def restaurant_profile(restaurant_id):
# Get details for profile window
restaurant_profile_df = algorithm.restaurant_profile(restaurant_id)
#restaurant_profile = restaurant_profile_df.astype(str) # Issue #7
restaurant_profile = restaurant_profile_df
# Get first/second order nodes for graph, and info for like nodes across bottom of page
like_restaurants_df = algorithm.like_restaurants(restaurant_id,
competitor_limit)
#like_restaurants_str_df = like_restaurants_df[['restaurant', 'restaurant_id', 'compete_score']].astype(str).drop_duplicates().head(like_limit) # Issue #7
like_restaurants_str_df = like_restaurants_df[[
'restaurant', 'restaurant_id', 'compete_score'
]].drop_duplicates().head(like_limit)
# Get third order nodes for graph
customer_prospects_df = algorithm.customer_prospects(
like_restaurants_df, prospect_limit)
# Transform graph info into nodes and links lists
restaurant_graph_dict = algorithm.restaurant_graph(restaurant_id,
restaurant_profile_df,
like_restaurants_df,
customer_prospects_df)
return restaurant_profile, restaurant_graph_dict, like_restaurants_str_df
def direct_competitor_similarity(active_restaurant_id, selected_restaurant_id):
##restaurant_similarity_json
active_restaurant_profile_df = algorithm.restaurant_profile(
active_restaurant_id)
# Returns restaurant details with categories and attributes csv lists
active_like_restaurants_df = algorithm.like_restaurants(
active_restaurant_id, competitor_limit)
restaurant_similarity = active_like_restaurants_df[
active_like_restaurants_df['restaurant_id'] == selected_restaurant_id][
[
'shared_val', 'shared_score', 'overlap_val', 'overlap_score',
'rate_sim_val', 'rate_sim_score', 'zip_val', 'zip_score',
'compete_score'
]].astype(str)
restaurant_similarity_json = restaurant_similarity.to_json(
orient='records')
##comparsion
customer_prospects_df = algorithm.customer_prospects(
active_like_restaurants_df, prospect_limit)
restaurant_graph_dict = algorithm.restaurant_graph(
active_restaurant_id, active_restaurant_profile_df,
active_like_restaurants_df, customer_prospects_df)
# Global nodes and links dataframes are created
nodes = restaurant_graph_dict['nodes']
nodes_users = nodes[nodes['order'] == 2][['name', 'score']]
links = restaurant_graph_dict['links'].to_json(orient='records')
nodes = restaurant_graph_dict['nodes'].to_json(orient="records")
graph = {"links": links, "nodes": nodes}
comparison_json = nodes_users.to_json(orient="records")
##likes
restaurant_profile_df = algorithm.restaurant_profile(
selected_restaurant_id)
like_restaurants_df = algorithm.like_restaurants(selected_restaurant_id,
competitor_limit)
like_restaurants = like_restaurants_df[[
'restaurant', 'restaurant_id', 'compete_score'
]].astype(str).drop_duplicates().head(10)
like_restaurants_json = like_restaurants.to_json(orient="records")
result = {
"graph": graph,
"restaurant_similarity": restaurant_similarity_json,
"comparison": comparison_json,
"likes": like_restaurants_json
}
return result
def direct_competitor_similarity(active_restaurant_id, selected_restaurant_id):
# Get active restaurant info for other routines
active_restaurant_profile_df = algorithm.restaurant_profile(
active_restaurant_id)
#restaurant_profile = active_restaurant_profile_df.astype(str) # Issue #7
#restaurant_profile = active_restaurant_profile_df
# Get first/second order nodes for graph
active_like_restaurants_df = algorithm.like_restaurants(
active_restaurant_id, competitor_limit)
# Pull off details on selected direct competitor similarity for upper right window
restaurant_similarity = active_like_restaurants_df[
active_like_restaurants_df['restaurant_id'] == selected_restaurant_id][
[
'shared_val', 'shared_score', 'overlap_val', 'overlap_score',
'review_sim_val', 'review_sim_score', 'rate_sim_val',
