def missing_value_treatment(self, min_threshold):
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(self.ds1_df)
print(missing_value_feats)
# Calculate Missing Value percentage and Visualize
missing_values_perc_df = f.missing_val_perc(missing_value_feats,
self.ds1_df)
val = missing_values_perc_df[0].sort_values(ascending=False)
f.plot_bar(val.index, (50, 10), val)
# Check direct imputations such as remove the records for attributes which contain less than 5% of null values or remove
# attributes which contain more than 65% of null values.
self.ds1_df = f.impute_values(self.ds1_df,
missing_value_feats,
min_threshold,
action=True)
self.ds1_df.reset_index(drop=True)
# How row in dataframe having more than x% NaN values
na_row_cnt = f.get_rowcnt_most_missing_val(self.ds1_df, 30)
print('No of rows having more than 30% NA Values', na_row_cnt)
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(self.ds1_df)
print(missing_value_feats)
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
def missing_value_imputations(self):
#################################### MISSING VALUES #############################
# Since the numerical univariate distribution are symmetrical now with no difference
# between median and mean. Lets impute all the numerical missing values with mean
# Record missing values for further validations:
#indicator = MissingIndicator(missing_values=np.nan)
#mask_missing_values_only = indicator.fit_transform(self.ds1_df)
#mask_missing_values_only.shape
num_feats_imp_df, cat_feats_imp_df = self.seperate_cat_num_var(
self.ds1_df)
# Num missing values imputations
self.ds1_df[num_feats_imp_df] = self.ds1_df[num_feats_imp_df].fillna(
value=self.ds1_df[num_feats_imp_df].mean())
# Left missing values are categorical.
missing_feats_cat = f.get_missing_value_feats(self.ds1_df)
par_num_df, par_cat_df = f.get_params(self.ds1_df, num_feats_imp_df,
cat_feats_imp_df)
# Categorical values where mode frequency is more than 80% - Impute na with Mode
# If not then use the KNN model to impute the values
mode_threshold = 80
for feature in missing_feats_cat:
if par_cat_df.loc[feature]['MODE_PERCENTAGE'] > mode_threshold:
self.ds1_df[feature].fillna(
value=par_cat_df.loc[feature]['MODE'], inplace=True)
print("Method : MODE , Feature : {} , Mode_Percentage : {}".
format(feature,
par_cat_df.loc[feature]['MODE_PERCENTAGE']))
else:
imp_list, score = f.impute_knn_classifier(
self.ds1_df, feature, 5)
self.ds1_df[feature].fillna(value=imp_list, inplace=True)
print(
"Method : KNN , Feature : {} , Imputation Accuracy Score : {}"
.format(feature, score))
return par_num_df, par_cat_df
color_list = ['green','blue','orange','yellow','red','violet','cyan']
# In[5]:
val = x_df.isna().sum().sort_values(ascending=False)
f.plot_bar(val.index,(70,10),val,30)
# In[6]:
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)
# Import Libraries
import Model.FunctionLib as f
# Import working dataset
train_df = pd.read_csv(train_dataset)
# Create a new dataset same as train data
x_df = train_df.sample(frac=0.1, random_state=1).reset_index(drop=True)
# 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')
