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nnnba.py
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nnnba.py
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import json
import pandas as pd
import sys
import numpy as np
from logger import *
import logging
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import normalize
from sklearn.pipeline import Pipeline
from sklearn import linear_model
from sklearn import ensemble
from sklearn.preprocessing import PolynomialFeatures
from sklearn import svm
from scipy.stats import skew
import xgboost as xgb
import prepare_data
pd.set_option('display.max_columns', None)
class NNNBA:
"""
NNNBA class, which contains all the calculated information
"""
default_model_type = "lasso"
assumed_max_salary = 35350000.0
__threshold_per_col = {"OFF_RATING": 12, "PIE":0.11, "NET_RATING": 18, "GP": 50, "DEF_RATING": 7, "USG_PCT": 0.12, "FGA": None, "FGM": None, "FG3A": None, "PTS": None, "FTM": None, "FGM": None, "REB_PCT": None, "AGE": 4}
__outlier_cols_upper = [] #["OFF_RATING", "PIE", "NET_RATING", "USG_PCT", "PTS"]
__outlier_cols_lower = [] #["DEF_RATING"]
__ridge_init_alpha = [0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1, 3, 6, 10, 30, 60]
__lasso_init_alpha = [0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1]
__elasticnet_init = {
"l1_ratio":[0.1, 0.3, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 1],
"alpha":[0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1, 3, 6]
}
def __realpha__(self, alpha):
"""
Function to recalculate alpha
"""
return [alpha * .6, alpha * .65, alpha * .7, alpha * .75, alpha * .8, alpha * .85, alpha * .9, alpha * .95, alpha, alpha * 1.05, alpha * 1.1, alpha * 1.15, alpha * 1.25, alpha * 1.3, alpha * 1.35, alpha * 1.4]
def __reratio__(self, ratio):
"""
Function to recalculate ratio
"""
return [ratio * .85, ratio * .9, ratio * .95, ratio, ratio * 1.05, ratio * 1.1, ratio * 1.15]
def __baseline_model__():
"""
Base Neural Network model
"""
input=39
model = Sequential()
model.add(Dense(input, input_dim=input, kernel_initializer='normal', activation='relu'))
model.add(Dense(int(input/2), kernel_initializer='normal', activation='relu'))
model.add(Dense(input, kernel_initializer='normal', activation='relu'))
model.add(Dense(int(input/2), kernel_initializer='normal', activation='relu'))
model.add(Dense(int(input/4), kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
def __idx_of_median_outlier__(self, col, threshold=None, upper_outlier=True): #may need threshold=2
"""
Find index of outlier based on distance from median
Distance from median = threshold, which is either passed in or calculated as a function of std from the passed in data
"""
if threshold is None:
threshold = col.std()*2.5
logger.debug("median: " + str(col.median()) + " threshold: " + str(threshold))
diff = col - col.median()
if upper_outlier:
outlier = diff > threshold
else:
outlier = -1*diff > threshold
return list(outlier.index[outlier])
models = {
"linear regression": linear_model.LinearRegression(fit_intercept=True),
"ridge": linear_model.RidgeCV(alphas = __ridge_init_alpha, fit_intercept=True),
"lasso": linear_model.LassoCV( alphas = __lasso_init_alpha, max_iter = 5000, cv = 10, fit_intercept = True),
"bayes ridge": linear_model.BayesianRidge(),
"keras regressor": KerasRegressor(build_fn=__baseline_model__, nb_epoch=100, batch_size=5, verbose=0),
"xgb": xgb.XGBRegressor(n_estimators=1500, max_depth=2, learning_rate=0.01),
"elasticnet": linear_model.ElasticNetCV(l1_ratio = __elasticnet_init["l1_ratio"], alphas = __elasticnet_init["alpha"], max_iter = 1000, cv = 3),
"theilsen": linear_model.TheilSenRegressor(),
"polynomial": Pipeline([('poly', PolynomialFeatures(degree=2)), ('linear', linear_model.LinearRegression(fit_intercept=True))])