'rate_sim_score', 'zip_val', 'zip_score', 'compete_score'
]]
# Get third order nodes for graph
customer_prospects_df = algorithm.customer_prospects(
active_like_restaurants_df, prospect_limit)
# Transform graph info into nodes and links lists
restaurant_graph_dict = algorithm.restaurant_graph(
active_restaurant_id, active_restaurant_profile_df,
active_like_restaurants_df, customer_prospects_df)
# Get info for comparison bar graph
nodes = restaurant_graph_dict[
'nodes'] # Call these nodes and nodes_comparison
nodes_comparison = nodes[nodes['order'] == 2][['name', 'score']]
nodes_comparison.sort_values(by=['score'], ascending=False, inplace=True)
nodes_comparison = nodes_comparison.round({'score': 2})
##likes
restaurant_profile_df = algorithm.restaurant_profile(
selected_restaurant_id) # Not needed - delete
# Prepare the like user nodes
like_restaurants_df = algorithm.like_restaurants(
selected_restaurant_id,
competitor_limit)[['restaurant', 'restaurant_id', 'compete_score']]
like_restaurants_df['restaurant'] = like_restaurants_df[
'restaurant'].str.encode('utf8')
like_restaurants_df['restaurant_id'] = like_restaurants_df[
'restaurant_id'].str.encode('utf8')
like_restaurants_df['compete_score'] = like_restaurants_df[
'compete_score'].astype(str)
like_restaurants_df = like_restaurants_df.drop_duplicates().head(
like_limit) # Issue #7
return restaurant_similarity, nodes_comparison, like_restaurants_df
def rating_by_user(active_restaurant_id, user_id):
restaurant_profile_df = algorithm.restaurant_profile(active_restaurant_id)
like_restaurants_df = algorithm.like_restaurants(restaurant_id,
competitor_limit)
# Returns name and restaurant_id info to build like restaurant nodes at bottom of screen.
# (Also provides info for bar graph in Analyze mode when a second order node is selected)
customer_prospects_df = algorithm.customer_prospects(
like_restaurants_df, prospect_limit)
restaurant_graph_dict = algorithm.restaurant_graph(restaurant_id,
restaurant_profile_df,
like_restaurants_df,
customer_prospects_df)
# Global nodes and links dataframes are created
nodes = restaurant_graph_dict['nodes']
nodes_users = nodes[nodes['order'] == 1][['name', 'score']]
links = restaurant_graph_dict['links'].to_json(orient='records')
nodes = restaurant_graph_dict['nodes'].to_json(orient="records")
graph = {"links": links, "nodes": nodes}
comparison_json = nodes_users.to_json(orient="records")
rating_df = algorithm.user_restaurant_rating(user_id, active_restaurant_id)
# Returns details of how restaurant was rated by user
rating = rating_df[[
'restaurant', 'user', 'rating', 'date', 'review', 'useful', 'funny',
'cool'
]].astype(str)
rating_json = rating.to_json(orient='records')
##likes json
user_profile_df = algorithm.user_profile(user_id)
# Get first/second order nodes for graph, and info for like nodes across bottom of page
# This is for explore mode, all like nodes are LMU for active users
like_users_df = algorithm.like_users(user_profile_df, user_id, lmu_limit)
like_users = like_users_df[['user', 'user_id', 'lmu_score'
]].astype(str).drop_duplicates().head(10)
like_users_json = like_users.to_json(orient="records")
result = {
"graph": graph,
"rating": rating_json,
"comparison": comparison_json,
"likes": like_users_json
}
return result
def rating_by_user(active_restaurant_id, user_id):
# Get active restaurant info for other routines
restaurant_profile_df = algorithm.restaurant_profile(active_restaurant_id)
#restaurant_profile = restaurant_profile_df.astype(str) # Issue #7
#restaurant_profile = restaurant_profile_df
# Get first/second order nodes for graph
like_restaurants_df = algorithm.like_restaurants(active_restaurant_id,
competitor_limit)
# Get third order nodes for graph
customer_prospects_df = algorithm.customer_prospects(
