8000 Migliorato esempio di uso della funzione Train e Predict, con un picc… · mathcoding/Programmazione2@aab872c · GitHub
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Migliorato esempio di uso della funzione Train e Predict, con un piccolo campione di dati di test
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‎Assignments/hw2/compito_131313.py

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# -*- coding: utf-8 -*-
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"""
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Created on Fri Apr 21 17:59:25 2017
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@author: gualandi
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"""
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def NomeCognome():
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return "Kunta Kid 131313"
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def sex2int(x):
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""" Converti sesso"""
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try:
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return int(x=='male')
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except:
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return -1
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def Train(Xs):
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def ConvertInput(data):
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data['Sex'] = data['Sex'].apply(sex2int)
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return data
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def FilterInput(data):
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data = data[data['Sex'] >= 0]
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return data
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def Predict(x_test):
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x_test = ConvertInput(x_test)
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# Se avete fatto un fitting qui dovete richiamare il
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# metodo per fare le previsioni
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y_pred = [sex for sex in x_test['Sex']]
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return y_pred
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# Parte principale della funzione 'Train'
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# Elabora i dati
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Xs = ConvertInput(Xs)
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Xs = FilterInput(Xs)
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# Fitting dei dati con il vostro metodo scelto
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# Esempio insignificanti: sopravvivono solo i maschi
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# NO FITTING
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return Predict

‎Assignments/hw2/compito_MATRICOLA.py

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‎Assignments/hw2/test.csv

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< 10000 div data-testid="addition diffstat" class="DiffSquares-module__diffSquare--h5kjy DiffSquares-module__addition--jeNtt">
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PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
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892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
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893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
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894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
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895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
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896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S
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897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S
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898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q
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899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S
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900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C

‎Assignments/hw2/test.py

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# -*- coding: utf-8 -*-
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"""
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Created on Fri Apr 21 12:08:27 2017
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@author: gualandi
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"""
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import pandas as pd
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import csv
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from sklearn.metrics import accuracy_score
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#------------------------------------------
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# MAIN ENTRY POINT
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#------------------------------------------
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if __name__ == "__main__":
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from compito_131313 import *
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print(NomeCognome())
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df = pd.read_csv('train.csv')
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P = Train(df)
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dt = pd.read_csv('test.csv')
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y_pred = P(dt)
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with open('solution.csv','w') as csv_file:
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writer = csv.writer(csv_file, delimiter=',', lineterminator='\n')
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writer.writerow(['PassengerId','Survived'])
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for i,p in zip(dt['PassengerId'], y_pred):
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writer.writerow([i,p])

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