wip
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wea_ondara
235
itsnew.py
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235
itsnew.py
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import statsmodels.api as sm
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import sys
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from datetime import datetime
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from datetime import timedelta
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from dateutil.relativedelta import relativedelta
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from common import calc_intervals, printnoln, rprint, DAYS_NEW_USER, FIG_SIZE, CHANGE_DATE, difftime
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from loader import load, dmt, cms
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from sentiments import readtoxleveltxt
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colors = ['red', 'green', 'blue', 'orange', 'deeppink']
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thresholds = [6, 9, 12, 15]
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def main(folder, intervl):
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users, posts, firstcontrib, sumcontrib = load(folder)
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intervals = calc_intervals(posts, intervl)
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start = cms()
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printnoln("reading sentiments ...")
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(_, cachedsentiments) = readtoxleveltxt(folder + "/output/sentiments.txt")
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rprint("reading sentiments ... took " + str(cms() - start) + "ms")
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outputdir = folder + "/output/itsnew/"
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os.system("mkdir -p " + outputdir)
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data = []
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datasingle = []
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count = []
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for (option_date_from, option_date_to) in intervals:
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if option_date_to <= datetime.fromisoformat("2015-01-01T00:00:00"):
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datasingle.append(float("nan"))
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data.append(float("nan"))
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count.append(float("nan"))
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continue
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print(option_date_from.strftime("%d-%m-%Y") + " to " + option_date_to.strftime("%d-%m-%Y"))
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# avg sentiments
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filtered = (dmt(posts).map(lambda p: [cachedsentiments[a['Id']]['compound']
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for a in p['Answers']
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if option_date_from <= p['CreationDate'] < option_date_to # post in interval
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and firstcontrib[p['OwnerUserId']] + timedelta(days=DAYS_NEW_USER) > p['CreationDate'] # post created withon 1 week of 1st contrib
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and p['CreationDate'] + timedelta(days=DAYS_NEW_USER) > a['CreationDate']]) # answer within 1 week of post creation
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.filter(lambda p: p != [])
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.reduce(lambda a, b: a + b, lambda a, b: a + b, lambda: [])
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.getresults())
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datasingle.append(filtered)
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avg = np.average(filtered) if len(filtered) > 0 else float("nan")
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data.append(avg)
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count.append(len(filtered))
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avgcount = np.mean([x for x in count if str(x) != "nan"])
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stdcount = np.std([x for x in count if str(x) != "nan"])
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for i in range(len(count)):
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if str(count[i]) == "nan": # or np.abs((count[i] - avgcount) / stdcount) > 3:
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datasingle[i] = float("nan")
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data[i] = float("nan")
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count[i] = float("nan")
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# filter nan entries
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for i in range(len(data)):
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while i < len(data) and str(data[i]) == "nan":
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del datasingle[i]
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del data[i]
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del intervals[i]
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del count[i]
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# deseason
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# mins = [min([data[j] for j in range(len(data)) if j % 12 == i]) for i in range(0, 12)]
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mins = [np.average([data[j] for j in range(len(data)) if j % 12 == i]) for i in range(0, 12)]
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# mins = [min(d) / count(d) for d in [[data[j] for j in range(len(data)) if j % 12 == i] for i in range(0, 12)]]
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# mins = [data[i] for i in range(0, 12)]
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mins = [m - min(mins) for m in mins]
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print("mins", mins)
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dsdata = [data[i] - mins[i % 12] for i in range(len(data))]
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dsdatasingle = [[d - mins[i % 12] for d in datasingle[i]] for i in range(len(datasingle))]
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# data = dsdata
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# datasingle = dsdatasingle
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print("Computing full ITS")
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# t_s = np.reshape(np.array([data[i] for i in range(len(datasingle)) for j in datasingle[i]]), (-1, 1))
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t = np.reshape(np.array([i for i in range(len(dsdatasingle)) for j in dsdatasingle[i]]), (-1, 1))
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x = np.reshape(np.array([(0 if intervals[i][0] <= CHANGE_DATE else 1) for i in range(len(dsdatasingle)) for j in dsdatasingle[i]]), (-1, 1))
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X = np.array(t) # b1
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X = np.concatenate((X, x), 1) # b2
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X = np.concatenate((X, np.multiply(t, x)), 1) # 3
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# X = np.concatenate((X, t_s), 1) # 4
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X = sm.add_constant(X) # b0
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y = np.reshape(np.array([d for a in dsdatasingle for d in a]), (-1, 1))
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res = sm.OLS(y, X).fit()
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p2 = res.pvalues
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print("coef ols: " + str(res.params))
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print("sum ols: " + str(res.summary()))
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coef2ols = np.reshape(np.array(res.params), (-1, 1))
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# coef2ols[4] = 0
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its2ols = X.dot(coef2ols)
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dsits2ols = np.copy(its2ols)
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# its2ols = np.add(its2ols, np.reshape(np.array([mins[i % 12] for i in range(len(data)) for j in dsdatasingle[i]]), (-1, 1)))
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minavg = np.average(mins)
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its2ols = np.add(its2ols, np.reshape(np.array([minavg for i in range(len(data)) for j in dsdatasingle[i]]), (-1, 1)))
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with open(outputdir + "/summary-i" + str(intervl) + ".txt", "w") as file:
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file.write(str(res.summary()))
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thresdata = []
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thresols = []
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thresiv = []
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thresp = []
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print("Computing threshold ITS")
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for ti in thresholds:
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# print(1, CHANGE_DATE - relativedelta(months=ti))
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# print(2, CHANGE_DATE + relativedelta(months=ti))
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z = [(i, x) for (i, x) in zip(intervals, datasingle) if i[0] >= CHANGE_DATE - relativedelta(months=ti) and i[1] <= CHANGE_DATE + relativedelta(months=ti)]
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iv = [i for (i, x) in z]
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# print("iv " + str(iv))
