Python sklearn.datasets.fetch_california_housing() Examples

The following are 3 code examples for showing how to use sklearn.datasets.fetch_california_housing(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File:    License: MIT License 5 votes vote down vote up
def fetch(*args, **kwargs):
    return fetch_california_housing(*args, download_if_missing=False, **kwargs) 
Example 2
Project: scikit-optimize   Author: scikit-optimize   File:    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def load_data_target(name):
    Loads data and target given the name of the dataset.
    if name == "Boston":
        data = load_boston()
    elif name == "Housing":
        data = fetch_california_housing()
        dataset_size = 1000 # this is necessary so that SVR does not slow down too much
        data["data"] = data["data"][:dataset_size]
        data["target"] =data["target"][:dataset_size]
    elif name == "digits":
        data = load_digits()
    elif name == "Climate Model Crashes":
            data = fetch_mldata("climate-model-simulation-crashes")
        except HTTPError as e:
            url = ""
            data = urlopen(url).read().split('\n')[1:]
            data = [[float(v) for v in d.split()] for d in data]
            samples = np.array(data)
            data = dict()
            data["data"] = samples[:, :-1]
            data["target"] = np.array(samples[:, -1],
        raise ValueError("dataset not supported.")
    return data["data"], data["target"] 
Example 3
Project: nonlinearIB   Author: artemyk   File:    License: MIT License 5 votes vote down vote up
def load_housing():
    from sklearn.datasets import fetch_california_housing
    d['data'] -= d['data'].mean(axis=0)
    d['data'] /= d['data'].std(axis=0)
    # Housing prices above 5 are all collapsed to 5, which makes the Y distribution very strange. Drop these
    d['data']   = d['data'][d['target'] < 5]
    d['target'] = d['target'][d['target'] < 5]
    d['target'] = np.log(d['target'])
    permutation = np.random.permutation(len(d['data']))
    d['data']   = d['data'][permutation]
    d['target'] = d['target'][permutation]
    l = int(len(d['data'])*0.8)
    data = {'err':'mse',
            'trn_X': d['data'][:l],
            'trn_Y': np.atleast_2d(d['target'][:l]).T,
            'tst_X': d['data'][l:],
            'tst_Y': np.atleast_2d(d['target'][l:]).T,
    return data