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convert_format.py
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convert_format.py
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import sklearn.decomposition as sk_decomposition
import os
import pandas as pd
import numpy as np
import random
import json
import gc
from gensim.models import Word2Vec
import multiprocessing
from collections import Counter
from sklearn import preprocessing
import scipy.special as special
from pandas import DataFrame, Series
from collections import Counter
np.random.seed(2019)
random.seed(2019)
## SVD to
for file,dim in [('data/video_w2v.pkl',32),('data/audio_w2v.pkl',64)]:
if file[-3:]=="pkl":
df=pd.read_pickle(file)
else:
df=pd.read_csv(file)
pca = sk_decomposition.PCA(n_components=dim,whiten=False,svd_solver='auto')
pca.fit(df[df.columns[1:]])
df1=pd.DataFrame(pca.transform(df[df.columns[1:]]))
df1.columns=df.columns[1:dim+1]
df1[df.columns[0]]=df[df.columns[0]].values
df1.to_pickle(file[:-4]+'_svd_'+str(dim)+'.pkl')
def norm(train_df,test_df,features):
df=pd.concat([train_df,test_df])[features]
scaler = preprocessing.QuantileTransformer(random_state=0)
scaler.fit(df[features])
train_df[features]=scaler.transform(train_df[features])
test_df[features]=scaler.transform(test_df[features])
for path1,path2,flag in [('data/train_dev.pkl','data/dev.pkl','dev')]:
print(path1,path2)
train_df=pd.read_pickle(path1)
test_df=pd.read_pickle(path2)
print(train_df.shape,test_df.shape)
float_features=['uid_did_nunique', 'uid_did_count', 'uid_channel_nunique', 'did_video_duration_min',
'did_video_duration_max', 'did_video_duration_mean', 'did_video_duration_std',
'channel_video_duration_min', 'channel_video_duration_max', 'channel_video_duration_mean',
'channel_video_duration_std', 'uid_item_id_unique_mean', 'uid_author_id_unique_mean',
'uid_channel_unique_mean', 'did_item_id_unique_mean', 'did_author_id_unique_mean',
'did_channel_unique_mean', 'uid_item_id_unique_var', 'uid_author_id_unique_var',
'uid_channel_unique_var', 'did_item_id_unique_var', 'did_author_id_unique_var',
'did_channel_unique_var', 'author_id_title_cont_skew', 'author_id_title_cont_mean',
'author_id_title_cont_std', 'did_title_cont_skew', 'did_title_cont_mean',
'did_title_cont_std', 'uid_channel_title_cont_skew', 'uid_channel_title_cont_mean',
'uid_channel_title_cont_std', 'item_id_uid_nunique', 'item_id_uid_count',
'author_id_item_id_nunique', 'author_id_item_id_count', 'uid_user_city_nunique',
'uid_author_id_nunique', 'channel_user_city_nunique', 'did_video_duration_skew',
'channel_video_duration_skew', 'title_mean', 'uid_title_mean_mean',
'uid_title_mean_std', 'uid_title_mean_skew', 'author_id_title_mean_mean',
'author_id_title_mean_std', 'author_id_title_mean_skew', 'did_title_mean_mean',
'did_title_mean_std', 'did_title_mean_skew', 'uid_channel_title_mean_mean',
'uid_channel_title_mean_std', 'uid_channel_title_mean_skew',
'uid_num_of_author_mean','uid_num_of_author_var','uid_num_of_author_fft_var',]
train_df=train_df.fillna(-1)
test_df=test_df.fillna(-1)
norm(train_df,test_df,float_features)
print(train_df[float_features])
k=10
train_df=train_df.sample(frac=1)
test_df=test_df.sample(frac=0.1)
train=[(path2[:-4]+'_NN.pkl',test_df)]
for i in range(k):
train.append((path1[:-4]+'_NN_'+str(i)+'.pkl',train_df.iloc[int(i/k*len(train_df)):int((i+1)/k*len(train_df))]))
del train_df
gc.collect()
for file,temp in train:
print(file,temp.shape)
for f1,f2 in [('uid','item_id'),('uid','author_id'),('did','item_id'),('did','author_id')]:
col=f1
df = pd.read_pickle( 'data/' +f1+'_'+ f2+'_'+col +'_'+flag +'_deepwalk_64.pkl')
df = df.drop_duplicates([col])
fs = list(df)
fs.remove(col)
temp = pd.merge(temp, df, on=col, how='left')
print(temp.shape)
col=f2
df = pd.read_pickle( 'data/' +f1+'_'+ f2+'_'+col +'_'+flag +'_deepwalk_64.pkl')
df = df.drop_duplicates([col])
fs = list(df)
fs.remove(col)
temp = pd.merge(temp, df, on=col, how='left')
print(temp.shape)
print("done 1!")
for col in ['video','audio','author_id','uid','did','item_id']:
if col in ['audio']:
df = pd.read_pickle( 'data/' + col + '_w2v_svd_64.pkl')
col='item_id'
elif col in ['video']:
df = pd.read_pickle( 'data/' + col + '_w2v_svd_32.pkl')
col='item_id'
else:
df = pd.read_pickle( 'data/' + col + '_'+flag+'_w2v_128.pkl')
df = df.drop_duplicates([col])
fs = list(df)
fs.remove(col)
print(temp.shape)
temp = pd.merge(temp, df, on=col, how='left')
print(temp.shape)
print("done 2!")
temp=temp.fillna(0)
temp.to_pickle(file)
del temp
gc.collect()