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analyze.py
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analyze.py
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import pandas as pd
import numpy as np
import sys
import matplotlib.pyplot as plt
from helpers.function.read import read_many_file, read_reactions
from numpy import append, array, reshape
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.cluster import KMeans
from gensim.models import Doc2Vec
if __name__ == "__main__":
print(">> Reading reactions from file")
reactions = read_reactions()
print(">> Total interactions: %d" % len(reactions))
# Get person has most interaction
user_id = reactions.drop_duplicates(['author_id'], keep = 'last').drop(['status_id', 'reaction_status'], axis = 1)
print(">> Number of people liked page: %d" % len(user_id))
count = reactions.groupby(['author_id'], as_index=False).size().reset_index().rename(columns={0:'count'})['count'].tolist()
user_id = user_id.assign(num_reactions = count)
print(user_id)
user_id.to_excel("Reactions.xlsx")
# print(">> Reading from file")
# posts = read_many_file(["lhpconfessions", "NthersConfessions", "PtnkConfession", "rmitvnconf"], 'raw_input/Confessions')
# model = Doc2Vec.load("gensim.txt")
# X = array([])
# count = 0
# for vec in model.docvecs:
# count += 1
# if count == 1:
# print(vec)
# if count % 100 == 0:
# sys.stdout.flush()
# sys.stdout.write("\r>> process to %d over %d" % (count, len(model.docvecs)))
# X = append(X, vec)
# X = X.reshape(len(model.docvecs), 100)
# print(X.shape)
# print(">> Running KMeans")
# kmeans = KMeans(n_clusters=8, random_state=0).fit(X)
# data = pd.DataFrame()
# data['content'] = posts['status_message'].dropna(how='any')
# data = data.assign(label=kmeans.labels_)
# data = data.sort('label', ascending=True)
# print(data)
# writer = pd.ExcelWriter('output.xlsx')
# data.to_excel(writer, 'Sheet 1')
# writer.save()
# print(">> Learning iPCA")
# n_components = 2
# ipca = IncrementalPCA(n_components=n_components, batch_size=10)
# X_ipca = ipca.fit_transform(X)
# print(">> Running KMeans")
# kmeans = KMeans(n_clusters=8, random_state=0).fit(X_ipca)
# print(">> Start plotting")
# colors = ['red', 'yellow', 'green','turquoise', 'black', 'blue', 'orange', 'violet']
# plt.figure(figsize=(8, 8))
# count = -1
# for X_transformed, Y_transformed in X_ipca:
# count += 1
# if count % 100 == 0:
# sys.stdout.flush()
# sys.stdout.write("\r>> add to plt %d over %d" % (count, len(X_ipca)))
# color = colors[kmeans.labels_[count]]
# plt.scatter(X_transformed, Y_transformed, color=color, lw=1)
# plt.legend(loc="best", shadow=False, scatterpoints=1)
# plt.axis([-10, 10, -10, 10])
# plt.savefig('PCA_KMeans.png')
# plt.show()