like_restaurants_df, prospect_limit)
# Transform graph info into nodes and links lists
restaurant_graph_dict = algorithm.restaurant_graph(active_restaurant_id,
restaurant_profile_df,
like_restaurants_df,
customer_prospects_df)
# Get info for comparison bar graph
nodes = restaurant_graph_dict['nodes']
nodes_comparison = nodes[nodes['order'] == 1][['name', 'score']]
nodes_comparison.sort_values(by=['score'], ascending=False, inplace=True)
nodes_comparison = nodes_comparison.round({'score': 2})
# Returns details of how active restaurant was rated by selected user
rating_df = algorithm.user_restaurant_rating(user_id, active_restaurant_id)
#rating_str_df = rating_df[['restaurant', 'user', 'rating', 'date', 'review', 'useful', 'funny', 'cool']].astype(str) # Issue #7
rating_str_df = rating_df[[
'restaurant', 'user', 'rating', 'date', 'review', 'useful', 'funny',
'cool'
]]
## Get info for like nodes across bottom of page
user_profile_df = algorithm.user_profile(user_id)
like_users_df = algorithm.like_users(user_profile_df, user_id, lmu_limit)
#like_users_str_df = like_users_df[['user', 'user_id', 'lmu_score']].astype(str).drop_duplicates().head(like_limit) # Issue #7
like_users_str_df = like_users_df[['user', 'user_id', 'lmu_score'
]].drop_duplicates().head(like_limit)
# Return should be: restaurant_graph_dict, like_users, rating, nodes_comparison
return rating_str_df, nodes_comparison, like_users_str_df
def restaurant_profile(restaurant_id):
# Get details for profile window
restaurant_profile_df = algorithm.restaurant_profile(restaurant_id)
restaurant_profile = restaurant_profile_df.astype(str)
restaurant_profile_json = restaurant_profile.to_json(orient="records")
#display(restaurant_profile_json)
# Get first/second order nodes for graph, and info for like nodes across bottom of page
like_restaurants_df = algorithm.like_restaurants(restaurant_id,
competitor_limit)
like_restaurants = like_restaurants_df[[
'restaurant', 'restaurant_id', 'compete_score'
]].astype(str).drop_duplicates().head(10)
like_restaurants_json = like_restaurants.to_json(orient="records")
#display(like_restaurants_json)
# Get third order nodes for graph
customer_prospects_df = algorithm.customer_prospects(
like_restaurants_df, prospect_limit)
#display(customer_prospects_df)
# Transform graph info into nodes and links lists
restaurant_graph_dict = algorithm.restaurant_graph(restaurant_id,
restaurant_profile_df,
like_restaurants_df,
customer_prospects_df)
#display(restaurant_graph_dict)
links = restaurant_graph_dict['links'].to_json(orient='records')
nodes = restaurant_graph_dict['nodes'].to_json(orient="records")
graph = {"links": links, "nodes": nodes}
result = {
"profile": restaurant_profile_json,
"graph": graph,
"likes": like_restaurants_json
}
return result
def customer_prospect_relevance(active_restaurant_id, selected_user_id):
##pre-calcualtion
active_restaurant_profile_df = algorithm.restaurant_profile(
active_restaurant_id)
active_like_restaurants_df = algorithm.like_restaurants(
active_restaurant_id, competitor_limit)
customer_prospects_df = algorithm.customer_prospects(
like_restaurants_df, prospect_limit)
restaurant_graph_dict = algorithm.restaurant_graph(restaurant_id,
restaurant_profile_df,
like_restaurants_df,
customer_prospects_df)
##graph
links = restaurant_graph_dict['links'].to_json(orient='records')
nodes = restaurant_graph_dict['nodes'].to_json(orient="records")
graph = {"links": links, "nodes": nodes}
##relevance
customer_prospect_relevance = customer_prospects_df[
customer_prospects_df['user_id'] == selected_user_id]
relevance_df = customer_prospect_relevance[[
'restaurant', 'user_rating', 'compete_score', 'contribution',
'relevancy'
]]
if (customer_prospect_relevance.shape[0] == 1):
