59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# A function for loading the open-source Boston Housing dataset.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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import os
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this_dir, this_filename = os.path.split(__file__)
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DATA_PATH = os.path.join(this_dir, "data", "boston_housing.csv")
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def boston_housing_data():
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"""Boston Housing dataset.
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Source : https://archive.ics.uci.edu/ml/datasets/Housing
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Number of samples : 506
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Continuous target variable : MEDV
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MEDV = Median value of owner-occupied homes in $1000's
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Dataset Attributes:
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- 1) CRIM per capita crime rate by town
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- 2) ZN proportion of residential land zoned for lots over
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25,000 sq.ft.
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- 3) INDUS proportion of non-retail business acres per town
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- 4) CHAS Charles River dummy variable (= 1 if tract bounds
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river; 0 otherwise)
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- 5) NOX nitric oxides concentration (parts per 10 million)
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- 6) RM average number of rooms per dwelling
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- 7) AGE proportion of owner-occupied units built prior to 1940
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- 8) DIS weighted distances to five Boston employment centres
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- 9) RAD index of accessibility to radial highways
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- 10) TAX full-value property-tax rate per $10,000
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- 11) PTRATIO pupil-teacher ratio by town
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- 12) B 1000(Bk - 0.63)^2 where Bk is the prop. of b. by town
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- 13) LSTAT % lower status of the population
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Returns
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--------
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X, y : [n_samples, n_features], [n_class_labels]
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X is the feature matrix with 506 housing samples as rows
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and 13 feature columns.
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y is a 1-dimensional array of the continuous target variable MEDV
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/data/boston_housing_data/
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"""
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tmp = np.genfromtxt(fname=DATA_PATH, delimiter=',', dtype=float)
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X, y = tmp[:, :-1], tmp[:, -1]
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return X, y
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