reg_keras.py

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import numpy as np
import glob
import cv2
import os
 
def load_house_attributes(inputPath):
	# initialize the list of column names in the CSV file and then
	# load it using Pandas
	cols = ["bedrooms", "bathrooms", "area", "zipcode", "price"]
	df = pd.read_csv(inputPath, sep=" ", header=None, names=cols)
	

#   bedrooms  bathrooms  area  zipcode     price
#0         4        4.0  4053    85255  869500.0
#1         4        3.0  3343    36372  865200.0	

# determine (1) the unique zip codes and (2) the number of data
# points with each zip code
	zipcodes = df["zipcode"].value_counts().keys().tolist()
	counts = df["zipcode"].value_counts().tolist()
 
# loop over each of the unique zip codes and their corresponding
# count
	for (zipcode, count) in zip(zipcodes, counts):
		# the zip code counts for our housing dataset is *extremely*
		# unbalanced (some only having 1 or 2 houses per zip code)
		# so let's sanitize our data by removing any houses with less
		# than 25 houses per zip code
		if count < 25:
			idxs = df[df["zipcode"] == zipcode].index
			df.drop(idxs, inplace=True)
 
# return the data frame
	return df
	
# one-hot encode the zip code categorical data (by definition of
# one-hot encoing, all output features are now in the range [0, 1])
#	zipBinarizer = LabelBinarizer().fit(df["zipcode"])
#	trainCategorical = zipBinarizer.transform(train["zipcode"])
#	testCategorical = zipBinarizer.transform(test["zipcode"])
 
# construct our training and testing data points by concatenating
# the categorical features with the continuous features
#	trainX = np.hstack([trainCategorical, trainContinuous])
#	testX = np.hstack([testCategorical, testContinuous])
 
# return the concatenated training and testing data
#	return (trainX, testX)


# preprocess data
def process_house_attributes(df, train, test):
	# initialize the column names of the continuous data
	continuous = ["bedrooms", "bathrooms", "area"]
 
	# performin min-max scaling each continuous feature column to
	# the range [0, 1]
	cs = MinMaxScaler()
	trainContinuous = cs.fit_transform(train[continuous])
	testContinuous = cs.transform(test[continuous])
	
# preprocess categorical features
	# one-hot encode the zip code categorical data (by definition of
	# one-hot encoing, all output features are now in the range [0, 1])
	zipBinarizer = LabelBinarizer().fit(df["zipcode"])
	trainCategorical = zipBinarizer.transform(train["zipcode"])
	testCategorical = zipBinarizer.transform(test["zipcode"])
 
	# construct our training and testing data points by concatenating
	# the categorical features with the continuous features
	trainX = np.hstack([trainCategorical, trainContinuous])
	testX = np.hstack([testCategorical, testContinuous])
 
	# return the concatenated training and testing data
	return (trainX, testX)