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deep_learn.py
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deep_learn.py
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#deep learning implementation
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import StandardScaler
import cbb_web_scraper
from os import getcwd
from os.path import join, exists
import yaml
from tqdm import tqdm
from time import sleep
from pandas import DataFrame, concat, read_csv, isnull
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
# from sklearn.ensemble import RandomForestClassifier
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# from sys import argv
import joblib
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from difflib import get_close_matches
# from datetime import datetime, timedelta
# from sklearn.metrics import roc_curve
import seaborn as sns
#TODO: CREATE A FEATURE OF opp_simple_rating_system
class cbbDeep():
def __init__(self):
print('instantiate class cbbClass')
self.all_data = DataFrame()
# if exists(join(getcwd(),'randomForestModelTuned.joblib')):
# self.RandForRegressor=joblib.load("./randomForestModelTuned.joblib")
def get_teams(self):
year_list_find = []
year_list = [2023,2022,2021,2019,2018,2017,2016,2015,2014,2013,2012] # ,2014,2013,2012,2011,2010
if exists(join(getcwd(),'year_count.yaml')):
with open(join(getcwd(),'year_count.yaml')) as file:
year_counts = yaml.load(file, Loader=yaml.FullLoader)
else:
year_counts = {'year':year_list_find}
#Remove any years that have already been collected
if year_counts['year']:
year_list_check = year_counts['year']
year_list_find = year_counts['year']
year_list = [i for i in year_list if i not in year_list_check]
print(f'Need data for year: {year_list}')
#Collect data per year
if year_list:
for year in tqdm(year_list):
all_teams = cbb_web_scraper.get_teams_year(year_list[-1],year_list[0])
team_names = sorted(all_teams)
final_list = []
self.year_store = year
for abv in tqdm(team_names):
try:
print() #tqdm things
print(f'current team: {abv}, year: {year}')
basic = 'https://www.sports-reference.com/cbb/schools/' + abv + '/' + str(self.year_store) + '-gamelogs.html'
adv = 'https://www.sports-reference.com/cbb/schools/' + abv + '/' + str(self.year_store) + '-gamelogs-advanced.html'
df_inst = cbb_web_scraper.html_to_df_web_scrape_cbb(basic,adv,abv,self.year_store)
df_inst['pts'].replace('', np.nan, inplace=True)
df_inst.dropna(inplace=True)
final_list.append(df_inst)
except Exception as e:
print(e)
print(f'{abv} data are not available')
sleep(4) #I get get banned for a small period of time if I do not do this
final_data = concat(final_list)
if exists(join(getcwd(),'all_data_regressor.csv')):
self.all_data = read_csv(join(getcwd(),'all_data_regressor.csv'))
self.all_data = concat([self.all_data, final_data.dropna()])
if not exists(join(getcwd(),'all_data_regressor.csv')):
self.all_data.to_csv(join(getcwd(),'all_data_regressor.csv'),index=False)
self.all_data.to_csv(join(getcwd(),'all_data_regressor.csv'),index=False)
year_list_find.append(year)
print(f'year list after loop: {year_list_find}')
with open(join(getcwd(),'year_count.yaml'), 'w') as write_file:
yaml.dump(year_counts, write_file)
print(f'writing {year} to yaml file')
else:
self.all_data = read_csv(join(getcwd(),'all_data_regressor.csv'))
print('len data: ', len(self.all_data))
self.all_data = self.all_data.drop_duplicates(keep='last')
print(f'length of data after duplicates are dropped: {len(self.all_data)}')
def convert_to_float(self):
for col in self.all_data.columns:
self.all_data[col].replace('', np.nan, inplace=True)
self.all_data[col] = self.all_data[col].astype(float)
def split(self):
