Wann-Jiun

y_final = (1*np.ravel(y_test_pred_xgb) + 1*np.ravel(y_test_pred_kridge) + 1*np.ravel(y_test_pred_lasso))/3
y_final.shape

y_pred = np.exp(y_final)

# Final Conversion.
output_file = 'xgboost_lasso_kridge_weights_1_1_1'
final_file = '0108_'+ output_file +'.csv'

pred_df = pd.DataFrame(y_pred, index=test_df["Id"], columns=["SalePrice"])

Wann-Jiun

from xgboost import plot_importance
from matplotlib import pyplot

xgb_regr = xgb.XGBRegressor(
                 colsample_bytree=0.2,
                 gamma=0.0,
                 learning_rate=0.01,
                 max_depth=4,
                 min_child_weight=1.5,
                 n_estimators=7200,                                                                  

Wann-Jiun

# Extra Trees Regressor

et_regr = ExtraTreesRegressor()
et_regr.fit(train_df_munged, label_df)

# Run prediction on training set to get a rough idea of how well it does.
y_pred = et_regr.predict(train_df_munged)
y_test = label_df
print("Extra Trees Regressor score on training set: ", rmse(y_test, y_pred))

Wann-Jiun

df = pd.read_csv('ratings.csv', sep=',')
df_id = pd.read_csv('links.csv', sep=',')
df = pd.merge(df, df_id, on=['movieId'])

rating_matrix = np.zeros((df.userId.unique().shape[0], max(df.movieId)))
for row in df.itertuples():
    rating_matrix[row[1]-1, row[2]-1] = row[3]
rating_matrix = rating_matrix[:,:9000]

Wann-Jiun

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]

Wann-Jiun

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

Wann-Jiun

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)):

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) )

Wann-Jiun

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

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)