def impute_data(df, cohort):
#import IPython
#IPython.embed()
if isinstance(df, str):
df = ml.read_data(df)
#########################
## IMPUTE MISSING DATA ##
#########################
print "Imputing missing data..."
#change msam to missing is msam_NA==1
nanList = ['g6_g6msam_nan', 'g7_g7msam_nan', 'g8_g8msam_nan', 'g9_g8msam_nan']
varList = [[ 'g6_g6msam_Advanced', 'g6_g6msam_Basic', 'g6_g6msam_Proficient'], ['g7_g7msam_Advanced', 'g7_g7msam_Basic', 'g7_g7msam_Proficient'], ['g8_g8msam_Advanced', 'g8_g8msam_Basic', 'g8_g8msam_Proficient'],['g9_g8msam_Advanced', 'g9_g8msam_Basic', 'g9_g8msam_Proficient']]
for x in range(0,len(nanList)):
nacol = nanList[x]
colList = varList[x]
for col in colList:
df.loc[df[nacol] == 1, col] = np.nan
#pred missing data using any available data
wordList = ['absrate', 'mapr', 'msam_Advanced', 'msam_Basic', 'msam_Proficient', 'mobility', 'nsusp', 'mpa', 'tardyr', 'psatm', 'psatv', 'retained']
for word in wordList:
colList = [col for col in df.columns if word in col]
rowMean = df[colList].mean(axis=1)
for col in colList:
print df[col].value_counts(dropna=False)
df.loc[:,col].fillna(rowMean, inplace=True)
print df[col].value_counts(dropna=False)
'''
############################
# IMPUTE NEIGHBORHOOD DATA #
############################
print "Imputing missing school neighborhood data..."
## Fill missing school neighborhood data
print "Fixing neighborhood columns..."
neighborhood_cols = ['suspensionrate', 'mobilityrateentrantswithdra', 'attendancerate', 'avg_class_size', 'studentinstructionalstaffratio', 'dropoutrate', 'grade12documenteddecisionco', 'grade12documenteddecisionem', 'grade12documenteddecisionmi', 'grad12docdec_col_emp', 'graduationrate', 'studentsmeetinguniversitysyste', 'Est_Households_2012', 'Est_Population_2012', 'Med_Household_Income_2012', 'Mean_Household_Income_2012', 'Pop_Below_Poverty_2012', 'Percent_Below_Poverty_2012', 'Pop_Under18_2012', 'Under18_Below_Poverty_2012', 'Under18_Below_Poverty_Percent_2012', 'Housholds_on_Food_stamps_with_Children_Under18_2012', 'Housholds_Pop_on_Food_Stamps_2012', 'Pop_BlackAA_2012', 'Pop_White_2012', 'Bt_18_24_percent_less_than_High_School_2012', 'Bt_18_24_percent_High_School_2012', 'Bt_18_24_percent_Some_College_or_AA_2012', 'Bt_1824_percent_BA_or_Higher_2012', 'Over_25_percent_less_than_9th_grade_2012', 'Over_25_percent_9th_12th_2012', 'Over_25_percent_High_School_2012', 'Over_25__percent_Some_College_No_Deg_2012', 'Over_25_percent_AA_2012', 'Over_25_percent_Bachelors_2012', 'Over_25_percent_Graduate_or_Professionals_2012']
ml.replace_with_mean(df, neighborhood_cols)
'''
#summary = ml.summarize(df)
#print summary.T
#ml.print_to_csv(summary.T, 'updated_summary_stats_vertical.csv')
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/imputed_data_cohort' + str(cohort) + '.csv'
ml.print_to_csv(df, return_file)
#IPython.embed()
print "Done!"
import IPython
IPython.embed()
return df
def deal_with_dummies(df, cohort):
if isinstance(df, str):
df = ml.read_data(df)
###################################
## CREATE DUMMY VARIABLE COLUMNS ##
###################################
print "Creating dummy variables..."
school_ids = [col for col in df.columns if 'school_id' in col]
df[school_ids] = df.loc[:,school_ids].astype(str, copy=False)
string_cols = list(df.select_dtypes(include=['object']))
dummys = pd.get_dummies(df[string_cols], dummy_na=True)
df = pd.concat([df, dummys], axis=1)
df.drop(string_cols, axis=1, inplace=True)
## Save clean version
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/clean_data_cohort' + str(cohort) + '.csv'
ml.print_to_csv(df, return_file)
return df
def choose_rows(df, grade):
#Find rows to use
print "Choosing rows..."
