def plot_age_groups():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
age_checkins = []
for age_group in xrange(1, 9):
query = select([func.count()], and_(consolidated.c["visits_joined_places_place_label"] == place_label, consolidated.c["demographics_age_group"] == age_group))
result = connection.execute(query).fetchall()
age_checkins.append(result[0][0])
fig, ax = plt.subplots()
rects = ax.bar(xrange(1, 9), age_checkins)
ax.set_ylabel("Count", fontsize=30, fontweight='bold')
ax.set_xlabel("Age groups", fontsize=30, fontweight='bold')
ax.set_title(LABEL_PLACE_MAPPING[place_label] + " Visits across Age Groups", fontsize=36, fontweight='bold')
xticks_values = [AGE_MAPPING[i] for i in xrange(1, 9)]
ax.set_xticks([i + 0.35 for i in xrange(1, 9)])
ax.set_xticklabels(xticks_values)
#autolabel(rects, age_checkins)
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(";", "_").replace(" ", "_") + "_" + "age.png"
fig.set_size_inches((15, 12))
fig.savefig(filename, dpi=100)
plt.close(fig)
def plot_start_time_day():
day_dict = [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday"
]
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
query = select(
[consolidated.c["visits_joined_" + table + "_time_start"]],
consolidated.c["visits_joined_places_place_label"] ==
place_label)
start_times = connection.execute(query).fetchall()
days = [0 for i in xrange(7)]
for start_time in start_times:
current_day = datetime.fromtimestamp(start_time[0]).weekday()
days[current_day] += 1
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(';', '_').replace(" ",
"_") + "_day.png"
draw_barplot(days,
x_ticks=day_dict,
xlabel="Day of week",
ylabel="Number of Checkins",
title="%s Visits by Days" % (place_name),
save_as=os.path.join("/local", "thesis", "plots",
filename))
def plot_age_groups():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
age_checkins = []
for age_group in xrange(1, 9):
query = select(
[func.count()],
and_(
consolidated.c["visits_joined_places_place_label"] ==
place_label,
consolidated.c["demographics_age_group"] == age_group))
result = connection.execute(query).fetchall()
age_checkins.append(result[0][0])
fig, ax = plt.subplots()
rects = ax.bar(xrange(1, 9), age_checkins)
ax.set_ylabel("Count", fontsize=30, fontweight='bold')
ax.set_xlabel("Age groups", fontsize=30, fontweight='bold')
ax.set_title(LABEL_PLACE_MAPPING[place_label] +
" Visits across Age Groups",
fontsize=36,
fontweight='bold')
xticks_values = [AGE_MAPPING[i] for i in xrange(1, 9)]
ax.set_xticks([i + 0.35 for i in xrange(1, 9)])
ax.set_xticklabels(xticks_values)
#autolabel(rects, age_checkins)
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(";", "_").replace(
" ", "_") + "_" + "age.png"
fig.set_size_inches((15, 12))
fig.savefig(filename, dpi=100)
plt.close(fig)
def plot_gender():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
print connection.execute(select([func.count()], consolidated.c["demographics_gender"] == 2)).fetchall()
gender_checkins = []
for gender in (0, 1):
gender_checkins.append([])
for place_label in xrange(1, 11):
query = select([func.count()], and_(consolidated.c["visits_joined_places_place_label"] == place_label, consolidated.c["demographics_gender"] == gender + 1))
result = connection.execute(query).fetchall()
gender_checkins[gender].append(result[0][0])
fig, ax = plt.subplots()
width = 0.35
rects1 = ax.bar(xrange(1, 11), gender_checkins[0], width, color='r')
rects2 = ax.bar([i + width for i in xrange(1, 11)], gender_checkins[1], width, color='g')
ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
ax.set_ylabel("Count", fontsize = 24, fontweight = 'bold')
ax.set_xlabel("Place Category", fontsize=24, fontweight = 'bold')
ax.set_title("Visits Across Gender", fontsize=32, fontweight='bold')
xticks_values = [LABEL_PLACE_MAPPING[i] for i in xrange(1, 11)]
xticks_values = [textwrap.fill(text,10) for text in xticks_values]
ax.set_xticks([i + width for i in xrange(1, 11)])
ax.set_xticklabels(xticks_values)
#autolabel(rects1, gender_checkins[0])
#autolabel(rects2, gender_checkins[1])
plt.show()
def plot_working_groups():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
working_checkins = []
for working_group in xrange(1, 9):
query = select([func.count()], and_(consolidated.c["visits_joined_places_place_label"] == place_label, consolidated.c["demographics_working"] == working_group))
result = connection.execute(query).fetchall()
working_checkins.append(result[0][0])
#fig, ax = plt.subplots()