# self.delete_opp()
for col in self.all_data.columns:
if 'Unnamed' in col:
self.all_data.drop(columns=col,inplace=True)
self.convert_to_float()
self.y = self.all_data['game_result'].astype(int)
self.x = self.all_data.drop(columns=['game_result'])
self.pre_process()
#Dropna and remove all data from subsequent y data
real_values = ~self.x_no_corr.isna().any(axis=1)
self.x_no_corr.dropna(inplace=True)
self.y = self.y.loc[real_values]
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.x_no_corr, self.y, train_size=0.8)
# normalize data
self.scaler = StandardScaler()
self.x_train = self.scaler.fit_transform(self.x_train)
self.x_test = self.scaler.transform(self.x_test)
def pre_process(self):
# Remove features with a correlation coef greater than 0.85
corr_matrix = np.abs(self.x.astype(float).corr())
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
to_drop = [column for column in upper.columns if any(upper[column] >= 0.90)]
self.drop_cols = to_drop
self.x_no_corr = self.x.drop(columns=to_drop)
cols = self.x_no_corr.columns
print(f'Columns dropped >= 0.90: {to_drop}')
#Drop samples that are outliers
print(f'old feature dataframe shape before outlier removal: {self.x_no_corr.shape}')
for col_name in cols:
Q1 = np.percentile(self.x_no_corr[col_name], 25)
Q3 = np.percentile(self.x_no_corr[col_name], 75)
IQR = Q3 - Q1
upper = np.where(self.x_no_corr[col_name] >= (Q3+2.5*IQR)) #1.5 is the standard, use two to see if more data helps improve model performance
lower = np.where(self.x_no_corr[col_name] <= (Q1-2.5*IQR))
self.x_no_corr.drop(upper[0], inplace = True)
self.x_no_corr.drop(lower[0], inplace = True)
self.y.drop(upper[0], inplace = True)
self.y.drop(lower[0], inplace = True)
if 'level_0' in self.x_no_corr.columns:
self.x_no_corr.drop(columns=['level_0'],inplace = True)
self.x_no_corr.reset_index(inplace = True)
self.y.reset_index(inplace = True, drop=True)
self.x_no_corr.drop(columns=['level_0','index'],inplace = True)
print(f'new feature dataframe shape after outlier removal: {self.x_no_corr.shape}')
top_corr_features = corr_matrix.index
def create_model(self, neurons=32, learning_rate=0.001, dropout_rate=0.2, alpha=0.1):
model = keras.Sequential([
layers.Dense(neurons, input_shape=(self.x_no_corr.shape[1],)),
layers.LeakyReLU(alpha=alpha),
layers.Dropout(dropout_rate),
layers.Dense(neurons),
layers.LeakyReLU(alpha=alpha),
layers.Dropout(dropout_rate),
layers.Dense(neurons),
layers.LeakyReLU(alpha=alpha),
layers.Dropout(dropout_rate),
layers.Dense(1, activation='sigmoid')
])
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
return model
def deep_learn(self):
if exists('deep_learning.h5'):
self.model = keras.models.load_model('deep_learning.h5')
else:
#best params
# Best: 0.999925 using {'alpha': 0.1, 'batch_size': 32, 'dropout_rate': 0.2,
# 'learning_rate': 0.001, 'neurons': 16}
optimizer = keras.optimizers.Adam(learning_rate=0.001)
self.model = keras.Sequential([
layers.Dense(16, input_shape=(self.x_no_corr.shape[1],)),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(16),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(16),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(16),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(16),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(16),
layers.LeakyReLU(alpha=0.1),
layers.BatchNormalization(),
layers.Dropout(0.2),
layers.Dense(1, activation='sigmoid')
])
self.model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
history = self.model.fit(self.x_train, self.y_train,
epochs=50, batch_size=32,
validation_data=(self.x_test,self.y_test))
#validation_split=0.2)
