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dietary_trends.py
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dietary_trends.py
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import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pyspark import SparkContext, SparkConf
from pyspark.mllib.clustering import KMeans
from src.preprocess import extract_demo_data, extract_diet_data_xtot, extract_diet_data_2tot
from src.statistics import get_totals_from_data, report_trends, get_trends, report_total_intake, get_mean_intake_percentages, report_class_counts, get_class_counts, compute_mean_nutrient_intake
from src.statistics import report_mean_overall, report_mean_lower, report_mean_middle, report_mean_upper, get_class_wise_nutrient_trends, report_overall_average_intake_per_person
from src.statistics import report_lower_average_intake_per_person, report_middle_average_intake_per_person, report_upper_average_intake_per_person, compute_average_intake_per_person
from src.statistics import get_average_intake_per_person_nutrient_wise, get_average_intake_per_person_class_wise, normalize_nutrient_data, generate_box_plot
from src.plots import show_bar_analysis, show_pie_analysis, show_trend_analysis, get_scatter_plot_data, show_scatter_plot
from src.clustering import calculate_wssse, get_cluster_ids, plot_clusters, categorize_values, plot_as_4d
# Creating a Spark Context
if os.name=='nt':
import findspark
findspark.init(r'C:/Spark/spark-2.3.0-bin-hadoop2.7')
# Create configuration object
conf = SparkConf()
# Set driver and executor memory and max result size
conf = conf.set('spark.executor.memory', '2G')\
.set('spark.driver.memory', '5G')\
.set('spark.driver.maxResultSize', '5G')
# Create Spark Context
sc = SparkContext.getOrCreate(conf=conf)
# Check that memory is set
#~ print('Spark Driver Memory:', sc._conf.get('spark.driver.memory'))
#~ print('Spark Executor Memory:', sc._conf.get('spark.executor.memory'))
def get_children_data(relevant_demo_data, relevant_diet_data, year):
"""Gets children financial status & macro nutrient intake data."""
# children upto 13 years = 156 months
upto_13 = 156
children_demo_data = relevant_demo_data.filter(lambda x: x[1][0]<upto_13)
# Identifying Sequence Number and Poverty Income Ratio of these children
target_children = children_demo_data.map(lambda x: (x[0],x[1][1]))
raw_relevant_diet_data = relevant_diet_data.map(
lambda x: (x[0], x[1].tolist()))
raw_target_pairs = target_children.join(raw_relevant_diet_data)
raw_target_pairs = raw_target_pairs.map(lambda x: [x[1][0], x[1][1]])
raw_target_data = raw_target_pairs.map(
lambda x: [x[0], x[1][0], x[1][1], x[1][2], x[1][3]])
raw_target_data_arrays = raw_target_data.map(
lambda x: np.array(x, dtype='float32'))
target_data_arrays = raw_target_data_arrays.filter(
lambda x: not np.any(np.isnan(x)))
target_data_lists = target_data_arrays.map(lambda x: x.tolist())
return target_data_arrays, target_data_lists
def get_children_nutrient_intake(target_data_lists):
"""Gets numpy array of macro-nutrient intake data"""
nutrient_diet = target_data_lists.map(
lambda x: np.array((x[1], x[2], x[3], x[4]), dtype='float32'))
return nutrient_diet
def get_financial_class_wise_nutrient_details(target_data_arrays):
"""Gets financial class-wise macro-nutrient intake data."""