#ax.legend((xrange(1,9)), xrange(1, 9))
#rects = ax.bar(xrange(1, 9), working_checkins)
#ax.set_ylabel("Count", fontsize=30, fontweight='bold')
#ax.set_xlabel("Working groups", fontsize=30, fontweight='bold')
#ax.set_title(LABEL_PLACE_MAPPING[place_label] + " Visits across Work Groups", fontsize=36, fontweight='bold')
x_ticks = [WORKING_MAPPING[i] for i in xrange(1, 9)]
#xticks_values = [textwrap.fill(text,7) for text in xticks_values]
#ax.set_xticks([i + 0.3 for i in xrange(1, 9)])
#ax.set_xticklabels(xticks_values)
#autolabel(rects, working_checkins)
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(";", "_").replace(" ", "_") + "_" + "workgroup.png"
#fig.set_size_inches((15, 12))
#fig.savefig(filename, dpi=100)
#plt.close(fig)
draw_barplot(working_checkins, x_ticks=[textwrap.fill(text,10) for text in x_ticks], xlabel="Working Status", ylabel="Visits", title=LABEL_PLACE_MAPPING[place_label] + " Visits across Employment Status", save_as=os.path.join("/local", "thesis", "plots", "working",filename), width=0.35)
def plot_demographics():
metadata, connection = setup_database()
demographics = get_table("demographics", metadata)
gender_query = select([demographics.c.gender, func.count(demographics.c.gender)]).group_by(demographics.c.gender)
result = connection.execute(gender_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [GENDER_MAPPING[r[0]] for r in result]
filename = "gender.png"
draw_barplot(vals, x_ticks=x_ticks, xlabel="Gender", ylabel="Count", title="Gender Distribution", save_as=os.path.join("/local", "thesis", "plots", filename), width=0.35)
age_query = select([demographics.c.age_group, func.count(demographics.c.age_group)]).group_by(demographics.c.age_group)
result = connection.execute(age_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [AGE_MAPPING[r[0]] for r in result]
filename = "age.png"
draw_barplot(vals, x_ticks=x_ticks, xlabel="Age Group", ylabel="Count", title="Age Distribution", save_as=os.path.join("/local", "thesis", "plots", filename), width=0.35)
working_query = select([demographics.c.working, func.count(demographics.c.working)]).group_by(demographics.c.working)
result = connection.execute(working_query).fetchall()
print result
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [WORKING_MAPPING[r[0]] for r in result]
filename = "working.png"
draw_barplot(vals, x_ticks=[textwrap.fill(text,10) for text in x_ticks], xlabel="Employment Status", ylabel="Count", title="Employment Status Distribution", save_as=os.path.join("/local", "thesis", "plots", filename), width=0.35)
bill_query = select([demographics.c.phone_bill, func.count(demographics.c.phone_bill)]).group_by(demographics.c.phone_bill)
result = connection.execute(bill_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [BILL_MAPPING[r[0]] for r in result]
filename = "bill.png"
draw_barplot(vals, x_ticks=x_ticks, xlabel="Bill", ylabel="Count", title="Bill Distribution", save_as=os.path.join("/local", "thesis", "plots", filename), width=0.35)
bill_query = select([demographics.c.nb_12, demographics.c.nb_12_18, demographics.c.nb_18_30, demographics.c.nb_30_40, demographics.c.nb_40_50, demographics.c.nb_50_65, demographics.c.nb_65])
result = connection.execute(bill_query).fetchall()
result = [sum([a for a in r if a is not None]) for r in result if r is not None]
s = set(result)
print s
vals = []
x_ticks = []
for elem in s:
if elem > 13:
continue
x_ticks.append(elem)
vals.append(result.count(elem))
#vals = [r[1] for r in result]
#x_ticks = [BILL_MAPPING[r[0]] for r in result]
filename = "family.png"
draw_barplot(vals, x_ticks=x_ticks, xlabel="Number of members in family", ylabel="Count", title="Number of Family Members Distribution", save_as=os.path.join("/local", "thesis", "plots", filename), width=0.35)
def plot_start_time_hour():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
query = select([consolidated.c["visits_joined_" + table + "_time_start"]], consolidated.c["visits_joined_places_place_label"] == place_label)
start_times = connection.execute(query).fetchall()
hours = [0 for i in xrange(24)]
for start_time in start_times:
hours[datetime.fromtimestamp(start_time[0]).hour] += 1
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(';', '_').replace(" ", "_") + "_hours.png"
draw_barplot(hours, x_ticks=xrange(24), xlabel="Hour of Day", ylabel="Number of Checkins", title="%s Visits by Hours" % (place_name), save_as=os.path.join("/local", "thesis", "plots", filename))
def plot_start_time_day():
