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wea_ondara
2020-01-23 22:40:28 +01:00
parent 8877747692
commit 69221ac8b3
6 changed files with 78 additions and 63 deletions
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its.py
+38 -62
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@@ -1,19 +1,20 @@
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import os
import statsmodels.api as sm
from datetime import datetime
from datetime import timedelta
from sklearn.linear_model import LinearRegression
from dateutil.relativedelta import relativedelta
from common import calc_intervals, printnoln, rprint, DAYS_NEW_USER
from loader import load, dmt, cms
from sentiments import readtoxleveltxt
import statsmodels.api as sm
colors = ['red', 'green', 'blue', 'orange', 'deeppink']
thresholds = [2, 3, 4, 5, 6]
changedate = datetime.fromisoformat("2018-09-01T00:00:00")
def main(folder, intervl):
@@ -55,42 +56,13 @@ def main(folder, intervl):
del data[i]
del intervals[i]
# print("Computing ITS ...")
# t = np.reshape(np.array([i for i in range(len(data))]), (-1, 1))
# x = np.reshape(np.array([(0 if option_date_to <= datetime.fromisoformat("2018-09-01T00:00:00") else 1) for (option_date_from, option_date_to) in intervals]), (-1, 1))
# X = np.array(t)
# X = np.concatenate((X, x), 1)
# X = np.concatenate((X, np.multiply(t, x)), 1)
# y = np.reshape(np.array(data), (-1, 1))
# # print("Xfin", X)
# # print("y", y)
# reg = LinearRegression()
# reg.fit(X, y)
# score = reg.score(X, y)
# coef = np.reshape(np.array(reg.coef_), (-1, 1))
# its = X.dot(coef) + reg.intercept_
# print("score: " + str(score))
# print("coef: " + str(coef))
# print("its: " + str(its))
print("Computing full ITS")
t = np.reshape(np.array([i for i in range(len(datasingle)) for j in datasingle[i]]), (-1, 1))
x = np.reshape(np.array([(0 if intervals[i][1] <= datetime.fromisoformat("2018-09-01T00:00:00") else 1) for i in range(len(datasingle)) for j in datasingle[i]]), (-1, 1))
x = np.reshape(np.array([(0 if intervals[i][1] <= changedate else 1) for i in range(len(datasingle)) for j in datasingle[i]]), (-1, 1))
X = np.array(t)
X = np.concatenate((X, x), 1)
X = np.concatenate((X, np.multiply(t, x)), 1)
y = np.reshape(np.array([d for a in datasingle for d in a]), (-1, 1))
# print("Xfin", X)
# print("y", y)
# reg = LinearRegression()
# reg.fit(X, y)
# score2 = reg.score(X, y)
# coef2 = np.reshape(np.array(reg.coef_), (-1, 1))
# its2 = X.dot(coef2) + reg.intercept_
# print("intercept: " + str(reg.intercept_))
# print("score: " + str(score2))
# print("coef: " + str(coef2))
# print("its: " + str(its2))
X = sm.add_constant(X)
res = sm.OLS(y, X).fit()
p2 = res.pvalues
@@ -101,29 +73,35 @@ def main(folder, intervl):
with open(outputdir + "/summary-i" + str(intervl) + ".txt", "w") as file:
file.write(str(res.summary()))
# print("Computing segmented ITS before")
# X = np.reshape(np.array([i for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] <= datetime.fromisoformat("2018-09-01T00:00:00")]), (-1, 1))
# y = np.reshape(np.array([j for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] <= datetime.fromisoformat("2018-09-01T00:00:00")]), (-1, 1))
# reg = LinearRegression()
# reg.fit(X, y)
# scoreb = reg.score(X, y)
# coefb = np.reshape(np.array(reg.coef_), (-1, 1))
# itsb = X.dot(coefb) + reg.intercept_
# print("scoreb: " + str(scoreb))
# print("coefb: " + str(coefb))
# print("itsb: " + str(itsb))
# print("Computing segmented ITS after")
# X = np.reshape(np.array([i for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] > datetime.fromisoformat("2018-09-01T00:00:00")]), (-1, 1))
# y = np.reshape(np.array([j for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] > datetime.fromisoformat("2018-09-01T00:00:00")]), (-1, 1))
# reg = LinearRegression()
# reg.fit(X, y)
# scorea = reg.score(X, y)
# coefa = np.reshape(np.array(reg.coef_), (-1, 1))
# itsa = X.dot(coefa) + reg.intercept_
# print("scorea: " + str(scorea))
# print("coefa: " + str(coefa))
# print("itsa: " + str(itsa))
thresdata = []