data9 = df[df['g6_dropout'] != 1]
data9 = data9[data9['g7_dropout'] != 1]
data9 = data9[data9['g8_dropout'] != 1]
data10 = data9[data9['g9_dropout'] != 1]
data11 = data10[data10['g10_dropout'] != 1]
data12 = data11[data11['g11_dropout'] != 1]
if grade == 9:
data9 = data9[data9['g8_missing'] != 1]
return data9
elif grade == 10:
data10 = data10[data10['g9_missing'] != 1]
return data10
elif grade == 11:
data11 = data11[data11['g10_missing'] != 1]
return data11
elif grade == 12:
data12 = data12[data12['g11_missing'] != 1]
ml.print_to_csv(
df,
'/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/subset_test.csv'
)
return data12
def deal_with_dummies(df, cohort):
if isinstance(df, str):
df = ml.read_data(df)
###################################
## CREATE DUMMY VARIABLE COLUMNS ##
###################################
print "Creating dummy variables..."
school_ids = [col for col in df.columns if 'school_id' in col]
df[school_ids] = df.loc[:, school_ids].astype(str, copy=False)
string_cols = list(df.select_dtypes(include=['object']))
dummys = pd.get_dummies(df[string_cols], dummy_na=True)
df = pd.concat([df, dummys], axis=1)
df.drop(string_cols, axis=1, inplace=True)
## Save clean version
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/clean_data_cohort' + str(
cohort) + '.csv'
ml.print_to_csv(df, return_file)
return df
def deal_with_dummies(dataset):
df = ml.read_data(dataset)
###################################
## CREATE DUMMY VARIABLE COLUMNS ##
###################################
print "Creating dummy variables..."
string_cols = list(df.select_dtypes(include=['object']))
print string_cols
df = ml.get_dummys(df, string_cols, dummy_na=True)
for col in string_cols:
print col
df.drop(col, axis=1, inplace=True)
## Save clean version
ml.print_to_csv(df, 'data/clean_data.csv')
def run_classifiers(csv_file, y): ## LOAD PREPARED DATA df = ml.read_data(csv_file) ################################ # Build & Evaluate Classifiers # ################################ print "Evaluating classifiers..." ## USE TOP FEATURES TO COMPARE CLASSIFIER PERFORMACE features = ['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome', 'age', 'NumberOfTimes90DaysLate', 'NumberOfOpenCreditLinesAndLoans'] X = df[features].as_matrix() y = df[y].as_matrix() #print ml.build_classifiers(X,y) ml.print_to_csv(ml.build_classifiers(X, y), 'compare_classifiers.csv')
def choose_rows(df, grade):
#Find rows to use
print "Choosing rows..."
data9 = df[df['g6_dropout'] !=1]
data9 = data9[data9['g7_dropout'] !=1]
data9 = data9[data9['g8_dropout'] !=1]
data10 = data9[data9['g9_dropout'] !=1]
data11 = data10[data10['g10_dropout'] !=1]
data12 = data11[data11['g11_dropout'] !=1]
if grade == 9:
data9 = data9[data9['g8_missing'] !=1]
return data9
elif grade == 10:
data10 = data10[data10['g9_missing'] !=1]
return data10
elif grade == 11:
data11 = data11[data11['g10_missing'] !=1]
return data11
elif grade == 12:
data12 = data12[data12['g11_missing'] !=1]
ml.print_to_csv(df, '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/subset_test.csv')
return data12
def clean_data(df, cohort):
print "Cleaning data..."
################################
## DROP UNNECESSARY VARIABLES ##
################################
print "Dropping unnecessary variables..."
if cohort == 1:
print "for cohort 1..."
variables_to_drop = ['g6_tardyr','g6_school_name', 'g7_school_name', 'g8_school_name', 'g9_school_name', 'g10_school_name', 'g11_school_name', 'g12_school_name','g6_year', 'g6_gradeexp', 'g6_grade', 'g6_wcode', 'g7_year', 'g7_gradeexp', 'g7_grade', 'g7_wcode', 'g8_year', 'g8_gradeexp', 'g8_grade', 'g8_wcode', 'g9_year', 'g9_gradeexp', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_gradeexp', 'g10_grade', 'g10_wcode', 'g11_year', 'g11_gradeexp', 'g11_grade', 'g11_wcode', 'g12_year', 'g12_gradeexp', 'g12_grade', 'g12_wcode']
df.drop(variables_to_drop, axis=1, inplace=True)
elif cohort == 2:
print "for cohort 2..."
variables_to_drop = ['g6_school_name', 'g7_school_name', 'g8_school_name', 'g9_school_name', 'g10_school_name', 'g11_school_name', 'g12_school_name','g6_year', 'g6_grade', 'g6_wcode', 'g7_year', 'g7_grade', 'g7_wcode', 'g8_year', 'g8_grade', 'g8_wcode', 'g9_year', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_grade', 'g10_wcode', 'g11_year', 'g11_grade', 'g11_wcode', 'g12_year', 'g12_grade', 'g12_wcode']
df.drop(variables_to_drop, axis=1, inplace=True)
#######################
## COMBINE VARIABLES ##
#######################
## Create single column for birth year
print "Correcting birthdays..."
