def comparemodels(cross=True):
X = np.array(get_features(f,wo_penalties,only_fcb_shots))
X_alt = np.array(get_features(f_alt,wo_penalties,only_fcb_shots))
y = np.array(get_results(wo_penalties,only_fcb_shots))
if cross:
prob = model.crosspredict(X,y)
prob_alt = model_alt.crosspredict(X_alt,y)
else:
model.fit(X,y)
model_alt.fit(X,y)
prob = model.predict(X)
prob_alt = model.predict(X)
#compare_models(X, y, prob, prob_alt, f, f_plot=True)
plot_roc_curve(y, prob, prob_alt)
def plot_features_bins2():
X = np.array(get_features(fs,wo_penalties,only_fcb_shots))
y = np.array(get_results(wo_penalties,only_fcb_shots))
n = len(fs)
names = {'Distance' : 'Afstand tot doel (m)' ,
'Angle' : 'Zichthoek (radialen)',
'Surface' : 'Oppervlakte driehoek (m^2)',
'NbOfPassesInTimewindow' : 'Aantal passes in de laatste 10 seconden',
'SpeedInTimewindow' : 'Afgelegde afstand in de laatste 10 seconden (m)',
'AngleInTimewindow' : 'Hoek in de laatste 10 seconden (radialen)',
'NbOfPassesInPhase' : "passes in fase",
'NbOfEventsInPhase' : "events in fase",
'NbOfEventsInTimewindow' : 'events in de laatste 10 seconden'}
for i in range(0,n):
fig,ax = plt.subplots(1,1)
fig.set_size_inches(7*0.7,5*0.7,forward=True)
name = fs[i]
subax = ax if n > 1 else ax
x = X[...,i] if n > 1 else X
if name== 'Distance' or name == 'SpeedInTimewindow': x = [float(a)/100 for a in x]
if name== 'Surface': x = [float(a)/10000 for a in x]
if name in ['NbOfPassesInPhase','NbOfEventsInPhase','NbOfPassesInTimewindow','NbOfEventsInTimewindow']:
plot_passes(x,y,names[name],subax)
else:
plot_feature_bins(x,y,names[name], subax)
plt.tight_layout()
#plt.savefig("C:/Users/Temp/Dropbox/thesis/reports/thesis/img/model2/" + name + ".pdf")
plt.show()
def analysistest():
f = [#'IsPenalty',
'Distance',
'Angle',
'Surface',
#'LastEvent',
#'NbOfPassesInPhase',
#'NbOfEventsInPhase',
'NbOfPassesInTimewindow',
'NbOfEventsInTimewindow',
'SpeedInTimewindow',
'AngleInTimewindow',
#'Random'
]
wo_penalties = True
only_fcb_shots = False
skmodel = lambda: RandomForestClassifier(n_estimators=1000)
skmodel_alt = lambda: ExtraTreesClassifier(n_estimators=1000)
model = SKLearnModel(skmodel,True)
model_alt = SKLearnModel(LogisticRegression,True)
X = np.array(get_features(f,wo_penalties,only_fcb_shots))
y = np.array(get_results(wo_penalties,only_fcb_shots))
prob = model.crosspredict(X, y)
#prob_alt = model_alt.crosspredict(X, y)
plot_model_analysis(X,y,prob,f,indexes=[0])
print_scores(get_scores(y, prob))
#print_scores(get_scores(y, prob))
#print_scores(get_scores(y, prob_alt))
#compare_models(X, y, prob, prob_alt, f, True,[0])
plt.show()
def build_shotclasstable(cross=True):
from db.prozoneDB import DB
c = DB.c
conn = DB.conn
idsX = np.array(get_features(["shotid"] + f,wo_penalties,only_fcb_shots))
X = idsX[:,1:]
y = np.array(get_results(wo_penalties,only_fcb_shots))
if cross:
prob = model.crosspredict(X,y)
else:
model.fit(X,y)
prob = model.predict(X)
#print prob
c.execute("drop table if exists Shotvalue")
c.execute("create table Shotvalue (shotid int,esv real)")
for t in zip(idsX[:,0],prob):
print(t)
c.execute("insert into Shotvalue values (?,?)",t)
conn.commit()
conn.close()
print("Shotclass table succcesfully built")
print_scores(get_scores(y,prob))
plot_roc_curve(y,prob)
def comparemodelsroc(models,labels):
preds = []
X = np.array(get_features(f, wo_penalties, only_fcb_shots))
y = np.array(get_results(wo_penalties, only_fcb_shots))
for model in models:
preds.append(model.crosspredict(X,y))
plot_roc_curves(y,preds,labels)
def plot_features_bins():
X = np.array(get_features(fs,wo_penalties,only_fcb_shots))
y = np.array(get_results(wo_penalties,only_fcb_shots))
n = len(fs)
fig,ax = plt.subplots(1,n)
fig.set_size_inches(n*5,5,forward=True)
for i in range(0,n):
name = fs[i]
subax = ax[i] if n > 1 else ax
x = X[...,i] if n > 1 else X
plot_feature_bins(x,y,name, subax)
plt.tight_layout()
def analyze_model(cross=True):
X = np.array(get_features(f,wo_penalties,only_fcb_shots))
y = np.array(get_results(wo_penalties,only_fcb_shots))
if cross:
prob = model.crosspredict(X,y)
else:
model.fit(X,y)
prob = model.predict(X)
# print prob
# plt.hist(prob)
# plt.show()
#plot_model_analysis(X,y,prob,f)
print_scores(get_scores(y,prob))
plot_roc_curve(y,prob)
x,y,r,a = [],[],[],[]
for shot in shots:
x.append(shot[0])
y.append(shot[1])
#p = model.predict(shot)
#r.append(p)
#a.append(p)
plt.scatter(x,y)#,c = r,cmap="YlOrRd")
plt.show()
f = [
"xcoordinate",
"ycoordinate",
"distance",
"angle",
"surface",
"random",
"nbofpassesinphase",
"NbOfEventsInPhase",
"NbOfPassesInTimewindow",
"NbOfEventsInTimewindow",
"SpeedInTimewindow",
"AngleInTimewindow"
]
wo_penalties,only_fcb_shots = True,False
shotfeatures = get_features(f, wo_penalties, only_fcb_shots)
#shots = [dict(x=x,y=y,r=r)
# for (x,y,r)
# in get_features(["XCoordinate","YCoordinate","Result"])]
plot_shots(shotfeatures)
