healthsea-demo / support_functions.py
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import pandas as pd
import difflib
import numpy as np
from numpy import dot
from numpy.linalg import norm
from pyvis.network import Network
import streamlit.components.v1 as components
color_code_node = {
0: '#4B9EFF',
1: '#4BD4FF',
2: '#3CDFCB',
3: '#37DF8E',
4: '#A0C159',
5: '#CA804B',
6: '#CA524B',
7: '#CA4B97',
8: '#C04BCA',
9: '#5D4BCA',
10: '#213ABA',
11: '#0E6697',
}
class HealthseaSearch:
def __init__(self, _health_aspects, _products, _conditions, _benefits):
self.health_aspects = _health_aspects
self.products = _products
self.conditions = _conditions
self.benefits = _benefits
def __call__(self, query):
return query
# Load product meta
def get_products(self, _aspect, n):
product_list = []
product_ids = {}
_n = n
_aspect = _aspect.replace(" ", "_")
if _aspect in self.health_aspects:
aspect = self.health_aspects[_aspect]
else:
_aspect = difflib.get_close_matches("_aspect", self.health_aspects.keys())[
0
]
aspect = self.health_aspects[_aspect]
product_scoring = aspect["products"]
if n != 0:
if n > len(product_scoring):
n = len(product_scoring)
product_scoring = aspect["products"][:n]
for product in product_scoring:
if product[1] not in product_ids:
product_list.append((product[0], self.products[product[1]], _aspect))
product_ids[product[1]] = 1
for alias in aspect["alias"]:
n = _n
_product_scoring = self.health_aspects[alias]["products"]
if n != 0:
if n > len(_product_scoring):
n = len(_product_scoring)
_product_scoring = self.health_aspects[alias]["products"][:n]
for product in _product_scoring:
if product[1] not in product_ids:
product_list.append((product[0], self.products[product[1]], alias))
product_ids[product[1]] = 1
n = _n
if len(product_list) > n and n != 0:
product_list = product_list[:n]
product_list = sorted(product_list, key=lambda tup: tup[0], reverse=True)
return product_list
# Load product meta and return as DataFrame
def get_products_df(self, _aspect, n):
product_list = self.get_products(_aspect, n)
product_data = {
"product": [],
"score": [],
"health_aspect": [],
"rating": [],
"reviews": [],
}
for product in product_list:
product_data["score"].append(product[0])
product_data["product"].append(product[1]["name"])
product_data["health_aspect"].append(product[2])
product_data["rating"].append(product[1]["rating"])
product_data["reviews"].append(product[1]["review_count"])
datatypes = {
"product": str,
"score": int,
"health_aspect": str,
"rating": str,
"reviews": int,
}
df = pd.DataFrame(data=product_data)
df = df.astype(datatypes)
return df
# Get health aspect
def get_aspect(self, _aspect):
_aspect = _aspect.replace(" ", "_")
if _aspect in self.health_aspects:
return self.health_aspects[_aspect]
else:
_aspect = difflib.get_close_matches("_aspect", self.health_aspects.keys())[
0
]
return self.health_aspects[_aspect]
# Get health aspect meta
def get_aspect_meta(self, _aspect):
_aspect = _aspect.replace(" ", "_")
if _aspect in self.conditions:
return self.conditions[_aspect]
elif _aspect in self.benefits:
return self.benefits[_aspect]
else:
_aspect = difflib.get_close_matches("_aspect", self.conditions.keys())[0]
return self.conditions[_aspect]
def pyvis(self, vectors):
net = Network(height='500px', width='700px', bgcolor="#0E1117", font_color="#ffffff")
net.barnes_hut(gravity=-2500)
net.set_edge_smooth("dynamic")
net.toggle_stabilization(False)
net.add_node(vectors[0][0], label=vectors[0][0], color="#4EA0DB", value=100, shape="circle") # node id = 1 and label = Node 1
for vector in vectors[1:]:
net.add_node(vector[0], label=vector[0], color="#FE51B9", value=70, shape="circle") # node id = 1 and label = Node 1
for i, vector in enumerate(vectors):
current_vector = vectors[i]
if i < len(vectors):
if i == 0:
for _vector in vectors[i+1:]:
sim = self.calculate_cosine_sim(current_vector[1],_vector[1])
net.add_edge(current_vector[0],_vector[0], weight=sim, value=sim, title=sim)
else:
for _vector in vectors[i+1:]:
sim = self.calculate_cosine_sim(current_vector[1],_vector[1])
net.add_edge(current_vector[0],_vector[0], weight=sim, value=sim/2, title=sim)
#for _vector in vectors[1:]:
# sim = self.calculate_cosine_sim(vectors[0][1],_vector[1])
# net.add_edge(vectors[0][0],_vector[0], weight=sim, value=sim*0.1, title=sim)
net.save_graph("viz.html")
HtmlFile = open("viz.html", 'r', encoding='utf-8')
source_code = HtmlFile.read()
components.html(source_code, height = 500, width=700)
# Experimental
def get_recursive_alias(self, _aspect, n, node_list, edge_list, _max):
aspect = self.get_aspect(_aspect)
aspect_name = aspect["name"].replace(" ","_")
if aspect_name not in node_list:
node_list[aspect_name] = {"level":n}
aspect_alias = aspect["alias"]
