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handle.py
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handle.py
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# -*- coding: utf-8 -*-
"""
Created on Sat May 21 01:00:09 2016
@author: Zero
"""
import utils as u
import pandas as pd
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('fivethirtyeight') # Good looking plots
pd.set_option('display.max_columns', None) # Display any number of columns
#bins = np.linspace(df.Var1.min(), df.Var1.max(), 10)
#groups = pd.cut(df.Var1, bins)
#print groups
#label='upselling'
def loadFile(file,header=0):
df=pd.read_table(file,header=header)
return df
def loadData():
# comment start
# read data
#comment end
data="orange_small_train.data"
appetency='orange_small_train_appetency.labels'
churn='orange_small_train_churn.labels'
upselling='orange_small_train_upselling.labels'
df=loadFile(data)
appetency=loadFile(appetency,None)
churn=loadFile(churn,None)
upselling=loadFile(upselling,None)
churn.columns=['churn']
appetency.columns=['appetency']
upselling.columns=['upselling']
df=pd.concat([df,churn,appetency,upselling],axis=1) # concat data and label
df_all=df.dropna(axis='columns',how='all')
df_all_cat=u.to_category(df_all).describe()
df_all_cat=df_all_cat.transpose()
df_all_cat=df_all_cat[df_all_cat.unique>1]
df=df[df_all_cat.index]
df_all=df_all[df_all_cat.index]
missing_count=df.notnull().sum(axis=1)
df['missing_count']=missing_count
df=df.sort_values('missing_count',axis='index')
return df
def split_data(df,label):
df_=df[df[label]==-1] # choose the label == -1
df=df[df[label]==1]# choose the label == 1
split=df_.shape[0]/df.shape[0]
df_split=np.array_split(df_, split)
df_split=[pd.concat([df,i]) for i in df_split]
return df_split
def handle_missing(df_all,label,is_common=True,replace=0):
labels=['churn','appetency','upselling']
isnull=df_all.isnull().sum()/df_all.shape[0]
filter_var=isnull=isnull[isnull<0.8].index.values.tolist()
df_all=df_all[filter_var]
df_all_cat=u.to_category(df_all).describe()
df_all_cat=df_all_cat.transpose()
df_all_cat=df_all_cat[df_all_cat.unique>1]
df_all=df_all[df_all_cat.index]
df_n=df_all[df_all[label]==-1]
num_n=df_n.isnull().sum(axis=1).sort_values(ascending=False,kind='quicksort') /df_all.shape[1]
num=df_all.isnull().sum(axis=1).sort_values(ascending=False,kind='quicksort') /df_all.shape[1]
# df_all=df_all.drop(num_n[num_n>0.5].index)
category=df_all.select_dtypes(include=['object']).columns.values.tolist()
temp=df_all.drop(df_all.dropna(axis='columns',how='any').columns.values,axis='columns').columns.values.tolist()
null=pd.isnull(df_all[temp]).as_matrix().astype(np.int)
temp=[s + '_' for s in temp]
null=pd.DataFrame(null,columns=temp)
# df_all=pd.concat([df_all,null],axis='columns')
df_fill=df_all.copy()
if(is_common):
df_fill=df_fill.fillna(replace)
df_fill[category]=df_fill[category].apply(u.convert_train,axis='index')
else:
df_=df_all[df_all[label]==-1] # choose the label == -1
df=df_all[df_all[label]==1]# choose the label == 1
df_cat=u.to_category(df).describe()
df_cat=df_cat.transpose()
df_n_cat=u.to_category(df_).describe()
df_n_cat=df_n_cat.transpose()
df_all_cat['freq_ratio']=df_all_cat['freq']/df_all_cat['count']
df_all_cat['unique_ratio']=df_all_cat['unique']/df_all_cat['count']
features_fill_freq=df_all_cat[df_all_cat.freq_ratio>=0.5].drop(labels,axis='rows').index.values.tolist()
features_fill_median=df_all_cat.drop(category,axis='rows')[(df_all_cat.freq_ratio<0.5) & (df_all_cat.unique_ratio>=0.5)].index.values.tolist()
features_fill_mean=df_all_cat.drop(category,axis='rows')[(df_all_cat.freq_ratio<0.5) & (df_all_cat.unique_ratio<0.5)].index.values.tolist()
use_all=False
if(use_all):
mean=df_all.mean()
median=df_all.median()
df_fill=df_fill.fillna(median)
else:
median= df_all[features_fill_median].median()
mean_= df[features_fill_mean].mean()
mean_n= df_[features_fill_mean].mean()
use_p=True
if(use_p):
mean=(mean_+mean_n)/2
else:
mean= df_all[features_fill_mean].mean()
freq=df_all_cat[df_all_cat.freq_ratio>=0.5].drop(labels,axis='rows')['top']
df_fill[features_fill_freq]=df_all[features_fill_freq].fillna(freq)
df_fill[features_fill_median]=df_all[features_fill_median].fillna(median)
df_fill[features_fill_mean]=df_all[features_fill_mean].fillna(mean)
df_fill=df_fill.fillna(0)
#mean_=pd.DataFrame([mean,mean_,mean_n]).transpose().mean(axis='columns')
df_fill[category]=df_fill[category].apply(u.convert_train,axis='index')
return df_fill
class HandleMissingValues(object):
