def regressior_baseline(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip() for line in open(test_business_list).readlines()]
train_raw_x, test_raw_x = gen_data_no_review(bus_df, test_ids)
print "baseline lasso no review"
# 335 rmse: 0.792623
# 410 rmse: 1.255996
print "rmse: %f" % baseline_lasso_leave_one_out(
train_raw_x, test_raw_x, test_ids, alpha=0.001, result_path="regression_lasso_no_review.txt"
)
print "baseline extra no review"
# 335 rmse: 0.818625
print "rmse: %f" % baseline_extra_leave_one_out(
train_raw_x, test_raw_x, test_ids, n=20, d=10, result_path="regression_extra_no_review.txt"
)
train_raw_x, test_raw_x = gen_data_with_top_review(bus_df, test_ids, city)
print "baseline lasso with 10 reviews"
# 335 rmse: 0.808528
# 410 rmse: 1.256638
print "rmse: %f" % baseline_lasso_leave_one_out(
train_raw_x, test_raw_x, test_ids, alpha=10e-4, result_path="regression_with_review.txt"
)
print "baseline extra no review"
print "rmse: %f" % baseline_extra_leave_one_out(
train_raw_x, test_raw_x, test_ids, n=40, d=40, result_path="regression_extra_with_review.txt"
)
def build_feature(input_file, top_review_number=50):
bus_df = get_data()
all_stars_dist = pd.value_counts(
bus_df.stars, normalize=True).sort_index(ascending=False)
all_stars_dist_dict = all_stars_dist.to_dict()
stars_dict = dict(zip(bus_df.business_id, bus_df.stars))
x = []
y = []
xi = {}
now_b_id = ""
count = 0
for line in open(input_file):
line = line.replace("BUSINESS#", "")
source, dest, pagerank = line.strip().split(',')
if now_b_id != source:
if xi:
row = []
for k in sorted(xi.keys()):
row.append((xi[k] / count) - all_stars_dist_dict[k])
x.append(row)
xi = {k: 0.0 for k in all_stars_dist_dict.keys()}
now_b_id = source
count = 0
y.append(stars_dict[now_b_id])
if count < top_review_number:
xi[stars_dict[dest]] += 1
count += 1
row = []
for k in sorted(xi.keys()):
row.append((xi[k] / count) - all_stars_dist_dict[k])
x.append(row)
return np.array(x), np.array(y)
def baseline_city_average(city="Phoenix"):
bus_df = get_data(city)
# test_ids = bus_df.business_id[:100].tolist()
test_ids = [line.strip()
for line in open(test_business_list).readlines()]
x = bus_df[['business_id', 'review_count', 'categories']]
test_x = x[bus_df['business_id'].isin(test_ids)].reset_index()
predict = []
for test_id in test_ids:
predict.append(x[x.business_id != test_id].review_count.mean())
rmse = mean_squared_error(predict, test_x.review_count.as_matrix()) ** 0.5
print rmse
def search_n_d(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip() for line in open(test_business_list).readlines()]
print "no review"
train_raw_x, test_raw_x = gen_data_no_review(bus_df, test_ids)
train_x = train_raw_x[~train_raw_x.business_id.isin(test_ids)]
train_y = train_x.stars.as_matrix()
train_x = train_x.drop(["business_id", "stars"], 1).as_matrix()
tune_extra(train_x, train_y)
print "with top 10 review"
train_raw_x, test_raw_x = gen_data_with_top_review(bus_df, test_ids, city)
train_x = train_raw_x[~train_raw_x.business_id.isin(test_ids)]
train_y = train_x.stars.as_matrix()
train_x = train_x.drop(["business_id", "stars"], 1).as_matrix()
tune_extra(train_x, train_y)
def baseline_city_average(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip() for line in open(test_business_list).readlines()]
x = bus_df[["business_id", "stars"]]
test_x = x[bus_df["business_id"].isin(test_ids)].reset_index()
predict = []
for test_id in test_ids:
predict.append(x[x.business_id != test_id].stars.mean())
rmse = mean_squared_error(predict, test_x.stars.as_matrix()) ** 0.5
result = pd.DataFrame([], columns=["stars", "predict"])
result["stars"] = test_x.stars
result["predict"] = predict
result = result.sort("stars", ascending=0)
result.to_csv("city.csv", index=False)
print rmse
def baseline_category_average(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip()
for line in open(test_business_list).readlines()]
x = bus_df[['business_id', 'review_count', 'categories']]
test_x = x[bus_df['business_id'].isin(test_ids)].reset_index()
predict = []
for test_id in test_ids:
categories = set(x[x.business_id == test_id].categories.tolist()[0])
if categories:
c = x.categories.apply(
lambda x: len(categories.intersection(set(x))) > 0)
predict.append(x[c].review_count.mean())
else:
predict.append(x[x.business_id != test_id].review_count.mean())
rmse = mean_squared_error(predict, test_x.review_count.as_matrix()) ** 0.5
print rmse
def search_alpha(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip() for line in open(test_business_list).readlines()]
print "no review"
train_raw_x, test_raw_x = gen_data_no_review(bus_df, test_ids)
train_x = train_raw_x[~train_raw_x.business_id.isin(test_ids)]
train_y = train_x.stars.as_matrix()
train_x = train_x.drop(["business_id", "stars"], 1).as_matrix()
coefs, scores, alpha = tune_lasso(train_x, train_y)
best_alpha = alpha[scores.index(min(scores))]
print "best alpha=%f" % best_alpha
print "with top 10 review"
train_raw_x, test_raw_x = gen_data_with_top_review(bus_df, test_ids, city)
train_x = train_raw_x[~train_raw_x.business_id.isin(test_ids)]
train_y = train_x.stars.as_matrix()
train_x = train_x.drop(["business_id", "stars"], 1).as_matrix()
coefs, scores, alpha = tune_lasso(train_x, train_y)
best_alpha = alpha[scores.index(min(scores))]
print "best alpha=%f" % best_alpha
def baseline_category_average(city="Phoenix"):
bus_df = get_data(city)
test_ids = [line.strip() for line in open(test_business_list).readlines()]
x = bus_df[["business_id", "stars", "categories"]]
test_x = x[bus_df["business_id"].isin(test_ids)].reset_index()
predict = []
for test_id in test_ids:
categories = set(x[x.business_id == test_id].categories.tolist()[0])
if categories:
c = x.categories.apply(lambda x: len(categories.intersection(set(x))) > 0)
predict.append(x[c].stars.mean())
else:
predict.append(x[x.business_id != test_id].stars.mean())
result = pd.DataFrame([], columns=["stars", "predict"])
result["stars"] = test_x.stars
result["predict"] = predict
result = result.sort("stars", ascending=0)
result.to_csv("category.csv", index=False)
rmse = mean_squared_error(predict, test_x.stars.as_matrix()) ** 0.5
print rmse
