[PYTHON] Specific implementation method to add horse past performance data to machine learning features


Predict horse racing with machine learning and aim for a recovery rate of 100%.

What to do this time

Last time, I made a machine learning model that predicts horses that will be in the top 3 with LightGBM. This time, I would like to add "past performance of horses" as a feature, but scraping and data processing are quite difficult when actually trying to do it. So, I would like to summarize what kind of code should be written and implemented </ font>. スクリーンショット 2020-07-09 18.13.19.png

Source code

First of all, scraping the past results of all horses running in 2019 from netkeiba.com. On netkeiba.com, horse_id is given for each horse, and the page URL of the past performance result is 「https://db.netkeiba.com/horse/(horse_id)」 Since it has the structure, scrape the necessary horse_id (and the jockey id) by processing the scrape_race_results function created in Previous article.

import time
from tqdm.notebook import tqdm
import requests
from bs4 import BeautifulSoup
import re
import pandas as pd

def scrape_race_results(race_id_list, pre_race_results={}):
    race_results = pre_race_results
    for race_id in tqdm(race_id_list):
        if race_id in race_results.keys():
            url = "https://db.netkeiba.com/race/" + race_id
            df = pd.read_html(url)[0]

            # horse_id and jockey_scraping id
            html = requests.get(url)
            html.encoding = "EUC-JP"
            soup = BeautifulSoup(html.text, "html.parser")
            # horse_id
            horse_id_list = []
            horse_a_list = soup.find("table", attrs={"summary": "Race result"}).find_all(
                "a", attrs={"href": re.compile("^/horse")}
            for a in horse_a_list:
                horse_id = re.findall(r"\d+", a["href"])
                #If you use backslash in qiita, it will be buggy, so it is capitalized.
            # jockey_id
            jockey_id_list = []
            jockey_a_list = soup.find("table", attrs={"summary": "Race result"}).find_all(
                "a", attrs={"href": re.compile("^/jockey")}
            for a in jockey_a_list:
                jockey_id = re.findall(r"\d+", a["href"])

            df["horse_id"] = horse_id_list
            df["jockey_id"] = jockey_id_list
            race_results[race_id] = df
        except IndexError:
        except Exception as e:
    return race_results

Convert to DataFrame type referring to the previous article. This will give you a list of the horse_ids you need.

results = scrape_race_results(race_id_list)
results = pd.concat([results[key] for key in results])
horse_id_list = results['horse_id'].unique()

This is used to scrape past performance data.

def scrape_horse_results(horse_id_list, pre_horse_id=[]):
    horse_results = {}
    for horse_id in tqdm(horse_id_list):
        if horse_id in pre_horse_id:
            url = 'https://db.netkeiba.com/horse/' + horse_id
            df = pd.read_html(url)[3]
            if df.columns[0]=='Award history':
                df = pd.read_html(url)[4]
            horse_results[horse_id] = df
        except IndexError:
        except Exception as e:
            import traceback
    return horse_results

It takes a long time, but after scraping, make it a DataFrame type again and save it in a pickle file.

horse_results = scrape_horse_results(horse_id_list)
for key in horse_results:
    horse_results[key].index = [key] * len(horse_results[key])
df = pd.concat([horse_results[key] for key in horse_results])

Next, create a class called HorseResults and implement a function that merges the order of arrival and the average of the prize money.

class HorseResults:
    def __init__(self, horse_results):
        self.horse_results = horse_results[['date', 'Order of arrival', 'Prize money']]
    def preprocessing(self):
        df = self.horse_results.copy()

        #Remove items that contain non-numeric character strings in the order of arrival
        df['Order of arrival'] = pd.to_numeric(df['Order of arrival'], errors='coerce')
        df.dropna(subset=['Order of arrival'], inplace=True)
        df['Order of arrival'] = df['Order of arrival'].astype(int)

        df["date"] = pd.to_datetime(df["date"])
        df.drop(['date'], axis=1, inplace=True)
        #Fill the prize NaN with 0
        df['Prize money'].fillna(0, inplace=True)
        self.horse_results = df
    def average(self, horse_id_list, date, n_samples='all'):
        target_df = self.horse_results.loc[horse_id_list]
        #Specify how many runs in the past
        if n_samples == 'all':
            filtered_df = target_df[target_df['date'] < date]
        elif n_samples > 0:
            filtered_df = target_df[target_df['date'] < date].\
                sort_values('date', ascending=False).groupby(level=0).head(n_samples)
            raise Exception('n_samples must be >0')
        average = filtered_df.groupby(level=0)[['Order of arrival', 'Prize money']].mean()
        return average.rename(columns={'Order of arrival':'Order of arrival_{}R'.format(n_samples), 'Prize money':'Prize money_{}R'.format(n_samples)})
    def merge(self, results, date, n_samples='all'):
        df = results[results['date']==date]
        horse_id_list = df['horse_id']
        merged_df = df.merge(self.average(horse_id_list, date, n_samples), left_on='horse_id',
                             right_index=True, how='left')
        return merged_df
    def merge_all(self, results, n_samples='all'):
        date_list = results['date'].unique()
        merged_df = pd.concat([self.merge(results, date, n_samples) for date in tqdm(date_list)])
        return merged_df

With this, for example, if you want to add the results of the past 5 races to the feature quantity, you can implement it as follows.

hr = HorseResults(horse_results)
results_5R = hr.merge_all(results_p, n_samples=5)

Now you can see that the rightmost two columns have added the finish order and the average of the last five races of prize money. スクリーンショット 2020-07-09 18.41.01.png

Details are explained in the video ↓ Data analysis / machine learning starting with horse racing prediction スクリーンショット 2020-07-11 15.00.29.png

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