target_data_lower = target_data_arrays.filter(lambda x: x[0]<2)
lower_iprs = target_data_lower.map(lambda x: x[0])
nutrients_lower_lists = target_data_lower.map(
lambda x: (x[1], x[2], x[3], x[4]))
nutrients_lower_arrays = target_data_lower.map(
lambda x: np.array((x[1], x[2], x[3], x[4]), dtype='float32'))
target_data_middle = target_data_arrays.filter(
lambda x: (x[0]>=2 and x[0]<4))
middle_iprs = target_data_middle.map(lambda x: x[0])
nutrients_middle_lists = target_data_middle.map(
lambda x: (x[1], x[2], x[3], x[4]))
nutrients_middle_arrays = target_data_middle.map(
lambda x: np.array((x[1], x[2], x[3], x[4]), dtype='float32'))
target_data_upper = target_data_arrays.filter(lambda x: x[0]>=4)
upper_iprs = target_data_upper.map(lambda x: x[0])
nutrients_upper_lists = target_data_upper.map(
lambda x: (x[1], x[2], x[3], x[4]))
nutrients_upper_arrays = target_data_upper.map(
lambda x: np.array((x[1], x[2], x[3], x[4]), dtype='float32'))
return nutrients_lower_lists, nutrients_middle_lists, nutrients_upper_lists, nutrients_lower_arrays, nutrients_middle_arrays, nutrients_upper_arrays
def get_carb_fat_intake(
nutrients_lower_lists, nutrients_middle_lists,nutrients_upper_lists):
"""Gets financial class-wise carb and fat intake data."""
lower_carb_fat = nutrients_lower_lists.map(lambda x: (x[0], x[2]))
middle_carb_fat = nutrients_middle_lists.map(lambda x: (x[0], x[2]))
upper_carb_fat = nutrients_upper_lists.map(lambda x: (x[0], x[2]))
return lower_carb_fat, middle_carb_fat, upper_carb_fat
def get_fiber_protein_intake(
nutrients_lower_lists, nutrients_middle_lists,nutrients_upper_lists):
"""Gets financial class-wise fibee and protein intake data."""
lower_fiber_prot = nutrients_lower_lists.map(lambda x: (x[1], x[3]))
middle_fiber_prot = nutrients_middle_lists.map(lambda x: (x[1], x[3]))
upper_fiber_prot = nutrients_upper_lists.map(lambda x: (x[1], x[3]))
return lower_fiber_prot, middle_fiber_prot, upper_fiber_prot
def nutrient_12yr_intake_trend(years, macro_nutrients):
"""Gets Nutrient intake trend over 12 years.
First Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
nutrient_diet = get_children_nutrient_intake(relevant_data_lists)
nutrient_totals, total_value = get_totals_from_data(nutrient_diet)
nutrient_trends = report_trends(nutrient_totals)
nutrient_totals, total_value = report_total_intake(
nutrient_totals, total_value)
if year=='2011':
get_trends(
nutrient_trends, years, 'Years', 'Intake',
'Nutrient Intake Trend', macro_nutrients)
def class_wise_12yr_food_composition(years, ideal_percentages, macro_nutrients):
"""Gets Food compositions over 12 years across classes.
Second Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
nutrient_intake = get_children_nutrient_intake(relevant_data_lists)
nutrient_totals, total_value = get_totals_from_data(nutrient_intake)
nutrient_totals, total_value = report_total_intake(
nutrient_totals, total_value)
nutrients_lower_lists, nutrients_middle_lists, nutrients_upper_lists, nutrients_lower_arrays, nutrients_middle_arrays, nutrients_upper_arrays = get_financial_class_wise_nutrient_details(
relevant_data_arrays)
lower_nutrient_totals, lower_total_value = get_totals_from_data(
nutrients_lower_arrays)
lower_nutrient_totals, lower_total_value = report_total_intake(
lower_nutrient_totals, lower_total_value)
middle_nutrient_totals, middle_total_value = get_totals_from_data(
nutrients_middle_arrays)
middle_nutrient_totals, middle_total_value = report_total_intake(
middle_nutrient_totals, middle_total_value)
upper_nutrient_totals, upper_total_value = get_totals_from_data(
nutrients_upper_arrays)
upper_nutrient_totals, upper_total_value = report_total_intake(
upper_nutrient_totals, upper_total_value)
if year=='2011':
get_mean_intake_percentages(