# param_grid = {
# 'neurons': [16, 32, 64],
# 'learning_rate': [0.01, 0.001, 0.0001],
# 'dropout_rate': [0.1, 0.2, 0.3],
# 'alpha': [0.01, 0.1, 0.2],
# 'batch_size': [16, 32, 64]
# }
# param_grid = {
# 'neurons': [16, 32],
# 'learning_rate': [0.01, 0.001],
# 'dropout_rate': [0.2],
# 'alpha': [0.1],
# 'batch_size': [32, 64]
# }
# model = KerasClassifier(build_fn=self.create_model,
# epochs=50, batch_size=32, verbose=4)
# grid = GridSearchCV(estimator=model,
# param_grid=param_grid,
# cv=3,
# verbose=3)
# self.grid_result = grid.fit(self.x_train, self.y_train)
# print("Best: %f using %s" % (self.grid_result.best_score_, self.grid_result.best_params_))
# self.model = self.grid_result
# input()
self.model.save('deep_learning.h5')
plt.figure()
plt.plot(history.history['accuracy'], label='training accuracy')
plt.plot(history.history['val_accuracy'], label='validation accuracy')
plt.title('Accuracy History')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.savefig('Accuracy.png',dpi=300)
plt.close()
# plot loss history
plt.figure()
plt.plot(history.history['loss'], label='training loss')
plt.plot(history.history['val_loss'], label='validation loss')
plt.title('Loss History')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig('Loss.png',dpi=300)
plt.close()
def predict_two_teams(self):
teams_sports_ref = read_csv('teams_sports_ref_format.csv')
while True:
try:
team_1 = input('team_1: ')
if team_1 == 'exit':
break
team_2 = input('team_2: ')
#Game location
game_loc_team1 = int(input(f'{team_1} : #home = 0, away = 1, N = 2: '))
if game_loc_team1 == 0:
game_loc_team2 = 1
elif game_loc_team1 == 1:
game_loc_team2 = 0
elif game_loc_team1 == 2:
game_loc_team2 = 2
#Check to see if the team was spelled right
team_1 = get_close_matches(team_1,teams_sports_ref['teams'].tolist(),n=1)[0]
team_2 = get_close_matches(team_2,teams_sports_ref['teams'].tolist(),n=1)[0]
#2023 data
year = 2023
sleep(4)
basic = 'https://www.sports-reference.com/cbb/schools/' + team_1.lower() + '/' + str(year) + '-gamelogs.html'
adv = 'https://www.sports-reference.com/cbb/schools/' + team_1.lower() + '/' + str(year) + '-gamelogs-advanced.html'
team_1_df2023 = cbb_web_scraper.html_to_df_web_scrape_cbb(basic,adv,team_1.lower(),year)
sleep(4) #I get get banned for a small period of time if I do not do this
basic = 'https://www.sports-reference.com/cbb/schools/' + team_2.lower() + '/' + str(year) + '-gamelogs.html'
adv = 'https://www.sports-reference.com/cbb/schools/' + team_2.lower() + '/' + str(year) + '-gamelogs-advanced.html'
team_2_df2023 = cbb_web_scraper.html_to_df_web_scrape_cbb(basic,adv,team_2.lower(),year)
#Remove empty cells
team_1_df2023['pts'].replace('', np.nan, inplace=True)
team_1_df2023.replace('', np.nan, inplace=True)
team_1_df2023.dropna(inplace=True)
team_2_df2023['pts'].replace('', np.nan, inplace=True)
team_2_df2023.replace('', np.nan, inplace=True)
team_2_df2023.dropna(inplace=True)
#Remove pts and game result
# for col in team_1_df2023.columns:
# if 'opp' in col:
# team_1_df2023.drop(columns=col,inplace=True)
# for col in team_2_df2023.columns:
# if 'opp' in col:
# team_2_df2023.drop(columns=col,inplace=True)
team_1_df2023.drop(columns=['game_result'],inplace=True)
team_2_df2023.drop(columns=['game_result'],inplace=True)
#Drop the correlated features
team_1_df2023.drop(columns=self.drop_cols, inplace=True)
team_2_df2023.drop(columns=self.drop_cols, inplace=True)
ma_range = np.arange(2,5,1) #2 was the most correct value for mean and 8 was the best for the median; chose 9 for tiebreaking
team_1_count = 0
team_2_count = 0
team_1_count_mean = 0
team_2_count_mean = 0
team_1_ma_win = []
team_1_ma_loss = []
team_2_ma = []
#get SRS
team_srs = cbb_web_scraper.get_latest_srs(team_1)
for ma in tqdm(ma_range):
