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poisson_preprocessing.py
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poisson_preprocessing.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 15 20:10:49 2020
@author: lukeum
"""
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from multiprocessing import Pool
columns = ["Time","Subreddits", "Words", 'nodes', 'edges', 'density',
'assortativity', 'local_cluster','global_cluster', 'avg_degree', 'max_degree',
'min_degree', 'large_com','singletons', 'betweenness', 'closeness', 'degree',
'eigen', 'pagerank','users','posts','r_assort','r_gc','r_lc']
word_path = './summaries/word_count_csv'
graph_path = "./summaries/graph_stats"
baseline_path = './summaries/graph_baseline'
outpath = "./summaries/innovation_stats"
subreddits = os.listdir(word_path)
bgraph= pd.read_csv("./summaries/bgraphs_stats_2")
activities = pd.read_csv('./summaries/user_stats',index_col=1)
activities = activities.drop(columns=['Unnamed: 0'])
activities.rename(columns={'subreddits':'Subreddits'},
inplace=True)
subreddits = os.listdir(word_path)
filtered_words = pd.read_csv("./summaries/filtered_words",sep='\t',header=None)
filtered_words = set(filtered_words[0].unique().tolist())
def generate_innovation_stats(s):
# get word statistics
word_counts = pd.read_csv(os.path.join(word_path,s),index_col=0)
selected_words = set(list(word_counts))
selected_words = selected_words.intersection(filtered_words)
word_counts = word_counts.loc[:,list(selected_words)]
# get graph statistics
graph = pd.read_csv(os.path.join(graph_path,s),index_col=0)
# get baseline statistics
baseline = pd.read_csv(os.path.join(baseline_path,s),index_col=0)
baseline.rename(columns={'assortativity':'r_assort',
'local_cluster':'r_lc',
'global_cluster':'r_gc'},
inplace=True)
data = pd.DataFrame(np.zeros((len(graph),len(columns))),columns=columns)
data["Time"] = graph.index
data = data.set_index("Time")
data.loc[graph.index,list(graph)] = graph.values
inter = bgraph[bgraph["Subreddits"]==s]
inter = inter.set_index("time")
data.loc[inter.index,list(inter)] = inter.values
users = activities[activities['Subreddits']==s]
data.loc[users.index,list(users)] = users.values
data.loc[baseline.index,list(baseline)] = baseline.values
for w in list(word_counts):
series = word_counts.loc[:,w]
series = series[series!=0]
initial = sorted(series.index)[0]
data.loc[initial,"Words"] += 1
data.to_csv(os.path.join(outpath,s))
print("%s done!"%s)
with Pool() as P:
P.map(generate_innovation_stats,subreddits)
def merge_innovation_data(inpath,outpath):
innovation = pd.DataFrame()
files = os.listdir(inpath)
for t in tqdm(files):
data = pd.read_csv(os.path.join(inpath,t))
innovation = pd.concat([innovation,data],ignore_index=True)
innovation= innovation[innovation["Subreddits"]!="0.0"]
innovation['adjusted_assort'] = (innovation['assortativity']-innovation['r_assort'])/innovation['r_assort']
innovation['adjusted_gc'] = (innovation['global_cluster']-innovation['r_gc'])/innovation['r_gc']
innovation['adjusted_lc'] = (innovation['local_cluster']-innovation['r_lc'])/innovation['r_lc']
innovation['activity'] = np.log(innovation['posts']/innovation['users'])
innovation = innovation.dropna()
innovation = innovation[innovation["adjusted_lc"]!=np.inf]
innovation.to_csv(outpath+"innovation_data",index=False)
merge_innovation_data("./summaries/innovation_stats","./summaries/")
innovation = pd.read_csv("./summaries/innovation_data")
#sns.distplot(innovation["adjusted_gc"])
#plt.xscale("log")
#plt.yscale("log")
'''
Add delta features
'''
selected = ['nodes', 'edges', 'density',
'avg_degree', 'max_degree', 'large_com', 'singletons', 'betweenness',
'closeness', 'degree', 'eigen', 'pagerank',
'adjusted_assort', 'adjusted_gc', 'adjusted_lc']
d_selected = ['d_'+i for i in selected]
def merge_innovation_data(inpath,outpath):
innovation = pd.DataFrame()
files = os.listdir(inpath)
for t in tqdm(files):
data = pd.read_csv(os.path.join(inpath,t))
data['adjusted_assort'] = (data['assortativity']-data['r_assort'])/data['r_assort']
data['adjusted_gc'] = (data['global_cluster']-data['r_gc'])/data['r_gc']
data['adjusted_lc'] = (data['local_cluster']-data['r_lc'])/(data['r_lc']+1e-4)
data['activity'] = np.log(data['posts']/data['users'])
data = data.sort_values('Time')
feat = data.loc[:,selected]
last = np.zeros_like(feat)
last[1:,:] = feat.to_numpy()[:-1,:]
delta = feat.to_numpy() - last
data.loc[:,d_selected] = delta
innovation = pd.concat([innovation,data],ignore_index=True)
innovation= innovation[innovation["Subreddits"]!="0.0"]
innovation = innovation.replace([np.inf,-np.inf],np.nan)
innovation = innovation.dropna()
# innovation = innovation[innovation["adjusted_lc"]!=np.inf]
innovation.to_csv(outpath+"innovation_data_delta",index=False)
merge_innovation_data("./summaries/innovation_stats","./summaries/")