df['birthday'] = df['g11_byrmm']
birthday_cols = ['g12_byrmm', 'g10_byrmm', 'g9_byrmm', 'g8_byrmm', 'g7_byrmm', 'g6_byrmm']
for c in birthday_cols:
ml.replace_if_missing(df, 'birthday', c)
df.drop(c, axis=1, inplace=True)
df['birth_year'] = df.loc[:,'birthday'].astype(str, copy=False)[:4]
df['birth_month'] = df.loc[:,'birthday'].astype(str, copy=False)[4:]
df.drop('birthday', axis=1, inplace=True)
## Create single column for gender
print "Correcting gender..."
df['gender'] = df['g11_gender']
gender_cols = ['g12_gender', 'g11_gender', 'g10_gender', 'g9_gender', 'g8_gender', 'g7_gender', 'g6_gender']
for c in gender_cols:
ml.replace_if_missing(df, 'gender', c)
df.drop(c, axis=1, inplace=True)
################
## CLEAN DATA ##
################
print "Cleaning data..."
retained_cols = ['g11_retained', 'g12_retained', 'g9_newmcps', 'g10_newmcps', 'g11_newmcps', 'g12_newmcps', 'g9_newus', 'g10_newus', 'g11_newus', 'g12_newus']
for col in retained_cols:
df[col] = df[col].notnull()
###############################
## CREATE MISSING DATA FLAGS ##
###############################
print "Creating missing data flags..."
## Create flag if a given student is missing a year's worth of data
grade_id = ['g6_pid', 'g7_pid', 'g8_pid', 'g9_pid', 'g10_pid', 'g11_pid', 'g12_pid']
year = 6
for g in grade_id:
col_name = 'g' + str(year) + '_missing'
df[col_name] = df[g].isnull()
df.drop(g, axis=1, inplace=True)
year+=1
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/predummy_data_cohort' + str(cohort) + '.csv'
ml.print_to_csv(df, return_file)
return df
def clean_data(df, cohort):
print "Cleaning data..."
################################
## DROP UNNECESSARY VARIABLES ##
################################
print "Dropping unnecessary variables..."
if cohort == 'cohort1':
print "for cohort 1..."
variables_to_drop = ['g6_tardyr','g6_school_name', 'g7_school_name', 'g8_school_name', 'g9_school_name', 'g10_school_name', 'g11_school_name', 'g12_school_name','g6_year', 'g6_gradeexp', 'g6_grade', 'g6_wcode', 'g7_year', 'g7_gradeexp', 'g7_grade', 'g7_wcode', 'g8_year', 'g8_gradeexp', 'g8_grade', 'g8_wcode', 'g9_year', 'g9_gradeexp', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_gradeexp', 'g10_grade', 'g10_wcode', 'g11_year', 'g11_gradeexp', 'g11_grade', 'g11_wcode', 'g12_year', 'g12_gradeexp', 'g12_grade', 'g12_wcode']
for v in variables_to_drop:
df.drop(v, axis=1, inplace=True)
elif cohort == 'cohort2':
print "for cohort 2..."
variables_to_drop = ['g6_tardyr','g6_school_name', 'g7_school_name', 'g8_school_name', 'g9_school_name', 'g10_school_name', 'g11_school_name', 'g12_school_name','g6_year', 'g6_grade', 'g6_wcode', 'g7_year', 'g7_grade', 'g7_wcode', 'g8_year', 'g8_grade', 'g8_wcode', 'g9_year', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_grade', 'g10_wcode', 'g11_year', 'g11_grade', 'g11_wcode', 'g12_year', 'g12_grade', 'g12_wcode']
for v in variables_to_drop:
df.drop(v, axis=1, inplace=True)
else:
pass
#######################
## COMBINE VARIABLES ##
#######################
## Create single column for birth year
print "Correcting birthdays..."
df['birthday'] = df['g11_byrmm']
birthday_cols = ['g12_byrmm', 'g11_byrmm', 'g10_byrmm', 'g9_byrmm', 'g8_byrmm', 'g7_byrmm', 'g6_byrmm']
for c in birthday_cols:
ml.replace_with_other_col(df, 'birthday', c)
df.drop(c, axis=1, inplace=True)
#print ml.summarize(df['birthday'])
## Create single column for gender
print "Correcting gender..."
df['gender'] = df['g11_gender']
gender_cols = ['g12_gender', 'g11_gender', 'g10_gender', 'g9_gender', 'g8_gender', 'g7_gender', 'g6_gender']
for c in gender_cols:
ml.replace_with_other_col(df, 'gender', c)
df.drop(c, axis=1, inplace=True)
#print df['gender'].value_counts()
################
## CLEAN DATA ##
################
print "Cleaning data..."
retained_cols = ['g11_retained', 'g12_retained', 'g9_newmcps', 'g10_newmcps', 'g11_newmcps', 'g12_newmcps', 'g9_newus', 'g10_newus', 'g11_newus', 'g12_newus']
for col in retained_cols:
for index, row in df.iterrows():
if pd.isnull(row[col]):
df.ix[index, col] = 0
else:
df.ix[index, col] = 1
df[col] = df[col].astype(int)
###############################
## CREATE MISSING DATA FLAGS ##
###############################
print "Creating missing data flags..."