# data1_median = team_1_df2023.rolling(ma).median()
# data1_median['game_loc'] = game_loc_team1
# data2_median = team_2_df2023.rolling(ma).median()
# data2_median['game_loc'] = game_loc_team2
# data1_mean_old = team_1_df2023.rolling(ma).mean()
# data2_mean_old = team_2_df2023.rolling(ma).mean()
# TEAM 1
data1_mean = team_1_df2023.ewm(span=ma,min_periods=ma-1).mean()
data1_mean['game_loc'] = game_loc_team1
data2_mean = team_2_df2023.ewm(span=ma,min_periods=ma-1).mean()
data2_mean['game_loc'] = game_loc_team2
# team_1_predict_median = self.RandForclass.predict(data1_median.iloc[-1:])
# team_2_predict_median = self.RandForclass.predict(data2_median.iloc[-1:])
#Here replace opponent metrics with the features of the second team
for col in team_1_df2023.columns:
if "opp" in col:
if col == 'opp_trb':
# new_col = col.replace("opp_", "")
data1_mean.loc[data1_mean.index[-1], 'opp_trb'] = data2_mean.loc[data2_mean.index[-1], 'total_board']
else:
new_col = col.replace("opp_", "")
data1_mean.loc[data1_mean.index[-1], col] = data2_mean.loc[data2_mean.index[-1], new_col]
#get latest SRS value
data1_mean.loc[data1_mean.index[-1], 'simple_rating_system'] = team_srs
# data1_mean['simple_rating_system'].iloc[-1] = cbb_web_scraper.get_latest_srs(team_1)# float(input(f'input {team_1} current simple rating system value: '))
#TEAM 1 Prediction
x_new = self.scaler.transform(data1_mean.iloc[-1:])
prediction = self.model.predict(x_new)
print(f'prediction: {prediction[0]*100}%')
probability = prediction[0]
if probability > 0.5:
team_1_count += 1
elif probability < 0.5:
team_2_count += 1
# team_1_predict_mean = self.RandForclass.predict_proba(data1_mean.iloc[-1:])
#TEAM
# data1_mean_change = team_1_df2023.ewm(span=ma,min_periods=ma-1).mean()
# data1_mean_change['game_loc'] = game_loc_team1
# data2_mean_change = team_2_df2023.ewm(span=ma,min_periods=ma-1).mean()
# data2_mean_change['game_loc'] = game_loc_team2
# x_new = self.scaler.transform(data2_mean_change.iloc[-1:])
# prediction = self.model.predict(x_new)
# probability = prediction[0]
# team_1_predict_median = self.RandForclass.predict(data1_median.iloc[-1:])
# team_2_predict_median = self.RandForclass.predict(data2_median.iloc[-1:])
#Here replace opponent metrics with the features of the second team
# for col in team_2_df2023.columns:
# if "opp" in col:
# if col == 'opp_trb':
# # new_col = col.replace("opp_", "")
# data2_mean_change.loc[data2_mean_change.index[-1], 'opp_trb'] = data1_mean_change.loc[data1_mean_change.index[-1], 'total_board']
# else:
# new_col = col.replace("opp_", "")
# data2_mean_change.loc[data2_mean_change.index[-1], col] = data1_mean_change.loc[data1_mean_change.index[-1], new_col]
# team_2_predict_mean = self.RandForclass.predict_proba(data2_mean_change.iloc[-1:])
# team_2_ma.append(team_2_predict_mean[0][1])
# print('===============================================================')
# print(f'{team_1} win probability {round(np.mean(team_1_ma_win),4)*100}%')
# print(f'{team_2} win probability {round(np.median(team_2_predict_mean),4)*100}%')
# print(f'{team_2} winning: {np.mean(team_2_ma)}%')
print('===============================================================')
# if np.mean(team_1_ma_win) > np.mean(team_1_ma_loss):
# print(f'{team_1} wins over {team_2}')
# else:
# print(f'{team_2} wins over {team_1}')
if team_1_count > team_2_count:
print(f'{team_1} wins over {team_2}')
elif team_1_count < team_2_count:
print(f'{team_2} wins over {team_1}')
print('===============================================================')
except Exception as e:
print(f'The error: {e}')
def run_analysis(self):
self.get_teams()
self.split()
self.deep_learn()
self.predict_two_teams()
def main():
cbbDeep().run_analysis()
if __name__ == '__main__':
main()