}
def __remodel__(self, model_type, regr, __X_train, __Y_train):
"""
Function to retrain certain models based on optimal alphas and/or ratios
"""
if model_type == "ridge":
alpha = regr.alpha_
regr = linear_model.RidgeCV(alphas = self.__realpha__(alpha), cv = 10)
elif model_type == "lasso":
alpha = regr.alpha_
regr = linear_model.LassoCV(alphas = self.__realpha__(alpha), max_iter = 5000, cv = 10)
elif model_type == "elasticnet":
alpha = regr.alpha_
ratio = regr.l1_ratio_
regr = linear_model.ElasticNetCV(l1_ratio = self.__reratio__(ratio), alphas = self.__elasticnet_init["alpha"], max_iter = 1000, cv = 3)
regr.fit(__X_train, __Y_train)
return regr
def __normalize_salary__(self, col, max_salary=assumed_max_salary): # scales out to max contract; max taken from https://www.hoopsrumors.com/2017/05/nba-maximum-salary-projections-for-201718.html
"""
Function to normalize salary so that the max is maximum salary possible, as yoy max salary changes
"""
min_salary = min(col)
local_max_salary = max(col)
return max_salary - (local_max_salary - col)/(local_max_salary - min_salary) * (max_salary - min_salary)
def __init__(self, debug=False):
logger.setLevel( logging.DEBUG if debug else logging.ERROR)
with open("crawled_data/raw_data.json", "r") as data_file:
raw_data = json.load(data_file)
columns = raw_data[0]["header"]
unique_columns = list(set( raw_data[0]["header"]))
position_names = ["Point Guard", "Shooting Guard", "Small Forward", "Power Forward", "Center"]
positions = []
for i, val in enumerate(position_names):
positions.append((val, i))
positions_convert = dict(positions)
self.X_df = pd.DataFrame(columns=columns)
Y_df = pd.DataFrame(columns=["SALARIES"])
age = []
positions_df = pd.DataFrame(columns = position_names)
names = pd.DataFrame(columns=[ "NAME", "PROJECTED_SALARIES" ])
logger.debug("Processing data")
for i, player in enumerate(raw_data):
if "2016_17" in player["salaries"] and "2016-17" in player["stats"]:
Y_df.loc[len(Y_df)] = player["salaries"]["2016_17"]
self.X_df.loc[len(self.X_df)] = player["stats"]["2016-17"]
age.append(player["age"])
positions_df.loc[len(positions_df)] = [0,0,0,0,0]
for position in player["positions"]:
positions_df[position][len(positions_df)] = 1
projected_salaries = 0
try:
projected_salaries = player["projected_salaries"][0]
except:
pass
names.loc[len(names)] = [ player["name"], projected_salaries ]
else:
continue
for col in []:
try:
self.X_df[col] = np.tanh(self.X_df[col])
except:
pass
self.X_df = self.X_df.T.drop_duplicates().T
self.X_df = pd.concat([self.X_df, pd.Series(age, name="AGE"), positions_df], axis=1)
self.X_df = self.X_df.drop(["FGA", "L", "AGE", "PCT_TOV", "BLKA", "AST_PCT", "AST_RATIO", "OREB_PCT", "DREB_PCT", "REB_PCT", "TM_TOV_PCT", "PACE", "OPP_PTS_OFF_TOV", "OPP_PTS_FB", "OPP_PTS_PAINT", 'OPP_PTS_2ND_CHANCE', 'OPP_PTS_FB', 'PCT_FGA_2PT', 'PCT_FGA_3PT', 'PCT_PTS_2PT', 'PCT_PTS_2PT_MR', 'PCT_PTS_3PT', 'PCT_PTS_FB', 'PCT_PTS_FT', 'PCT_PTS_OFF_TOV','PCT_PTS_PAINT', 'PCT_AST_2PM', 'PCT_UAST_2PM', 'PCT_AST_3PM', 'PCT_UAST_3PM', 'PCT_AST_FGM', 'PCT_UAST_FGM', 'PCT_FGM', 'PCT_FGA','PCT_FG3M', 'PCT_FG3A', 'PCT_FTM', 'PCT_FTA', 'PCT_OREB', 'PCT_DREB','PCT_REB', 'PCT_AST', 'PCT_STL', 'PCT_BLK', 'PCT_BLKA', 'PTS_OFF_TOV', 'PTS_FB', 'PTS_PAINT'], 1)
logger.debug("Columns: " + ", ".join(self.X_df.columns))
# remove players who's played less than 15 games
idx_of_lt_gp = self.X_df.index[(self.X_df["GP"] < 15)]
self.X_df = self.X_df.drop(idx_of_lt_gp)
Y_df = Y_df.drop(idx_of_lt_gp)
age = pd.Series(age).drop(idx_of_lt_gp)
positions_df = positions_df.drop(idx_of_lt_gp)
names = names.drop(idx_of_lt_gp)
# Remove outliers
logger.debug("Remove outliers")
X_train = self.X_df.copy()
Y_train = Y_df.copy()
logger.debug("No of rows before removing outliers: " + str(X_train.shape[0]))
