']).round(5)\ndf2['diff_odds'] = ((df2['odds_x']  - df2['odds_y'])*df2['lambd'])\nfeature_benchmark = ['diff_position','diff_odds']\ntrain, test, X_train, y_train, X_test, y_test = train_test_split(df2, 0.8, features)\nmodels_benchmark = modelling(X_train,y_train,SVM=False,GB=False,RF=False,DT=False,NB=False,KNN=False,LR=True)\nresult_benchmark, preds_benchmark, names_benchmark = model_eval(models_benchmark,X_test,y_test)\nresult_team_benchmark = race_prob(preds_benchmark,test,df)\n# Just take series resuting from this dataframe acc_winner_by_time_benchmark\noverall_acc_winner_benchmakr, acc_winner_by_time_benchmark = accuray_winner(result_team_benchmark)\nacc_winner_by_time_benchmark","metadata":{"datalore":{"node_id":"LTTCXO0YhvWCryJ6XHx2kO","type":"CODE","hide_input_from_viewers":false,"hide_output_from_viewers":false,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots()\n\nax.plot(pd.DataFrame(acc_winner_by_time).rolling(10).mean())","metadata":{"datalore":{"node_id":"DtNkOhTMfIm7Sz3LnwAkdU","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true,"report_properties":{}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_team = pd.read_csv(\"result_team.csv\", index_col=\"Unnamed: 0\")\nacc_winner_by_time = pd.read_csv(\"acc_winner_by_time.csv\", index_col=\"Unnamed: 0\")\nacc_winner_by_time_benchmark = pd.read_csv(\"acc_winner_by_time_benchmark.csv\", index_col=\"Unnamed: 0\")","metadata":{"datalore":{"node_id":"xEC1e5s3uP5255Tj68MLKl","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax= plt.subplots(nrows = 2, gridspec_kw={\"hspace\": 0.2})\nfig.set_size_inches(20, 15)\nax[0].grid(True)\nax[1].grid(True)\n\nax[0].plot(acc_winner_by_time.rolling(30, min_periods=1).mean(), label = \"Our Model\", color = 'blue', linewidth = 2, linestyle = '--')\nax[0].plot(acc_winner_by_time_benchmark.rolling(30, min_periods=1).mean(), label = \"Benchmark: Final Position = Current Position\", color = 'lightcoral', linewidth = 2, linestyle = '--')\nax[0].axhline(pos_finish_1, label = \"Benchmark: Odds\", color = 'mediumseagreen', linewidth = 2, linestyle = '--')\n\nax[0].plot(pos_finish_1)\nax[0].legend(fontsize = 15)\nax[0].set_xlabel(\"% of race completed\", fontsize = 14)\nax[0].set_ylabel(\"Accuracy\", fontsize = 14)\nax[0].set_title(\"Average accuracy at predicting the winner of the race by fraction of race completed\", fontsize = 16)\n\nax[1].set_title(\"Average accuracy at predicting all positions by fraction of race completed\", fontsize = 16)\nax[1].plot(pd.DataFrame(accuracy_team_pos(result_team)[1]).rolling(10).mean(), label = \"Our Model\", linestyle = '--', linewidth = 2, color = 'blue')\nax[1].axhline(odds_pred_correct, color = 'mediumseagreen', linestyle = '--', label = \"Benchmark: Odds\", )\nax[1].set_xlabel(\"% of race completed\", fontsize = 14)\nax[1].legend(fontsize = 15, loc = 2)\nax[1].set_ylim(0.15, 0.8)\nax[1].set_ylabel(\"Accuracy\", fontsize = 14)","metadata":{"datalore":{"node_id":"Sz04HSe4m6NCAj1cuZkBuU","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## The Bottom Line\n### Over the course of this project, we extensively explored factors influencing the probabilities of horse race finishing positions. In contrast to current practice, we leveraged data generated during the race to constantly update our prediction as the race progresses. Indeed, we can see the improvement in the predictive accuracy of our model as the race proceeds as one would expect. Consistently however, our model beat both benchmarks: pre-race odds as well as pre-race odds in combination with the current position. This shows the enormous value novel data-capturing technology can unlock. The resulting potential applications are manifold. \n\n### Through the tailoring of our model, we were able to see that significant performance improvement of our model on the validation set was realised when we decreased the importance of certain features as the race progresses. In particular odds and features based on historical data became less important. What matters towards the end of a race is the distance between the competing horses, and data that can indicate how tired a horse may be by that point.\n\n### In addition, we were able to see that slipstreaming, although an important tactic, is not particularly crucial for the winner of a race, but more for the horses vying for the other top positions. Further analysis of the proportion of time spent in slipstream, along with data on the aggressiveness of speed change, efficiency of path run, and horse weight would allow a future analysis to construct an \"exertion\" score for a horse. This could indicate the physical strain placed on a horse by each specific race, and, if utilised alongside medical data, could provide insights into what race conditions combine to create a dangerous situation for a horse to race in, and how these situations can be avoided.","metadata":{"datalore":{"node_id":"BQ64mliXW4BGlX1zwW9nGq","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"# Code appendix","metadata":{"datalore":{"node_id":"WKeV92DwCxAqMdKUvuPD85","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"markdown","source":"<a id='basic_features_code'></a>\n### Creation of basic features such as speed, distance and time/distance at finish\nWe now format the input dataframes, and create basic features such as speed, distance, and the time/distance travelled by a horse at the end of a race. Code *in ONLY the following cell* was taken from the notebook [Calculating when horses finish the race](https://www.kaggle.com/code/shapital/calculating-when-horses-finish-the-race) kindly uploaded by Kaggle users shapital and Ken Miller.","metadata":{"datalore":{"node_id":"eKYmX5zJZI2Gum8BIkgZOY","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# read the tracking, start and race table from the competition website\ndf = pd.read_csv('nyra_tracking_table.csv')\nstart_table_col_names = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']\nstart_table = pd.read_csv('nyra_start_table.csv', names=start_table_col_names)\nrace_table = pd.read_csv('nyra_race_table.csv')\nrace_table['race_index'] = race_table.index\n\n# create consistent program number for the start and the tracking table\npg_dict = {'1': '01', '1  ': '01', '10': '10', '10 ': '10', '11': '11', '11 ': '11', '12': '12', '12 ': '12','13': '13', '13 ': '13', '14': '14', '14 ': '14', '15': '15', '15 ': '15', '16': '16', '16 ': '16', '1A ': '01A', '1X ': '01X', '2': '02', '2  ': '02', '2B ': '02B', '3': '03', '3  ': '03', '3X ': '03X', '4': '04', '4  ': '04', '5': '05', '5  ': '05', '6': '06', '6  ': '06', '7': '07', '7  ': '07', '8': '08', '8  ': '08', '9': '09', '9  ': '09', '1A': '01A', '2B': '02B', '3X': '03X', '1X': '01X', 1:\"01\",2:\"02\",3:\"03\",4:\"04\",5:\"05\",6:\"06\",7:\"07\",8:\"08\",9:\"09\"}\ndf['program_number'] = df['program_number'].astype(str)\nstart_table['program_number'] = start_table['program_number'].astype(str)\ndf['pg'] = df['program_number'].map(pg_dict)\ndf['pg'].astype(str)\nstart_table['pg'] = start_table['program_number'].map(pg_dict)\n\n\n# merge the position_at_finish value from start table and distance_id + race_index values from race table to the tracking table\ndf = pd.merge(df, start_table[['track_id', 'race_date', 'race_number', 'pg', 'position_at_finish']], how='left', on=['track_id', 'race_date', 'race_number', 'pg'])\ndf = pd.merge(df, race_table[['track_id', 'race_date', 'race_number', 'distance_id', 'race_index']], how='left', on=['track_id', 'race_date', 'race_number'])\n# sort the tracking table\ndf = df.sort_values(['track_id', 'race_date', 'race_number', 'pg', 'trakus_index']).reset_index(drop=True)\n\n# convert lat and long to x and y\ndf['xy'] = np.array(utm.from_latlon(df['latitude'].values, df['longitude'].values)[:2]).T.tolist()\n# separate the xy column to an x column and a y column\ndf[['x', 'y']] = pd.Series(np.stack(df['xy'].values).T.tolist())\nmean_track_points = df.groupby('track_id')[['x', 'y']].mean()\nmean_track_points['xy'] = mean_track_points.swifter.apply(lambda row: np.array([row['x'], row['y']]), axis=1)\n# remove the mean of each track_id from the x and y values\ndf['x'] = df['x'] - df.groupby('track_id')['x'].transform('mean')\ndf['y'] = df['y'] - df.groupby('track_id')['y'].transform('mean')\ndf['xy'] = df[['x', 'y']].swifter.apply(lambda row: (row['x'], row['y']), axis=1)\n# create finish lines to track race end\nfinish_lines = {'AQU': np.array([40.67151, -73.83108]),\n                'BEL': np.array([40.713305, -73.722603]),\n                'SAR': np.array([43.071821, -73.77039])}\nxy_finish_lines = {track_id: utm.from_latlon(*finish_lines[track_id])[:2] - mean_track_points.loc[track_id]['xy'] for track_id in ['AQU', 'BEL', 'SAR']}\n\n# calculate speed of each horse for every trakus index\ndef get_speed(points):\n    speed = [0]\n    points = points.values\n    for i in range(1, len(points)):\n        speed.append(np.linalg.norm(np.array(points[i]) - np.array(points[i - 1])))\n    return speed\ndf['speed'] = 4 * df.groupby(['race_index', 'pg'])['xy'].transform(get_speed)\n\n# calculate the distance that horses travel thoughout the race \ndef get_distance(speed):\n    return np.cumsum(speed.values)\ndf['distance'] = df.groupby(['race_index', 'pg'])['speed'].transform(get_distance)\n\n# estimate the position of the race participants \ndef longitudinal_distance(p1, p2, p3):\n    return round(np.dot(p2-p1, p3-p2) / np.linalg.norm(p2-p1), 2)\ndef get_longitudinal_distances(points):\n    return [[longitudinal_distance(point1[0], point1[1], point2[1]) for point2 in points.values] for point1 in points.values]\ndef get_position(longitudinal_distances):\n    position = (np.array(longitudinal_distances)>=0).sum()\n    return np.nan if position == 0 else position\n\ndf_prev = df[['track_id', 'race_date', 'race_number', 'trakus_index', 'pg', 'xy']].copy()\ndf_prev['trakus_index'] = df_prev['trakus_index'] + 1\ndf = pd.merge(df, df_prev, on=['track_id', 'race_date', 'race_number', 'pg', 'trakus_index'], suffixes=['', '_prev'], how='left')\ndf['xy_pair'] = df.swifter.apply(lambda row: [np.array(row['xy_prev']), np.array(row['xy'])], axis=1)\ndf['longitudinal_distances'] = df.groupby(['track_id', 'race_date', 'race_number', 'trakus_index'])['xy_pair'].transform(get_longitudinal_distances)\ndf['position'] = df['longitudinal_distances'].swifter.apply(get_position)\n\n# calculate the maximum trakus index of each race and each horse, and mark horses that did not finish the race (dnf)\ndf['race_max_trakus_index'] = df.groupby('race_index')['trakus_index'].transform(max)\ndf['pg_max_trakus_index'] = df.groupby(['race_index', 'pg'])['trakus_index'].transform(max)\ndf['dnf'] = (df['race_max_trakus_index'] > df['pg_max_trakus_index']).astype(int)\n\n# mark finished for all indeces after the first finished mark\ndef get_finished(f):\n    if 1 in f.values:\n        min_finished_index = f[f == 1].index[0]\n        f[f.index.values >= min_finished_index] = 1\n        return f.values\n    return f.values\n\n# calculate distance between point p3 and point p1 along vector from p1 to p2\ndef longitudinal_distance2(p1, p2, p3):\n    return round(np.dot(p2-p1, p3-p2) / np.linalg.norm(p2-p1), 