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import os import tarfile from six.moves import urllib import pandas as pd import numpy as npDOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/" HOUSING_PATH = "datasets/housing" HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + "/housing.tgz"def fetch_housing_data ( housing_url=HOUSING_URL, housing_path=HOUSING_PATH) : # create datasets/housing directory if needed if not os.path.isdir(housing_path): os.makedirs(housing_path) tgz_path = os.path.join(housing_path, "housing.tgz" ) # retrieve tarfile urllib.request.urlretrieve(housing_url, tgz_path) # extract tarfile & close path housing_tgz = tarfile.open(tgz_path) housing_tgz.extractall(path=housing_path) housing_tgz.close()def load_housing_data ( housing_path=HOUSING_PATH) : csv_path = os.path.join(housing_path, "housing.csv" ) return pd.read_csv(csv_path)# do it #fetch_housing_data() -- already downloaded - static dataset housing = load_housing_data()housing.head()| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY |
# housing is a Pandas DataFrame. # untouched datafile: 20640 records, 10 cols (9 float, 1 text) housing.info()RangeIndex: 20640 entries, 0 to 20639Data columns (total 10 columns):longitude 20640 non-null float64latitude 20640 non-null float64housing_median_age 20640 non-null float64total_rooms 20640 non-null float64total_bedrooms 20433 non-null float64population 20640 non-null float64households 20640 non-null float64median_income 20640 non-null float64median_house_value 20640 non-null float64ocean_proximity 20640 non-null objectdtypes: float64(9), object(1)memory usage: 1.6+ MB# let's see if ocean_proximity can be lumped into categories: housing[ 'ocean_proximity' ].value_counts()<1H OCEAN 9136INLAND 6551NEAR OCEAN 2658NEAR BAY 2290ISLAND 5Name: ocean_proximity, dtype: int64# percentiles analysis of each feature housing.describe()| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | |
|---|---|---|---|---|---|---|---|---|---|
| count | 20640.000000 | 20640.000000 | 20640.000000 | 20640.000000 | 20433.000000 | 20640.000000 | 20640.000000 | 20640.000000 | 20640.000000 |
| mean | -119.569704 | 35.631861 | 28.639486 | 2635.763081 | 537.870553 | 1425.476744 | 499.539680 | 3.870671 | 206855.816909 |
| std | 2.003532 | 2.135952 | 12.585558 | 2181.615252 | 421.385070 | 1132.462122 | 382.329753 | 1.899822 | 115395.615874 |
| min | -124.350000 | 32.540000 | 1.000000 | 2.000000 | 1.000000 | 3.000000 | 1.000000 | 0.499900 | 14999.000000 |
| 25% | -121.800000 | 33.930000 | 18.000000 | 1447.750000 | 296.000000 | 787.000000 | 280.000000 | 2.563400 | 119600.000000 |
| 50% | -118.490000 | 34.260000 | 29.000000 | 2127.000000 | 435.000000 | 1166.000000 | 409.000000 | 3.534800 | 179700.000000 |
| 75% | -118.010000 | 37.710000 | 37.000000 | 3148.000000 | 647.000000 | 1725.000000 | 605.000000 | 4.743250 | 264725.000000 |
| max | -114.310000 | 41.950000 | 52.000000 | 39320.000000 | 6445.000000 | 35682.000000 | 6082.000000 | 15.000100 | 500001.000000 |
# feature histograms %matplotlib inline import matplotlib.pyplot as plthousing.hist(bins=, figsize=(,))array([[, , ], [, , ], [, , ]], dtype=object)
# split dataset into training (80%) and test (20%) subsets import numpy as np def split_train_test ( data, test_ratio) : shuffled_indices = np.random.permutation(len(data)) test_set_size = int(len(data) * test_ratio) test_indices = shuffled_indices[:test_set_size] train_indices = shuffled_indices[test_set_size:] return data.iloc[train_indices], data.iloc[test_indices]train_set, test_set = split_train_test(housing, 0.2 )print(len(train_set), "train +" , len(test_set), "test" )16512 train + 4128 test# create method for ensuring consistent test sets across multiple runs # (new test sets won't contain instances in previous training sets.) # example method: # compute hash of each instance # keep only the last byte # include instance in test set if value < 51 (20% of 256) import hashlib def test_set_check ( identifier, test_ratio, hash) : return hash(np.int64(identifier)).digest()[ -1 ] < * test_ratio def split_train_test_by_id ( data, test_ratio, id_column, hash=hashlib.md5) : ids = data[id_column] in_test_set = ids.apply( lambda id_: test_set_check( id_, test_ratio, hash)) return data.loc[~in_test_set], data.loc[in_test_set]# housing dataset doesn't have ID attribute, # so let's add an index to it. housing_with_id = housing.reset_index()train_set, test_set = split_train_test_by_id( housing_with_id, 0.2 , "index" )train_set.head()| index | longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 |
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