def main():
data = load_immobilien_data()
round_space = np.vectorize(lambda v: int(round(v / 10.0) * 10))
data["living_space"] = round_space(data["living_space"])
round_space = np.vectorize(lambda s: int(round(s / 10000.0) * 10000))
data["price"] = round_space(data["price"])
figure = pl.figure(1)
figure.clf()
ax = figure.add_subplot(111)
labels = dict([(d, i) for i, d in enumerate(data["distict"].unique())])
ncolors = len(labels)
for n, g in data.groupby("distict"):
ax.scatter(
g["living_space"],
g["price"],
c=np.random.rand(ncolors, 1),
s=(labels[n] + 2) * 3,
label=n.decode("utf-8"),
alpha=0.75,
)
ax.set_xlabel("Living space (sqm)")
ax.set_ylabel("Price (EUR)")
ax.grid(True)
pl.legend(shadow=True, fancybox=True, loc=0, scatterpoints=1)
pl.show()
def main():
data = load_immobilien_data()
round_feature = np.vectorize(lambda v: int(round(v)))
data['number_of_rooms'] = round_feature(data['number_of_rooms'])
figure = pl.figure(1)
figure.clf()
pic = 321
for distict in disticts:
sdata = data[data['district'] == distict.encode('utf-8')]
ncolors = len(sdata['number_of_rooms'].unique())
ax = figure.add_subplot(pic)
for n, g in sdata.groupby('number_of_rooms'):
ax.scatter(g['living_space'], g['price'],
c=np.random.rand(ncolors, 1), s=n*4, label='%s rooms' % n)
ax.set_xlabel('Living space (sqm)')
ax.set_ylabel('Price (EUR)')
ax.set_title(distict)
ax.grid(True)
ax.legend(loc=0, scatterpoints=1)
pic += 1
pl.show()
def main():
data = load_immobilien_data()
round_feature = np.vectorize(lambda v: int(round(v)))
data['number_of_rooms'] = round_feature(data['number_of_rooms'])
figure = pl.figure(1)
figure.clf()
ax = figure.add_subplot(111)
ncolors = len(data['number_of_rooms'].unique())
for n, g in data.groupby('number_of_rooms'):
ax.scatter(g['living_space'], g['price'], c=np.random.rand(ncolors, 1),
s=n*6, label='%s rooms' % n)
ax.set_xlabel('Living space (sqm)')
ax.set_ylabel('Price (EUR)')
ax.grid(True)
pl.legend(shadow=True, fancybox=True, loc=0, scatterpoints=1)
pl.show()
def main():
data = load_immobilien_data()
figure = pl.figure(1)
show_hist(data['price'], 'Price', 'EUR', figure, 331)
show_hist(data['living_space'], 'Living space', 'sqm', figure, 332)
show_hist(data['number_of_rooms'], 'Number of rooms', 'rooms', figure, 333)
sdata = preprocessing.scale(
data[['price', 'living_space', 'number_of_rooms']])
show_hist(sdata[:,0], 'Price / scaled', 'EUR', figure, 334)
show_hist(sdata[:,1], 'Living space / scaled', '', figure, 335)
show_hist(sdata[:,2], 'Number of rooms / scaled', '', figure, 336)
min_max_scaler = preprocessing.MinMaxScaler()
sdata = min_max_scaler.fit_transform(
data[['price', 'living_space', 'number_of_rooms']])
show_hist(sdata[:,0], 'Price / min max scaled', 'EUR', figure, 337)
show_hist(sdata[:,1], 'Living space / min max scaled', '', figure, 338)
show_hist(sdata[:,2], 'Number of rooms / min max scaled', '', figure, 339)
pl.show()
def main():
data = load_immobilien_data()
figure = pl.figure()
ax = figure.add_subplot(111, projection='3d')
ax.scatter(data['price'], data['living_space'], data['number_of_rooms'])
ax.set_xlabel('price')
ax.set_ylabel('living_space')
ax.set_zlabel('number_of_rooms')
pl.show()
def split_data(district=None):
data = load_immobilien_data()
if district:
data = data[data['district'] == district]
m = data.shape[0]
# 30% of the data - test set
test_rows = random.sample(data.index, (m * 30 / 100))
test_set = data.ix[test_rows]
train_set = data.drop(test_rows)
return train_set, test_set
def main():
"""
In order to test for normality let's plot:
* histogram with the best fitting normal curve - check if it's
bell curve
* normal probability plot - should be linear
"""
data = load_immobilien_data()
ax = pl.subplot(121)
plot_histogram(data.price, ax)
pl.subplot(122)
plot_normal_probability(data.price)
pl.show()
def main():
data = load_immobilien_data()
figure = pl.figure(1)
show_hist(data['price'], 'Price', 'EUR', figure, 331)
show_hist(np.log(data['price']), 'log price', 'log(EUR)', figure, 332)
show_hist(np.sqrt(data['price']), 'sqrt price', 'sqrt(EUR)', figure, 333)
show_hist(data['living_space'], 'Living space', 'sqm', figure, 334)
living_space = data[data['living_space'] > 1]['living_space']
show_hist(np.log(living_space), 'log living space', 'log(sqm)', figure, 335)
show_hist(np.sqrt(data['living_space']), 'sqrt living space', 'sqrt(sqm)',
figure, 336)
show_hist(data['number_of_rooms'], 'Numbers of rooms', 'rooms', figure, 337)
show_hist(np.log(data['number_of_rooms']), 'log(rooms)', 'log(rooms)',
figure, 338)
show_hist(np.sqrt(data['number_of_rooms']), 'sqrt(rooms)', 'sqrt(rooms)',
figure, 339)
pl.show()
def main():
data = load_immobilien_data()
data = data.sort(['price']).groupby('district')['price']
data = [(d.decode('utf-8'), np.array(g)) for d, g in data]
data.sort(key=lambda (d, p): p.mean(), reverse=True)
prices = [p for d, p in data]
labels = [d for d, p in data]
#pl.boxplot(prices, 0, 'gD')
pl.boxplot(prices, 0, '')
pl.ylabel('Price in EUR')
pl.xlabel('Distict')
pl.xticks(np.arange(1, len(labels) + 1), labels, rotation=90)
pl.title('Berlin appartment prices')
pl.subplots_adjust(bottom=0.2, right=0.8, top=0.92)
pl.show()
def main():
data = load_immobilien_data()
pl.boxplot(data['price'], 0, 'gD')
pl.show()
def mad(data):
"""Median Absolute Deviation"""
# make it normally distributed
log_data = np.log(data)
# the number of standart deviations to include
m = 4
index = abs(log_data - np.mean(log_data)) < m * np.std(log_data)
return index
def remove_suspicious_data(data):
data = data[np.logical_and(
data['number_of_rooms'] > 0., data['living_space'] > 0.)]
index = mad(data['price'])
data = data[index]
return data
if __name__ == '__main__':
data = load_immobilien_data()
data = remove_suspicious_data(data)
encoder = preprocessing.LabelEncoder()
data['district'] = encoder.fit_transform(data['district'])
print data['district']
