EarlyPrediction/FeatureCreationPart2_.py at master · ash13/EarlyPrediction · GitHub

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import pandas as pd
import numpy as np


data=pd.read_csv('Filtered_skill_builders_16_17.csv')

print("original data lenth:",len(data.index))

unique_Actions=list(data['action_name'])
unique_Actions=set(unique_Actions)
print("unique 1",unique_Actions)

print(len(data.index))
data=data.loc[~data['action_name'].isin(['start','comment','end','work','resume'])]
print(len(data.index))
#data.to_csv('Filtered_skill_builders_16_17.csv')


#data=data.loc[data['action_name'] not in ('start','comment','end','work','resume')]
data=data.loc[~data['action_name'].isin(['start','comment','end','work','resume'])]
data_orig=data.loc[~data['action_name'].isin(['start','comment','end','work','resume'])]
unique_Actions2=list(data['action_name'])
print(set(unique_Actions2))


print(data.columns)
print(data['action_time'])


data['next_assignment_wheel_spin']=-1
users=list(set(data['user_id']))
#print(users)
new_df=pd.DataFrame()
#Creating the feature 1 : Next_assignment_wheel_spin
#data=data[0:1000]

#Creating bottom out hint cumaltive

#for each student grab the problem ids and for each problem id get all the rows and check if the row is for bottom out hint and update all the
#rows after it
#data=new_df1
new_df2=pd.DataFrame()
for i in users:
    user_df=data.loc[data['user_id']==i]
    user_df = user_df.sort_values(by=['action_time'])
    #print(user_df['problem_id'])
    problem_ids=np.unique(user_df["problem_id"].values)
    #print("Problem_ids=",problem_ids)

    for j in range(0,len(problem_ids)):
        bottom_out_hint=0
        #print("problem:",problem_ids[j])
        problem_df=user_df.loc[user_df['problem_id']==problem_ids[j]]
        #print(problem_df['problem_id'])
        problem_df = problem_df.reset_index(drop=True)
        problem_df['used_bottom_out_hint']=0

        for index in range(0, len(problem_df.index)):
            if(bottom_out_hint==0):
                #print("Problem bottom hint:",)
                if(problem_df.at[index, 'problem_bottom_hint'] == 1):
                    problem_df.at[index, 'used_bottom_out_hint'] = 1
                    bottom_out_hint=1
            else:
                problem_df.at[index, 'used_bottom_out_hint'] = 1

        new_df2=new_df2.append(problem_df)
        #print(problem_df['used_bottom_out_hint'])

print("New df 2")
print(len(data_orig.index),len(new_df2.index))
print(new_df2.columns)


#Creating feature used_penultimate_hint

data=new_df2
new_df3=pd.DataFrame()
for i in users:
    user_df=data.loc[data['user_id']==i]
    user_df = user_df.sort_values(by=['action_time'])
    #print(user_df['problem_id'])
    all_problems=np.unique(user_df["problem_id"].values)
    all_problems=list(all_problems)
    problem_hints = user_df[['problem_id', 'problem_hint_count','problem_total_hints']]
    problem_hints = problem_hints.drop_duplicates()
    problem_hints = problem_hints.dropna()
    problem_hints['hints_not_used']=problem_hints['problem_total_hints']-problem_hints['problem_hint_count']
    problem_ids=list(problem_hints.loc[problem_hints['hints_not_used']==1]['problem_id'])
    hint_counts=list(problem_hints.loc[problem_hints['hints_not_used']==1]['problem_total_hints'])

    #print(problem_ids)
    for j in range(0,len(all_problems)):
        #print("All problems:",all_problems[j])
        problem_df = user_df.loc[user_df['problem_id'] == all_problems[j]]
        if(all_problems[j] in problem_ids):
            problem_df=user_df.loc[user_df['problem_id']==all_problems[j]]
            problem_df['used_penultimate_hint'] = 0
            p=problem_ids.index(all_problems[j])
            count=hint_counts[p]
            curr_hint_count=0
            for index in range(0, len(problem_df.index)):
                if (problem_df.at[index, 'action_name'] == 'hint'):
                    curr_hint_count=curr_hint_count+1

                if (curr_hint_count>=(count-1)):
                    problem_df.at[index, 'used_penultimate_hint']=1

            new_df3=new_df3.append(problem_df)
        else:
            problem_df['used_penultimate_hint'] = 0
            new_df3 = new_df3.append(problem_df)

print("New df 3")
print(len(data_orig.index),len(new_df3.index))
print(new_df3.columns)


data=new_df3
new_df4=pd.DataFrame()
#Create previous 3 actions
for i in users:
    user_df = data.loc[data['user_id'] == i]
    user_df = user_df.sort_values(by=['action_time'])
    user_df['previous_3_states'] = str(np.NaN)
    user_df = user_df.reset_index(drop=True)
    for index in range(0,len(user_df.index)):
        #print("index=",index)

        if(index==0):
            user_df.at[index, 'previous_3_states'] = ('null', 'null', 'null')

        elif(index==1):
            user_df.at[index, 'previous_3_states'] = ('null', 'null',user_df.at[0, 'action_name'])

        elif(index==2):
            user_df.at[index, 'previous_3_states'] = ('null', user_df.at[index-2, 'action_name'],user_df.at[index - 1, 'action_name'])
        else:
            user_df.at[index,'previous_3_states']=(user_df.at[index-3,'action_name'],user_df.at[index-2,'action_name'],user_df.at[index-1,'action_name'])


    new_df4=new_df4.append(user_df)

print("FINAL DF")
print(new_df4.columns)
print("New df 4")
print(len(data_orig.index),len(new_df4.index))
new_df4.to_csv('features_created2.csv')
print("DONE creating new file")