def __init__(self,common=False,replace=0):
self.common=common
self.replace=replace
self.X=None
self.y=None
self.m
def fit(self,X,y,common=False,replace=0):
self.X=X
self.y=y
self.common=common
self.replace=replace
def fit_transform(self,X,y,common=False,replace=0):
self.X=X
self.y=y
self.common=common
self.replace=replace
# if(common):
#X=u.standardize_df(df_fill.drop(labels,axis='columns'))
#y=df_all[label],X=df_fill.drop(labels,axis='columns')
#X_trai,y_trai=u.test_rest(X.as_matrix(),y.as_matrix(),0,ratio=1)
#u.treeClassifer(X_trai,y_trai)
#X_train, X_test, y_train, y_test=u.split(X,y)
#df_fill=df_all.copy()
#df_all_cat['freq_ratio']=df_all_cat['freq']/df_all_cat['count']
#df_all_cat['unique_ratio']=df_all_cat['unique']/df_all_cat['count']
#features_fill_freq=df_all_cat[df_all_cat.freq_ratio>=0.5].drop(labels,axis='rows').index.values.tolist()
#features_fill_median=df_all_cat.drop(category,axis='rows')[(df_all_cat.freq_ratio<0.5) & (df_all_cat.unique_ratio>=0.5)].index.values.tolist()
#features_fill_mean=df_all_cat.drop(category,axis='rows')[(df_all_cat.freq_ratio<0.5) & (df_all_cat.unique_ratio<0.5)].index.values.tolist()
#median= df_all[features_fill_median].median()
#mean= df_all[features_fill_mean].mean()
#mean_= df[features_fill_mean].mean()
#mean_n= df_[features_fill_mean].mean()
##mean_=pd.DataFrame([mean,mean_,mean_n]).transpose().mean(axis='columns')
#freq=df_all_cat[df_all_cat.freq_ratio>=0.5].drop(labels,axis='rows')['top']
#df_fill[features_fill_freq]=df_all[features_fill_freq].fillna(freq)
#df_fill[features_fill_median]=df_all[features_fill_median].fillna(median)
#df_fill[features_fill_mean]=df_all[features_fill_mean].fillna(mean)
#df_fill=df_fill.fillna(0)
#df_obj_cat['freq_ratio']=df_obj_cat['freq']/df_obj_cat['count']
#df_obj_cat['unique_ratio']=df_obj_cat['unique']/df_obj_cat['count']
#df_obj_cat=df_obj_cat[df_obj_cat.freq_ratio>=0.5]
#df_obj_n_cat['freq_ratio']=df_obj_n_cat['freq']/df_obj_n_cat['count']
#df_obj_n_cat=df_obj_n_cat[df_obj_n_cat.freq_ratio>0.5]
#df_all_obj_cat['freq_ratio']=df_all_obj_cat['freq']/df_all_obj_cat['count']
#df_all_obj_cat['unique_ratio']=df_all_obj_cat['unique']/df_all_obj_cat['count']
#df_all_obj_cat=df_all_obj_cat[df_obj_cat.freq_ratio>0.5]
#df_cat=cat(df).describe()
#df_cat=df_cat.transpose()
#df_n_cat=cat(df_).describe()
#df_n_cat=df_n_cat.transpose()
#df_var=pd.read_csv('var.txt',delimiter=' ',header=None)
#print u.intersection(df_all_.index.values,df_var[4])
#num_all=df_all.isnull().sum(axis=1).sort_values(ascending=True,kind='quicksort')
#D=Counter(num_all)
#D_=Counter(num_per)
#D_n=Counter(num_per_n)
#plt.bar(range(len(D)), D.values(), align='center')
#plt.xticks(range(len(D)), D.keys())
#fig, ax = plt.subplots()
#ax.bar(D_.keys(),u.preprocessing.scale(D_.values()), 0.8, color='r')
#ax.bar(D_n.keys(),u.preprocessing.scale(D_n.values()), 0.5, color='g')
#plt.xlabel('missing count')
#plt.ylabel('sample count')
#ax.legend(('1','-1'))
#u.standardize_df(df_all_cat.drop('top',axis='columns')).plot()
#plt.show()
#num_all_s=u.standardize_df(pd.DataFrame(num_all,columns=['missing count']))
#num_all.plot()
#num_all_s.plot()
#temp_s[['var','var_n']].plot()
#temp_s[['mean','mean_n']].plot()
#temp_s.plot.scatter(x='var', y='var_n',c='c', s=50);
#temp_s[['min','min_n','max','max_n']].plot()
#temp_s.drop(['count_','count_n','mean','mean_n','min','min_n','max','max_n'],axis='columns').plot()
#if __name__ == "__main__":
# numerical_cate=cat(numerical).describe().transpose()
# numerical_n_cate=cat(numerical_n).describe().transpose()
# numerical_cate['type']=pd.Series(numerical.dtypes)
# numerical_n_cate['type']=pd.Series(numerical_n.dtypes)
# numerical_des=numerical.describe()
# numerical_n_des=numerical_n.describe()
# bug=True
# if(bug):
# count=numerical_cate['count']/numerical.shape[0]
# count.name='count_'
# count_n=numerical_n_cate['count']/numerical_n.shape[0]
# count_n.name='count_n'
# var=numerical.var()
# var.name='var'
# std=numerical.std()
# std.name='std'
# mean=numerical.mean()
# mean.name='mean'
# mean_n=numerical_n.mean()
# mean_n.name='mean_n'
# var_n=numerical_n.var()
# var_n.name='var_n'
# std_n=numerical_n.std()
# std_n.name='std_n'
# max_n=numerical_n.max()
# max_n.name='max_n'
# max_=numerical.max()
# max_.name='max'
# min_n=numerical_n.min()
# min_n.name='min_n'
# min_=numerical.min()
# min_.name='min'
# temp=pd.DataFrame([count,count_n,var,var_n,std,std_n,mean,mean_n,min_,min_n,max_,max_n]).transpose()
# temp_s=u.standardize_df(temp)