## Create flag if a given student is missing a year's worth of data
grade_id = ['g6_pid', 'g7_pid', 'g8_pid', 'g9_pid', 'g10_pid', 'g11_pid', 'g12_pid']
year = 6
for g in grade_id:
col_name = 'g' + str(year) + '_missing'
for index, row in df.iterrows():
if pd.isnull(row[g]):
df.ix[index, col_name] = 1
else:
df.ix[index, col_name] = 0
df.drop(g, axis=1, inplace=True)
year+=1
ml.print_to_csv(df, 'data/predummy_data.csv')
def impute_data(dataset, cohort):
df = ml.read_data(dataset)
##########################
## IMPUTE ACADEMIC DATA ##
##########################
print "Impute missing academic information..."
## Fill missing school data -- use mean imputation for now
school_vars = ['g6_school_id', 'g7_school_id', 'g8_school_id', 'g9_school_id', 'g10_school_id', 'g11_school_id', 'g12_school_id']
ml.replace_with_mean(df, school_vars)
## Fill missing grade and test score information -- use mean imputation for now
grades_tests = ['g6_q1mpa', 'g6_q2mpa', 'g6_q3mpa', 'g6_q4mpa', 'g6_g6mapr','g7_q1mpa', 'g7_q2mpa', 'g7_q3mpa', 'g7_q4mpa', 'g7_g7mapr', 'g8_q1mpa', 'g8_q2mpa', 'g8_q3mpa', 'g8_q4mpa', 'g8_g8mapr', 'g9_q1mpa', 'g9_q2mpa', 'g9_q3mpa', 'g9_q4mpa', 'g9_g8mapr', 'g10_q1mpa', 'g10_q2mpa', 'g10_q3mpa', 'g10_q4mpa', 'g10_psatv', 'g10_psatm', 'g11_q1mpa', 'g11_q2mpa', 'g11_q3mpa', 'g11_q4mpa', 'g11_psatv', 'g11_psatm', 'g12_q1mpa', 'g12_q2mpa', 'g12_q3mpa', 'g12_q4mpa', 'g12_psatv', 'g12_psatm']
ml.replace_with_mean(df, grades_tests)
## Fill in missing id with dummy
ml.replace_with_value(df, 'id', 0)
## Fill missing MSAM data
g6_msam = ['g6_g6msam_Advanced','g6_g6msam_Basic','g6_g6msam_Proficient']
ml.replace_dummy_null_mean(df, 'g6_g6msam_nan', g6_msam)
if cohort == 'cohort1':
g7_msam = ['g7_g7msam_Advanced','g7_g7msam_Basic','g7_g7msam_Proficient']
ml.replace_dummy_null_mean(df, 'g7_g7msam_nan', g7_msam)
elif cohort == 'cohort2':
g7_msam = ['g7_g7msam_ ','g7_g7msam_1','g7_g7msam_2', 'g7_g7msam_3']
ml.replace_dummy_null_mean(df, 'g7_g7msam_nan', g7_msam)
g8_msam = ['g8_g8msam_Advanced','g8_g8msam_Basic','g8_g8msam_Proficient']
ml.replace_dummy_null_mean(df, 'g8_g8msam_nan', g8_msam)
g9_msam = ['g9_g8msam_Advanced','g9_g8msam_Basic','g9_g8msam_Proficient']
ml.replace_dummy_null_mean(df,'g9_g8msam_nan', g9_msam)
############################
## IMPUTE BEHAVIORAL DATA ##
############################
print "Impute missing behavioral data..."
## Fill missing behavioral data -- use mean imputation for now
behavioral_cols = ['g6_absrate', 'g6_nsusp','g7_absrate', 'g7_tardyr', 'g7_nsusp', 'g8_absrate', 'g8_tardyr', 'g8_nsusp', 'g9_absrate', 'g9_nsusp', 'g10_absrate', 'g10_nsusp', 'g11_absrate', 'g11_nsusp','g12_absrate', 'g12_nsusp']
ml.replace_with_mean(df, behavioral_cols)
## Fill in missing birthday data
#ml.replace_with_mean(df, 'birthday')
############################
## IMPUTE ENROLLMENT DATA ##
############################
print "Imputing missing enrollment data..."
## Fill missing enrollment data
print "Fixing mobility columns..."
mobility_cols = ['g10_retained', 'g6_mobility', 'g7_mobility', 'g8_mobility', 'g9_mobility', 'g9_retained','g10_mobility', 'g11_mobility', 'g12_mobility', 'birthday']
# Includes g10_retained because it's coded as 0/1 already
ml.replace_with_mean(df, mobility_cols)
#########################
## IMPUTE DROPOUT DATA ##
#########################
print "Impute missing droput information..."
