Wann-Jiun

# creating matrices for sklearn:
x_train = np.array(train_df_munged)
x_test = np.array(test_df_munged)
y_train = label_df.values

ntrain = x_train.shape[0]
ntest = x_test.shape[0]

kf = KFold(ntrain, n_folds=NFOLDS, shuffle=True, random_state=SEED)

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from sklearn.metrics import mean_squared_error

prediction = similarity_user.dot(train_matrix) / np.array([np.abs(similarity_user).sum(axis=1)]).T
prediction = prediction[test_matrix.nonzero()].flatten()
test_vector = test_matrix[test_matrix.nonzero()].flatten()
mse = mean_squared_error(prediction, test_vector)

print 'MSE = ' + str(mse)

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Wann-Jiun

quality_dict = {None: 0, "Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5}

train_df["ExterQual"] = df["ExterQual"].map(quality_dict).astype(int)
train_df["ExterCond"] = df["ExterCond"].map(quality_dict).astype(int)
train_df["BsmtQual"] = df["BsmtQual"].map(quality_dict).astype(int)
train_df["BsmtCond"] = df["BsmtCond"].map(quality_dict).astype(int)
train_df["HeatingQC"] = df["HeatingQC"].map(quality_dict).astype(int)
train_df["KitchenQual"] = df["KitchenQual"].map(quality_dict).astype(int)
train_df["FireplaceQu"] = df["FireplaceQu"].map(quality_dict).astype(int)
train_df["GarageQual"] = df["GarageQual"].map(quality_dict).astype(int)

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import requests
import json

from IPython.display import Image
from IPython.display import display
from IPython.display import HTML

idx_to_movie = {}
for row in df_id.itertuples():
    idx_to_movie[row[1]-1] = row[2]

Wann-Jiun

similarity_user = train_matrix.dot(train_matrix.T) + 1e-9
norms = np.array([np.sqrt(np.diagonal(similarity_user))])
similarity_user = ( similarity_user / (norms * norms.T) )

similarity_movie = train_matrix.T.dot(train_matrix) + 1e-9
norms = np.array([np.sqrt(np.diagonal(similarity_movie))])
similarity_movie = ( similarity_movie / (norms * norms.T) )

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df_id = pd.read_csv('links.csv', sep=',')

idx_to_movie = {}
for row in df_id.itertuples():
    idx_to_movie[row[1]-1] = row[2]  
    
total_movies = 9000

movies = [0]*total_movies
for i in range(len(movies)):

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sparsity = float(len(ratings.nonzero()[0]))
sparsity /= (ratings.shape[0] * ratings.shape[1])
sparsity *= 100

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train_matrix = rating_matrix.copy()
test_matrix = np.zeros(ratings_matrix.shape)

for i in xrange(rating_matrix.shape[0]):
    rating_idx = np.random.choice(
        rating_matrix[i, :].nonzero()[0], 
        size=10, 
        replace=True)
    train_matrix[i, rating_idx] = 0.0
    test_matrix[i, rating_idx] = rating_matrix[i, rating_idx]