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153 lines
7.2 KiB
153 lines
7.2 KiB
import re
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import pandas as pd
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.tree import export_graphviz
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from sklearn.tree import plot_tree
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from sklearn.pipeline import Pipeline
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import classification_report
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from sklearn.metrics import confusion_matrix
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from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
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from sklearn.compose import make_column_transformer
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from sklearn.impute import SimpleImputer
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pd.options.display.max_columns = None
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pd.options.display.max_rows = None
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import sklearn
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def displayNumberOfNaNValues(df):
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# Create an empty list to store tuples of column index and number of NaN values
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na = []
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# Loop through each column in the DataFrame
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for index, col in enumerate(df):
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# Count the number of NaN values in each column and append the index and count to 'na'
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na.append((index, df[col].isna().sum()))
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# Make a copy of 'na' and sort it based on the count of NaN values in descending order
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na.sort(key=lambda x: x[1], reverse=True)
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# Iterate through the sorted list of columns
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for i in range(len(df.columns)):
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# Check if the count of NaN values for the current column is not zero
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if na[i][1] != 0:
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# Print the column name, count of NaN values, and "NaN"
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print(df.columns[na[i][0]], ":", na[i][1], "NaN")
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# Calculate and print the total number of features with NaN values
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print('Number of features with NaN values:', len([x[1] for x in na if x[1] > 0]))
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print("Total NaN in dataframe :" , df.isna().sum().sum())
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# i = index of the fighter's fight, 0 means the last fight, -1 means first fight
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def select_fight_row(df, name, i):
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df_temp = df[(df['R_fighter'] == name) | (df['B_fighter'] == name)] # filter df on fighter's name
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df_temp.reset_index(drop=True, inplace=True) # as we created a new temporary dataframe, we have to reset indexes
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idx = max(df_temp.index) # get the index of the oldest fight
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if i > idx: # if we are looking for a fight that didn't exist, we return nothing
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return
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arr = df_temp.iloc[i,:].values
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return arr
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# get all active UFC fighters (according to the limit_date parameter)
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def list_fighters(df, limit_date):
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# Filter the DataFrame to include only fights occurring after the specified limit date
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df_temp = df[df['date'] > limit_date]
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# Create a set of all fighters from the red corner ('R_fighter') in the filtered DataFrame
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set_R = set(df_temp['R_fighter'])
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# Create a set of all fighters from the blue corner ('B_fighter') in the filtered DataFrame
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set_B = set(df_temp['B_fighter'])
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# Combine the sets of fighters from the red and blue corners to get all unique fighters
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fighters = list(set_R.union(set_B))
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# Print the number of unique fighters included in the list
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# print("Number of fighters: " + str(len(fighters)))
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# Return the list of unique fighters
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return fighters
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def build_df(df, fighters, i):
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arr = [select_fight_row(df, fighters[f], i) for f in range(len(fighters)) if select_fight_row(df, fighters[f], i) is not None]
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cols = [col for col in df]
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df_fights = pd.DataFrame(data=arr, columns=cols)
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df_fights.drop_duplicates(inplace=True)
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df_fights['title_bout'] = df_fights['title_bout'].map({True: 1, False: 0})
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df_fights.drop(['R_fighter', 'B_fighter', 'date'], axis=1, inplace=True)
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return df_fights
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def build_df_all_but_last(df, fighters):
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cols = [col for col in df]
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print(len(cols))
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df_fights=pd.DataFrame(columns=cols)
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for f in range(len(fighters)):
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i=0
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while True:
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fight_row = select_fight_row(df, fighters[f], i)
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if fight_row is None:
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if not df_fights.empty:
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df_fights = df_fights.iloc[:-1]
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break
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fight_row = list(fight_row)
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dfTemp = pd.DataFrame(data=[fight_row], columns=cols)
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df_fights = df_fights.dropna(axis=1, how='all')
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df_fights = pd.concat([df_fights, dfTemp], ignore_index=True)
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i=i+1
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df_fights.drop_duplicates(inplace=True)
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df_fights = df_fights[~df_fights.apply(lambda row: 'Open Stance' in row.values, axis=1)].reset_index(drop=True)
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df_fights['title_bout'] = df_fights['title_bout'].map({True: 1, False: 0})
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df_fights.drop(['R_fighter', 'B_fighter', 'date'], axis=1, inplace=True)
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return df_fights
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def predict(df, pipeline, blue_fighter, red_fighter, weightclass, rounds, title_bout=False):
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#We build two dataframes, one for each figther
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f1 = df[(df['R_fighter'] == blue_fighter) | (df['B_fighter'] == blue_fighter)].copy()
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f1.reset_index(drop=True, inplace=True)
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f1 = f1[:1]
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f2 = df[(df['R_fighter'] == red_fighter) | (df['B_fighter'] == red_fighter)].copy()
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f2.reset_index(drop=True, inplace=True)
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f2 = f2[:1]
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print("OK 1")
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# if the fighter was red/blue corner on his last fight, we filter columns to only keep his statistics (and not the other fighter)
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# then we rename columns according to the color of the corner in the parameters using re.sub()
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if (f1.loc[0, ['R_fighter']].values[0]) == blue_fighter:
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result1 = f1.filter(regex='^R', axis=1).copy() #here we keep the red corner stats
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result1.rename(columns = lambda x: re.sub('^R','B', x), inplace=True) #we rename it with "B_" prefix because he's in the blue_corner
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else:
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result1 = f1.filter(regex='^B', axis=1).copy()
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if (f2.loc[0, ['R_fighter']].values[0]) == red_fighter:
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result2 = f2.filter(regex='^R', axis=1).copy()
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else:
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result2 = f2.filter(regex='^B', axis=1).copy()
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result2.rename(columns = lambda x: re.sub('^B','R', x), inplace=True)
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print("OK 2")
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fight = pd.concat([result1, result2], axis = 1) # we concatenate the red and blue fighter dataframes (in columns)
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fight.drop(['R_fighter','B_fighter'], axis = 1, inplace = True) # we remove fighter names
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fight.insert(0, 'title_bout', title_bout) # we add tittle_bout, weight class and number of rounds data to the dataframe
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fight.insert(1, 'weight_class', weightclass)
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fight.insert(2, 'no_of_rounds', rounds)
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fight['title_bout'] = fight['title_bout'].map({True: 1, False: 0})
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print("OK 3")
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pred = pipeline.predict(fight)
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proba = pipeline.predict_proba(fight)
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print("OK 4")
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if (pred == 1.0):
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print("The predicted winner is", red_fighter, 'with a probability of', round(proba[0][1] * 100, 2), "%")
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else:
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print("The predicted winner is", blue_fighter, 'with a probability of ', round(proba[0][0] * 100, 2), "%")
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return proba
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#predict(df, model, 'Kamaru Usman', 'Colby Covington', 'Welterweight', 3, True)
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#predict(df, model, 'Leon Edwards', 'Belal Muhammad', 'Welterweight', 3, True)
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#predict(df, model, 'Conor McGregor', 'Khabib Nurmagomedov', 'Lightweight', 5, True)
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#predict(df, model, 'Conor McGregor', 'Tai Tuivasa', 'Heavyweight', 5, True)
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#predict(df,model,'Charles Oliveira','Conor McGregor','Lightweight',5,True)
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#predict(df,model,'Charles Oliveira','Khabib Nurmagomedov','Lightweight',5,True)
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#predict(df, model, 'Leon Edwards', 'Kamaru Usman', 'Welterweight', 5, True)
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