In [69]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import KFold
In [97]:
# import data
filename= "data.csv"
raw = pd.read_csv(filename)
print (raw.shape)
raw.head()
Out[97]:
In [71]:
# 5000 for test
kobe = raw[pd.notnull(raw['shot_made_flag'])]
print (kobe.shape)
In [105]:
#plt.subplot(211) first is raw second Column
alpha = 0.02
plt.figure(figsize=(10,10))
# loc_x and loc_y
plt.subplot(121)
plt.scatter(kobe.loc_x, kobe.loc_y, color='R', alpha=alpha)
plt.title('loc_x and loc_y')
# lat and lon
plt.subplot(122)
plt.scatter(kobe.lon, kobe.lat, color='B', alpha=alpha)
plt.title('lat and lon')
Out[105]:
In [73]:
raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)
loc_x_zero = raw['loc_x'] == 0
#print (loc_x_zero)
raw['angle'] = np.array([0]*len(raw))
raw['angle'][~loc_x_zero] = np.arctan(raw['loc_y'][~loc_x_zero] / raw['loc_x'][~loc_x_zero])
raw['angle'][loc_x_zero] = np.pi / 2
In [74]:
raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']
In [106]:
print(kobe.action_type.unique())
print(kobe.combined_shot_type.unique())
print(kobe.shot_type.unique())
print(kobe.shot_type.value_counts())
In [76]:
kobe['season'].unique()
Out[76]:
In [77]:
raw['season'] = raw['season'].apply(lambda x: int(x.split('-')[1]) )
raw['season'].unique()
Out[77]:
In [78]:
print(kobe['team_id'].unique())
print(kobe['team_name'].unique())
In [79]:
pd.DataFrame({'matchup':kobe.matchup, 'opponent':kobe.opponent})
Out[79]:
In [80]:
plt.figure(figsize=(5,5))
plt.scatter(raw.dist, raw.shot_distance, color='blue')
plt.title('dist and shot_distance')
Out[80]:
In [81]:
gs = kobe.groupby('shot_zone_area')
print (kobe['shot_zone_area'].value_counts())
print (len(gs))
In [82]:
import matplotlib.cm as cm
plt.figure(figsize=(20,10))
def scatter_plot_by_category(feat):
alpha = 0.1
gs = kobe.groupby(feat)
cs = cm.rainbow(np.linspace(0, 1, len(gs)))
for g, c in zip(gs, cs):
plt.scatter(g[1].loc_x, g[1].loc_y, color=c, alpha=alpha)
# shot_zone_area
plt.subplot(131)
scatter_plot_by_category('shot_zone_area')
plt.title('shot_zone_area')
# shot_zone_basic
plt.subplot(132)
scatter_plot_by_category('shot_zone_basic')
plt.title('shot_zone_basic')
# shot_zone_range
plt.subplot(133)
scatter_plot_by_category('shot_zone_range')
plt.title('shot_zone_range')
Out[82]:
In [83]:
drops = ['shot_id', 'team_id', 'team_name', 'shot_zone_area', 'shot_zone_range', 'shot_zone_basic', \
'matchup', 'lon', 'lat', 'seconds_remaining', 'minutes_remaining', \
'shot_distance', 'loc_x', 'loc_y', 'game_event_id', 'game_id', 'game_date']
for drop in drops:
raw = raw.drop(drop, 1)
In [84]:
print (raw['combined_shot_type'].value_counts())
pd.get_dummies(raw['combined_shot_type'], prefix='combined_shot_type')[0:2]
Out[84]:
In [85]:
categorical_vars = ['action_type', 'combined_shot_type', 'shot_type', 'opponent', 'period', 'season']
for var in categorical_vars:
raw = pd.concat([raw, pd.get_dummies(raw[var], prefix=var)], 1)
raw = raw.drop(var, 1)
In [86]:
train_kobe = raw[pd.notnull(raw['shot_made_flag'])]
train_kobe = train_kobe.drop('shot_made_flag', 1)
train_label = train_kobe['shot_made_flag']
test_kobe = raw[pd.isnull(raw['shot_made_flag'])]
test_kobe = test_kobe.drop('shot_made_flag', 1)
In [87]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import confusion_matrix,log_loss
import time
In [4]:
import numpy as np
range_m = np.logspace(0,2,num=5).astype(int)
range_m
Out[4]:
In [90]:
# find the best n_estimators for RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import KFold
print('Finding best n_estimators for RandomForestClassifier...')
min_score = 100000
best_n = 0
scores_n = []
range_n = np.logspace(0,2,num=3).astype(int)
for n in range_n:
print("the number of trees : {0}".format(n))
t1 = time.time()
rfc_score = 0.
rfc = RandomForestClassifier(n_estimators=n)
for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
#rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
pred = rfc.predict(train_kobe.iloc[test_k])
rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
scores_n.append(rfc_score)
if rfc_score < min_score:
min_score = rfc_score
best_n = n
t2 = time.time()
print('Done processing {0} trees ({1:.3f}sec)'.format(n, t2-t1))
print(best_n, min_score)
# find best max_depth for RandomForestClassifier
print('Finding best max_depth for RandomForestClassifier...')
min_score = 100000
best_m = 0
scores_m = []
range_m = np.logspace(0,2,num=3).astype(int)
for m in range_m:
print("the max depth : {0}".format(m))
t1 = time.time()
rfc_score = 0.
rfc = RandomForestClassifier(max_depth=m, n_estimators=best_n)
for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
#rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
pred = rfc.predict(train_kobe.iloc[test_k])
rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
scores_m.append(rfc_score)
if rfc_score < min_score:
min_score = rfc_score
best_m = m
t2 = time.time()
print('Done processing {0} trees ({1:.3f}sec)'.format(m, t2-t1))
print(best_m, min_score)
In [91]:
plt.figure(figsize=(10,5))
plt.subplot(121)
plt.plot(range_n, scores_n)
plt.ylabel('score')
plt.xlabel('number of trees')
plt.subplot(122)
plt.plot(range_m, scores_m)
plt.ylabel('score')
plt.xlabel('max depth')
Out[91]:
In [ ]:
model = RandomForestClassifier(n_estimators=best_n, max_depth=best_m)
model.fit(train_kobe, train_label)
# 474241623