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d = [x for (i, x) in z]
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# t_s = np.reshape(np.array([data[i] for i in range(len(d)) for j in d[i]]), (-1, 1))
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t = np.reshape(np.array([i for i in range(len(d)) for j in d[i]]), (-1, 1))
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x = np.reshape(np.array([(0 if iv[i][0] <= CHANGE_DATE else 1) for i in range(len(d)) for j in d[i]]), (-1, 1))
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X = np.array(t) # b1
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X = np.concatenate((X, x), 1) # b2
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X = np.concatenate((X, np.multiply(t, x)), 1) # b3
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# X = np.concatenate((X, t_s), 1) # b4
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X = sm.add_constant(X) # 0
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y = np.reshape(np.array([v for a in d for v in a]), (-1, 1))
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res = sm.OLS(y, X).fit()
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tp = res.pvalues
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thresp.append(tp)
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# print("coef ols: " + str(res.params))
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# print("sum ols: " + str(res.summary()))
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coefthresols = np.reshape(np.array(res.params), (-1, 1))
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# coefthresols[4] = 0
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thresols.append(X.dot(coefthresols))
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thresiv.append(iv)
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thresdata.append(d)
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with open(outputdir + "/summary_threshold" + str(ti) + "-i" + str(intervl) + ".txt", "w") as file:
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file.write(str(res.summary()))
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fig = plt.figure(figsize=FIG_SIZE)
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plt.plot([difftime(i[0]) for i in intervals], data, label="average sentiment")
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# plt.plot([difftime(i[0]) for i in intervals], dsdata, label="average sentiment - deseason")
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plt.grid(True)
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for i in range(len(data)):
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va = "center"
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if 0 < i < len(data) - 1:
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if data[i - 1] < data[i] and data[i + 1] < data[i]:
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va = "bottom"
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elif data[i - 1] > data[i] and data[i + 1] > data[i]:
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va = "top"
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elif i == 0:
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if data[i + 1] < data[i]:
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va = "bottom"
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else:
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va = "top"
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elif i == len(data) - 1:
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if data[i - 1] < data[i]:
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va = "bottom"
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else:
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va = "top"
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plt.text(difftime(intervals[i][0]), data[i], ("n=" if i == 0 else "") + str(len(datasingle[i])), ha="center", va=va)
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plt.plot([difftime(intervals[i][0]) for i in range(len(datasingle)) for j in datasingle[i]], its2ols, label="sm single ITS")
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# plt.plot([difftime(intervals[i][0]) for i in range(len(dsdatasingle)) for j in dsdatasingle[i]], dsits2ols, label="sm single ITS - deseason")
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# print("shape: " + str(np.shape(thresdata)))
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for (ti, t) in enumerate(thresholds):
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# print("shape1: " + str(np.shape(thresdata[ti])))
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plt.plot([difftime(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")
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plt.title("Average sentiments for new users")
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plt.xticks(rotation=90)
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plt.xlabel("months")
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plt.ylabel("sentiment")
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plt.legend(loc="upper right")
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outfile = outputdir + "/average_sentiments-i" + str(intervl) + ".png"
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plt.savefig(outfile, bbox_inches='tight')
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plt.close(fig)
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# plot seasonality
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fig = plt.figure(figsize=FIG_SIZE)
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plt.plot([difftime(intervals[i][0]) for i in range(len(datasingle)) for j in datasingle[i]], [mins[i % 12] for i in range(len(datasingle)) for j in datasingle[i]], label="seasonality")
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# print("shape: " + str(np.shape(thresdata)))
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plt.title("Average sentiments for new users - seasonality")
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plt.xticks(rotation=90)
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plt.xlabel("months")
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plt.ylabel("sentiment - seasonality")
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plt.legend(loc="upper right")
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outfile = outputdir + "/season-i" + str(intervl) + ".png"
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plt.savefig(outfile, bbox_inches='tight')
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plt.close(fig)
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# plot seasonality post count
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pcmins = [len(datasingle[i]) for i in range(0, 12)]
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pcmins = [m - min(pcmins) for m in pcmins]
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fig = plt.figure(figsize=FIG_SIZE)
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plt.plot([difftime(intervals[i][0]) for i in range(len(datasingle))], [pcmins[i % 12] for i in range(len(datasingle))], label="seasonality")
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plt.title("post count for new users - seasonality")
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plt.xticks(rotation=90)
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plt.xlabel("months")
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plt.ylabel("post count - seasonality")
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plt.legend(loc="upper right")
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outfile = outputdir + "/season_postcount-i" + str(intervl) + ".png"
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plt.savefig(outfile, bbox_inches='tight')
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plt.close(fig)
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if __name__ == "__main__":
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# execute only if run as a script
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usage = sys.argv[0] + " <folder>"
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if len(sys.argv) < 2:
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print(usage)
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sys.exit(1)
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folder = sys.argv[1]
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if not os.path.isdir(folder):
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print(folder + " is not a folder")
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sys.exit(1)
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interval = 1
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if len(sys.argv) >= 3:
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if sys.argv[2].startswith("-i"):
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interval = sys.argv[2][2:]
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try:
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interval = int(interval)
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except ValueError:
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print("-i: int required")
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sys.exit(1)
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if interval < 1 or interval > 12:
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print("-i: only 1 - 12")
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sys.exit(1)
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else:
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print("unknown parameter: " + sys.argv[2])
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sys.exit(1)
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main(folder, interval)
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