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]