def define_dataset(self):
# Observe the features with missing values
f.get_missing_value_feats(self.ds1_df)
# Seperate the categorical and numerical features
self.ds1_df.shape
num_feats, cat_feats = self.seperate_cat_num_var(self.ds1_df)
# Change the datatype of categorical and numerical values
f.change_type(self.ds1_df, num_feats, count_threshold=5)
# Seperate the categorical and numerical features
num_feats, cat_feats = self.seperate_cat_num_var(self.ds1_df)
par_num_df_start, par_cat_df_start = f.get_params(
self.ds1_df, num_feats, cat_feats)
return par_num_df_start, par_cat_df_start
def define_dataset(self, df=None, ch_type=False, cnt_threshold=2):
# Observe the features with missing values
if df == None:
df = self.ds1_df
f.get_missing_value_feats(df)
# Seperate the categorical and numerical features
num_feats, cat_feats = self.seperate_cat_num_var(df)
# Change the datatype of categorical and numerical values
if ch_type == True:
f.change_type(df, num_feats, count_threshold=cnt_threshold)
# Seperate the categorical and numerical features
par_num_df_start, par_cat_df_start = self.define_params(df)
stats_df = f.feature_stats(df)
par_num_df_start = par_num_df_start.join(stats_df, how='left')
par_cat_df_start = par_cat_df_start.join(stats_df, how='left')
return par_num_df_start, par_cat_df_start
f.get_missing_value_feats(x_df)
# In[5]:
# Seperate the categorical and numerical features
num_feats,cat_feats = f.distinct_feats(x_df)
print(len(num_feats),len(cat_feats))
num_feats.remove('TARGET')
# In[6]:
f.change_type(x_df,num_feats,10)
# In[7]:
# Seperate the categorical and numerical features
num_feats,cat_feats = f.distinct_feats(x_df)
print(len(num_feats),len(cat_feats))
# In[10]:
x_df_dum = pd.get_dummies(x_df)
x_df_Default_dum = x_df_dum[x_df_dum['TARGET']==1]
bal_df = bureau_balance_df.loc[bureau_balance_df[bureau_balance_df['SK_ID_BUREAU'].isin(b_df.SK_ID_BUREAU)].index]
bal_df.reset_index(drop=True)
del bureau_df, bureau_balance_df
################################ CHANGING THE DATA TYPES ################################
# Seperate the categorical and numerical features
num_feats_b,cat_feats_b = f.distinct_feats(b_df)
print(len(num_feats_b),len(cat_feats_b))
#num_feats_bal,cat_feats_bal = f.distinct_feats(bal_df)
#print(len(num_feats_bal),len(cat_feats_bal))
# Change the datatype of categorical and numerical values
f.change_type(b_df,num_feats_b,count_threshold=5)
# Seperate the categorical and numerical features
# Create dataframe with Skew kurt, Missing val and Outliers for num_feats_imp_df
num_feats_b,cat_feats_b = f.distinct_feats(b_df)
for i in ['SK_ID_BUREAU','SK_ID_CURR']:
num_feats_b.remove(i)
print(len(num_feats_b),len(cat_feats_b))
par_num_df_start, par_cat_df_start = f.get_params(b_df, num_feats_b, cat_feats_b)
############################# FEATURE TREATMENT AND EXTRACTION #########################
# As the features are expected to be extracted and grouped at SK_ID_CURR level
# to synchronise at the Loan Application Client level.Hence we need to extract
# aggregated information at Client level out of the dataset
# This means that treatment to individual columns would not be generallised as
# Delete the original dataset and work with Sample to free some space for processing.
del train_df
################################ CHANGING THE DATA TYPES ################################
# Observe the features with missing values
f.get_missing_value_feats(x_df)
# Seperate the categorical and numerical features
num_feats, cat_feats = f.distinct_feats(x_df)
print(len(num_feats), len(cat_feats))
num_feats.remove('TARGET')
num_feats.remove('SK_ID_CURR')
# Change the datatype of categorical and numerical values
f.change_type(x_df, num_feats, count_threshold=5)
# Seperate the categorical and numerical features
num_feats, cat_feats = f.distinct_feats(x_df)
print(len(num_feats), len(cat_feats))
num_feats.remove('TARGET')
num_feats.remove('SK_ID_CURR')
par_num_df_start, par_cat_df_start = f.get_params(x_df, num_feats, cat_feats)
############################# IDENTIFYING MISSING FEATS #########################
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(x_df)
missing_value_feats
# Calculate Missing Value percentage and Visualize
pos_cash_df = pd.read_csv(pos_cash_dataset)
p_df = pos_cash_df.loc[pos_cash_df[pos_cash_df['SK_ID_CURR'].isin(
train_clean_bureau_df.SK_ID_CURR)].index]
p_df.reset_index(drop=True)
del pos_cash_df
################################ CHANGING THE DATA TYPES ################################
# Seperate the categorical and numerical features
num_feats_p, cat_feats_p = f.distinct_feats(p_df)
print(len(num_feats_p), len(cat_feats_p))
# Change the datatype of categorical and numerical values
f.change_type(p_df, num_feats_p, count_threshold=5)
# Seperate the categorical and numerical features
# Create dataframe with Skew kurt, Missing val and Outliers for num_feats_imp_df
num_feats_p, cat_feats_p = f.distinct_feats(p_df)
for i in ['SK_ID_PREV', 'SK_ID_CURR']:
num_feats_p.remove(i)
print(len(num_feats_p), len(cat_feats_p))
par_num_df_start, par_cat_df_start = f.get_params(p_df, num_feats_p,
cat_feats_p)
############################# FEATURE TREATMENT AND EXTRACTION #########################
# As the features are expected to be extracted and grouped at SK_ID_CURR level
# to synchronise at the Loan Application Client level.Hence we need to extract
# aggregated information at Client level out of the dataset
cc_bal_df = pd.read_csv(cc_bal_dataset)
c_df = cc_bal_df.loc[cc_bal_df[cc_bal_df['SK_ID_CURR'].isin(
train_bureau_poscash_instpmt_clean_df.SK_ID_CURR)].index]
c_df.reset_index(drop=True)
del cc_bal_df
################################ CHANGING THE DATA TYPES ################################
# Seperate the categorical and numerical features
num_feats_c, cat_feats_c = f.distinct_feats(c_df)
print(len(num_feats_c), len(cat_feats_c))
# Change the datatype of categorical and numerical values
f.change_type(c_df, num_feats_c, count_threshold=5)
# Seperate the categorical and numerical features
# Create dataframe with Skew kurt, Missing val and Outliers for num_feats_imp_df
num_feats_c, cat_feats_c = f.distinct_feats(c_df)
for i in ['SK_ID_CURR', 'SK_ID_PREV']:
num_feats_c.remove(i)
print(len(num_feats_c), len(cat_feats_c))
par_num_df_start, par_cat_df_start = f.get_params(c_df, num_feats_c,
cat_feats_c)
############################# FEATURE TREATMENT AND EXTRACTION #########################
# As the features are expected to be extracted and grouped at SK_ID_CURR level
# to synchronise at the Loan Application Client level.Hence we need to extract
# aggregated information at Client level out of the dataset
