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knn.py
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knn.py
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######################################
# Current Version #
######################################
import numpy as np
import pandas as pd
import sys
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import MinMaxScaler
import time
# change this to your own path
data = pd.read_csv("/Users/drewhoo/Desktop/all-data.csv")
# usr_title = sys.argv[1] # String input for title [DEPRECATED]
# usr_author = sys.argv[2] # String input for author [DEPRECATED]
attempts = 5
for attempt in xrange(attempts):
try:
usr_title = raw_input("Enter Title: ")
usr_author = raw_input("Enter Author: ")
found = data[(data.book_name == usr_title) & (data.author == usr_author)]
found_book = found['ID']
id_target = found_book.tolist()[0]
except IndexError:
print "**** Title and/or Author Incorrect or Book is not in corpus****\nPlease try again"
# sys.exit(1)
else:
print "************** Searching for Book *******************"
break
# Start timer
start_time = time.time()
# status = "**************** Adjusting Data *******************"
# print status
data = data.drop('>', 1)
data = data.drop('(', 1)
data = data.drop('[', 1)
data = data.drop('{', 1)
data = data.drop('#', 1)
data = data.drop('&', 1)
data = data.drop('/', 1)
data = data.drop('\\', 1)
data = data.drop('*', 1)
data = data.drop('@', 1)
data = data.drop('_', 1)
data = data.drop('^', 1)
# data = pd.concat([data, pd.get_dummies(data['Cluster'])], axis=1)
data = pd.concat([data, pd.get_dummies(data['smallClusterId'], prefix="general")], axis=1) #and for each cluster
data = pd.concat([data, pd.get_dummies(data['mediumClusterId'], prefix="medium")], axis=1)
data = pd.concat([data, pd.get_dummies(data['largeClusterId'], prefix="specific")], axis=1)
data = pd.concat([data, pd.get_dummies(data['author'])], axis=1) #now you have a binary variable for each author
titles = data['book_name']
authors = data['author']
IDs = data['ID']
data = data.drop('ID', 1)
data = data.drop('book_name', 1)
data = data.drop('mediumClusterId', 1)
data = data.drop('smallClusterId', 1)
data = data.drop('largeClusterId', 1)
data = data.drop('author', 1)
data = data.drop('filename', 1)
data = data.fillna(0)
scaler = MinMaxScaler()
data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
# data.to_csv("Normalized output.csv")
data['ID'] = IDs #had to remove this earlier because we didn't want IDs to be scaled with the other columns
print("Completed in %.3f seconds..." % (time.time() - start_time))
status = "*********** Finding Recommendations ***************"
print status
start_time = time.time()
example = data[data['ID']==id_target]
example = example.drop('ID', 1)
data = data.drop('ID', 1)
neigh = NearestNeighbors(5)
neigh = neigh.fit(data)
# print "example ", example
output = neigh.kneighbors(example, 15)
a = output[0]
b = output[1]
# print "Distance ", a
# print "Nearest Neighbor ", b
b = b.tolist()
b = b[0]
fulldata = data
fulldata['author'] = authors
fulldata['book_name'] = titles
results = data.iloc[b,:]
# print results.shape
results = results[['book_name','author']]
print("Completed in %.3f seconds..." % (time.time() - start_time))
status = "************* Your Recommendations ***************"
print status
print results