## Fill missing dropout information with 0
dropout_vars = ['g6_dropout', 'g7_dropout', 'g8_dropout', 'g9_dropout', 'g10_dropout', 'g11_dropout', 'g12_dropout', 'dropout']
ml.replace_with_value(df, dropout_vars, [0,0,0,0,0,0,0,0])
#variables = list(df.columns.values)
#print variables
############################
# IMPUTE NEIGHBORHOOD DATA #
############################
print "Imputing missing school neighborhood data..."
## Fill missing school neighborhood data
print "Fixing neighborhood columns..."
"""
neighborhood_cols = ['suspensionrate', 'mobilityrateentrantswithdra', 'attendancerate', 'avg_class_size', 'studentinstructionalstaffratio', 'dropoutrate', 'grade12documenteddecisionco', 'grade12documenteddecisionem', 'grade12documenteddecisionmi', 'grad12docdec_col_emp', 'graduationrate', 'studentsmeetinguniversitysyste', 'Est_Households_2012', 'Est_Population_2012', 'Med_Household_Income_2012', 'Mean_Household_Income_2012', 'Pop_Below_Poverty_2012', 'Percent_Below_Poverty_2012', 'Pop_Under18_2012', 'Under18_Below_Poverty_2012', 'Under18_Below_Poverty_Percent_2012', 'Housholds_on_Food_stamps_with_Children_Under18_2012', 'Housholds_Pop_on_Food_Stamps_2012', 'Pop_BlackAA_2012', 'Pop_White_2012', 'Bt_18_24_percent_less_than_High_School_2012', 'Bt_18_24_percent_High_School_2012', 'Bt_18_24_percent_Some_College_or_AA_2012', 'Bt_1824_percent_BA_or_Higher_2012', 'Over_25_percent_less_than_9th_grade_2012', 'Over_25_percent_9th_12th_2012', 'Over_25_percent_High_School_2012', 'Over_25__percent_Some_College_No_Deg_2012', 'Over_25_percent_AA_2012', 'Over_25_percent_Bachelors_2012', 'Over_25_percent_Graduate_or_Professionals_2012']
"""
neighborhood_cols = ['g9_suspensionrate', 'g10_suspensionrate', 'g11_suspensionrate', 'g12_suspensionrate', 'g9_mobilityrateentrantswithdra', 'g10_mobilityrateentrantswithdra', 'g11_mobilityrateentrantswithdra', 'g12_mobilityrateentrantswithdra', 'g9_attendancerate', 'g10_attendancerate', 'g11_attendancerate', 'g12_attendancerate','g9_avg_class_size', 'g10_avg_class_size', 'g11_avg_class_size', 'g12_avg_class_size','g9_studentinstructionalstaffratio', 'g10_studentinstructionalstaffratio', 'g11_studentinstructionalstaffratio', 'g12_studentinstructionalstaffratio','g9_dropoutrate', 'g10_dropoutrate', 'g11_dropoutrate', 'g12_dropoutrate', 'g9_grade12documenteddecisionco', 'g10_grade12documenteddecisionco', 'g11_grade12documenteddecisionco', 'g12_grade12documenteddecisionco','g9_grade12documenteddecisionem', 'g10_grade12documenteddecisionem', 'g11_grade12documenteddecisionem', 'g12_grade12documenteddecisionem','g9_grade12documenteddecisionmi', 'g10_grade12documenteddecisionmi', 'g11_grade12documenteddecisionmi', 'g12_grade12documenteddecisionmi', 'g9_grad12docdec_col_emp', 'g10_grad12docdec_col_emp', 'g11_grad12docdec_col_emp', 'g12_grad12docdec_col_emp', 'g9_graduationrate', 'g10_graduationrate', 'g11_graduationrate', 'g12_graduationrate','g9_studentsmeetinguniversitysyste', 'g10_studentsmeetinguniversitysyste', 'g11_studentsmeetinguniversitysyste', 'g12_studentsmeetinguniversitysyste', 'g9_Est_Households_2012', 'g10_Est_Households_2012', 'g11_Est_Households_2012', 'g12_Est_Households_2012','g9_Est_Population_2012', 'g10_Est_Population_2012', 'g11_Est_Population_2012', 'g12_Est_Population_2012', 'g9_Med_Household_Income_2012', 'g10_Med_Household_Income_2012', 'g11_Med_Household_Income_2012', 'g12_Med_Household_Income_2012', 'g9_Mean_Household_Income_2012', 'g10_Mean_Household_Income_2012', 'g11_Mean_Household_Income_2012', 