Load Pandas, we are going to need it for manipulating data
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import pandas as pd
import numpy as np
from IPython.display import Image
np.set_printoptions(precision = 3)
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Image(filename='books.png')
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Now load the data
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data = pd.read_csv("user_ratings.csv")
d = data.to_latex()
text_file = open("Output.txt", "w")
text_file.write(d)
text_file.close()
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n_features = 2
user_ratings = data.values
user_preferences = np.random.random((user_ratings.shape[0], n_features))
item_features = np.random.random((user_ratings.shape[1],n_features))
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item_features
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user_preferences
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def predict_rating(user_id,item_id):
""" Predict a rating given a user_id and an item_id.
"""
user_preference = user_preferences[user_id]
item_preference = item_features[item_id]
return user_preference.dot(item_preference)
def train(user_id, item_id, rating,alpha = 0.0001):
#print item_id
prediction_rating = predict_rating(user_id, item_id)
err = alpha * ( prediction_rating- rating );
#print err
user_pref_values = user_preferences[user_id][:]
user_preferences[user_id] -= err * item_features[item_id]
item_features[item_id] -= err * user_pref_values
return err
def sgd(iterations = 30000):
""" Iterate over all users and all items and train for
a certain number of iterations
"""
for iteration in range(0,iterations):
error = []
for user_id in range(0,user_preferences.shape[0]):
for item_id in range(0,item_features.shape[0]):
rating = user_ratings[user_id][item_id]
if(not np.isnan(rating)):
err = train(user_id,item_id,rating)
error.append(err)
mse = (np.array(error) ** 2).mean()
print mse
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#train(0,0,10)
sgd()
#user_preferences
#item_features
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predictions = user_preferences.dot(item_features.T)
predictions
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values = [zip(user_ratings[i], predictions[i]) for i in range(0,predictions.shape[0])]
comparison_data = pd.DataFrame(values)
comparison_data.columns = data.columns
comparison_data.applymap(lambda (x,y): "(%2.3f|%2.3f)"%(x,y))
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comparison_data
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d = comparison_data.to_latex()
text_file = open("comparison.txt", "w")
text_file.write(d)
text_file.close()
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