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nba_ml_kg.py
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nba_ml_kg.py
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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from operator import itemgetter
import xgboost as xgb
import random
import time
from sklearn.metrics import average_precision_score
import matplotlib.pyplot as plt
from numpy import genfromtxt
import seaborn as sns
from sklearn import preprocessing
from sklearn.metrics import roc_curve, auc, recall_score, precision_score, accuracy_score, f1_score
import datetime as dt
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_output
# print(check_output(["ls", "../input"]).decode("utf8"))
def create_feature_map(features):
outfile = open('xgb.fmap', 'w')
for i, feat in enumerate(features):
outfile.write('{0}\t{1}\tq\n'.format(i, feat))
outfile.close()
def get_importance(gbm, features):
create_feature_map(features)
importance = gbm.get_fscore(fmap='xgb.fmap')
importance = sorted(importance.items(), key=itemgetter(1), reverse=True)
return importance
def get_features(train, test):
trainval = list(train.columns.values)
output = trainval
return sorted(output)
def run_single(train, test, features, target, random_state=0):
eta = 0.1
max_depth = 6
subsample = 1
colsample_bytree = 1
min_chil_weight = 1
start_time = time.time()
print(
'XGBoost params. ETA: {}, MAX_DEPTH: {}, SUBSAMPLE: {}, COLSAMPLE_BY_TREE: {}'.format(eta, max_depth, subsample,
colsample_bytree))
params = {
"objective": "binary:logistic",
"booster": "gbtree",
"eval_metric": "auc",
"eta": eta,
"tree_method": 'exact',
"max_depth": max_depth,
"subsample": subsample,
"colsample_bytree": colsample_bytree,
"silent": 1,
"min_chil_weight": min_chil_weight,
"seed": random_state,
# "num_class" : 22,
}
num_boost_round = 500
early_stopping_rounds = 20
test_size = 0.1
X_train, X_valid = train_test_split(train, test_size=test_size, random_state=random_state)
print('Length train:', len(X_train.index))
print('Length valid:', len(X_valid.index))
y_train = X_train[target]
y_valid = X_valid[target]
dtrain = xgb.DMatrix(X_train[features], y_train)
dvalid = xgb.DMatrix(X_valid[features], y_valid)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds,
verbose_eval=True)
gbm.save_model('0001.model')
print("Validating...")
check = gbm.predict(xgb.DMatrix(X_valid[features]), ntree_limit=gbm.best_iteration + 1)
print('check: %s' % check)
# area under the precision-recall curve
score = average_precision_score(X_valid[target].values, check)
print('area under the precision-recall curve: {:.6f}'.format(score))
check2 = check.round()
print('check2: %s' % check2)
score = precision_score(X_valid[target].values, check2)
print('precision score: {:.6f}'.format(score))
score = recall_score(X_valid[target].values, check2)
print('recall score: {:.6f}'.format(score))
score = accuracy_score(X_valid[target].values, check2)
print('accurary score: {:.6f}'.format(score))
score = f1_score(X_valid[target].values, check2)
print('f1 score: {:.6f}'.format(score))
RMSE = np.sqrt(mean_squared_error(X_valid[target].values, check2))
print('RMSE: {:.6f}'.format(RMSE.round(4)))
imp = get_importance(gbm, features)
print('Importance array: ', imp)
print("Predict test set... ")
test_prediction = gbm.predict(xgb.DMatrix(test[features]), ntree_limit=gbm.best_iteration + 1)
score = average_precision_score(test[target].values, test_prediction)
print('area under the precision-recall curve test set: {:.6f}'.format(score))
############################################ ROC Curve
# Compute micro-average ROC curve and ROC area
fpr, tpr, _ = roc_curve(X_valid[target].values, check)
roc_auc = auc(fpr, tpr)
# xgb.plot_importance(gbm)
# plt.show()
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([-0.02, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC curve')
plt.legend(loc="lower right")
plt.show()
##################################################
test['prob'] = test_prediction
print('Training time: {} minutes'.format(round((time.time() - start_time) / 60, 2)))
return test_prediction, imp, gbm.best_iteration + 1, test
# Any results you write to the current directory are saved as output.
start_time = dt.datetime.now()
print("Start time: ", start_time)
# data = pd.read_csv('../data/creditcard.csv')
data = pd.read_csv('data/Seasons_Stats.csv')
# data.loc[data['Player'] == 'LeBron James'].T
data = data.replace('?', np.nan)
zero_data = np.zeros(shape=(len(data), 1))
data = data.assign(all_nba=zero_data)
allnba = pd.read_csv('data/all_nba_teams_clean_binary.csv')
res = allnba.set_index(['Year', 'Player']) \
.combine_first(data.set_index(['Year', 'Player'])) \
.reset_index()
df_1955 = res[res.Year > 1975]
print(df_1955.Player == 'Michael Jordan')
# data = df_1955[['2P', '3P', '3P%', 'AST',
# 'AST%', 'BLK', 'BLK%', 'DRB',
# 'eFG%', 'FT', 'FT%', 'FTA',
# 'G', 'MP', 'ORB', 'STL',
# 'TOV', 'TOV%', 'TRB', 'TRB%', 'TS%', 'PTS', 'PER',
# 'all_nba']].copy()
data = df_1955[['2P', '3P', 'eFG%', 'AST', 'BLK', 'FT', 'MP', 'ORB', 'DRB', 'TRB', 'STL', 'PTS', 'G', 'all_nba']].copy()
# data = df_1955[['all_nba']].copy()
train, test = train_test_split(data, test_size=.1, random_state=random.seed(2016))
test = df_1955[(df_1955.Player == 'Michael Jordan') |
(df_1955.Player == 'Tim Duncan ') |
(df_1955.Player == 'LeBron James') |
(df_1955.Player == 'Dwyane Wade') |
(df_1955.Player == 'Kevin Durant') |
(df_1955.Player == 'Giannis Antetokounmpo') |
(df_1955.Player == 'Russell Westbrook') |
(df_1955.Player == 'Zach Randolph') |
(df_1955.Player == 'Deron Williams') |
(df_1955.Player == 'Vince Carter') |
(df_1955.Player == 'Mike Muscala') |
(df_1955.Player == 'Shane Battier') |
(df_1955.Player == 'Mike Miller')
]
test = test[['Player', 'Year', '2P', '3P', 'eFG%', 'AST', 'BLK', 'FT', 'MP', 'ORB', 'DRB', 'TRB', 'STL', 'PTS', 'G',
'all_nba']].copy()
features = list(train.columns.values)
features.remove('all_nba')
print(features)
print("Building model.. ", dt.datetime.now() - start_time)
preds, imp, num_boost_rounds, test_prob = run_single(train, test, features, 'all_nba', 42)
print(dt.datetime.now() - start_time)
print(test_prob.to_string())