2)\n\n# iterate over the races and calculate when each horse finished each race\nfinished = []\nfor race_index in tqdm(sorted(df['race_index'].unique())):\n    dff = df.query(f'race_index == {race_index}').copy()\n    dff_prev = dff[['track_id', 'race_date', 'race_number', 'trakus_index', 'pg', 'x', 'y']].copy()\n    dff_prev['trakus_index'] = dff_prev['trakus_index'] + 1\n    dff = pd.merge(dff, dff_prev, on=['track_id', 'race_date', 'race_number', 'pg', 'trakus_index'], suffixes=['', '_prev'], how='left')\n#     finish_xy = dff.query('trakus_index == race_max_trakus_index').sort_values('position_at_finish', ascending=False)[['x', 'y']].values[0]\n    finish_xy = xy_finish_lines[dff.iloc[0]['track_id']]\n    dff['finished'] = dff[['x', 'y', 'x_prev', 'y_prev', 'distance', 'distance_id']].swifter.apply(lambda row: longitudinal_distance2(np.array([row['x_prev'], row['y_prev']]), np.array([row['x'], row['y']]), finish_xy) <= 0 and \\\n                                                                                           row['distance'] > 8 * row['distance_id'], axis=1).astype(int)\n    dff['finished'] = dff.groupby('pg')['finished'].transform(get_finished)\n    finished.append(dff[['race_index', 'pg', 'trakus_index', 'finished']])\n\n# merge the finished results with the tracking table\nif 'finished' in df.columns:\n    df.drop(columns=['finished'], inplace=True)\ndf = pd.merge(df, pd.concat(finished, ignore_index=True), on=['race_index', 'pg', 'trakus_index'], how='left')\ndf.loc[(df['dnf'] == 0) & (df['distance'] > 8 * df['distance_id']) & (df['speed'] == 0) & (\n            df['finished'] == 0), 'finished'] = 1\n\n# calculate the distance that the competitors have traveled at the end of the race \ndef get_distance_at_finish(g):\n    if g.iloc[0]['dnf'] == 1:\n        return np.nan\n    if g.iloc[-1]['finished'] == 0:\n        return g.iloc[-1]['distance']\n    return g.query('finished == 1').iloc[0]['distance']\n\n\ndaf = df.groupby(['race_index', 'pg'], as_index=False).apply(lambda g: pd.Series(index=['distance_at_finish'],\n                                                                                 data=get_distance_at_finish(g)))\ndf = pd.merge(df, daf, on=['race_index', 'pg'], how='left')\ndf['portion_completed'] = df['distance'] / df['distance_at_finish']","metadata":{"datalore":{"node_id":"1kzrzunXejp9pNRMTtyiB6","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='Slipstream_code'></a>\n##### Slipstream feature - based on our own track calculation\nThe dataframes containing the coordinates of the inner tracks can be found as csv files in the submitted folder - see inner track files. The code to produce these csv files is in the [Inner Track Cell](attachment:./#inner_tracks) at the bottom of the notebook.","metadata":{"datalore":{"node_id":"wG9xIJzTocq5LoLmP01HLX","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# creation of \n\ndef norm(x1,x2):\n    return np.sqrt(x1**2 + x2**2)\n\ndf['lat_shift'] = df.groupby(['track_id','race_date','race_number','pg'])['latitude'].shift(1)\ndf['long_shift'] = df.groupby(['track_id','race_date','race_number','pg'])['longitude'].shift(1)\n\ndf['lat_change'] = df['latitude'] - df['lat_shift']\ndf['long_change'] = df['longitude'] - df['long_shift']\n\ndf['circle_lat_centre1'] = df['latitude'] - df['lat_change']\ndf['circle_long_centre1'] = df['longitude'] - df['long_change']\n\ndf['circle_lat_centre2'] = df['latitude'] - 2*df['lat_change']\ndf['circle_long_centre2'] = df['longitude'] - 2*df['long_change']\n\ndf['circle_lat_centre3'] = df['latitude'] - 4*df['lat_change']\ndf['circle_long_centre3'] = df['longitude'] - 4*df['long_change']\n\ndf['grad'] = df['long_change'] / df['lat_change']\n\nnaive_dist = df.groupby(['track_id','race_date','race_number','pg']).\\\n    apply(lambda a: norm(a.lat_change, a.long_change))\n\ndf['naive_dist']= naive_dist.reset_index(level=[0,1,2,3],drop=True)\ndf['naive_dist'] = df['naive_dist'].fillna(0)\n\ndf_for_slip = pd.merge(\n    df,\n    df[['track_id','race_date','race_number','trakus_index','pg','latitude', 'longitude']],\n    on=['track_id','race_date','race_number','trakus_index'],\n    how='left')\n\ndf_for_slip.sort_values(['track_id', 'race_date', 'race_number', 'pg_x','pg_y']).reset_index(drop=True)\ndf_for_slip = df_for_slip[df_for_slip['pg_x'] != df_for_slip['pg_y']]\n\ndf_for_slip = df_for_slip.set_index(['track_id', 'race_date', 'race_number'])\n\ndf_for_slip.reset_index(inplace = True)\n\ndef is_slipstream(centre_x1, centre_y1, centre_x2, centre_y2, centre_x3, centre_y3, \\\n     major_radius1, minor_radius1, major_radius2, minor_radius2, major_radius3, minor_radius3,gradient, x, y, speed):\n\n    dist1 = (((y-centre_y1) - gradient*(x-centre_x1))**2)/((minor_radius1**2)*(1+gradient**2)) + \\\n        ((gradient*(y-centre_y1) - (x-centre_x1))**2)/((major_radius1**2)*(1+gradient**2))\n\n    dist2 = (((y-centre_y2) - gradient*(x-centre_x2))**2)/((minor_radius2**2)*(1+gradient**2)) + \\\n        ((gradient*(y-centre_y2) - (x-centre_x2))**2)/((major_radius2**2)*(1+gradient**2))\n\n    dist3 = (((y-centre_y3) - gradient*(x-centre_x3))**2)/((minor_radius3**2)*(1+gradient**2)) + \\\n        ((gradient*(y-centre_y3) - (x-centre_x3))**2)/((major_radius3**2)*(1+gradient**2))\n\n    if dist1 < 1:\n        return 1*speed**2\n    elif dist2 < 1:\n        return 1*speed**2\n    elif dist3 < 1:\n        return 0.25*speed**2\n    else:\n        return 0\n\nwith_slipstream = df_for_slip.swifter.apply(lambda a: is_slipstream(a.circle_lat_centre1, a.circle_long_centre1, \\\na.circle_lat_centre2, a.circle_long_centre2, \\\na.circle_lat_centre3, a.circle_long_centre3, \\\na.naive_dist, 0.5*a.naive_dist, \\\n    1.8*a.naive_dist, a.naive_dist, \\\n        2*a.naive_dist, 1.5*a.naive_dist, \\\n            a.grad, a.latitude_y, a.longitude_y, a.speed), axis=1)\n\ndf_for_slip['slipstream']= with_slipstream\n\nslip_sum = df_for_slip.groupby([\"pg_y\", 'track_id', 'race_date', 'race_number', 'trakus_index'])['slipstream'].sum()\nslip_sum1 = slip_sum.reset_index(level=[0,1,2,3,4])\n\nslip_sum1.rename(columns={'pg_y' :'pg'}, inplace=True)\n\ndf = df.merge(slip_sum1, on=[\"pg\", 'track_id', 'race_date', 'race_number', 'trakus_index'])\n\ndf['prop_slipstream'] = df.groupby([\"pg\", 'track_id', 'race_date', 'race_number'])['slipstream'].cumsum()\n\ndf.drop(columns=['lat_shift','long_shift', 'lat_change', 'long_change', 'circle_lat_centre1',\n       'circle_long_centre1', 'circle_lat_centre2', 'circle_long_centre2',\n       'circle_lat_centre3', 'circle_long_centre3'], inplace=True)","metadata":{"datalore":{"node_id":"xqyD581M5hf8xGb61lSC60","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='dist_to_inner_code'></a>\n#### Code for finding how far a horse is from the inner track at every time point.\nThe dataframes containing the coordinates of the inner tracks can be found as csv files in the submitted folder - see inner track files. The code to produce these csv files is in the [Inner Track Cell](attachment:./#inner_tracks) at the bottom of the notebook.","metadata":{"datalore":{"node_id":"QrgZrzXRa12qjbquG4l6p0","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"##### Distance of race participants to inner track  \nfrom scipy import spatial\n\naqu_inner = pd.read_csv('aqu_inner_tracks_df_reduced.csv')\nbel_inner = pd.read_csv('bel_inner_tracks_df_reduced.csv')\nsar_inner = pd.read_csv('sar_inner_tracks_df_reduced.csv')\n\ncoords_aqu_T = np.array(aqu_inner[['aqu_T_lat', 'aqu_T_lon']].values.tolist())\ncoords_aqu_D = np.array(aqu_inner[['aqu_D_lat', 'aqu_D_lon']].values.tolist())\ncoords_aqu_O = np.array(aqu_inner[['aqu_O_lat', 'aqu_O_lon']].values.tolist())\n\ntree_aqu_T = spatial.KDTree(coords_aqu_T)\ntree_aqu_D = spatial.KDTree(coords_aqu_D)\ntree_aqu_O = spatial.KDTree(coords_aqu_O)\n\ncoords_bel_T = np.array(bel_inner[['bel_T_lat', 'bel_T_lon']].values.tolist())\ncoords_bel_D = np.array(bel_inner[['bel_D_lat', 'bel_D_lon']].values.tolist())\ncoords_bel_I = np.array(bel_inner[['bel_I_lat', 'bel_I_lon']].values.tolist())\ncoords_bel_M = np.array(bel_inner[['bel_M_lat', 'bel_M_lon']].values.tolist())\n\ntree_bel_T = spatial.KDTree(coords_bel_T)\ntree_bel_D = spatial.KDTree(coords_bel_D)\ntree_bel_I = spatial.KDTree(coords_bel_I)\ntree_bel_M = spatial.KDTree(coords_bel_M)\n\ncoords_sar_T = np.array(sar_inner[['sar_T_lat', 'sar_T_lon']].values.tolist())\ncoords_sar_D = np.array(sar_inner[['sar_D_lat', 'sar_D_lon']].values.tolist())\ncoords_sar_I = np.array(sar_inner[['sar_I_lat', 'sar_I_lon']].values.tolist())\ncoords_sar_M = np.array(sar_inner[['sar_M_lat', 'sar_M_lon']].values.tolist())\n\ntree_sar_T = spatial.KDTree(coords_sar_T)\ntree_sar_D = spatial.KDTree(coords_sar_D)\ntree_sar_I = spatial.KDTree(coords_sar_I)\ntree_sar_M = spatial.KDTree(coords_sar_M)\n\nlat_lon_coords = pd.DataFrame(np.array(df[['latitude', 'longitude']].values.tolist()))\nlat_lon_coordstrn = lat_lon_coords.values.T\nlat_long_coords_combined = list(zip(lat_lon_coordstrn[0], lat_lon_coordstrn[1]))\ndf['lat_long'] = lat_long_coords_combined\n\ndef dist_to_inner(x):\n    if x.track_id=='Aqueduct':\n        if x.course_type=='Dirt':\n            q = tree_aqu_D.query(x['lat_long'], 1)\n            return q[0], aqu_inner['aqu_D_section'][q[1]]\n        elif x.course_type=='turf':\n            q = tree_aqu_T.query(x['lat_long'], 1)\n            return q[0], aqu_inner['aqu_T_section'][q[1]]\n        elif x.course_type=='Outer turf':\n            q = tree_aqu_O.query(x['lat_long'], 1)\n            return q[0], aqu_inner['aqu_O_section'][q[1]]\n\n\n    if x.track_id=='Saratoga':\n        if x.course_type=='Dirt':\n            q = tree_sar_D.query(x['lat_long'], 1)\n            return q[0], sar_inner['sar_D_section'][q[1]]\n        elif x.course_type=='turf':\n            q = tree_sar_T.query(x['lat_long'], 1)\n            return q[0], sar_inner['sar_T_section'][q[1]]\n        elif x.course_type=='Hurdle':\n            q = tree_sar_M.query(x['lat_long'], 1)\n            return q[0], sar_inner['sar_M_section'][q[1]]\n        elif x.course_type=='Inner turf':\n            q = tree_sar_I.query(x['lat_long'], 1)\n            return q[0], sar_inner['sar_I_section'][q[1]]\n\n    if x.track_id=='Belmont':\n        if x.course_type=='Dirt':\n            q = tree_bel_D.query(x['lat_long'], 1)\n            return q[0], bel_inner['bel_D_section'][q[1]]\n        elif x.course_type=='turf':\n            q = tree_bel_T.query(x['lat_long'], 1)\n            return q[0], bel_inner['bel_T_section'][q[1]]\n        elif x.course_type=='Hurdle':\n            q = tree_bel_M.query(x['lat_long'], 1)\n            return q[0], bel_inner['bel_M_section'][q[1]]\n        elif x.course_type=='Inner turf':\n            q = tree_bel_I.query(x['lat_long'], 1)\n            return q[0], bel_inner['bel_I_section'][q[1]]\n    else:\n        return 0, np.nan\n\ndist_inner = df.swifter.apply(lambda x: dist_to_inner(x), axis=1)\nl1, l2 = zip(*dist_inner)\n\ndf['dist_to_inner'] = np.array(l1)\ndf['track_section'] = np.array(l2)\n\nrol_mn = df.groupby([\"pg\", 'track_id', 'race_date', 'race_number']).apply(lambda x: x['dist_to_inner'].expanding().mean())\n\ndf['mean_inner_dist'] = rol_mn.reset_index(level=[0,1,2,3], drop=True)\n\ndf['mean_inner_dist']  = df['mean_inner_dist'].fillna(0)\nsave11 = df","metadata":{"datalore":{"node_id":"wDxeAyHGMUqbpiZegehtMU","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='external_data_code'></a>\n##### External data to make races more interpretable\nWe also create standardized position to ensure that race outcomes are comparable even if the number of participants is different  ","metadata":{"datalore":{"node_id":"TQ1BVSXoPPg0GbRlhg4rhv","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"horse_names = pd.read_csv('horse_names.csv')\nhorse_ids = pd.read_csv('horse_ids.csv')\nhorse_ids = horse_ids.merge(horse_names, on='horse_id', how='inner')\nhorse_ids['program_number'] = horse_ids['program_number'].swifter.apply(lambda x: x.ljust(3, ' '))\nstart_table = start_table.merge(horse_ids, left_on=['track_id', 'race_date', 'race_number', 'program_number'], right_on=['track_id', 'race_date', 'race', 'program_number'], how='left')\nstart_table = start_table.merge(race_table, on=['track_id', 'race_date', 'race_number'], how='inner')\nstart_table.sort_values(['track_id', 'race_date', 'race_number', 'pg']).reset_index(drop=True)\n# create column to account for number of participants and standerdize finished position\nparticipants = start_table.groupby(['track_id', 'race_date', 'race_number'])['pg'].count()\nparticipants = participants.reset_index()\nparticipants = participants.rename({'pg': 'participants_race'}, axis=1)\nstart_table = pd.merge(start_table,participants, on = ['track_id', 'race_date', 'race_number'],how='left',copy=False)\nstart_table['standardized_position_at_finish'] = start_table['position_at_finish'] / start_table['participants_race']\nstart_table.drop(['program_number', 'position_at_finish', 'distance_id'], axis=1, inplace=True)\nmaster_data = pd.merge(df, start_table, left_on=['track_id', 'race_date', 'race_number', 'pg'],\n                       right_on=['track_id', 'race_date', 'race_number', 'pg'], how='left', copy=False)\nmaster_data.drop(['program_number'], axis=1, inplace=True)\n# Make data more interpretable by adding official track names and race specific descriptions \nrace_type_dict =   {'STK': 'Stakes', 'WCL': 'Waiver Claiming', 'WMC': 'Waiver Maiden Claiming', 'SST': 'Starter Stakes', 'SHP': 'Starter Handicap', 'CLM': 'Claiming','STR': 'Starter Allowance', 'AOC': 'Allowance Optionl Claimer', 'SOC': 'Starter Optional Claimer', 'MCL': 'Maiden Claiming', 'ALW': 'Allowance', 'MSW': 'Maiden Special Weight'}\ntrack_id_dict = dict(AQU = 'Aqueduct', BEL = 'Belmont', SAR = 'Saratoga')\ncourse_type_dict = dict(M = 'Hurdle', D = 'Dirt', O = 'Outer turf', I = 'Inner turf', T = 'turf')\ntrack_condition_dict =  dict(YL = 'Yielding', FM ='Firm', SY = 'Sloppy', GD = 'Good', FT = 'Fast', MY = 'Muddy', SF = 'Soft')\nmaster_data['race_type'] = master_data['race_type'].map(race_type_dict)\nmaster_data['track_id'] = master_data['track_id'].map(track_id_dict)\nmaster_data['course_type'] = master_data['course_type'].map(course_type_dict)\n\nmaster_data['track_condition'] = master_data['track_condition'].apply(lambda x:x.replace(\" \", ''))\nmaster_data['track_condition'] = master_data['track_condition'].map(track_condition_dict)\nmaster_data['standardized_position'] = master_data['position'] / master_data['participants_race']\nmaster_data = master_data.sort_values(['track_id', 'race_date', 'race_number', 'pg']).reset_index(drop=True)\nmaster_data.drop(['xy', 'x', 'y', 'xy_prev', 'xy_pair', 'longitudinal_distances', 'finishing_place'], axis=1,\n                 inplace=True)","metadata":{"datalore":{"node_id":"QsWmHqYPwpanOSrSVgajig","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='historical_info_code'></a>\n##### Add historical information regarding horses and jockeys to create further measurments informing our model on their past performance ","metadata":{"datalore":{"node_id":"aOzS0kB6IP6MP09UufkjKx","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# add external performance information about jockey performance data \ndata_df = df\nadd_jockey_info = pd.read_csv(\"2018_equibase_jockey_data.csv\")\nadd_jockey_info = add_jockey_info.rename({'winPercentage':'winPercentage jockey 2018', 'jockeyName':'jockey', 'earnings':'earnings jockey 2018', 'rank':'rank jockey 2018','place':'place jockey 2018', 'identity':'identity jockey 2018', 'starts':'starts jockey 2018', 'perStart':'perStart jockey 2018', 'topThreePercentage':'topThreePercentage jockey 2018', 'show':'show jockey 2018', 'win':'win jockey', 'topThree':'topThree jockey 2018'}, axis=1)\n\njockey = pd.DataFrame(data_df['jockey'].unique())\nlist1 = data_df['jockey'].unique().tolist()\nlist2 = add_jockey_info['jockey'].unique().tolist()\n\n# some of the jockey names do not match with the ones given in the competition dataframe - we consequently use a fuzzy matching algorithm to find similarties between the names and map them\n# it can be expected that nearly all name matches that reach a level above 70 are corresponding to equivalent persons - for enteries with a value bewlow 70 we use the mean performance of the riders as performance indicator  \nthreshold = 70\nmat1=[]\nfor i in list1:\n    mat1.append(process.extract(i, list2, limit=1))\njockey[['matching names jockey','jockey name score']] = pd.Series(mat1).explode().tolist()\njockey.rename({0:'jockey'},inplace=True,axis=1)\ndata_df = pd.merge(data_df,jockey, how='left',on='jockey',copy=False)\nadd_jockey_info.drop_duplicates(\"jockey\", inplace=True)\ndata_df = pd.merge(data_df,add_jockey_info,left_on='matching names jockey', right_on='jockey',how='left',copy=False)\n\n# adding additional information about the horses \nadd_horse_info  = pd.read_csv(\"2018_Equibase_Horse_Rank.csv\")\nadd_horse_info = add_horse_info.rename({'winPercentage':'winPercentage horse 2018','horseName':'horse_name', 'referenceNumber':'referenceNumber horse 2018','earnings':'earnings horse 2018', 'rank':'rank horse 2018', 'place':'place horse 2018', 'sireReferenceNumber':'sireReferenceNumber horse 2018', 'starts':'starts horse 2018','sireName':'sireName horse 2018', 'speedFigure':'speedFigure horse 2018', 'perStart':'perStart horse 2018', 'topThreePercentage':'topThreePercentage horse 2018', 'show':'show horse 2018',\n       'win':'win horse 2018', 'topThree':'topThree horse 2018', 'registry':'registry horse 2018' }, axis=1)\nadd_horse_info.drop_duplicates(\"horse_name\", inplace=True)\n\ndata_df = pd.merge(data_df, add_horse_info, on = 'horse_name', how='left', copy=False)\n\ndata_df.drop(['matching names jockey', 'registry horse 2018',\n              'referenceNumber horse 2018', 'sireReferenceNumber horse 2018', 'sireName horse 2018'], axis=1,\n             inplace=True)\njockey_cols = list(data_df.columns[(data_df.columns.str.contains('jockey'))])\njockey_cols.remove('jockey_x')\njockey_cols.remove('jockey name score')\nhorse_cols = list(data_df.columns[data_df.columns.str.contains('horse')])\nhorse_cols.remove('horse_id')\nhorse_cols.remove('horse_name')\n\nfor column in jockey_cols:\n    data_df[column] = np.where(data_df[\"jockey name score\"] < threshold, np.nan, data_df[column])\n\ndata_df.rename({'jockey_x': 'jockey'}, axis=1, inplace=True)\ndata_df.loc[:, jockey_cols + horse_cols] = data_df.loc[:, jockey_cols + horse_cols].fillna(\n    data_df.loc[:, jockey_cols + horse_cols].mean())","metadata":{"datalore":{"node_id":"A3OqYO4prn5Jg6d9FA4tmu","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='exp_win_rate_code'></a>\n##### Experience & Win-rate","metadata":{"datalore":{"node_id":"Q1fBBK2BNRzuRuca8rLKC6","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# general experience of jockeys and horses\none_trakus = data_df[data_df['trakus_index'] == 3]\n\nrace_count_jockey = pd.DataFrame(one_trakus.groupby(['jockey'])['race'].cumcount())\nrace_count_jockey.rename({0: 'experience_jockey'}, axis=1,inplace=True)\none_trakus = pd.merge(one_trakus, race_count_jockey, how='left', copy=False, left_index=True, right_index=True)\n\nrace_count_horse = pd.DataFrame(one_trakus.groupby(['horse_id'])['race'].cumcount())\nrace_count_horse.rename({0: 'experience_horse'}, axis=1,inplace=True)\none_trakus = pd.merge(one_trakus, race_count_horse, how='left', left_index=True, right_index=True, copy=False)\n\ndata_df = pd.merge(data_df, one_trakus[['track_id', 'race_date', 'race_number', 'pg','experience_jockey','experience_horse']], how='left', on=['track_id', 'race_date', 'race_number', 'pg'], copy=False)\n\ndata_df[\"experience_horse\"] = data_df[\"experience_horse\"] + data_df[\"starts horse 2018\"]\ndata_df[\"experience_jockey\"] = data_df[\"experience_jockey\"] + data_df[\"starts jockey 2018\"]\nrace_level_data = data_df[data_df[\"trakus_index\"] == 3]","metadata":{"datalore":{"node_id":"lCUKS1sopzDgIQbJuyjDhx","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Win-rate\none_trakus = data_df[data_df['trakus_index'] == 3]\n\nwin_count_jockey = pd.DataFrame(one_trakus[one_trakus[\"position_at_finish\"] == 1].groupby([\"jockey\"]).cumcount())\nwin_count_jockey.rename({0: 'wins_jockey'}, axis=1 ,inplace=True)\none_trakus = pd.merge(one_trakus, win_count_jockey, how='left', copy=False, left_index=True, right_index=True)\ndata_df = pd.merge(data_df, one_trakus[['track_id', 'race_date', 'race_number', 'pg','wins_jockey']], how='left', on=['track_id', 'race_date', 'race_number', 'pg'], copy=False)\n\n# adding wins of 2018 and before to rolling cumulative wins of 2019\ndata_df[\"wins_jockey\"] = data_df.apply(lambda x : x[\"wins_jockey\"]  + x[\"win jockey\"] if ~np.isnan(x[\"wins_jockey\"]) else x[\"win jockey\"], axis=1)\n\n# calculating overall win rate of jockeys\ndata_df[\"win_rate_jockey\"] = data_df[\"wins_jockey\"] / data_df[\"experience_jockey\"]\nrace_level_data = data_df[data_df[\"trakus_index\"] == 3]","metadata":{"datalore":{"node_id":"4HiH1okMp6BjSC8RWhuvha","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='performance_trend_code'></a>\n##### Performance trend ","metadata":{"datalore":{"node_id":"WZNsqsSgcXVXQINAhgxphx","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# Trend performance of jockeys and horses\none_trakus = data_df[data_df['trakus_index'] == 3]\none_trakus.reset_index(inplace=True, drop=\"index\")\none_trakus.set_index(['race', 'track_id', 'race_date', 'race_number'], inplace=True)\n\ntrend = pd.DataFrame(one_trakus.groupby(['jockey']).apply(lambda x: x['standardized_position_at_finish'].shift().fillna(0.5).ewm(alpha=0.05).mean()))\ntrend.reset_index(inplace=True)\ntrend = trend.rename({'standardized_position_at_finish': 'trend_jockey'}, axis=1)\ndata_df = pd.merge(data_df, trend, how='left', on=['race', 'track_id', 'race_date', 'race_number', 'jockey'], copy=False)","metadata":{"datalore":{"node_id":"32zEoy1of5YIZEOzbKSfVU","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Trend performance of horses\ntrend = pd.DataFrame(one_trakus.groupby(['horse_id']).apply(lambda x: x['standardized_position_at_finish'].fillna(np.nanmean(x['standardized_position_at_finish'].shift())).ewm(alpha=0.05).mean()))\ntrend.reset_index(inplace=True)\ntrend = trend.rename({'standardized_position_at_finish': 'trend_horse'}, axis=1)\ndata_df = pd.merge(data_df, trend, how='left', on=['race', 'track_id', 'race_date', 'race_number', 'horse_id'], copy=False)\n# drop the now unused columns from external datasource \ndata_df.drop(['winPercentage jockey 2018', 'earnings jockey 2018', 'rank jockey 2018',\n       'place jockey 2018', 'identity jockey 2018', 'starts jockey 2018',\n       'perStart jockey 2018', 'topThreePercentage jockey 2018',\n       'show jockey 2018', 'win jockey', 'topThree jockey 2018',\n       'winPercentage horse 2018', 'earnings horse 2018', 'rank horse 2018',\n       'place horse 2018', 'starts horse 2018', 'speedFigure horse 2018',\n       'perStart horse 2018', 'topThreePercentage horse 2018',\n       'show horse 2018', 'win horse 2018', 'topThree horse 2018'], inplace=True, axis=1)\ndf = data_df","metadata":{"datalore":{"node_id":"qsHoayNpZWm5xYKtY3y3E3","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='performance_by_track_condition_code'></a>\n### Performance of competitors by different track condition ","metadata":{"datalore":{"node_id":"YCRcfAsq8F3JCClAVOJQVE","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"\njockey_track_condition = df.iloc[:int(0.8*len(df)), :].groupby(['jockey', 'track_condition']).mean()['standardized_position_at_finish']\njockey_track_condition = pd.DataFrame(jockey_track_condition)\njockey_track_condition.columns = ['jockey_standardised_position_by_race_condition']\ndf.set_index(['jockey', 'track_condition'], inplace = True)\ndf = df.join(jockey_track_condition, on = ['jockey', 'track_condition'])\ndf['jockey_standardised_position_by_race_condition'].fillna(df['standardized_position_at_finish'], inplace = True)\ndf.reset_index(inplace = True)\n\n# calculating speed feature\ndf['speed'] = np.where(df['speed'] > df['speed'].quantile(0.99995), df['speed'].quantile(0.99995), df['speed']).tolist()\n\n# max trakus index by unique race - when did the first horse finish the race exactly \ndistance = df.distance_id.value_counts()\nmax_trakus_by_unique_race = pd.DataFrame(df.groupby(['race_date', 'track_id', 'race_number'])['trakus_index'].max()).rename(columns = {\"trakus_index\": \"max_trakus\"})\ndf = df.join(max_trakus_by_unique_race, on = ['race_date', 'track_id', 'race_number'])\n# categorizing races in terms of distance necessary for average position \nshort_races = [(i, \"Short\") for i in distance.index if i <= 700]\nmedium_races = [(i, \"Medium\") for i in distance.index if i > 700 and i <= 1200]\nlong_races= {(i, \"Long\") for i in distance.index if i > 1200}\n\nshort = dict(short_races)\nmedium = dict(medium_races)\nlong = dict(long_races)\nshort.update(medium)\nshort.update(long)\ndf['dist_type'] = df['distance_id'].map(short)\n# Performance on different tracks and indicator of jockey has a strong finish sprint \n\ndef add_average_position_by_characteristic( df, characteristic):\n\n    characteristic = 'track_id'\n\n    grouped = df.groupby([characteristic, 'trakus_index', 'standardized_position_at_finish'])\n    grouped_mean = grouped.mean()\n    grouped_mean = grouped_mean.reset_index()\n    mean_strong_finisher = grouped_mean[grouped_mean['standardized_position_at_finish'] <= 0.3]\n    mean_weak_finisher = grouped_mean[grouped_mean['standardized_position_at_finish'] >= 0.7]\n\n    mean_strong_finisher = mean_strong_finisher.reset_index().groupby(['trakus_index', characteristic]).mean()\n    mean_weak_finisher = mean_weak_finisher.reset_index().groupby(['trakus_index', characteristic]).mean()\n\n    mean_strong_finisher_plot = mean_strong_finisher.reset_index()\n    mean_weak_finisher_plot = mean_weak_finisher.reset_index()\n\n    categories = list(mean_strong_finisher_plot[characteristic].unique())\n\n    mean_strong_finisher = mean_strong_finisher[['speed']]\n\n    df = df.reset_index().set_index(['trakus_index', characteristic])\n\n    mean_strong_finisher = mean_strong_finisher.rename(columns = {'speed': f'speed_of_winning_rider_by_{characteristic}'})\n\n    df = pd.merge(df, mean_strong_finisher, left_index=True, right_index=True, copy = False)\n    df.reset_index(inplace=True)\n    return df\n\ncharacteristic = 'track_id'\ndf = add_average_position_by_characteristic(df, characteristic)\ndf['speed_diff_from_typical_winning_rider'] = (df['speed'] - df[f'speed_of_winning_rider_by_{characteristic}'])","metadata":{"datalore":{"node_id":"r4SdMk9g1LmPmZJRuSUISF","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='std_speed_code'></a>\n### Standard Deviation Speed","metadata":{"datalore":{"node_id":"LQ22XoOMTP3mZeMVn5G6XX","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# Standard Deviation Speed\nstd = pd.DataFrame(df.groupby(['track_id', 'race_date', 'race_number', 'pg'])[['speed']].apply(lambda x: x.expanding().std()))\nstd.reset_index(inplace = True)\ndf = df.set_index(['track_id', 'race_date', 'race_number', 'pg'])\nstd = pd.DataFrame(std).set_index(df.index).rename(columns ={'speed': 'std_speed'})\ndel std['index']\nstd['std_speed'].fillna(0, inplace = True)\ndf['std_speed'] = std['std_speed'].values\n#df['std_speed'].fillna(0, inplace = True)\ndf.reset_index(inplace=True)","metadata":{"datalore":{"node_id":"uepO9OzUH92UocYa9WfxQM","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='writing_masterfile_code'></a>\n### Export Masterdata File ","metadata":{"datalore":{"node_id":"VicPvrABEmU1Y4Aj3XarDf","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"# race number for filtering when uploading the dataset\ndf = df.sort_values(['race_date','track_id', 'race_number','trakus_index','pg']).reset_index(drop=True)\ndf['races'] = df.groupby(['race_date','track_id','race_number']).ngroup()\ncolumns =[\"track_id\",\"race_date\",\"race_number\",\"program_number\",\"trakus_index\",\"latitude\",\"longitude\",\"distance_id\",\"course_type\",\"track_condition\",\"run_up_distance\",\"race_type\",\"purse\",\"post_time\",\"weight_carried\",\"jockey\",\"odds\",\"position_at_finish\"]\ndf.to_csv('df.csv')","metadata":{"datalore":{"node_id":"te6rTbgpdZf9yADgdczAFz","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='inner_tracks'></a>\n### Below is the entire code to find the inner tracks. The output of the code below, i.e. dataframes of the coordinates for the inner tracks has been submitted as CSV files alongside the notebook which are used in the code above. You can run the following code to obtain those CSV files for yourself, however it takes 5-6 minutes to run. ","metadata":{"datalore":{"node_id":"ZmrQrRH7pbPm0xaVdewZ4Y","type":"MD","hide_input_from_viewers":true,"hide_output_from_viewers":true}}},{"cell_type":"code","source":"def rotate_y(origin, point, angle):\n    \"\"\"\n    Rotate a point counterclockwise by a given angle around a given origin.\n\n    The angle should be given in radians.\n    \"\"\"\n    ox, oy = origin\n    px, py = point\n\n    qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)\n    qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)\n    return qy\n\ndef rotate_x(origin, point, angle):\n    \"\"\"\n    Rotate a point counterclockwise by a given angle around a given origin.\n\n    The angle should be given in radians.\n    \"\"\"\n    ox, oy = origin\n    px, py = point\n\n    qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)\n    qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)\n    return qx\n\n\n## AQU\naqu = data_for_tracks[data_for_tracks['track_id']=='AQU']\naqu_D = aqu[aqu['course_type']=='D']\naqu_O = aqu[aqu['course_type']=='O']\naqu_T = aqu[aqu['course_type']=='T']\n\naqu_coords_D = np.array(aqu_D[['latitude', 'longitude']].values.tolist())\naqu_coords_O = np.array(aqu_O[['latitude', 'longitude']].values.tolist())\naqu_coords_T = np.array(aqu_T[['latitude', 'longitude']].values.tolist())\n\n## Outer tracks\n\naqu_outer_D = alphashape.alphashape(aqu_coords_D, 5000)\naqu_outer_D_pts = aqu_outer_D.exterior.coords.xy\naqu_outer_O = alphashape.alphashape(aqu_coords_O, 2000)\naqu_outer_O_pts = aqu_outer_O.exterior.coords.xy\naqu_outer_T = alphashape.alphashape(aqu_coords_T, 2000)\naqu_outer_T_pts = aqu_outer_T.exterior.coords.xy\n\naqu_outer_D_pts_lat = np.array(aqu_outer_D_pts[0])\naqu_outer_D_pts_lon = np.array(aqu_outer_D_pts[1])\naqu_outer_O_pts_lat = np.array(aqu_outer_O_pts[0])\naqu_outer_O_pts_lon = np.array(aqu_outer_O_pts[1])\naqu_outer_T_pts_lat = np.array(aqu_outer_T_pts[0])\naqu_outer_T_pts_lon = np.array(aqu_outer_T_pts[1])\n\n\naqu_outer_track_df = pd.DataFrame((e for e in itertools.zip_longest(aqu_outer_D_pts_lat, aqu_outer_D_pts_lon, aqu_outer_O_pts_lat, aqu_outer_O_pts_lon, aqu_outer_T_pts_lat,aqu_outer_T_pts_lon)), \ncolumns=['D_lat', 'D_lon', 'O_lat', 'O_lon', 'T_lat', 'T_lon'])\naqu_outer_track_df.to_csv('aqu_outer_track_df.csv')\n\n\n\n## AQU Inner Track D\n\naqu_hull_D = alphashape.alphashape(aqu_coords_D, 0)\naqu_hull_D_pts = aqu_hull_D.exterior.coords.xy\n\n#Next find outer straight via inspection of plots\n#plt.plot(aqu_hull_D_pts[0][11:13], aqu_hull_D_pts[1][11:13], 'r-')\n#plt.plot(aqu_hull_D_pts[0][0:11], aqu_hull_D_pts[1][0:11], 'b-')\n#plt.plot(aqu_hull_D_pts[0][13:], aqu_hull_D_pts[1][13:], 'g-')\n# plt.show()\n\n# find centre of two centroids when splitting coordinates into two clusters\nkmeans = KMeans(n_clusters=2)\nlabs_aqu_D = kmeans.fit_predict(aqu_coords_D)\nk2_centroids_aqu_D = kmeans.cluster_centers_\ncentre_aqu_D = [(k2_centroids_aqu_D[0,0] + k2_centroids_aqu_D[1,0])/2, (k2_centroids_aqu_D[0,1] + k2_centroids_aqu_D[1,1])/2]\n\n# gradient of separating line\ngrad_aqu_D = (aqu_hull_D_pts[1][14] - aqu_hull_D_pts[1][11]) / (aqu_hull_D_pts[0][14] - aqu_hull_D_pts[0][11])\n\n# equation of separating line\ndef sepline_aqu_D(x):\n    y = centre_aqu_D[1] + grad_aqu_D*(x-centre_aqu_D[0])\n    return y\n\nyvals_aqu_D = []\nfor x in aqu_hull_D_pts[0]:\n    yvals_aqu_D.append(centre_aqu_D[1]+grad_aqu_D*(x-centre_aqu_D[0]))\n\naqu_coords_df_D = pd.DataFrame(aqu_coords_D)\naqu_coordstrn_D = aqu_coords_df_D.values.T\naqu_a_D = list(zip(aqu_coordstrn_D[0], aqu_coordstrn_D[1]))\naqu_coords_df_D['coords'] = aqu_a_D\naqu_coords_df_D['line_y'] = aqu_coords_df_D[0].apply(lambda x: sepline_aqu_D(x))\n\nlower_aqu_D = aqu_coords_df_D[aqu_coords_df_D[1]<aqu_coords_df_D.line_y]\nupper_aqu_D = aqu_coords_df_D[aqu_coords_df_D[1]>aqu_coords_df_D.line_y]\n\nmin_x_upr_aqu_D = min(upper_aqu_D[0])\nmax_x_upr_aqu_D = max(upper_aqu_D[0])\nxvals_upr_aqu_D = np.linspace(min_x_upr_aqu_D, max_x_upr_aqu_D, 200)\nxvals_upr_use_aqu_D = []\nyvals_upr_aqu_D = []\nfor i in range(1,199,2):\n    subset = upper_aqu_D[upper_aqu_D[0].between(xvals_upr_aqu_D[i-1], xvals_upr_aqu_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_D(xvals_upr_aqu_D[i]))\n        arg = series.argmin()\n        yvals_upr_aqu_D.append(subset[1].iloc[arg])\n        xvals_upr_use_aqu_D.append(xvals_upr_aqu_D[i])\n\nmin_x_lwr_aqu_D = min(lower_aqu_D[0])\nmax_x_lwr_aqu_D = max(upper_aqu_D[0])\nxvals_lwr_aqu_D = np.linspace(min_x_lwr_aqu_D, max_x_lwr_aqu_D, 200)\nxvals_lwr_use_aqu_D = []\nyvals_lwr_aqu_D = []\nfor i in range(1,199,2):\n    subset = lower_aqu_D[lower_aqu_D[0].between(xvals_lwr_aqu_D[i-1], xvals_lwr_aqu_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_D(xvals_lwr_aqu_D[i]))\n        arg = series.argmin()\n        