'g12_Mean_Household_Income_2012', 'g9_Pop_Below_Poverty_2012', 'g10_Pop_Below_Poverty_2012', 'g11_Pop_Below_Poverty_2012', 'g12_Pop_Below_Poverty_2012', 'g9_Percent_Below_Poverty_2012', 'g10_Percent_Below_Poverty_2012', 'g11_Percent_Below_Poverty_2012', 'g12_Percent_Below_Poverty_2012', 'g9_Pop_Under18_2012', 'g10_Pop_Under18_2012', 'g11_Pop_Under18_2012', 'g12_Pop_Under18_2012', 'g9_Under18_Below_Poverty_2012', 'g10_Under18_Below_Poverty_2012', 'g11_Under18_Below_Poverty_2012', 'g12_Under18_Below_Poverty_2012', 'g9_Under18_Below_Poverty_Percent_2012', 'g10_Under18_Below_Poverty_Percent_2012', 'g11_Under18_Below_Poverty_Percent_2012', 'g12_Under18_Below_Poverty_Percent_2012', 'g9_Housholds_on_Food_stamps_with_Children_Under18_2012', 'g10_Housholds_on_Food_stamps_with_Children_Under18_2012', 'g11_Housholds_on_Food_stamps_with_Children_Under18_2012', 'g12_Housholds_on_Food_stamps_with_Children_Under18_2012', 'g9_Housholds_Pop_on_Food_Stamps_2012', 'g10_Housholds_Pop_on_Food_Stamps_2012', 'g11_Housholds_Pop_on_Food_Stamps_2012', 'g12_Housholds_Pop_on_Food_Stamps_2012', 'g9_Pop_BlackAA_2012', 'g10_Pop_BlackAA_2012', 'g11_Pop_BlackAA_2012', 'g12_Pop_BlackAA_2012', 'g9_Pop_White_2012', 'g10_Pop_White_2012', 'g11_Pop_White_2012', 'g12_Pop_White_2012', 'g9_Bt_18_24_percent_less_than_High_School_2012', 'g10_Bt_18_24_percent_less_than_High_School_2012', 'g11_Bt_18_24_percent_less_than_High_School_2012', 'g12_Bt_18_24_percent_less_than_High_School_2012', 'g9_Bt_18_24_percent_High_School_2012', 'g10_Bt_18_24_percent_High_School_2012', 'g11_Bt_18_24_percent_High_School_2012', 'g12_Bt_18_24_percent_High_School_2012', 'g9_Bt_18_24_percent_Some_College_or_AA_2012', 'g10_Bt_18_24_percent_Some_College_or_AA_2012', 'g11_Bt_18_24_percent_Some_College_or_AA_2012', 'g12_Bt_18_24_percent_Some_College_or_AA_2012', 'g9_Bt_1824_percent_BA_or_Higher_2012', 'g10_Bt_1824_percent_BA_or_Higher_2012', 'g11_Bt_1824_percent_BA_or_Higher_2012', 'g12_Bt_1824_percent_BA_or_Higher_2012', 'g9_Over_25_percent_less_than_9th_grade_2012', 'g10_Over_25_percent_less_than_9th_grade_2012', 'g11_Over_25_percent_less_than_9th_grade_2012', 'g12_Over_25_percent_less_than_9th_grade_2012', 'g9_Over_25_percent_9th_12th_2012', 'g10_Over_25_percent_9th_12th_2012', 'g11_Over_25_percent_9th_12th_2012', 'g12_Over_25_percent_9th_12th_2012', 'g9_Over_25_percent_High_School_2012', 'g10_Over_25_percent_High_School_2012', 'g11_Over_25_percent_High_School_2012', 'g12_Over_25_percent_High_School_2012', 'g9_Over_25__percent_Some_College_No_Deg_2012', 'g10_Over_25__percent_Some_College_No_Deg_2012', 'g11_Over_25__percent_Some_College_No_Deg_2012', 'g12_Over_25__percent_Some_College_No_Deg_2012', 'g9_Over_25_percent_AA_2012', 'g10_Over_25_percent_AA_2012', 'g11_Over_25_percent_AA_2012', 'g12_Over_25_percent_AA_2012', 'g9_Over_25_percent_Bachelors_2012', 'g10_Over_25_percent_Bachelors_2012', 'g11_Over_25_percent_Bachelors_2012', 'g12_Over_25_percent_Bachelors_2012', 'g9_Over_25_percent_Graduate_or_Professionals_2012', 'g10_Over_25_percent_Graduate_or_Professionals_2012', 'g11_Over_25_percent_Graduate_or_Professionals_2012', 'g12_Over_25_percent_Graduate_or_Professionals_2012']
ml.replace_with_mean(df, neighborhood_cols)
summary = ml.summarize(df)
print summary.T
#ml.print_to_csv(summary.T, 'updated_summary_stats_vertical.csv')
ml.print_to_csv(df, 'data/imputed_data.csv')
#ml.print_to_csv(df, '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/imputed_data.csv')
print "Done!"
def clean_data(df, cohort):
print "Cleaning data..."