to_be_dropped = []
## remove upper
for col in self.__outlier_cols_upper:
logger.debug(col)
idx_of_median_outlier = self.__idx_of_median_outlier__(X_train[col], self.__threshold_per_col[col])
logger.debug(col +" should drop "+ ", ".join(names["NAME"][idx_of_median_outlier].values))
to_be_dropped = to_be_dropped + idx_of_median_outlier
## remove lower
for col in self.__outlier_cols_lower:
logger.debug(col)
idx_of_median_outlier = self.__idx_of_median_outlier__(X_train[col], self.__threshold_per_col[col], upper_outlier=False)
logger.debug(col +" should drop "+ ", ".join(names["NAME"][idx_of_median_outlier].values))
to_be_dropped = to_be_dropped + idx_of_median_outlier
to_be_dropped = list(set(to_be_dropped))
logger.debug("Outliers: " + ", ".join(names["NAME"][to_be_dropped].values))
X_train = X_train.drop(to_be_dropped)
Y_train = Y_train.drop(to_be_dropped)
logger.debug("No of rows after removing outliers: " + str(X_train.shape))
logger.debug("No of rows after removing outliers: " + str(Y_train.shape))
__X_train = X_train.values # training data only includes non-rookies
__Y_train = np.log1p(Y_train["SALARIES"].values) # y = log(1+y)
self.Y_df = Y_df
self.model_results = {}
self.names = names
for model_type, regr in self.models.items():
logger.debug("Started " + model_type)
this_results = names.copy()
regr.fit(__X_train, __Y_train)
regr = self.__remodel__(model_type, regr, __X_train, __Y_train)
results = self.__normalize_salary__(np.expm1(regr.predict(self.X_df.values))) # y = exp(y) - 1
this_results['WORTH'] = results
diffY = this_results["PROJECTED_SALARIES"].values - results
this_results['SALARY_DIFF'] = diffY
this_results = this_results.sort_values(by="SALARY_DIFF", ascending=False)
self.models[model_type] = regr
self.model_results[model_type] = this_results
logger.debug("Finished " + model_type)
#get avg
this_results = self.model_results["linear regression"].copy()
this_results["WORTH"] = self.__normalize_salary__((1.*self.model_results["bayes ridge"]["WORTH"] + 1.*self.model_results["lasso"]["WORTH"] + 1.*self.model_results["elasticnet"]["WORTH"])/3)
diffY = this_results["PROJECTED_SALARIES"].values - this_results["WORTH"]
this_results['SALARY_DIFF'] = diffY
self.model_results["avg"] = this_results
def getUndervalued(self, model_type=default_model_type):
names = self.model_results[model_type]
print(names.loc[(names["SALARY_DIFF"] < 0) & (names["PROJECTED_SALARIES"] > 0)])
def getPlayerValue(self, player_name, model_type=default_model_type):
names = self.model_results[model_type]
idx = names[names["NAME"] == player_name].index[0]
print("\nPaid: " + '${:,.2f}'.format(float(self.Y_df.loc[idx]["SALARIES"])) + "\tFuture Salary: " + '${:,.2f}'.format(float(self.names["PROJECTED_SALARIES"][idx])) + "\tWorth: " + '${:,.2f}'.format(float(names["WORTH"][idx])) + "\n")
self.getPlayerStats(player_name, trim=True)
def getPlayerStats(self, player_name, trim=False):
columns = self.X_df.columns
if trim:
columns = columns[:30]
print(self.X_df.loc[self.names["NAME"] == player_name, columns])
def getMostValuablePlayers(self, model_type=default_model_type):
names = self.model_results[model_type]
print(names.sort_values(by="WORTH")
)
def showAvailableModels(self):
for model in self.models:
print(model)
def getPlayerNameByIndex(self, index):
return self.names[self.name.index == index]
def getCoefFromModel(self, model_type= default_model_type):
return pd.DataFrame(self.models[model_type].coef_, index=self.X_df.columns, columns=["coef"]).sort_values(by="coef")
def plotXCol(self, col_name, X = None):
import matplotlib.pyplot as plt
if X is None:
X = self.X_df.sort_values(by=col_name)[col_name].values
plt.figure()
plt.scatter(range(len(X)), X)
plt.show()
def get_data(parallel=True):
prepare_data.start(parallel=parallel)