nutrient_totals, total_value, macro_nutrients)
get_mean_intake_percentages(
lower_nutrient_totals, lower_total_value, macro_nutrients)
get_mean_intake_percentages(
middle_nutrient_totals, middle_total_value, macro_nutrients)
get_mean_intake_percentages(
upper_nutrient_totals, upper_total_value, macro_nutrients)
show_pie_analysis(
ideal_percentages, macro_nutrients,
'Recommended Food Composition')
def average_12yr_person_intake_per_class(years, macro_nutrients, classes):
"""Gets average intake per person over 12 years across classes
Third Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
nutrient_intake = get_children_nutrient_intake(relevant_data_lists)
nutrient_totals, total_value = get_totals_from_data(nutrient_intake)
nutrient_totals, total_value = report_total_intake(
nutrient_totals, total_value)
nutrients_lower_lists, nutrients_middle_lists, nutrients_upper_lists,nutrients_lower_arrays, nutrients_middle_arrays, nutrients_upper_arrays = get_financial_class_wise_nutrient_details(
relevant_data_arrays)
report_class_counts(nutrient_intake, nutrients_lower_arrays,
nutrients_middle_arrays, nutrients_upper_arrays)
lower_nutrient_totals, lower_total_value = get_totals_from_data(
nutrients_lower_arrays)
lower_nutrient_totals, lower_total_value = report_total_intake(
lower_nutrient_totals, lower_total_value)
middle_nutrient_totals, middle_total_value = get_totals_from_data(
nutrients_middle_arrays)
middle_nutrient_totals, middle_total_value = report_total_intake(
middle_nutrient_totals, middle_total_value)
upper_nutrient_totals, upper_total_value = get_totals_from_data(
nutrients_upper_arrays)
upper_nutrient_totals, upper_total_value = report_total_intake(
upper_nutrient_totals, upper_total_value)
if year=='2011':
overall_intake_averages = report_overall_average_intake_per_person(
nutrient_totals)
lower_intake_averages = report_lower_average_intake_per_person(
lower_nutrient_totals)
middle_intake_averages = report_middle_average_intake_per_person(
middle_nutrient_totals)
upper_intake_averages = report_upper_average_intake_per_person(
upper_nutrient_totals)
compute_average_intake_per_person(overall_intake_averages)
compute_average_intake_per_person(lower_intake_averages)
compute_average_intake_per_person(middle_intake_averages)
compute_average_intake_per_person(upper_intake_averages)
get_average_intake_per_person_nutrient_wise(macro_nutrients, classes)
get_average_intake_per_person_class_wise(
overall_intake_averages, lower_intake_averages,
middle_intake_averages, upper_intake_averages,
classes, macro_nutrients)
def nutrient_intake_density(years):
"""Gets Nutrient intake density across classes
Fourth Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
nutrients_lower_lists, nutrients_middle_lists, nutrients_upper_lists, nutrients_lower_arrays, nutrients_middle_arrays, nutrients_upper_arrays = get_financial_class_wise_nutrient_details(
relevant_data_arrays)
lower_carb_fat, middle_carb_fat, upper_carb_fat = get_carb_fat_intake(
nutrients_lower_lists, nutrients_middle_lists,nutrients_upper_lists)
lower_carb, lower_fat = get_scatter_plot_data(lower_carb_fat)
middle_carb, middle_fat = get_scatter_plot_data(middle_carb_fat)
upper_carb, upper_fat = get_scatter_plot_data(upper_carb_fat)
show_scatter_plot(
lower_carb, lower_fat, 'Carbohydrate Intake', 'Fat Intake',
'Fat vs Carbs among Lower Income Class')
show_scatter_plot(
middle_carb, middle_fat, 'Carbohydrate Intake', 'Fat Intake',
'Fat vs Carbs among Middle Income Class')
show_scatter_plot(
upper_carb, upper_fat, 'Carbohydrate Intake', 'Fat Intake',
'Fat vs Carbs among Upper Income Class')
lower_fiber_prot, middle_fiber_prot, upper_fiber_prot = get_fiber_protein_intake(nutrients_lower_lists, nutrients_middle_lists,
nutrients_upper_lists)