day_dict = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
query = select([consolidated.c["visits_joined_" + table + "_time_start"]], consolidated.c["visits_joined_places_place_label"] == place_label)
start_times = connection.execute(query).fetchall()
days = [0 for i in xrange(7)]
for start_time in start_times:
current_day = datetime.fromtimestamp(start_time[0]).weekday()
days[current_day] += 1
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(';', '_').replace(" ", "_") + "_day.png"
draw_barplot(days, x_ticks=day_dict, xlabel="Day of week", ylabel="Number of Checkins", title="%s Visits by Days" % (place_name), save_as=os.path.join("/local", "thesis", "plots", filename))
def plot_working_groups():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
working_checkins = []
for working_group in xrange(1, 9):
query = select(
[func.count()],
and_(
consolidated.c["visits_joined_places_place_label"] ==
place_label, consolidated.c["demographics_working"] ==
working_group))
result = connection.execute(query).fetchall()
working_checkins.append(result[0][0])
#fig, ax = plt.subplots()
#ax.legend((xrange(1,9)), xrange(1, 9))
#rects = ax.bar(xrange(1, 9), working_checkins)
#ax.set_ylabel("Count", fontsize=30, fontweight='bold')
#ax.set_xlabel("Working groups", fontsize=30, fontweight='bold')
#ax.set_title(LABEL_PLACE_MAPPING[place_label] + " Visits across Work Groups", fontsize=36, fontweight='bold')
x_ticks = [WORKING_MAPPING[i] for i in xrange(1, 9)]
#xticks_values = [textwrap.fill(text,7) for text in xticks_values]
#ax.set_xticks([i + 0.3 for i in xrange(1, 9)])
#ax.set_xticklabels(xticks_values)
#autolabel(rects, working_checkins)
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(";", "_").replace(
" ", "_") + "_" + "workgroup.png"
#fig.set_size_inches((15, 12))
#fig.savefig(filename, dpi=100)
#plt.close(fig)
draw_barplot(working_checkins,
x_ticks=[textwrap.fill(text, 10) for text in x_ticks],
xlabel="Working Status",
ylabel="Visits",
title=LABEL_PLACE_MAPPING[place_label] +
" Visits across Employment Status",
save_as=os.path.join("/local", "thesis", "plots",
"working", filename),
width=0.35)
def plot_gender():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
print connection.execute(
select([func.count()],
consolidated.c["demographics_gender"] == 2)).fetchall()
gender_checkins = []
for gender in (0, 1):
gender_checkins.append([])
for place_label in xrange(1, 11):
query = select(
[func.count()],
and_(
consolidated.c["visits_joined_places_place_label"] ==
place_label,
consolidated.c["demographics_gender"] == gender + 1))
result = connection.execute(query).fetchall()
gender_checkins[gender].append(result[0][0])
fig, ax = plt.subplots()
width = 0.35
rects1 = ax.bar(xrange(1, 11), gender_checkins[0], width, color='r')
rects2 = ax.bar([i + width for i in xrange(1, 11)],
gender_checkins[1],
width,
color='g')
ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
ax.set_ylabel("Count", fontsize=24, fontweight='bold')
ax.set_xlabel("Place Category", fontsize=24, fontweight='bold')
ax.set_title("Visits Across Gender", fontsize=32, fontweight='bold')
xticks_values = [LABEL_PLACE_MAPPING[i] for i in xrange(1, 11)]
xticks_values = [textwrap.fill(text, 10) for text in xticks_values]
ax.set_xticks([i + width for i in xrange(1, 11)])
ax.set_xticklabels(xticks_values)
#autolabel(rects1, gender_checkins[0])
#autolabel(rects2, gender_checkins[1])
plt.show()
def plot_start_time_hour():
metadata, connection = setup_database()
tables = ["visits_10min"]
for table in tables:
consolidated = return_joined_table(table, metadata)
for place_label in xrange(1, 11):
query = select(
[consolidated.c["visits_joined_" + table + "_time_start"]],
consolidated.c["visits_joined_places_place_label"] ==
place_label)
start_times = connection.execute(query).fetchall()
hours = [0 for i in xrange(24)]
for start_time in start_times:
hours[datetime.fromtimestamp(start_time[0]).hour] += 1
place_name = LABEL_PLACE_MAPPING[place_label]
filename = place_name.replace(';', '_').replace(" ",
"_") + "_hours.png"
draw_barplot(hours,
x_ticks=xrange(24),
xlabel="Hour of Day",
ylabel="Number of Checkins",
title="%s Visits by Hours" % (place_name),
save_as=os.path.join("/local", "thesis", "plots",
filename))
import sys
import csv
import datetime
import math
from spams.db.utils import setup_database, get_table
from sqlalchemy.sql import select
from sqlalchemy import and_, func
from sklearn.neighbors import DistanceMetric
metadata, connection = setup_database()
