def predict_dataset(data_frame, city, columns):
"""
Predicts prices of listings from a dataset
:params data_frame dataframe:
:params city str:
:params columns list:
:returns predictions array:
"""
x_var, y_var = cm.to_matrix(data_frame, columns)
x_var = boxcox1p(x_var, 0.15) + 1
price_length = len(y_var)
#imports models
regressor_a = joblib.load(DATA_DIR + '/' + city + c.MODEL_1_SUFFIX)
regressor_b = joblib.load(DATA_DIR + '/' + city + c.MODEL_2_SUFFIX)
outlier_boundary = min(do.detect_outlier(y_var))
inbound = (y_var < outlier_boundary)
outbound = (y_var >= outlier_boundary)
#populates predictions depending on current price
predictions = np.zeros(price_length)
predictions[inbound] = np.expm1(regressor_b.predict(x_var[inbound]))
predictions[outbound] = np.expm1(regressor_a.predict(x_var[outbound]))
return predictions
def test_to_matrix_check_dtype(self):
"""
Tests that to_matrix will not run if datatype is not dataframe.
:params self:
:return boolean:
"""
with self.assertRaises(ValueError):
ctm.to_matrix('check', con.LISTING_COLUMNS)
ctm.to_matrix(1, con.LISTING_COLUMNS)
ctm.to_matrix([1, 2, 3], con.LISTING_COLUMNS)
import unittest
import zillowbnb.test.submodule_path
import constants as co
import convert_to_matrix as cm
import get_data as gd
import get_cleaned_listings as gcl
import train_model as tm
DATA = gd.download_dataset(co.DATASET_PROPERTIES, co.LISTINGS_DATA)
DATAFRAME = gcl.get_listings_dataframe(DATA, co.LISTING_COLUMNS)
X_VAR, Y_VAR = cm.to_matrix(DATAFRAME, co.LISTING_COLUMNS)
class TrainModelTest(unittest.TestCase):
"""
This class runs unit tests for the train_model module.
"""
def test_input_size(self):
"""
Tests that train_model will not run if sizes of x_var and y_var
do not match.
:params self:
:returns boolean:
"""
with self.assertRaises(IndexError):
tm.train_model(X_VAR[:500], Y_VAR, 'Hawaii')