if len(aspect_alias) > 0 and n <= _max:
for alias in aspect_alias:
if alias not in node_list:
edge_list.append((aspect_name,alias,n))
self.get_recursive_alias(alias, n+1, node_list, edge_list,_max)
return node_list, edge_list
else:
return node_list, edge_list
def add_to_network(self, network, node_list, edge_list):
for node in node_list:
value = 100-(15*node_list[node]["level"])
network.add_node(node, label=node, color=color_code_node[node_list[node]["level"]], value=value, shape="dot", title = str(node_list[node]["level"]))
for edge in edge_list:
value = 1-(0.15*edge[2])
network.add_edge(edge[0], edge[1], value=value)
def pyvis2(self, node_list, edge_list):
net = Network(height='500px', width='700px', bgcolor="#0E1117", font_color="#ffffff")
net.barnes_hut(gravity=-2500-(len(node_list)*2))
net.set_edge_smooth("dynamic")
self.add_to_network(net, node_list, edge_list)
net.save_graph("viz.html")
HtmlFile = open("viz.html", 'r', encoding='utf-8')
source_code = HtmlFile.read()
components.html(source_code, height = 500, width=700)
def calculate_cosine_sim(self,a,b):
cos_sim = dot(a, b)/(norm(a)*norm(b))
return cos_sim
# Load substance meta
def get_substances(self, _aspect, n):
substance_list = []
substance_ids = {}
exclude = ["sodium", "sugar", "sugar_alcohol"]
_n = n
_aspect = _aspect.replace(" ", "_")
if _aspect in self.health_aspects:
aspect = self.health_aspects[_aspect]
else:
_aspect = difflib.get_close_matches("_aspect", self.health_aspects.keys())[
0
]
aspect = self.health_aspects[_aspect]
substance_scoring = aspect["substance"]
if n != 0:
if n > len(substance_scoring):
n = len(substance_scoring)
substance_scoring = aspect["substance"][:n]
for substance in substance_scoring:
if substance[1] in exclude:
continue
if substance[1] not in substance_ids:
substance_list.append((substance[0], substance[1], _aspect))
substance_ids[substance[1]] = 1
for alias in aspect["alias"]:
n = _n
_substance_scoring = self.health_aspects[alias]["substance"]
if n != 0:
if n > len(_substance_scoring):
n = len(_substance_scoring)
_substance_scoring = self.health_aspects[alias]["substance"][:n]
for substance in _substance_scoring:
if substance[1] in exclude:
continue
if substance[1] not in substance_ids:
substance_list.append((substance[0], substance[1], alias))
substance_ids[substance[1]] = 1
n = _n
if len(substance_list) > n and n != 0:
substance_list = substance_list[:n]
substance_list = sorted(substance_list, key=lambda tup: tup[0], reverse=True)
return substance_list
# Load substance meta and return as DataFrame
def get_substances_df(self, _aspect, n):
substance_list = self.get_substances(_aspect, n)
substance_data = {"substance": [], "score": [], "health_aspect": []}
for substance in substance_list:
substance_data["score"].append(substance[0])
substance_data["substance"].append(substance[1])
substance_data["health_aspect"].append(substance[2])
datatypes = {"substance": str, "score": int, "health_aspect": str}
df = pd.DataFrame(data=substance_data)
df = df.astype(datatypes)
return df
# Get all health aspect indices
def get_all_conditions(self):
condition_list = []
for condition_key in self.conditions:
if condition_key in self.health_aspects:
alias = len(self.health_aspects[condition_key]["alias"])
else:
alias = 0
condition_list.append((self.conditions[condition_key]["frequency"],condition_key,alias))
condition_list = sorted(condition_list, key=lambda tup: tup[0], reverse=True)
return condition_list
def get_all_conditions_df(self):
condition_list = self.get_all_conditions()[:100]
condition_data = {
"Condition": [],
"Frequency": [],
"Alias": []
}
for condition in condition_list:
condition_data["Frequency"].append(condition[0])
condition_data["Condition"].append(condition[1])
condition_data["Alias"].append(condition[2])
datatypes = {
"Frequency": int,
"Condition": str,
"Alias": int
}
df = pd.DataFrame(data=condition_data)
df = df.astype(datatypes)
return df
def get_all_benefits(self):
benefit_list = []
for benefit_key in self.benefits:
if benefit_key in self.health_aspects:
alias = len(self.health_aspects[benefit_key]["alias"])
else:
alias = 0
benefit_list.append((self.benefits[benefit_key]["frequency"],benefit_key,alias))
benefit_list = sorted(benefit_list, key=lambda tup: tup[0], reverse=True)
return benefit_list
def get_all_benefits_df(self):
benefit_list = self.get_all_benefits()[:100]
benefit_data = {
"Benefit": [],
"Frequency": [],
"Alias": []
}
for benefit in benefit_list:
benefit_data["Frequency"].append(benefit[0])
benefit_data["Benefit"].append(benefit[1])
benefit_data["Alias"].append(benefit[2])
datatypes = {
"Frequency": int,
"Benefit": str,
"Alias": int
}
df = pd.DataFrame(data=benefit_data)
df = df.astype(datatypes)
return df