################################
## DROP UNNECESSARY VARIABLES ##
################################
print "Dropping unnecessary variables..."
if cohort == 1:
print "for cohort 1..."
variables_to_drop = [
'g6_tardyr', 'g6_school_name', 'g7_school_name', 'g8_school_name',
'g9_school_name', 'g10_school_name', 'g11_school_name',
'g12_school_name', 'g6_year', 'g6_gradeexp', 'g6_grade',
'g6_wcode', 'g7_year', 'g7_gradeexp', 'g7_grade', 'g7_wcode',
'g8_year', 'g8_gradeexp', 'g8_grade', 'g8_wcode', 'g9_year',
'g9_gradeexp', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_gradeexp',
'g10_grade', 'g10_wcode', 'g11_year', 'g11_gradeexp', 'g11_grade',
'g11_wcode', 'g12_year', 'g12_gradeexp', 'g12_grade', 'g12_wcode'
]
df.drop(variables_to_drop, axis=1, inplace=True)
elif cohort == 2:
print "for cohort 2..."
variables_to_drop = [
'g6_school_name', 'g7_school_name', 'g8_school_name',
'g9_school_name', 'g10_school_name', 'g11_school_name',
'g12_school_name', 'g6_year', 'g6_grade', 'g6_wcode', 'g7_year',
'g7_grade', 'g7_wcode', 'g8_year', 'g8_grade', 'g8_wcode',
'g9_year', 'g9_grade', 'g9_wcode', 'g10_year', 'g10_grade',
'g10_wcode', 'g11_year', 'g11_grade', 'g11_wcode', 'g12_year',
'g12_grade', 'g12_wcode'
]
df.drop(variables_to_drop, axis=1, inplace=True)
#######################
## COMBINE VARIABLES ##
#######################
## Create single column for birth year
print "Correcting birthdays..."
df['birthday'] = df['g11_byrmm']
birthday_cols = [
'g12_byrmm', 'g10_byrmm', 'g9_byrmm', 'g8_byrmm', 'g7_byrmm',
'g6_byrmm'
]
for c in birthday_cols:
ml.replace_if_missing(df, 'birthday', c)
df.drop(c, axis=1, inplace=True)
df['birth_year'] = df.loc[:, 'birthday'].astype(str, copy=False)[:4]
df['birth_month'] = df.loc[:, 'birthday'].astype(str, copy=False)[4:]
df.drop('birthday', axis=1, inplace=True)
## Create single column for gender
print "Correcting gender..."
df['gender'] = df['g11_gender']
gender_cols = [
'g12_gender', 'g11_gender', 'g10_gender', 'g9_gender', 'g8_gender',
'g7_gender', 'g6_gender'
]
for c in gender_cols:
ml.replace_if_missing(df, 'gender', c)
df.drop(c, axis=1, inplace=True)
################
## CLEAN DATA ##
################
print "Cleaning data..."
retained_cols = [
'g11_retained', 'g12_retained', 'g9_newmcps', 'g10_newmcps',
'g11_newmcps', 'g12_newmcps', 'g9_newus', 'g10_newus', 'g11_newus',
'g12_newus'
]
for col in retained_cols:
df[col] = df[col].notnull()
###############################
## CREATE MISSING DATA FLAGS ##
###############################
print "Creating missing data flags..."
## Create flag if a given student is missing a year's worth of data
grade_id = [
'g6_pid', 'g7_pid', 'g8_pid', 'g9_pid', 'g10_pid', 'g11_pid', 'g12_pid'
]
year = 6
for g in grade_id:
col_name = 'g' + str(year) + '_missing'
df[col_name] = df[g].isnull()
df.drop(g, axis=1, inplace=True)
year += 1
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/predummy_data_cohort' + str(
cohort) + '.csv'
ml.print_to_csv(df, return_file)
return df
def impute_data(df, cohort):
#import IPython
#IPython.embed()
if isinstance(df, str):
df = ml.read_data(df)
#########################
## IMPUTE MISSING DATA ##
#########################
print "Imputing missing data..."
#change msam to missing is msam_NA==1
nanList = [
'g6_g6msam_nan', 'g7_g7msam_nan', 'g8_g8msam_nan', 'g9_g8msam_nan'
]
varList = [
['g6_g6msam_Advanced', 'g6_g6msam_Basic', 'g6_g6msam_Proficient'],
['g7_g7msam_Advanced', 'g7_g7msam_Basic', 'g7_g7msam_Proficient'],
['g8_g8msam_Advanced', 'g8_g8msam_Basic', 'g8_g8msam_Proficient'],
['g9_g8msam_Advanced', 'g9_g8msam_Basic', 'g9_g8msam_Proficient']
]
for x in range(0, len(nanList)):
nacol = nanList[x]
colList = varList[x]
for col in colList:
df.loc[df[nacol] == 1, col] = np.nan
#pred missing data using any available data
wordList = [
'absrate', 'mapr', 'msam_Advanced', 'msam_Basic', 'msam_Proficient',
'mobility', 'nsusp', 'mpa', 'tardyr', 'psatm', 'psatv', 'retained'
]
for word in wordList:
colList = [col for col in df.columns if word in col]
rowMean = df[colList].mean(axis=1)
for col in colList:
print df[col].value_counts(dropna=False)
df.loc[:, col].fillna(rowMean, inplace=True)
print df[col].value_counts(dropna=False)
'''
############################
# IMPUTE NEIGHBORHOOD DATA #
############################
print "Imputing missing school neighborhood data..."