places_location = get_table("places_location", metadata)
visits_10min = get_table("visits_10min", metadata)
records = get_table("records", metadata)
def relative_frequency(place, user):
count_place = connection.execute(
select([visits_10min.c.userid, visits_10min.c.placeid]).where(
and_(visits_10min.c.userid == user,
visits_10min.c.placeid == place))).rowcount
count_all_places = connection.execute(
select([visits_10min.c.userid
]).where(visits_10min.c.userid == user)).rowcount
return (count_place * 1.0) / (count_all_places * 1.0)
def distance_from_most_visited_place(place, user):
q = select([func.count(), visits_10min.c.placeid
]).where(visits_10min.c.userid == user).group_by(
visits_10min.c.placeid).order_by(func.count().desc())
most_visited_places = [r[1] for r in connection.execute(q).fetchall()]
import csv
import datetime
import math
from spams.db.utils import setup_database, get_table
from sqlalchemy.sql import select
from sqlalchemy import and_, func
from sklearn.neighbors import DistanceMetric
import numpy as np
from itertools import izip
from extract_features import write_features_to_csv
import logging
logging.basicConfig(filename='motion.log',level=logging.DEBUG)
metadata, connection = setup_database()
places_location = get_table("places_location", metadata)
visits_10min = get_table("visits_10min", metadata)
records = get_table("records", metadata)
accel = get_table("accel", metadata)
def get_features(data):
X = [d[0] for d in data]
Y = [d[1] for d in data]
Z = [d[2] for d in data]
x_mean = np.mean(X)
y_mean = np.mean(Y)
z_mean = np.mean(Z)
x_var = np.var(X)
y_var = np.var(Y)
z_var = np.var(Z)
def plot_demographics():
metadata, connection = setup_database()
demographics = get_table("demographics", metadata)
gender_query = select(
[demographics.c.gender,
func.count(demographics.c.gender)]).group_by(demographics.c.gender)
result = connection.execute(gender_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [GENDER_MAPPING[r[0]] for r in result]
filename = "gender.png"
draw_barplot(vals,
x_ticks=x_ticks,
xlabel="Gender",
ylabel="Count",
title="Gender Distribution",
save_as=os.path.join("/local", "thesis", "plots", filename),
width=0.35)
age_query = select([
demographics.c.age_group,
func.count(demographics.c.age_group)
]).group_by(demographics.c.age_group)
result = connection.execute(age_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [AGE_MAPPING[r[0]] for r in result]
filename = "age.png"
draw_barplot(vals,
x_ticks=x_ticks,
xlabel="Age Group",
ylabel="Count",
title="Age Distribution",
save_as=os.path.join("/local", "thesis", "plots", filename),
width=0.35)
working_query = select(
[demographics.c.working,
func.count(demographics.c.working)]).group_by(demographics.c.working)
result = connection.execute(working_query).fetchall()
print result
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [WORKING_MAPPING[r[0]] for r in result]
filename = "working.png"
draw_barplot(vals,
x_ticks=[textwrap.fill(text, 10) for text in x_ticks],
xlabel="Employment Status",
ylabel="Count",
title="Employment Status Distribution",
save_as=os.path.join("/local", "thesis", "plots", filename),
width=0.35)
bill_query = select([
demographics.c.phone_bill,
func.count(demographics.c.phone_bill)
]).group_by(demographics.c.phone_bill)
result = connection.execute(bill_query).fetchall()
result = [r for r in result if r[0] is not None]
result = sorted(result, key=lambda x: x[0])
vals = [r[1] for r in result]
x_ticks = [BILL_MAPPING[r[0]] for r in result]
filename = "bill.png"
draw_barplot(vals,
x_ticks=x_ticks,
xlabel="Bill",
ylabel="Count",
title="Bill Distribution",
save_as=os.path.join("/local", "thesis", "plots", filename),
width=0.35)
bill_query = select([
demographics.c.nb_12, demographics.c.nb_12_18, demographics.c.nb_18_30,
demographics.c.nb_30_40, demographics.c.nb_40_50,
demographics.c.nb_50_65, demographics.c.nb_65
])
result = connection.execute(bill_query).fetchall()
result = [
sum([a for a in r if a is not None]) for r in result if r is not None
]
s = set(result)
print s
vals = []
x_ticks = []
for elem in s:
if elem > 13:
continue
x_ticks.append(elem)
vals.append(result.count(elem))
#vals = [r[1] for r in result]
#x_ticks = [BILL_MAPPING[r[0]] for r in result]
filename = "family.png"
draw_barplot(vals,
x_ticks=x_ticks,
xlabel="Number of members in family",
ylabel="Count",
title="Number of Family Members Distribution",
save_as=os.path.join("/local", "thesis", "plots", filename),
width=0.35)
