I have 3 (or more) Dataframes with this structure:
| ID | Percentage |
|---|---|
| 00001 | 3 |
| 00002 | 15 |
| 00003 | 73 |
| 00004 | 90 |
| ... | ... |
each csv have unique predicted percentage values
among these csv, one csv have very good MAE so i want to give it bigger weight, also if 2 or more predicted the same value i want it to be considered (even if the values close to each other i want to take the avg or mean of the value)
here is my code :
df1 = pd.read_csv("BlahBlahBlah01.csv",index_col=0)
df2 = pd.read_csv("BlahBlahBlah02.csv",index_col=0)
df3 = pd.read_csv("BlahBlahBlah03.csv",index_col=0)
dfGold = pd.read_csv("BlahBlahBlahGold.csv",index_col=0)
# all dataframes have the same shape
lenOfDF = 1000
newCSV = pd.DataFrame(columns = ['ID','Percentage'])
newCSV['ID'] = df1['ID']
for i in range(lenOfDF):
pred01 = df1['Percentage'][i]
pred02 = df2['Percentage'][i]
pred03 = df3['Percentage'][i]
predGold = dfGold['Percentage'][i]
# all lines below are not real code (((Just pseudocode)))
if pred01 == Any(pred02,pred03,predGold):
newCSV['Percentage'][i] = pred01
elif pred02 == Any(pred01,pred03,predGold):
newCSV['Percentage'][i] = pred02
elif pred03 == Any(pred01,pred02,predGold):
newCSV['Percentage'][i] = pred03
else:
newCSV['Percentage'][i] = predGold
I know it's very basic and doesn't provide good prediction, so i need help to fix it.
like i said above i want to give weight also i want to consider values that are close to each other with +- 5
i know there is ensembling techniques for that, but i have csv files not the model.
thank you...
csv_list = ['BlahBlahBlah01','BlahBlahBlah02','BlahBlahBlah03','BlahBlahBlahGold']
preds = []
for i, pred in enumerate(csv_list):
pred = pd.read_csv(f"./{pred}.csv", index_col=0)
pred.rename(columns={"Percentage": i}, inplace=True)
preds.append(pred)
preds = pd.concat(preds, axis=1)
preds["Percentage"] = preds.mode(axis=1)[0]
df= pd.read_csv("BlahBlahBlah01.csv", index_col=0)
preds["Id"]=df.index
preds.to_csv("output.csv" ,columns=['Id', 'Percentage'], index=False)