## Fill missing school neighborhood data
print "Fixing neighborhood columns..."
neighborhood_cols = ['suspensionrate', 'mobilityrateentrantswithdra', 'attendancerate', 'avg_class_size', 'studentinstructionalstaffratio', 'dropoutrate', 'grade12documenteddecisionco', 'grade12documenteddecisionem', 'grade12documenteddecisionmi', 'grad12docdec_col_emp', 'graduationrate', 'studentsmeetinguniversitysyste', 'Est_Households_2012', 'Est_Population_2012', 'Med_Household_Income_2012', 'Mean_Household_Income_2012', 'Pop_Below_Poverty_2012', 'Percent_Below_Poverty_2012', 'Pop_Under18_2012', 'Under18_Below_Poverty_2012', 'Under18_Below_Poverty_Percent_2012', 'Housholds_on_Food_stamps_with_Children_Under18_2012', 'Housholds_Pop_on_Food_Stamps_2012', 'Pop_BlackAA_2012', 'Pop_White_2012', 'Bt_18_24_percent_less_than_High_School_2012', 'Bt_18_24_percent_High_School_2012', 'Bt_18_24_percent_Some_College_or_AA_2012', 'Bt_1824_percent_BA_or_Higher_2012', 'Over_25_percent_less_than_9th_grade_2012', 'Over_25_percent_9th_12th_2012', 'Over_25_percent_High_School_2012', 'Over_25__percent_Some_College_No_Deg_2012', 'Over_25_percent_AA_2012', 'Over_25_percent_Bachelors_2012', 'Over_25_percent_Graduate_or_Professionals_2012']
ml.replace_with_mean(df, neighborhood_cols)
'''
#summary = ml.summarize(df)
#print summary.T
#ml.print_to_csv(summary.T, 'updated_summary_stats_vertical.csv')
return_file = '/mnt/data2/education_data/mcps/DATA_DO_NOT_UPLOAD/imputed_data_cohort' + str(
cohort) + '.csv'
ml.print_to_csv(df, return_file)
#IPython.embed()
print "Done!"
import IPython
IPython.embed()
return df
def prepare_data(dataset): ####################################################### # Load Credit Data and Run Initial Summary Statistics # ####################################################### print "Loading data..." ## LOAD DATA df = ml.read_data(dataset) variables = list(df.columns.values) ## RUN INITIAL SUMMARY STATISTICS & GRAPH DISTRIBUTIONS summary = ml.summarize(df) #print_to_csv(summary, 'summary_stats.csv') for v in variables: ml.histogram(df, v) ## FOR FUTURE: Drop rows where 'percentage' fields have values > 1 ############################ # Deal with missing values # ############################ print "Handling missing values..." print "Correcting dependents column..." ''' DEPENDENTS: Missing values are likely zeros. If someone didn't provide this info, they likely wouldn't have kids.''' variables = ['NumberOfDependents'] values = [0] ml.replace_with_value(df, variables, values) print "Correcting income column..." '''MONTHLY INCOME: It wouldn't make sense to determine missing values through replacing with a specific value. Instead, impute null values with the mean of income.''' variables = ['MonthlyIncome'] ml.replace_with_mean(df, variables) #ml.print_to_csv(df, 'credit-data-updated.csv') ##################### # Generate Features # ##################### print "Generating features..." ## FIND IMPORTANT FEATURES test_features = np.array(['RevolvingUtilizationOfUnsecuredLines', 'age', 'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate', 'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfDependents']) y = 'SeriousDlqin2yrs' ## Find initial best features #print ml.find_features(df, test_features, y) ## ENGINEER ADDITIONAL FEATURES print "Engineering income buckets..." '''MONTHLY INCOME: Break this into buckets, adjusting for outliers''' df['MonthlyIncome_adjust'] = df.MonthlyIncome.apply(lambda x: ml.adjust_outliers(x, 15000)) ml.bin_variable(df, 'MonthlyIncome_adjust', 15, False) #print pd.value_counts(df['MonthlyIncome_adjust_bins']) print "Engineering age buckets..." '''AGE: Break this into buckets''' bins = [-1] + range(20, 80, 5) + [120] ml.bin_variable(df, 'age', bins, False) #print pd.value_counts(df['age_bins']) #print df.head() ## RECALCULATE IMPORTANT FEATURES new_features = np.array(['MonthlyIncome_adjust_bins', 'age_bins']) all_features = np.hstack((test_features, new_features)) #print all_features #print ml.summarize(df) ## FIND BEST FEATURES #print ml.find_features(df, all_features, y) ### FOR FUTURE: It would be cool to be able to automatically point to the top ### five best features or focus on the features that meet a certain threshold. ### Then I could return that as well for the run_classifiers function. ## PRINT PREPARED DATA TO CSV file_name = "credit-data-clean.csv" ml.print_to_csv(df, file_name) return file_name, y