lower_fiber, lower_prot = get_scatter_plot_data(lower_fiber_prot)
middle_fiber, middle_prot = get_scatter_plot_data(middle_fiber_prot)
upper_fiber, upper_prot = get_scatter_plot_data(upper_fiber_prot)
show_scatter_plot(lower_fiber, lower_prot, 'Fiber Intake',
'Protein Intake', 'Fiber vs Protein among Lower Income Class')
show_scatter_plot(middle_fiber, middle_prot, 'Fiber Intake',
'Protein Intake', 'Fiber vs Protein among Middle Income Class')
show_scatter_plot(upper_fiber, upper_prot, 'Fiber Intake',
'Protein Intake', 'Fiber vs Protein among Upper Income Class')
def class_wise_nutrient_intake_clusters_parallel_coordinates(years, classes):
"""Gets K-Means clusters, 4D parallel coordinates across classes
Fifth Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
nutrients_lower_lists, nutrients_middle_lists, nutrients_upper_lists, nutrients_lower_arrays, nutrients_middle_arrays, nutrients_upper_arrays = get_financial_class_wise_nutrient_details(
relevant_data_arrays)
nutrient_intake = get_children_nutrient_intake(relevant_data_lists)
overall_nutrients_mean = compute_mean_nutrient_intake(nutrient_intake)
report_mean_overall(overall_nutrients_mean)
lower_nutrients_mean = compute_mean_nutrient_intake(nutrients_lower_arrays)
report_mean_lower(lower_nutrients_mean)
middle_nutrients_mean = compute_mean_nutrient_intake(
nutrients_middle_arrays)
report_mean_middle(middle_nutrients_mean)
upper_nutrients_mean = compute_mean_nutrient_intake(
nutrients_upper_arrays)
report_mean_upper(upper_nutrients_mean)
report_class_counts(nutrient_intake, nutrients_lower_arrays,
nutrients_middle_arrays, nutrients_upper_arrays)
if year=='1999' or year=='2003' or year=='2007' or year=='2011':
# calculate_wssse(nutrient_intake)
# ipr values are devided into 6 groups
overall_cluster_ids, overall_intake_clusters = get_cluster_ids(
nutrient_intake, 6)
lower_cluster_ids, lower_intake_clusters = get_cluster_ids(
nutrients_lower_arrays, 6)
middle_cluster_ids, middle_intake_clusters = get_cluster_ids(
nutrients_middle_arrays, 6)
upper_cluster_ids, upper_intake_clusters = get_cluster_ids(
nutrients_upper_arrays, 6)
plot_clusters(overall_cluster_ids, overall_intake_clusters, 6)
plot_clusters(lower_cluster_ids, lower_intake_clusters, 6)
plot_clusters(middle_cluster_ids, middle_intake_clusters, 6)
plot_clusters(upper_cluster_ids, upper_intake_clusters, 6)
plot_as_4d(relevant_data_lists, year)
if year=='2011':
get_class_counts()
get_class_wise_nutrient_trends(years, classes)
def nutrient_box_plot(years):
"""Gets intake comparison among classes
Sixth Analysis"""
for year in years:
demo_data = extract_demo_data(year)
if year=='1999' or year=='2001':
diet_data = extract_diet_data_xtot(year)
else:
diet_data = extract_diet_data_2tot(year)
relevant_data_arrays, relevant_data_lists = get_children_data(
demo_data, diet_data, year)
category_data, carb_normalized, fiber_normalized, fat_normalized, prot_normalized = normalize_nutrient_data(
relevant_data_lists)
generate_box_plot(category_data, carb_normalized, fiber_normalized,
fat_normalized, prot_normalized)
def main():
macro_nutrients = ['Carbohydrates', 'Fiber', 'Fat', 'Protein']
classes = ['overall', 'lower', 'middle', 'upper']
years = ['1999', '2001', '2003', '2005', '2007', '2009', '2011']
ideal_percentages = [53, 2, 27, 18]
# First Analysis
nutrient_12yr_intake_trend(years, macro_nutrients)
# Second Analysis
class_wise_12yr_food_composition(years, ideal_percentages, macro_nutrients)
# Third Analysis
average_12yr_person_intake_per_class(years, macro_nutrients, classes)
# Fourth Analysis
nutrient_intake_density(years)
# Fifth Analysis
class_wise_nutrient_intake_clusters_parallel_coordinates(years, classes)
# Sixth Analysis
nutrient_box_plot(years)
if __name__=="__main__":
main()