def do_POST(self):
"""Handle a post request"""
length = int(self.headers.getheader('content-length'))
data_string = self.rfile.read(length)
print(data_string)
if data_string == 'predict':
try:
result = pd.read_csv("prediction.csv")
result = getdata.select_columns(
result,
columns=['Date', 'HomeTeam', 'AwayTeam', 'Prediction'])
result = result.to_html()
result = "<div style = 'display: inline-block;'>" + result + "</div><br>"
except:
result = 'error'
else:
try:
result = getdata.last_six(data_string)
result = result.to_html()
result = "<h3>" + data_string + " Last Six Games</h3><div style = 'display: inline-block;'>" + result + "</div><br><h4> WDL: Team gets +1 for win, -1 for loss</h4>"
#result = unicode(result)
except:
result = 'error'
self.wfile.write(result)
def do_GET(self):
"""Handle a get request"""
length = int(self.headers.getheader("content-length"))
data_string = self.rfile.read(length)
try:
result = pd.read_csv("prediction.csv")
result = getdata.select_columns(result, columns=["Date", "HomeTeam", "AwayTeam", "Prediction"])
result = result.to_html()
except:
result = "error"
self.wfile.write(result)
def do_GET(self):
"""Handle a get request"""
length = int(self.headers.getheader('content-length'))
data_string = self.rfile.read(length)
try:
result = pd.read_csv("prediction.csv")
result = getdata.select_columns(
result, columns=['Date', 'HomeTeam', 'AwayTeam', 'Prediction'])
result = result.to_html()
except:
result = 'error'
self.wfile.write(result)
def do_POST(self):
"""Handle a post request"""
length = int(self.headers.getheader('content-length'))
data_string = self.rfile.read(length)
print(data_string)
if data_string == 'predict':
try:
result = pd.read_csv("prediction.csv")
result = getdata.select_columns(result, columns=['Date','HomeTeam','AwayTeam','Prediction'])
result = result.to_html()
result = "<div style = 'display: inline-block;'>"+result+"</div><br>"
except:
result = 'error'
else:
try:
result = getdata.last_six(data_string)
result = result.to_html()
result = "<h3>"+data_string+" Last Six Games</h3><div style = 'display: inline-block;'>"+result+"</div><br><h4> WDL: Team gets +1 for win, -1 for loss</h4>"
#result = unicode(result)
except:
result = 'error'
self.wfile.write(result)
from sklearn.ensemble import AdaBoostClassifier
import getdata
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return (df_mod, targets)
df = pd.read_csv("last6model.csv", index_col=0)
testdf = pd.read_csv("futuretestmodel.csv")
testdf1 = getdata.select_columns(testdf, ['HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC',
'H2hWDL', 'H2hGS', 'H2hGA', 'H2hRC'])
testbigdf = getdata.select_columns(df, ['HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC',
'H2hWDL', 'H2hGS', 'H2hGA', 'H2hRC'])
features = ['HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC',
'H2hWDL', 'H2hGS', 'H2hGA', 'H2hRC']
df, targets = encode_target(df, "FTR")
y = df["Target"]
X = df[features]
dt = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
dt.fit(X, y)
features = ['HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC',
'H2hWDL', 'H2hGS', 'H2hGA', 'H2hRC']
df, targets = encode_target(df, "FTR")
y = df["Target"]
X = df[features]
dt = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
dt.fit(X, y)
scores = cross_val_score(dt, X, y, cv=10)
testdf1 = getdata.select_columns(modeldf, ['HWDL', 'HGS', 'HGA', 'HRC', 'AWDL',
'AGS', 'AGA', 'ARC', 'H2hWDL',
'H2hGS', 'H2hGA', 'H2hRC'])
pred = dt.predict(testdf1)
pred1 = pd.DataFrame(data=pred, columns=["Pred"])
#newrow = pd.concat([modeldf, pred1], axis=1)
#newrow = decode_target(newrow)
import getdata
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return (df_mod, targets)
df = pd.read_csv("last6model.csv", index_col=0)
testdf = pd.read_csv("futuretestmodel.csv")
testdf1 = getdata.select_columns(testdf, [
'HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC', 'H2hWDL',
'H2hGS', 'H2hGA', 'H2hRC'
])
testbigdf = getdata.select_columns(df, [
'HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC', 'H2hWDL',
'H2hGS', 'H2hGA', 'H2hRC'
])
features = [
'HWDL', 'HGS', 'HGA', 'HRC', 'AWDL', 'AGS', 'AGA', 'ARC', 'H2hWDL',
'H2hGS', 'H2hGA', 'H2hRC'
]
df, targets = encode_target(df, "FTR")
y = df["Target"]
X = df[features]