yvals_lwr_aqu_D.append(subset[1].iloc[arg])\n        xvals_lwr_use_aqu_D.append(xvals_lwr_aqu_D[i])\n\ntrack_vals_aqu_D1 = np.array([(xvals_upr_use_aqu_D[i], yvals_upr_aqu_D[i]) for i in range(28,len(xvals_upr_use_aqu_D)-1)])\ntrack_vals_aqu_D2 = np.array([(xvals_lwr_use_aqu_D[i], yvals_lwr_aqu_D[i]) for i in range(len(xvals_lwr_use_aqu_D)-2,5,-1)])\ntrack_vals_aqu_D = np.concatenate((track_vals_aqu_D1, track_vals_aqu_D2))\n\ntck_aqu_D, u_aqu_D = splprep(track_vals_aqu_D.T, u=None, s=0.0, per=1) \nu_new_aqu_D = np.linspace(u_aqu_D.min(), u_aqu_D.max(), 100000)\nx_new_aqu_D, y_new_aqu_D = splev(u_new_aqu_D, tck_aqu_D, der=0)\n\n\n\n## AQU Inner Track O\n\naqu_hull_O = alphashape.alphashape(aqu_coords_O, 0)\naqu_hull_O_pts = aqu_hull_O.exterior.coords.xy\n\n# Next find outer straight via inspection of plots\n#plt.plot(aqu_hull_O_pts[0][37:50], aqu_hull_O_pts[1][37:50], 'r-')\n#plt.plot(aqu_hull_O_pts[0][0:35], aqu_hull_O_pts[1][0:35], 'b-')\n#plt.plot(aqu_hull_O_pts[0][50:], aqu_hull_O_pts[1][50:], 'g-')\n# plt.show()\n\nkmeans = KMeans(n_clusters=2)\nlabs_aqu_O = kmeans.fit_predict(aqu_coords_O)\nk2_centroids_aqu_O = kmeans.cluster_centers_\ncentre_aqu_O = [(k2_centroids_aqu_O[0,0] + k2_centroids_aqu_O[1,0])/2, (k2_centroids_aqu_O[0,1] + k2_centroids_aqu_O[1,1])/2]\n\ngrad_aqu_O = (aqu_hull_O_pts[1][50] - aqu_hull_O_pts[1][37]) / (aqu_hull_O_pts[0][50] - aqu_hull_O_pts[0][37])\n\ndef sepline_aqu_O(x):\n    y = centre_aqu_O[1] + grad_aqu_O*(x-centre_aqu_O[0])\n    return y\n\nyvals_aqu_O = []\nfor x in aqu_hull_O_pts[0]:\n    yvals_aqu_O.append(centre_aqu_O[1]+grad_aqu_O*(x-centre_aqu_O[0]))\n\naqu_coords_Of_O = pd.DataFrame(aqu_coords_O)\naqu_coordstrn_O = aqu_coords_Of_O.values.T\naqu_a_O = list(zip(aqu_coordstrn_O[0], aqu_coordstrn_O[1]))\naqu_coords_Of_O['coords'] = aqu_a_O\naqu_coords_Of_O['line_y'] = aqu_coords_Of_O[0].apply(lambda x: sepline_aqu_O(x))\n\nlower_aqu_O = aqu_coords_Of_O[aqu_coords_Of_O[1]<aqu_coords_Of_O.line_y]\nupper_aqu_O = aqu_coords_Of_O[aqu_coords_Of_O[1]>aqu_coords_Of_O.line_y]\n\nmin_x_upr_aqu_O = min(upper_aqu_O[0])\nmax_x_upr_aqu_O = max(upper_aqu_O[0])\nxvals_upr_aqu_O = np.linspace(min_x_upr_aqu_O, max_x_upr_aqu_O, 200)\nxvals_upr_use_aqu_O = []\nyvals_upr_aqu_O = []\nfor i in range(1,199,2):\n    subset = upper_aqu_O[upper_aqu_O[0].between(xvals_upr_aqu_O[i-1], xvals_upr_aqu_O[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_O(xvals_upr_aqu_O[i]))\n        arg = series.argmin()\n        yvals_upr_aqu_O.append(subset[1].iloc[arg])\n        xvals_upr_use_aqu_O.append(xvals_upr_aqu_O[i])\n\nmin_x_lwr_aqu_O = min(lower_aqu_O[0])\nmax_x_lwr_aqu_O = max(upper_aqu_O[0])\nxvals_lwr_aqu_O = np.linspace(min_x_lwr_aqu_O, max_x_lwr_aqu_O, 200)\nxvals_lwr_use_aqu_O = []\nyvals_lwr_aqu_O = []\nfor i in range(1,199,2):\n    subset = lower_aqu_O[lower_aqu_O[0].between(xvals_lwr_aqu_O[i-1], xvals_lwr_aqu_O[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_O(xvals_lwr_aqu_O[i]))\n        arg = series.argmin()\n        yvals_lwr_aqu_O.append(subset[1].iloc[arg])\n        xvals_lwr_use_aqu_O.append(xvals_lwr_aqu_O[i])\n\ntrack_vals_aqu_O1 = np.array([(xvals_upr_use_aqu_O[i], yvals_upr_aqu_O[i]) for i in range(4,len(xvals_upr_use_aqu_O)-2)])\ntrack_vals_aqu_O2 = np.array([(xvals_lwr_use_aqu_O[i], yvals_lwr_aqu_O[i]) for i in range(len(xvals_lwr_use_aqu_O)-3,5,-1)])\ntrack_vals_aqu_O = np.concatenate((track_vals_aqu_O1, track_vals_aqu_O2))\n\ntck_aqu_O, u_aqu_O = splprep(track_vals_aqu_O.T, u=None, s=0.0, per=1) \nu_new_aqu_O = np.linspace(u_aqu_O.min(), u_aqu_O.max(), 100000)\nx_new_aqu_O, y_new_aqu_O = splev(u_new_aqu_O, tck_aqu_O, der=0)\n\n\n## AQU Inner Track T\n\n# Firstly since AQU T starts inside the inner track we consider coordinates after the start of the race\naqu_T = aqu_T[aqu_T['trakus_index'] > 50]\naqu_coords_T = np.array(aqu_T[['latitude', 'longitude']].values.tolist())\n\naqu_hull_T = alphashape.alphashape(aqu_coords_T, 0)\naqu_hull_T_pts = aqu_hull_T.exterior.coords.xy\n\n# Next find outer straight via inspection of plots\n#plt.plot(aqu_hull_T_pts[0][30:50], aqu_hull_T_pts[1][30:50], 'r-')\n#plt.plot(aqu_hull_T_pts[0][0:30], aqu_hull_T_pts[1][0:30], 'b-')\n#plt.plot(aqu_hull_T_pts[0][50:], aqu_hull_T_pts[1][50:], 'g-')\n# plt.show()\n\nkmeans = KMeans(n_clusters=2)\nlabs_aqu_T = kmeans.fit_predict(aqu_coords_T)\nk2_centroids_aqu_T = kmeans.cluster_centers_\ncentre_aqu_T = [(k2_centroids_aqu_T[0,0] + k2_centroids_aqu_T[1,0])/2, (k2_centroids_aqu_T[0,1] + k2_centroids_aqu_T[1,1])/2]\n\ngrad_aqu_T = (aqu_hull_T_pts[1][50] - aqu_hull_T_pts[1][30]) / (aqu_hull_T_pts[0][50] - aqu_hull_T_pts[0][30])\n\ndef sepline_aqu_T(x):\n    y = centre_aqu_T[1] + grad_aqu_T*(x-centre_aqu_T[0])\n    return y\n\nyvals_aqu_T = []\nfor x in aqu_hull_T_pts[0]:\n    yvals_aqu_T.append(centre_aqu_T[1]+grad_aqu_T*(x-centre_aqu_T[0]))\n\naqu_coords_Tf_T = pd.DataFrame(aqu_coords_T)\naqu_coordstrn_T = aqu_coords_Tf_T.values.T\naqu_a_T = list(zip(aqu_coordstrn_T[0], aqu_coordstrn_T[1]))\naqu_coords_Tf_T['coords'] = aqu_a_T\naqu_coords_Tf_T['line_y'] = aqu_coords_Tf_T[0].apply(lambda x: sepline_aqu_T(x))\n\nlower_aqu_T = aqu_coords_Tf_T[aqu_coords_Tf_T[1]<aqu_coords_Tf_T.line_y]\nupper_aqu_T = aqu_coords_Tf_T[aqu_coords_Tf_T[1]>aqu_coords_Tf_T.line_y]\n\nmin_x_upr_aqu_T = min(upper_aqu_T[0])\nmax_x_upr_aqu_T = max(upper_aqu_T[0])\nxvals_upr_aqu_T = np.linspace(min_x_upr_aqu_T, max_x_upr_aqu_T, 200)\nxvals_upr_use_aqu_T = []\nyvals_upr_aqu_T = []\nfor i in range(1,199,2):\n    subset = upper_aqu_T[upper_aqu_T[0].between(xvals_upr_aqu_T[i-1], xvals_upr_aqu_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_T(xvals_upr_aqu_T[i]))\n        arg = series.argmin()\n        yvals_upr_aqu_T.append(subset[1].iloc[arg])\n        xvals_upr_use_aqu_T.append(xvals_upr_aqu_T[i])\n\nmin_x_lwr_aqu_T = min(lower_aqu_T[0])\nmax_x_lwr_aqu_T = max(upper_aqu_T[0])\nxvals_lwr_aqu_T = np.linspace(min_x_lwr_aqu_T, max_x_lwr_aqu_T, 200)\nxvals_lwr_use_aqu_T = []\nyvals_lwr_aqu_T = []\nfor i in range(1,199,2):\n    subset = lower_aqu_T[lower_aqu_T[0].between(xvals_lwr_aqu_T[i-1], xvals_lwr_aqu_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_aqu_T(xvals_lwr_aqu_T[i]))\n        arg = series.argmin()\n        yvals_lwr_aqu_T.append(subset[1].iloc[arg])\n        xvals_lwr_use_aqu_T.append(xvals_lwr_aqu_T[i])\n\ntrack_vals_aqu_T1 = np.array([(xvals_upr_use_aqu_T[i], yvals_upr_aqu_T[i]) for i in range(6,len(xvals_upr_use_aqu_T)-1)])\ntrack_vals_aqu_T2 = np.array([(xvals_lwr_use_aqu_T[i], yvals_lwr_aqu_T[i]) for i in range(len(xvals_lwr_use_aqu_T)-3,5,-1)])\ntrack_vals_aqu_T = np.concatenate((track_vals_aqu_T1, track_vals_aqu_T2))\n\ntck_aqu_T, u_aqu_T = splprep(track_vals_aqu_T.T, u=None, s=0.0, per=1) \nu_new_aqu_T = np.linspace(u_aqu_T.min(), u_aqu_T.max(), 100000)\nx_new_aqu_T, y_new_aqu_T = splev(u_new_aqu_T, tck_aqu_T, der=0)\n\n## Now plot the tracks and by inspection set the coordinates to classify the track into sections\n# plt.plot(x_new_aqu_T[0:3000], y_new_aqu_T[0:3000], 'r-')\n# plt.plot(x_new_aqu_T[86000:100000], y_new_aqu_T[86000:100000], 'r-')\n# plt.plot(x_new_aqu_T[3000:35000], y_new_aqu_T[3000:35000], 'b-')\n# plt.plot(x_new_aqu_T[35000:53000], y_new_aqu_T[35000:53000], 'g-')\n# plt.plot(x_new_aqu_T[53000:86000], y_new_aqu_T[53000:86000], 'k-')\n\n# plt.plot(x_new_aqu_O[0:5000], y_new_aqu_O[0:5000], 'r-')\n# plt.plot(x_new_aqu_O[84000:100000], y_new_aqu_O[84000:100000], 'r-')\n# plt.plot(x_new_aqu_O[5000:33000], y_new_aqu_O[5000:33000], 'b-')\n# plt.plot(x_new_aqu_O[33000:55000], y_new_aqu_O[33000:55000], 'g-')\n# plt.plot(x_new_aqu_O[55000:84000], y_new_aqu_O[55000:84000], 'k-')\n\n# plt.plot(x_new_aqu_D[0:8000], y_new_aqu_D[0:8000], 'r-')\n# plt.plot(x_new_aqu_D[83000:], y_new_aqu_D[83000:], 'r-')\n# plt.plot(x_new_aqu_D[8000:33000], y_new_aqu_D[8000:33000], 'b-')\n# plt.plot(x_new_aqu_D[33000:57000], y_new_aqu_D[33000:57000], 'g-')\n# plt.plot(x_new_aqu_D[57000:83000], y_new_aqu_D[57000:83000], 'k-')\n# plt.show()\n\nl1_aqu_T = ['A' for i in range(3000)]\nl2_aqu_T = ['B' for i in range(3000,35000)]\nl3_aqu_T = ['C' for i in range(35000,53000)]\nl4_aqu_T = ['D' for i in range(53000,86000)]\nl5_aqu_T = ['A' for i in range(86000,100000)]\n\naqu_T_sec = l1_aqu_T + l2_aqu_T + l3_aqu_T + l4_aqu_T + l5_aqu_T\n\nl1_aqu_O = ['A' for i in range(5000)]\nl2_aqu_O = ['B' for i in range(5000,33000)]\nl3_aqu_O = ['C' for i in range(33000,55000)]\nl4_aqu_O = ['D' for i in range(55000,84000)]\nl5_aqu_O = ['A' for i in range(84000,100000)]\n\naqu_O_sec = l1_aqu_O + l2_aqu_O + l3_aqu_O + l4_aqu_O + l5_aqu_O\n\nl1_aqu_D = ['A' for i in range(8000)]\nl2_aqu_D = ['B' for i in range(8000,33000)]\nl3_aqu_D = ['C' for i in range(33000,57000)]\nl4_aqu_D = ['D' for i in range(57000,83000)]\nl5_aqu_D = ['A' for i in range(83000,100000)]\n\naqu_D_sec = l1_aqu_D + l2_aqu_D + l3_aqu_D + l4_aqu_D + l5_aqu_D\n\naqu_inner_tracks_dict = {\n'aqu_T_lat' : x_new_aqu_T,\n'aqu_T_lon' : y_new_aqu_T,\n'aqu_T_section' : aqu_T_sec,\n'aqu_D_lat' : x_new_aqu_D,\n'aqu_D_lon' : y_new_aqu_D,\n'aqu_D_section' : aqu_D_sec,\n'aqu_O_lat' : x_new_aqu_O,\n'aqu_O_lon' : y_new_aqu_O,\n'aqu_O_section' : aqu_O_sec,\n}\n\naqu_inner_tracks_df = pd.DataFrame(aqu_inner_tracks_dict)\naqu_inner_tracks_df.to_csv('aqu_inner_tracks_df_reduced.csv')\n\n\n## BELMONT TRACKS\n\nbel = data_for_tracks[data_for_tracks['track_id']=='BEL']\nbel_coords = np.array(bel[['latitude', 'longitude']].values.tolist())\n\nbel_I = bel[bel['course_type']=='I']\nbel_T = bel[bel['course_type']=='T']\nbel_D = bel[bel['course_type']=='D']\nbel_M = bel[bel['course_type']=='M']\n\nbel_coords_I = np.array(bel_I[['latitude', 'longitude']].values.tolist())\nbel_coords_T = np.array(bel_T[['latitude', 'longitude']].values.tolist())\nbel_coords_D = np.array(bel_D[['latitude', 'longitude']].values.tolist())\nbel_coords_M = np.array(bel_M[['latitude', 'longitude']].values.tolist())\n\n## BEL OUTER TRACKS\nbel_outer_T = alphashape.alphashape(bel_coords_T, 0)\nbel_outer_T_pts = bel_outer_T.exterior.coords.xy\n\nbel_outer_D = alphashape.alphashape(bel_coords_D, 0)\nbel_outer_D_pts = bel_outer_D.exterior.coords.xy\n\nbel_outer_M = alphashape.alphashape(bel_coords_M, 0)\nbel_outer_M_pts = bel_outer_M.exterior.coords.xy\n\nbel_outer_I = alphashape.alphashape(bel_coords_I, 0)\nbel_outer_I_pts = bel_outer_I.exterior.coords.xy\n\n\nimport itertools\nbel_outer_D_pts_lat = np.array(bel_outer_D_pts[0])\nbel_outer_D_pts_lon = np.array(bel_outer_D_pts[1])\n\nbel_outer_I_pts_lat = np.array(bel_outer_I_pts[0])\nbel_outer_I_pts_lon = np.array(bel_outer_I_pts[1])\n\nbel_outer_T_pts_lat = np.array(bel_outer_T_pts[0])\nbel_outer_T_pts_lon = np.array(bel_outer_T_pts[1])\n\nbel_outer_M_pts_lat = np.array(bel_outer_M_pts[0])\nbel_outer_M_pts_lon = np.array(bel_outer_M_pts[1])\n\n\nbel_outer_track_df = pd.DataFrame((e for e in itertools.zip_longest(bel_outer_D_pts_lat, bel_outer_D_pts_lon, bel_outer_I_pts_lat, bel_outer_I_pts_lon, bel_outer_T_pts_lat,bel_outer_T_pts_lon, bel_outer_M_pts_lat,bel_outer_M_pts_lon)), \ncolumns=['D_lat', 'D_lon', 'I_lat', 'I_lon', 'T_lat', 'T_lon', 'M_lat', 'M_lon'])\nbel_outer_track_df.to_csv('bel_outer_track_df.csv')\n\n## BEL INNER TRACKS\n\nkmeans = KMeans(n_clusters=2)\nlabs_bel = kmeans.fit_predict(bel_coords_I)\nk2_centroids_bel = kmeans.cluster_centers_\ncentre_bel = [(k2_centroids_bel[0,0] + k2_centroids_bel[1,0])/2, (k2_centroids_bel[0,1] + k2_centroids_bel[1,1])/2]\n\n# For better accuracy we rotate the points around the centre point of the track so that the track lies at a shallower gradient to the x axis\nbel['rotated_lon'] = bel.apply(lambda x: rotate_y((centre_bel),(x.latitude, x.longitude),0.5*np.pi), axis=1)\nbel['rotated_lat'] = bel.apply(lambda x: rotate_x((centre_bel),(x.latitude, x.longitude),0.5*np.pi), axis=1)\n\n\n# We now proceed with rotated coordinates\n\nbel_I = bel[bel['course_type']=='I']\nbel_T = bel[bel['course_type']=='T']\nbel_D = bel[bel['course_type']=='D']\nbel_M = bel[bel['course_type']=='M']\n\nbel_coords_I = np.array(bel_I[['rotated_lat', 'rotated_lon']].values.tolist())\nbel_coords_T = np.array(bel_T[['rotated_lat', 'rotated_lon']].values.tolist())\nbel_coords_D = np.array(bel_D[['rotated_lat', 'rotated_lon']].values.tolist())\nbel_coords_M = np.array(bel_M[['rotated_lat', 'rotated_lon']].values.tolist())\n\n## Bel Inner track I\nbel_hull_I = alphashape.alphashape(bel_coords_I, 0)\nbel_hull_I_pts = bel_hull_I.exterior.coords.xy\n\n# plt.plot(bel_hull_I_pts[0][62:72], bel_hull_I_pts[1][62:72], 'r-')\n# plt.plot(bel_hull_I_pts[0][0:62], bel_hull_I_pts[1][0:62], 'b-')\n# plt.plot(bel_hull_I_pts[0][72:], bel_hull_I_pts[1][72:], 'g-')\n# plt.show()\n\ngrad_bel_I = (bel_hull_I_pts[1][72] - bel_hull_I_pts[1][62]) / (bel_hull_I_pts[0][72] - bel_hull_I_pts[0][62])\n\ndef sepline_bel_I(x):\n    y = centre_bel[1] + grad_bel_I*(x-centre_bel[0])\n    return y\n\nyvals_bel_I = []\nfor x in bel_hull_I_pts[0]:\n    yvals_bel_I.append(centre_bel[1]+grad_bel_I*(x-centre_bel[0]))\n\nbel_coords_If_I = pd.DataFrame(bel_coords_I)\nbel_coordstrn_I = bel_coords_If_I.values.T\nbel_a_I = list(zip(bel_coordstrn_I[0], bel_coordstrn_I[1]))\nbel_coords_If_I['coords'] = bel_a_I\nbel_coords_If_I['line_y'] = bel_coords_If_I[0].apply(lambda x: sepline_bel_I(x))\n\nlower_bel_I = bel_coords_If_I[bel_coords_If_I[1]<bel_coords_If_I.line_y]\nupper_bel_I = bel_coords_If_I[bel_coords_If_I[1]>bel_coords_If_I.line_y]\n\nmin_x_upr_bel_I = min(upper_bel_I[0])\nmax_x_upr_bel_I = max(upper_bel_I[0])\nxvals_upr_bel_I = np.linspace(min_x_upr_bel_I, max_x_upr_bel_I, 200)\nxvals_upr_use_bel_I = []\nyvals_upr_bel_I = []\nfor i in range(1,199,2):\n    subset = upper_bel_I[upper_bel_I[0].between(xvals_upr_bel_I[i-1], xvals_upr_bel_I[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_I(xvals_upr_bel_I[i]))\n        arg = series.argmin()\n        yvals_upr_bel_I.append(subset[1].iloc[arg])\n        xvals_upr_use_bel_I.append(xvals_upr_bel_I[i])\n\nmin_x_lwr_bel_I = min(lower_bel_I[0])\nmax_x_lwr_bel_I = max(upper_bel_I[0])\nxvals_lwr_bel_I = np.linspace(min_x_lwr_bel_I, max_x_lwr_bel_I, 200)\nxvals_lwr_use_bel_I = []\nyvals_lwr_bel_I = []\nfor i in range(1,199,2):\n    subset = lower_bel_I[lower_bel_I[0].between(xvals_lwr_bel_I[i-1], xvals_lwr_bel_I[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_I(xvals_lwr_bel_I[i]))\n        arg = series.argmin()\n        yvals_lwr_bel_I.append(subset[1].iloc[arg])\n        xvals_lwr_use_bel_I.append(xvals_lwr_bel_I[i])\n\ntrack_vals_bel_I1 = np.array([(xvals_upr_use_bel_I[i], yvals_upr_bel_I[i]) for i in range(2,len(xvals_upr_use_bel_I)-1)])\ntrack_vals_bel_I2 = np.array([(xvals_lwr_use_bel_I[i], yvals_lwr_bel_I[i]) for i in range(len(xvals_lwr_use_bel_I)-2,1,-1)])\ntrack_vals_bel_I = np.concatenate((track_vals_bel_I1, track_vals_bel_I2))\n\ntck_bel_I, u_bel_I = splprep(track_vals_bel_I.T, u=None, s=0.0, per=1) \nu_new_bel_I = np.linspace(u_bel_I.min(), u_bel_I.max(), 100000)\nx_new_bel_I, y_new_bel_I = splev(u_new_bel_I, tck_bel_I, der=0)\n\n#Finally we rotate back to the original coordinates\nfinal_y_new_bel_I = rotate_y((centre_bel),(x_new_bel_I, y_new_bel_I),-0.5*np.pi)\nfinal_x_new_bel_I = rotate_x((centre_bel),(x_new_bel_I, y_new_bel_I),-0.5*np.pi)\n\n\n## Bel Course T inner track\n\nbel_hull_T = alphashape.alphashape(bel_coords_T, 0)\nbel_hull_T_pts = bel_hull_T.exterior.coords.xy\n\n# plt.plot(bel_hull_T_pts[0][22:35], bel_hull_T_pts[1][22:35], 'r-')\n# plt.plot(bel_hull_T_pts[0][0:22], bel_hull_T_pts[1][0:22], 'b-')\n# plt.plot(bel_hull_T_pts[0][35:], bel_hull_T_pts[1][35:], 'g-')\n# plt.show()\n\ngrad_bel_T = (bel_hull_T_pts[1][35] - bel_hull_T_pts[1][22]) / (bel_hull_T_pts[0][35] - bel_hull_T_pts[0][22])\n\ndef sepline_bel_T(x):\n    y = centre_bel[1] + grad_bel_T*(x-centre_bel[0])\n    return y\n\nyvals_bel_T = []\nfor x in bel_hull_T_pts[0]:\n    yvals_bel_T.append(centre_bel[1]+grad_bel_T*(x-centre_bel[0]))\n\nbel_coords_Tf_T = pd.DataFrame(bel_coords_T)\nbel_coordstrn_T = bel_coords_Tf_T.values.T\nbel_a_T = list(zip(bel_coordstrn_T[0], bel_coordstrn_T[1]))\nbel_coords_Tf_T['coords'] = bel_a_T\nbel_coords_Tf_T['line_y'] = bel_coords_Tf_T[0].apply(lambda x: sepline_bel_T(x))\n\nlower_bel_T = bel_coords_Tf_T[bel_coords_Tf_T[1]<bel_coords_Tf_T.line_y]\nupper_bel_T = bel_coords_Tf_T[bel_coords_Tf_T[1]>bel_coords_Tf_T.line_y]\n\nmin_x_upr_bel_T = min(upper_bel_T[0])\nmax_x_upr_bel_T = max(upper_bel_T[0])\nxvals_upr_bel_T = np.linspace(min_x_upr_bel_T, max_x_upr_bel_T, 200)\nxvals_upr_use_bel_T = []\nyvals_upr_bel_T = []\nfor i in range(1,199,2):\n    subset = upper_bel_T[upper_bel_T[0].between(xvals_upr_bel_T[i-1], xvals_upr_bel_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_T(xvals_upr_bel_T[i]))\n        arg = series.argmin()\n        yvals_upr_bel_T.append(subset[1].iloc[arg])\n        xvals_upr_use_bel_T.append(xvals_upr_bel_T[i])\n\nmin_x_lwr_bel_T = min(lower_bel_T[0])\nmax_x_lwr_bel_T = max(upper_bel_T[0])\nxvals_lwr_bel_T = np.linspace(min_x_lwr_bel_T, max_x_lwr_bel_T, 200)\nxvals_lwr_use_bel_T = []\nyvals_lwr_bel_T = []\nfor i in range(1,199,2):\n    subset = lower_bel_T[lower_bel_T[0].between(xvals_lwr_bel_T[i-1], xvals_lwr_bel_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_T(xvals_lwr_bel_T[i]))\n        arg = series.argmin()\n        yvals_lwr_bel_T.append(subset[1].iloc[arg])\n        xvals_lwr_use_bel_T.append(xvals_lwr_bel_T[i])\n\ntrack_vals_bel_T1 = np.array([(xvals_upr_use_bel_T[i], yvals_upr_bel_T[i]) for i in range(8,len(xvals_upr_use_bel_T)-1)])\ntrack_vals_bel_T2 = np.array([(xvals_lwr_use_bel_T[i], yvals_lwr_bel_T[i]) for i in range(len(xvals_lwr_use_bel_T)-3,3,-1)])\ntrack_vals_bel_T = np.concatenate((track_vals_bel_T1, track_vals_bel_T2))\n\ntck_bel_T, u_bel_T = splprep(track_vals_bel_T.T, u=None, s=0.0, per=1) \nu_new_bel_T = np.linspace(u_bel_T.min(), u_bel_T.max(), 100000)\nx_new_bel_T, y_new_bel_T = splev(u_new_bel_T, tck_bel_T, der=0)\n\nfinal_y_new_bel_T = rotate_y((centre_bel),(x_new_bel_T, y_new_bel_T),-0.5*np.pi)\nfinal_x_new_bel_T = rotate_x((centre_bel),(x_new_bel_T, y_new_bel_T),-0.5*np.pi)\n\nbel_hull_D = alphashape.alphashape(bel_coords_D, 0)\nbel_hull_D_pts = bel_hull_D.exterior.coords.xy\n\n# plt.plot(bel_hull_D_pts[0][22:27], bel_hull_D_pts[1][22:27], 'r-')\n# plt.plot(bel_hull_D_pts[0][0:22], bel_hull_D_pts[1][0:22], 'b-')\n# plt.plot(bel_hull_D_pts[0][27:], bel_hull_D_pts[1][27:], 'g-')\n# plt.show()\n\ngrad_bel_D = (bel_hull_D_pts[1][27] - bel_hull_D_pts[1][22]) / (bel_hull_D_pts[0][27] - bel_hull_D_pts[0][22])\n\ndef sepline_bel_D(x):\n    y = centre_bel[1] + grad_bel_D*(x-centre_bel[0])\n    return y\n\nyvals_bel_D = []\nfor x in bel_hull_D_pts[0]:\n    yvals_bel_D.append(centre_bel[1]+grad_bel_D*(x-centre_bel[0]))\n\nbel_coords_Df_D = pd.DataFrame(bel_coords_D)\nbel_coordstrn_D = bel_coords_Df_D.values.T\nbel_a_D = list(zip(bel_coordstrn_D[0], bel_coordstrn_D[1]))\nbel_coords_Df_D['coords'] = bel_a_D\nbel_coords_Df_D['line_y'] = bel_coords_Df_D[0].apply(lambda x: sepline_bel_D(x))\n\nlower_bel_D = bel_coords_Df_D[bel_coords_Df_D[1]<bel_coords_Df_D.line_y]\nupper_bel_D = bel_coords_Df_D[bel_coords_Df_D[1]>bel_coords_Df_D.line_y]\n\nmin_x_upr_bel_D = min(upper_bel_D[0])\nmax_x_upr_bel_D = max(upper_bel_D[0])\nxvals_upr_bel_D = np.linspace(min_x_upr_bel_D, max_x_upr_bel_D, 200)\nxvals_upr_use_bel_D = []\nyvals_upr_bel_D = []\nfor i in range(1,199,2):\n    subset = upper_bel_D[upper_bel_D[0].between(xvals_upr_bel_D[i-1], xvals_upr_bel_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_D(xvals_upr_bel_D[i]))\n        arg = series.argmin()\n        yvals_upr_bel_D.append(subset[1].iloc[arg])\n        xvals_upr_use_bel_D.append(xvals_upr_bel_D[i])\n\nmin_x_lwr_bel_D = min(lower_bel_D[0])\nmax_x_lwr_bel_D = max(upper_bel_D[0])\nxvals_lwr_bel_D = np.linspace(min_x_lwr_bel_D, max_x_lwr_bel_D, 200)\nxvals_lwr_use_bel_D = []\nyvals_lwr_bel_D = []\nfor i in range(1,199,2):\n    subset = lower_bel_D[lower_bel_D[0].between(xvals_lwr_bel_D[i-1], xvals_lwr_bel_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_D(xvals_lwr_bel_D[i]))\n        arg = series.argmin()\n        yvals_lwr_bel_D.append(subset[1].iloc[arg])\n        xvals_lwr_use_bel_D.append(xvals_lwr_bel_D[i])\n\ntrack_vals_bel_D1 = np.array([(xvals_upr_use_bel_D[i], yvals_upr_bel_D[i]) for i in range(15,len(xvals_upr_use_bel_D)-1)])\ntrack_vals_bel_D2 = np.array([(xvals_lwr_use_bel_D[i], yvals_lwr_bel_D[i]) for i in range(len(xvals_lwr_use_bel_D)-2,0,-1)])\ntrack_vals_bel_D = np.concatenate((track_vals_bel_D1, track_vals_bel_D2))\n\ntck_bel_D, u_bel_D = splprep(track_vals_bel_D.T, u=None, s=0.0, per=1) \nu_new_bel_D = np.linspace(u_bel_D.min(), u_bel_D.max(), 100000)\nx_new_bel_D, y_new_bel_D = splev(u_new_bel_D, tck_bel_D, der=0)\n\nfinal_y_new_bel_D = rotate_y((centre_bel),(x_new_bel_D, y_new_bel_D),-0.5*np.pi)\nfinal_x_new_bel_D = rotate_x((centre_bel),(x_new_bel_D, y_new_bel_D),-0.5*np.pi)\n\n## Bel Inner Track M\n\nbel_hull_M = alphashape.alphashape(bel_coords_M, 0)\nbel_hull_M_pts = bel_hull_M.exterior.coords.xy\n\n# plt.plot(bel_hull_M_pts[0][76:87], bel_hull_M_pts[1][76:87], 'r-')\n# plt.plot(bel_hull_M_pts[0][0:76], bel_hull_M_pts[1][0:76], 'b-')\n# plt.plot(bel_hull_M_pts[0][87:], bel_hull_M_pts[1][87:], 'g-')\n# plt.show()\n\ngrad_bel_M = (bel_hull_M_pts[1][87] - bel_hull_M_pts[1][76]) / (bel_hull_M_pts[0][87] - bel_hull_M_pts[0][76])\n\ndef sepline_bel_M(x):\n    y = centre_bel[1] + grad_bel_M*(x-centre_bel[0])\n    return y\n\nyvals_bel_M = []\nfor x in bel_hull_M_pts[0]:\n    yvals_bel_M.append(centre_bel[1]+grad_bel_M*(x-centre_bel[0]))\n\nbel_coords_Mf_M = pd.DataFrame(bel_coords_M)\nbel_coordstrn_M = bel_coords_Mf_M.values.T\nbel_a_M = list(zip(bel_coordstrn_M[0], bel_coordstrn_M[1]))\nbel_coords_Mf_M['coords'] = bel_a_M\nbel_coords_Mf_M['line_y'] = bel_coords_Mf_M[0].apply(lambda x: sepline_bel_M(x))\n\nlower_bel_M = bel_coords_Mf_M[bel_coords_Mf_M[1]<bel_coords_Mf_M.line_y]\nupper_bel_M = bel_coords_Mf_M[bel_coords_Mf_M[1]>bel_coords_Mf_M.line_y]\n\nmin_x_upr_bel_M = min(upper_bel_M[0])\nmax_x_upr_bel_M = max(upper_bel_M[0])\nxvals_upr_bel_M = np.linspace(min_x_upr_bel_M, max_x_upr_bel_M, 200)\nxvals_upr_use_bel_M = []\nyvals_upr_bel_M = []\nfor i in range(1,199,2):\n    subset = upper_bel_M[upper_bel_M[0].between(xvals_upr_bel_M[i-1], xvals_upr_bel_M[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_M(xvals_upr_bel_M[i]))\n        arg = series.argmin()\n        yvals_upr_bel_M.append(subset[1].iloc[arg])\n        