thresols = []
thresiv = []
thresp = []
print("Computing threshold ITS")
for ti in thresholds:
print(1, changedate - relativedelta(months=ti))
print(2, changedate + relativedelta(months=ti))
z = [(i, x) for (i, x) in zip(intervals, datasingle) if i[0] >= changedate - relativedelta(months=ti) and i[1] <= changedate + relativedelta(months=ti)]
iv = [i for (i, x) in z]
d = [x for (i, x) in z]
t = np.reshape(np.array([i for i in range(len(d)) for j in d[i]]), (-1, 1))
x = np.reshape(np.array([(0 if iv[i][1] <= changedate else 1) for i in range(len(d)) for j in d[i]]), (-1, 1))
X = np.array(t)
X = np.concatenate((X, x), 1)
X = np.concatenate((X, np.multiply(t, x)), 1)
y = np.reshape(np.array([v for a in d for v in a]), (-1, 1))
X = sm.add_constant(X)
res = sm.OLS(y, X).fit()
tp = res.pvalues
thresp.append(tp)
# print("coef ols: " + str(res.params))
# print("sum ols: " + str(res.summary()))
coefthresols = np.reshape(np.array(res.params), (-1, 1))
thresols.append(X.dot(coefthresols))
thresiv.append(iv)
thresdata.append(d)
with open(outputdir + "/summary_threshold" + str(ti) + "-i" + str(intervl) + ".txt", "w") as file:
file.write(str(res.summary()))
fig = plt.figure(figsize=(16, 12))
plt.plot([i[0] for i in intervals], data, label="average sentiment")
@@ -146,13 +124,11 @@ def main(folder, intervl):
else:
va = "top"
plt.text(intervals[i][0], data[i], ("n=" if i == 0 else "") + str(len(datasingle[i])), ha="center", va=va)
# plt.plot([i[0] for i in intervals], its, label="aggregated ITS (score " + str(score) + ")")
# plt.plot([intervals[i][0] for i in range(len(datasingle)) for j in datasingle[i]], its2, label="single ITS (score " + str(score2) + ", p " + str(p2) + ")")
plt.plot([intervals[i][0] for i in range(len(datasingle)) for j in datasingle[i]], its2ols, label="sm single ITS (pvalues " + str(p2) + ")")
# plt.plot([intervals[i][0] for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] <= datetime.fromisoformat("2018-09-01T00:00:00")], itsb,
# label="segmented ITS b (score " + str(scoreb) + ")")
# plt.plot([intervals[i][0] for i in range(len(datasingle)) for j in datasingle[i] if intervals[i][1] > datetime.fromisoformat("2018-09-01T00:00:00")], itsa,
# label="segmented ITS a (score " + str(scorea) + ")")
print("shape: " + str(np.shape(thresdata)))
for (ti, t) in enumerate(thresholds):
print("shape1: " + str(np.shape(thresdata[ti])))
plt.plot([thresiv[ti][i][0] for i in range(len(thresdata[ti])) for j in thresdata[ti][i]], thresols[ti], label="thres ITS " + str(t) + " months (pvalues " + str(thresp[ti]) + ")")
plt.title("Average sentiments for new users")
plt.xticks(rotation=90)
plt.xlabel("months")
text/0_doc.tex
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\documentclass[a4paper,10pt]{article}
\usepackage[utf8]{inputenc}
%opening
\title{}
\author{}
\begin{document}
\maketitle
\begin{abstract}
\end{abstract}
\include{1_intro}
\include{2_relwork}
\include{3_method}
\include{4_datasets}
\include{5_results}
\include{6_discussion}
\include{7_conclusion}
\end{document}
text/1_intro.tex
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\section{Introduction}
text/2_relwork.tex
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\section{Related Work}
%tutorial: Bernal et al. \cite{bernal2017interrupted}
%literatur analyse todo
text/bib.bib
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@article{bernal2017interrupted,
title={Interrupted time series regression for the evaluation of public health interventions: a tutorial},
author={Bernal, James Lopez and Cummins, Steven and Gasparrini, Antonio},
journal={International journal of epidemiology},
volume={46},
number={1},
pages={348--355},
year={2017},
publisher={Oxford University Press}
}