avg_rating = customer_prospect_relevance['avg_rating'].values[0]
user_similarity = 5.0
user_contribution = avg_rating * user_similarity
user_relevancy = customer_prospect_relevance['relevancy'].values[0]
user_df = pd.DataFrame({
'restaurant': ["User's Average Rating"],
"user_rating": [avg_rating],
"compete_score": [user_similarity],
"contribution": [user_contribution],
"relevancy": [user_relevancy]
})
relevance_df = pd.concat([relevance_df, user_df], axis=0)
relevance = relevance_df.astype(str)
relevance_json = relevance.to_json(orient='records')
##comparison
nodes = restaurant_graph_dict['nodes']
nodes_restaurants = nodes[nodes['order'] == 3][['name', 'score']]
comparison_json = nodes_restaurants.to_json(orient="records")
##likes
selected_user_profile_df = algorithm.user_profile(selected_user_id)
selected_like_users_df = algorithm.like_users(selected_user_profile_df,
selected_user_id, lmu_limit)
like_users = selected_like_users_df[[
'user', 'user_id', 'lmu_score'
]].astype(str).drop_duplicates().head(10)
like_users_json = like_users.to_json(orient="records")
result = {
"graph": graph,
"relevance": relevance_json,
"comparison": comparison_json,
"likes": like_users_json
}
return result
def customer_prospect_relevance(active_restaurant_id, selected_user_id):
# Get active restaurant info for other routines
active_restaurant_profile_df = algorithm.restaurant_profile(
active_restaurant_id)
#restaurant_profile = active_restaurant_profile_df.astype(str) # Issue #7
#restaurant_profile = active_restaurant_profile_df
# Get first/second order nodes for graph
active_like_restaurants_df = algorithm.like_restaurants(
active_restaurant_id, competitor_limit)
# Get third order nodes for graph
customer_prospects_df = algorithm.customer_prospects(
active_like_restaurants_df, prospect_limit)
# Transform graph info into nodes and links lists
restaurant_graph_dict = algorithm.restaurant_graph(
active_restaurant_id, active_restaurant_profile_df,
active_like_restaurants_df, customer_prospects_df)
## Pull off details on selected customer prospect relevance for upper right window
customer_prospect_relevance = customer_prospects_df[
customer_prospects_df['user_id'] == selected_user_id]
relevance_df = customer_prospect_relevance[[
'restaurant', 'user_rating', 'compete_score', 'contribution',
'relevancy'
]]
if (customer_prospect_relevance.shape[0] == 1):
avg_rating = customer_prospect_relevance['avg_rating'].values[0]
user_similarity = 5.0
user_contribution = avg_rating * user_similarity
user_relevancy = customer_prospect_relevance['relevancy'].values[0]
user_df = pd.DataFrame({
'restaurant': ["User's Average Rating"],
"user_rating": [avg_rating],
"compete_score": [user_similarity],
"contribution": [user_contribution],
"relevancy": [user_relevancy]
})
relevance_df = pd.concat([relevance_df, user_df], axis=0)
#relevance = relevance_df.astype(str) # Issue #7
relevance = relevance_df
# Get info for comparison bar graph
nodes = restaurant_graph_dict['nodes']
nodes_comparison = nodes[nodes['order'] == 3][['name', 'score']]
nodes_comparison.sort_values(by=['score'], ascending=False, inplace=True)
nodes_comparison = nodes_comparison.round({'score': 2})
# Prepare the like user nodes
selected_user_profile_df = algorithm.user_profile(selected_user_id)
selected_like_users_df = algorithm.like_users(selected_user_profile_df,
selected_user_id, lmu_limit)
#like_users_str_df = selected_like_users_df[['user', 'user_id', 'lmu_score']].astype(str).drop_duplicates().head(like_limit) # Issue #7
like_users_str_df = selected_like_users_df[[
'user', 'user_id', 'lmu_score'
]].drop_duplicates().head(like_limit)
# restaurant_graph_dict, like_users, relevance, nodes_comparison
return relevance, nodes_comparison, like_users_str_df