xvals_upr_use_bel_M.append(xvals_upr_bel_M[i])\n\nmin_x_lwr_bel_M = min(lower_bel_M[0])\nmax_x_lwr_bel_M = max(upper_bel_M[0])\nxvals_lwr_bel_M = np.linspace(min_x_lwr_bel_M, max_x_lwr_bel_M, 200)\nxvals_lwr_use_bel_M = []\nyvals_lwr_bel_M = []\nfor i in range(1,199,2):\n    subset = lower_bel_M[lower_bel_M[0].between(xvals_lwr_bel_M[i-1], xvals_lwr_bel_M[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_bel_M(xvals_lwr_bel_M[i]))\n        arg = series.argmin()\n        yvals_lwr_bel_M.append(subset[1].iloc[arg])\n        xvals_lwr_use_bel_M.append(xvals_lwr_bel_M[i])\n\ntrack_vals_bel_M1 = np.array([(xvals_upr_use_bel_M[i], yvals_upr_bel_M[i]) for i in range(2,len(xvals_upr_use_bel_M)-1)])\ntrack_vals_bel_M2 = np.array([(xvals_lwr_use_bel_M[i], yvals_lwr_bel_M[i]) for i in range(len(xvals_lwr_use_bel_M)-1,-1,-1)])\ntrack_vals_bel_M = np.concatenate((track_vals_bel_M1, track_vals_bel_M2))\n\ntck_bel_M, u_bel_M = splprep(track_vals_bel_M.T, u=None, s=0.0, per=1) \nu_new_bel_M = np.linspace(u_bel_M.min(), u_bel_M.max(), 100000)\nx_new_bel_M, y_new_bel_M = splev(u_new_bel_M, tck_bel_M, der=0)\n\nfinal_y_new_bel_M = rotate_y((centre_bel),(x_new_bel_M, y_new_bel_M),-0.5*np.pi)\nfinal_x_new_bel_M = rotate_x((centre_bel),(x_new_bel_M, y_new_bel_M),-0.5*np.pi)\n\n# plt.plot(final_x_new_bel_I[0:8000], final_y_new_bel_I[0:8000], 'r-')\n# plt.plot(final_x_new_bel_I[91000:100000], final_y_new_bel_I[91000:100000], 'r-')\n# plt.plot(final_x_new_bel_I[8000:40000], final_y_new_bel_I[8000:40000], 'b-')\n# plt.plot(final_x_new_bel_I[40000:58000], final_y_new_bel_I[40000:58000], 'g-')\n# plt.plot(final_x_new_bel_I[58000:91000], final_y_new_bel_I[58000:91000], 'k-')\n\n# plt.plot(final_x_new_bel_T[0:8000], final_y_new_bel_T[0:8000], 'r-')\n# plt.plot(final_x_new_bel_T[91000:100000], final_y_new_bel_T[91000:100000], 'r-')\n# plt.plot(final_x_new_bel_T[8000:40000], final_y_new_bel_T[8000:40000], 'b-')\n# plt.plot(final_x_new_bel_T[40000:58000], final_y_new_bel_T[40000:58000], 'g-')\n# plt.plot(final_x_new_bel_T[58000:91000], final_y_new_bel_T[58000:91000], 'k-')\n\n# plt.plot(final_x_new_bel_D[0:5000], final_y_new_bel_D[0:5000], 'r-')\n# plt.plot(final_x_new_bel_D[85000:], final_y_new_bel_D[85000:], 'r-')\n# plt.plot(final_x_new_bel_D[5000:36000], final_y_new_bel_D[5000:36000], 'b-')\n# plt.plot(final_x_new_bel_D[36000:54000], final_y_new_bel_D[36000:54000], 'g-')\n# plt.plot(final_x_new_bel_D[54000:85000], final_y_new_bel_D[54000:85000], 'k-')\n\n# plt.plot(final_x_new_bel_M[0:8000], final_y_new_bel_M[0:8000], 'r-')\n# plt.plot(final_x_new_bel_M[91000:100000], final_y_new_bel_M[91000:100000], 'r-')\n# plt.plot(final_x_new_bel_M[8000:40000], final_y_new_bel_M[8000:40000], 'b-')\n# plt.plot(final_x_new_bel_M[40000:58000], final_y_new_bel_M[40000:58000], 'g-')\n# plt.plot(final_x_new_bel_M[58000:91000], final_y_new_bel_M[58000:91000], 'k-')\n# plt.show()\n\nl1_bel_I = ['A' for i in range(8000)]\nl2_bel_I = ['B' for i in range(8000,40000)]\nl3_bel_I = ['C' for i in range(40000,58000)]\nl4_bel_I = ['D' for i in range(58000,91000)]\nl5_bel_I = ['A' for i in range(91000,100000)]\n\nbel_I_sec = l1_bel_I + l2_bel_I + l3_bel_I + l4_bel_I + l5_bel_I\n\nl1_bel_T = ['A' for i in range(8000)]\nl2_bel_T = ['B' for i in range(8000,40000)]\nl3_bel_T = ['C' for i in range(40000,58000)]\nl4_bel_T = ['D' for i in range(58000,91000)]\nl5_bel_T = ['A' for i in range(91000,100000)]\n\nbel_T_sec = l1_bel_T + l2_bel_T + l3_bel_T + l4_bel_T + l5_bel_T\n\nl1_bel_D = ['A' for i in range(5000)]\nl2_bel_D = ['B' for i in range(5000,36000)]\nl3_bel_D = ['C' for i in range(36000,54000)]\nl4_bel_D = ['D' for i in range(54000,85000)]\nl5_bel_D = ['A' for i in range(85000,100000)]\n\nbel_D_sec = l1_bel_D + l2_bel_D + l3_bel_D + l4_bel_D + l5_bel_D\n\nl1_bel_M = ['A' for i in range(8000)]\nl2_bel_M = ['B' for i in range(8000,40000)]\nl3_bel_M = ['C' for i in range(40000,58000)]\nl4_bel_M = ['D' for i in range(58000,91000)]\nl5_bel_M = ['A' for i in range(91000,100000)]\n\nbel_M_sec = l1_bel_M + l2_bel_M + l3_bel_M + l4_bel_M + l5_bel_M\n\nbel_inner_tracks_dict = {'bel_I_lat' : final_x_new_bel_I,\n'bel_I_lon' : final_y_new_bel_I,\n'bel_I_section' : bel_I_sec,\n'bel_T_lat' : final_x_new_bel_T,\n'bel_T_lon' : final_y_new_bel_T,\n'bel_T_section' : bel_T_sec,\n'bel_D_lat' : final_x_new_bel_D,\n'bel_D_lon' : final_y_new_bel_D,\n'bel_D_section' : bel_D_sec,\n'bel_M_lat' : final_x_new_bel_M,\n'bel_M_lon' : final_y_new_bel_M,\n'bel_M_section' : bel_M_sec,\n}\n\nbel_inner_tracks_df = pd.DataFrame(bel_inner_tracks_dict)\nbel_inner_tracks_df.to_csv('bel_inner_tracks_df_reduced.csv')\n\n\n\n\n\n\n\n\n\n## SARATOGA TRACKS\n\nsar = data_for_tracks[data_for_tracks['track_id']=='SAR']\nsar_coords = np.array(sar[['latitude', 'longitude']].values.tolist())\n\nsar_I = sar[sar['course_type']=='I']\nsar_T = sar[sar['course_type']=='T']\nsar_D = sar[sar['course_type']=='D']\nsar_M = sar[sar['course_type']=='M']\n\n## SAR OUTER TRACKS\n\nsar_coords_I = np.array(sar_I[['latitude', 'longitude']].values.tolist())\nsar_coords_T = np.array(sar_T[['latitude', 'longitude']].values.tolist())\nsar_coords_D = np.array(sar_D[['latitude', 'longitude']].values.tolist())\nsar_coords_M = np.array(sar_M[['latitude', 'longitude']].values.tolist())\n\n\nsar_outer_T = alphashape.alphashape(sar_coords_T, 0)\nsar_outer_T_pts = sar_outer_T.exterior.coords.xy\n\nsar_outer_D = alphashape.alphashape(sar_coords_D, 0)\nsar_outer_D_pts = sar_outer_D.exterior.coords.xy\n\nsar_outer_M = alphashape.alphashape(sar_coords_M, 0)\nsar_outer_M_pts = sar_outer_M.exterior.coords.xy\n\nsar_outer_I = alphashape.alphashape(sar_coords_I, 0)\nsar_outer_I_pts = sar_outer_I.exterior.coords.xy\n\n\nimport itertools\nsar_outer_D_pts_lat = np.array(sar_outer_D_pts[0])\nsar_outer_D_pts_lon = np.array(sar_outer_D_pts[1])\n\nsar_outer_I_pts_lat = np.array(sar_outer_I_pts[0])\nsar_outer_I_pts_lon = np.array(sar_outer_I_pts[1])\n\nsar_outer_T_pts_lat = np.array(sar_outer_T_pts[0])\nsar_outer_T_pts_lon = np.array(sar_outer_T_pts[1])\n\nsar_outer_M_pts_lat = np.array(sar_outer_M_pts[0])\nsar_outer_M_pts_lon = np.array(sar_outer_M_pts[1])\n\n\nsar_outer_track_df = pd.DataFrame((e for e in itertools.zip_longest(sar_outer_D_pts_lat, sar_outer_D_pts_lon, sar_outer_I_pts_lat, sar_outer_I_pts_lon, sar_outer_T_pts_lat,sar_outer_T_pts_lon, sar_outer_M_pts_lat,sar_outer_M_pts_lon)), \ncolumns=['D_lat', 'D_lon', 'I_lat', 'I_lon', 'T_lat', 'T_lon', 'M_lat', 'M_lon'])\nsar_outer_track_df.to_csv('sar_outer_track_df.csv')\n\n## ROTATING SAR COORDINATES\nsar_coords_I = np.array(sar_I[['latitude', 'longitude']].values.tolist())\nkmeans = KMeans(n_clusters=2)\nlabs_sar = kmeans.fit_predict(sar_coords_I)\nk2_centroids_sar = kmeans.cluster_centers_\ncentre_sar = [(k2_centroids_sar[0,0] + k2_centroids_sar[1,0])/2, (k2_centroids_sar[0,1] + k2_centroids_sar[1,1])/2]\nsar['rotated_lon'] = sar.apply(lambda x: rotate_y((centre_sar),(x.latitude, x.longitude),-0.25*np.pi), axis=1)\nsar['rotated_lat'] = sar.apply(lambda x: rotate_x((centre_sar),(x.latitude, x.longitude),-0.25*np.pi), axis=1)\n\nsar_I = sar[sar['course_type']=='I']\nsar_T = sar[sar['course_type']=='T']\nsar_D = sar[sar['course_type']=='D']\nsar_M = sar[sar['course_type']=='M']\n\nsar_coords_I = np.array(sar_I[['rotated_lat', 'rotated_lon']].values.tolist())\nsar_coords_T = np.array(sar_T[['rotated_lat', 'rotated_lon']].values.tolist())\nsar_coords_D = np.array(sar_D[['rotated_lat', 'rotated_lon']].values.tolist())\nsar_coords_M = np.array(sar_M[['rotated_lat', 'rotated_lon']].values.tolist())\n\n## SAR COURSE I INNER TRACK\nsar_hull_I = alphashape.alphashape(sar_coords_I, 0)\nsar_hull_I_pts = sar_hull_I.exterior.coords.xy\n\n# plt.plot(sar_hull_I_pts[0][45:65], sar_hull_I_pts[1][45:65], 'r-')\n# plt.plot(sar_hull_I_pts[0][0:45], sar_hull_I_pts[1][0:45], 'b-')\n# plt.plot(sar_hull_I_pts[0][65:], sar_hull_I_pts[1][65:], 'g-')\n# plt.show()\n\ngrad_sar_I = (sar_hull_I_pts[1][65] - sar_hull_I_pts[1][45]) / (sar_hull_I_pts[0][65] - sar_hull_I_pts[0][45])\n\ndef sepline_sar_I(x):\n    y = centre_sar[1] + grad_sar_I*(x-centre_sar[0])\n    return y\n\nyvals_sar_I = []\nfor x in sar_hull_I_pts[0]:\n    yvals_sar_I.append(centre_sar[1]+grad_sar_I*(x-centre_sar[0]))\n\nsar_coords_If_I = pd.DataFrame(sar_coords_I)\nsar_coordstrn_I = sar_coords_If_I.values.T\nsar_a_I = list(zip(sar_coordstrn_I[0], sar_coordstrn_I[1]))\nsar_coords_If_I['coords'] = sar_a_I\nsar_coords_If_I['line_y'] = sar_coords_If_I[0].apply(lambda x: sepline_sar_I(x))\n\nlower_sar_I = sar_coords_If_I[sar_coords_If_I[1]<sar_coords_If_I.line_y]\nupper_sar_I = sar_coords_If_I[sar_coords_If_I[1]>sar_coords_If_I.line_y]\n\nmin_x_upr_sar_I = min(upper_sar_I[0])\nmax_x_upr_sar_I = max(upper_sar_I[0])\nxvals_upr_sar_I = np.linspace(min_x_upr_sar_I, max_x_upr_sar_I, 200)\nxvals_upr_use_sar_I = []\nyvals_upr_sar_I = []\nfor i in range(1,199,2):\n    subset = upper_sar_I[upper_sar_I[0].between(xvals_upr_sar_I[i-1], xvals_upr_sar_I[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_I(xvals_upr_sar_I[i]))\n        arg = series.argmin()\n        yvals_upr_sar_I.append(subset[1].iloc[arg])\n        xvals_upr_use_sar_I.append(xvals_upr_sar_I[i])\n\nmin_x_lwr_sar_I = min(lower_sar_I[0])\nmax_x_lwr_sar_I = max(upper_sar_I[0])\nxvals_lwr_sar_I = np.linspace(min_x_lwr_sar_I, max_x_lwr_sar_I, 200)\nxvals_lwr_use_sar_I = []\nyvals_lwr_sar_I = []\nfor i in range(1,199,2):\n    subset = lower_sar_I[lower_sar_I[0].between(xvals_lwr_sar_I[i-1], xvals_lwr_sar_I[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_I(xvals_lwr_sar_I[i]))\n        arg = series.argmin()\n        yvals_lwr_sar_I.append(subset[1].iloc[arg])\n        xvals_lwr_use_sar_I.append(xvals_lwr_sar_I[i])\n\ntrack_vals_sar_I1 = np.array([(xvals_upr_use_sar_I[i], yvals_upr_sar_I[i]) for i in range(3,len(xvals_upr_use_sar_I)-1)])\ntrack_vals_sar_I2 = np.array([(xvals_lwr_use_sar_I[i], yvals_lwr_sar_I[i]) for i in range(len(xvals_lwr_use_sar_I)-2,1,-1)])\ntrack_vals_sar_I = np.concatenate((track_vals_sar_I1, track_vals_sar_I2))\n\ntck_sar_I, u_sar_I = splprep(track_vals_sar_I.T, u=None, s=0.0, per=1) \nu_new_sar_I = np.linspace(u_sar_I.min(), u_sar_I.max(), 100000)\nx_new_sar_I, y_new_sar_I = splev(u_new_sar_I, tck_sar_I, der=0)\n\nfinal_y_new_sar_I = rotate_y((centre_sar),(x_new_sar_I, y_new_sar_I),0.25*np.pi)\nfinal_x_new_sar_I = rotate_x((centre_sar),(x_new_sar_I, y_new_sar_I),0.25*np.pi)\n\n## SAR INNER TRACK T\n\nsar_hull_T = alphashape.alphashape(sar_coords_T, 0)\nsar_hull_T_pts = sar_hull_T.exterior.coords.xy\n\n# plt.plot(sar_hull_T_pts[0][57:62], sar_hull_T_pts[1][57:62], 'r-')\n# plt.plot(sar_hull_T_pts[0][0:57], sar_hull_T_pts[1][0:57], 'b-')\n# plt.plot(sar_hull_T_pts[0][62:], sar_hull_T_pts[1][62:], 'g-')\n# plt.show()\n\ngrad_sar_T = (sar_hull_T_pts[1][62] - sar_hull_T_pts[1][57]) / (sar_hull_T_pts[0][62] - sar_hull_T_pts[0][57])\n\ndef sepline_sar_T(x):\n    y = centre_sar[1] + grad_sar_T*(x-centre_sar[0])\n    return y\n\nyvals_sar_T = []\nfor x in sar_hull_T_pts[0]:\n    yvals_sar_T.append(centre_sar[1]+grad_sar_T*(x-centre_sar[0]))\n\nsar_coords_Tf_T = pd.DataFrame(sar_coords_T)\nsar_coordstrn_T = sar_coords_Tf_T.values.T\nsar_a_T = list(zip(sar_coordstrn_T[0], sar_coordstrn_T[1]))\nsar_coords_Tf_T['coords'] = sar_a_T\nsar_coords_Tf_T['line_y'] = sar_coords_Tf_T[0].apply(lambda x: sepline_sar_T(x))\n\nlower_sar_T = sar_coords_Tf_T[sar_coords_Tf_T[1]<sar_coords_Tf_T.line_y]\nupper_sar_T = sar_coords_Tf_T[sar_coords_Tf_T[1]>sar_coords_Tf_T.line_y]\n\nmin_x_upr_sar_T = min(upper_sar_T[0])\nmax_x_upr_sar_T = max(upper_sar_T[0])\nxvals_upr_sar_T = np.linspace(min_x_upr_sar_T, max_x_upr_sar_T, 200)\nxvals_upr_use_sar_T = []\nyvals_upr_sar_T = []\nfor i in range(1,199,2):\n    subset = upper_sar_T[upper_sar_T[0].between(xvals_upr_sar_T[i-1], xvals_upr_sar_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_T(xvals_upr_sar_T[i]))\n        arg = series.argmin()\n        yvals_upr_sar_T.append(subset[1].iloc[arg])\n        xvals_upr_use_sar_T.append(xvals_upr_sar_T[i])\n\nmin_x_lwr_sar_T = min(lower_sar_T[0])\nmax_x_lwr_sar_T = max(upper_sar_T[0])\nxvals_lwr_sar_T = np.linspace(min_x_lwr_sar_T, max_x_lwr_sar_T, 200)\nxvals_lwr_use_sar_T = []\nyvals_lwr_sar_T = []\nfor i in range(1,199,2):\n    subset = lower_sar_T[lower_sar_T[0].between(xvals_lwr_sar_T[i-1], xvals_lwr_sar_T[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_T(xvals_lwr_sar_T[i]))\n        arg = series.argmin()\n        yvals_lwr_sar_T.append(subset[1].iloc[arg])\n        xvals_lwr_use_sar_T.append(xvals_lwr_sar_T[i])\n\ntrack_vals_sar_T1 = np.array([(xvals_upr_use_sar_T[i], yvals_upr_sar_T[i]) for i in range(2,len(xvals_upr_use_sar_T)-2)])\ntrack_vals_sar_T2 = np.array([(xvals_lwr_use_sar_T[i], yvals_lwr_sar_T[i]) for i in range(len(xvals_lwr_use_sar_T)-3,1,-1)])\ntrack_vals_sar_T = np.concatenate((track_vals_sar_T1, track_vals_sar_T2))\n\ntck_sar_T, u_sar_T = splprep(track_vals_sar_T.T, u=None, s=0.0, per=1) \nu_new_sar_T = np.linspace(u_sar_T.min(), u_sar_T.max(), 100000)\nx_new_sar_T, y_new_sar_T = splev(u_new_sar_T, tck_sar_T, der=0)\n\nfinal_y_new_sar_T = rotate_y((centre_sar),(x_new_sar_T, y_new_sar_T),0.25*np.pi)\nfinal_x_new_sar_T = rotate_x((centre_sar),(x_new_sar_T, y_new_sar_T),0.25*np.pi)\n\n## SAR COURSE D INNER TRACK\n\nsar_hull_D = alphashape.alphashape(sar_coords_D, 0)\nsar_hull_D_pts = sar_hull_D.exterior.coords.xy\n\n# plt.plot(sar_hull_D_pts[0][27:29], sar_hull_D_pts[1][27:29], 'r-')\n# plt.plot(sar_hull_D_pts[0][0:27], sar_hull_D_pts[1][0:27], 'b-')\n# plt.plot(sar_hull_D_pts[0][29:], sar_hull_D_pts[1][29:], 'g-')\n# plt.show()\n\ngrad_sar_D = (sar_hull_D_pts[1][28] - sar_hull_D_pts[1][27]) / (sar_hull_D_pts[0][28] - sar_hull_D_pts[0][27])\n\ndef sepline_sar_D(x):\n    y = centre_sar[1] + grad_sar_D*(x-centre_sar[0])\n    return y\n\nyvals_sar_D = []\nfor x in sar_hull_D_pts[0]:\n    yvals_sar_D.append(centre_sar[1]+grad_sar_D*(x-centre_sar[0]))\n\nsar_coords_Df_D = pd.DataFrame(sar_coords_D)\nsar_coordstrn_D = sar_coords_Df_D.values.T\nsar_a_D = list(zip(sar_coordstrn_D[0], sar_coordstrn_D[1]))\nsar_coords_Df_D['coords'] = sar_a_D\nsar_coords_Df_D['line_y'] = sar_coords_Df_D[0].apply(lambda x: sepline_sar_D(x))\n\nlower_sar_D = sar_coords_Df_D[sar_coords_Df_D[1]<sar_coords_Df_D.line_y]\nupper_sar_D = sar_coords_Df_D[sar_coords_Df_D[1]>sar_coords_Df_D.line_y]\n\nmin_x_upr_sar_D = min(upper_sar_D[0])\nmax_x_upr_sar_D = max(upper_sar_D[0])\nxvals_upr_sar_D = np.linspace(min_x_upr_sar_D, max_x_upr_sar_D, 200)\nxvals_upr_use_sar_D = []\nyvals_upr_sar_D = []\nfor i in range(1,199,2):\n    subset = upper_sar_D[upper_sar_D[0].between(xvals_upr_sar_D[i-1], xvals_upr_sar_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_D(xvals_upr_sar_D[i]))\n        arg = series.argmin()\n        yvals_upr_sar_D.append(subset[1].iloc[arg])\n        xvals_upr_use_sar_D.append(xvals_upr_sar_D[i])\n\nmin_x_lwr_sar_D = min(lower_sar_D[0])\nmax_x_lwr_sar_D = max(upper_sar_D[0])\nxvals_lwr_sar_D = np.linspace(min_x_lwr_sar_D, max_x_lwr_sar_D, 200)\nxvals_lwr_use_sar_D = []\nyvals_lwr_sar_D = []\nfor i in range(1,199,2):\n    subset = lower_sar_D[lower_sar_D[0].between(xvals_lwr_sar_D[i-1], xvals_lwr_sar_D[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_D(xvals_lwr_sar_D[i]))\n        arg = series.argmin()\n        yvals_lwr_sar_D.append(subset[1].iloc[arg])\n        xvals_lwr_use_sar_D.append(xvals_lwr_sar_D[i])\n\ntrack_vals_sar_D1 = np.array([(xvals_upr_use_sar_D[i], yvals_upr_sar_D[i]) for i in range(8,len(xvals_upr_use_sar_D)-2)])\ntrack_vals_sar_D2 = np.array([(xvals_lwr_use_sar_D[i], yvals_lwr_sar_D[i]) for i in range(len(xvals_lwr_use_sar_D)-2,1,-1)])\ntrack_vals_sar_D = np.concatenate((track_vals_sar_D1, track_vals_sar_D2))\n\ntck_sar_D, u_sar_D = splprep(track_vals_sar_D.T, u=None, s=0.0, per=1) \nu_new_sar_D = np.linspace(u_sar_D.min(), u_sar_D.max(), 100000)\nx_new_sar_D, y_new_sar_D = splev(u_new_sar_D, tck_sar_D, der=0)\n\nfinal_y_new_sar_D = rotate_y((centre_sar),(x_new_sar_D, y_new_sar_D),0.25*np.pi)\nfinal_x_new_sar_D = rotate_x((centre_sar),(x_new_sar_D, y_new_sar_D),0.25*np.pi)\n\n## SAR M INNER TRACK\n\nsar_hull_M = alphashape.alphashape(sar_coords_M, 0)\nsar_hull_M_pts = sar_hull_M.exterior.coords.xy\n\n# plt.plot(sar_hull_M_pts[0][54:77], sar_hull_M_pts[1][54:77], 'r-')\n# plt.plot(sar_hull_M_pts[0][0:54], sar_hull_M_pts[1][0:54], 'b-')\n# plt.plot(sar_hull_M_pts[0][77:], sar_hull_M_pts[1][77:], 'g-')\n\ngrad_sar_M = (sar_hull_M_pts[1][76] - sar_hull_M_pts[1][54]) / (sar_hull_M_pts[0][76] - sar_hull_M_pts[0][54])\n\ndef sepline_sar_M(x):\n    y = centre_sar[1] + grad_sar_M*(x-centre_sar[0])\n    return y\n\nyvals_sar_M = []\nfor x in sar_hull_M_pts[0]:\n    yvals_sar_M.append(centre_sar[1]+grad_sar_M*(x-centre_sar[0]))\n\nsar_coords_Mf_M = pd.DataFrame(sar_coords_M)\nsar_coordstrn_M = sar_coords_Mf_M.values.T\nsar_a_M = list(zip(sar_coordstrn_M[0], sar_coordstrn_M[1]))\nsar_coords_Mf_M['coords'] = sar_a_M\nsar_coords_Mf_M['line_y'] = sar_coords_Mf_M[0].apply(lambda x: sepline_sar_M(x))\n\nlower_sar_M = sar_coords_Mf_M[sar_coords_Mf_M[1]<sar_coords_Mf_M.line_y]\nupper_sar_M = sar_coords_Mf_M[sar_coords_Mf_M[1]>sar_coords_Mf_M.line_y]\n\nmin_x_upr_sar_M = min(upper_sar_M[0])\nmax_x_upr_sar_M = max(upper_sar_M[0])\nxvals_upr_sar_M = np.linspace(min_x_upr_sar_M, max_x_upr_sar_M, 200)\nxvals_upr_use_sar_M = []\nyvals_upr_sar_M = []\nfor i in range(1,199,2):\n    subset = upper_sar_M[upper_sar_M[0].between(xvals_upr_sar_M[i-1], xvals_upr_sar_M[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_M(xvals_upr_sar_M[i]))\n        arg = series.argmin()\n        yvals_upr_sar_M.append(subset[1].iloc[arg])\n        xvals_upr_use_sar_M.append(xvals_upr_sar_M[i])\n\nmin_x_lwr_sar_M = min(lower_sar_M[0])\nmax_x_lwr_sar_M = max(upper_sar_M[0])\nxvals_lwr_sar_M = np.linspace(min_x_lwr_sar_M, max_x_lwr_sar_M, 200)\nxvals_lwr_use_sar_M = []\nyvals_lwr_sar_M = []\nfor i in range(1,199,2):\n    subset = lower_sar_M[lower_sar_M[0].between(xvals_lwr_sar_M[i-1], xvals_lwr_sar_M[i+1])]\n    if subset.shape[0] >0:\n        series = abs(subset[1]-sepline_sar_M(xvals_lwr_sar_M[i]))\n        arg = series.argmin()\n        yvals_lwr_sar_M.append(subset[1].iloc[arg])\n        xvals_lwr_use_sar_M.append(xvals_lwr_sar_M[i])\n\ntrack_vals_sar_M1 = np.array([(xvals_upr_use_sar_M[i], yvals_upr_sar_M[i]) for i in range(3,len(xvals_upr_use_sar_M)-1)])\ntrack_vals_sar_M2 = np.array([(xvals_lwr_use_sar_M[i], yvals_lwr_sar_M[i]) for i in range(len(xvals_lwr_use_sar_M)-2,0,-1)])\ntrack_vals_sar_M = np.concatenate((track_vals_sar_M1, track_vals_sar_M2))\n\ntck_sar_M, u_sar_M = splprep(track_vals_sar_M.T, u=None, s=0.0, per=1) \nu_new_sar_M = np.linspace(u_sar_M.min(), u_sar_M.max(), 100000)\nx_new_sar_M, y_new_sar_M = splev(u_new_sar_M, tck_sar_M, der=0)\n\nfinal_y_new_sar_M = rotate_y((centre_sar),(x_new_sar_M, y_new_sar_M),0.25*np.pi)\nfinal_x_new_sar_M = rotate_x((centre_sar),(x_new_sar_M, y_new_sar_M),0.25*np.pi)\n\n# plt.plot(final_x_new_sar_I[0:7000], final_y_new_sar_I[0:7000], 'r-')\n# plt.plot(final_x_new_sar_I[89000:100000], final_y_new_sar_I[89000:100000], 'r-')\n# plt.plot(final_x_new_sar_I[7000:39000], final_y_new_sar_I[7000:39000], 'b-')\n# plt.plot(final_x_new_sar_I[39000:56000], final_y_new_sar_I[39000:56000], 'g-')\n# plt.plot(final_x_new_sar_I[56000:89000], final_y_new_sar_I[56000:89000], 'k-')\n\n# plt.plot(final_x_new_sar_T[0:9000], final_y_new_sar_T[0:9000], 'r-')\n# plt.plot(final_x_new_sar_T[89000:100000], final_y_new_sar_T[89000:100000], 'r-')\n# plt.plot(final_x_new_sar_T[9000:40000], final_y_new_sar_T[9000:40000], 'b-')\n# plt.plot(final_x_new_sar_T[40000:58000], final_y_new_sar_T[40000:58000], 'g-')\n# plt.plot(final_x_new_sar_T[58000:89000], final_y_new_sar_T[58000:89000], 'k-')\n\n# plt.plot(final_x_new_sar_D[0:8000], final_y_new_sar_D[0:8000], 'r-')\n# plt.plot(final_x_new_sar_D[83000:], final_y_new_sar_D[83000:], 'r-')\n# plt.plot(final_x_new_sar_D[8000:38000], final_y_new_sar_D[8000:38000], 'b-')\n# plt.plot(final_x_new_sar_D[38000:58000], final_y_new_sar_D[38000:58000], 'g-')\n# plt.plot(final_x_new_sar_D[58000:87000], final_y_new_sar_D[58000:87000], 'k-')\n\n# plt.plot(final_x_new_sar_M[0:6000], final_y_new_sar_M[0:6000], 'r-')\n# plt.plot(final_x_new_sar_M[87000:100000], final_y_new_sar_M[87000:100000], 'r-')\n# plt.plot(final_x_new_sar_M[6000:38000], final_y_new_sar_M[6000:38000], 'b-')\n# plt.plot(final_x_new_sar_M[38000:56000], final_y_new_sar_M[38000:56000], 'g-')\n# plt.plot(final_x_new_sar_M[56000:87000], final_y_new_sar_M[56000:87000], 'k-')\n\n# plt.show()\n\nl1_sar_I = ['A' for i in range(7000)]\nl2_sar_I = ['B' for i in range(7000,39000)]\nl3_sar_I = ['C' for i in range(39000,56000)]\nl4_sar_I = ['D' for i in range(56000,89000)]\nl5_sar_I = ['A' for i in range(89000,100000)]\n\nsar_I_sec = l1_sar_I + l2_sar_I + l3_sar_I + l4_sar_I + l5_sar_I\n\nl1_sar_T = ['A' for i in range(9000)]\nl2_sar_T = ['B' for i in range(9000,40000)]\nl3_sar_T = ['C' for i in range(40000,58000)]\nl4_sar_T = ['D' for i in range(58000,89000)]\nl5_sar_T = ['A' for i in range(89000,100000)]\n\nsar_T_sec = l1_sar_T + l2_sar_T + l3_sar_T + l4_sar_T + l5_sar_T\n\nl1_sar_D = ['A' for i in range(8000)]\nl2_sar_D = ['B' for i in range(8000,38000)]\nl3_sar_D = ['C' for i in range(38000,58000)]\nl4_sar_D = ['D' for i in range(58000,87000)]\nl5_sar_D = ['A' for i in range(87000,100000)]\n\nsar_D_sec = l1_sar_D + l2_sar_D + l3_sar_D + l4_sar_D + l5_sar_D\n\nl1_sar_M = ['A' for i in range(6000)]\nl2_sar_M = ['B' for i in range(6000,38000)]\nl3_sar_M = ['C' for i in range(38000,56000)]\nl4_sar_M = ['D' for i in range(56000,87000)]\nl5_sar_M = ['A' for i in range(87000,100000)]\n\nsar_M_sec = l1_sar_M + l2_sar_M + l3_sar_M + l4_sar_M + l5_sar_M\n\nsar_inner_tracks_dict = {'sar_I_lat' : final_x_new_sar_I,\n'sar_I_lon' : final_y_new_sar_I,\n'sar_I_section' : sar_I_sec,\n'sar_T_lat' : final_x_new_sar_T,\n'sar_T_lon' : final_y_new_sar_T,\n'sar_T_section' : sar_T_sec,\n'sar_D_lat' : final_x_new_sar_D,\n'sar_D_lon' : final_y_new_sar_D,\n'sar_D_section' : sar_D_sec,\n'sar_M_lat' : final_x_new_sar_M,\n'sar_M_lon' : final_y_new_sar_M,\n'sar_M_section' : sar_M_sec,\n}\n\nsar_inner_tracks_df = pd.DataFrame(sar_inner_tracks_dict)\n\nsar_inner_tracks_df.to_csv('sar_inner_tracks_df_reduced.csv')","metadata":{"datalore":{"node_id":"3MdBk1qjJ4YDCrR9Mr0Luu","type":"CODE","hide_input_from_viewers":true,"hide_output_from_viewers":true}},"execution_count":null,"outputs":[]}]}