Revise documentation on hyperparameter search about dask-ml HOT 7 OPEN

scikit-learn GridSearchCV took 5.26 seconds for 16 candidate parameter settings.
scikit-learn GridSearchCV took 5.23 seconds for 16 candidate parameter settings.
scikit-learn GridSearchCV took 4.96 seconds for 16 candidate parameter settings.
scikit-learn GridSearchCV with memory took 5.55 seconds for 16 candidate parameter settings.
scikit-learn GridSearchCV with memory took 5.40 seconds for 16 candidate parameter settings.
scikit-learn GridSearchCV with memory took 4.90 seconds for 16 candidate parameter settings.
dashk-searchcv GridSearchCV took 6.63 seconds for 16 candidate parameter settings.
dashk-searchcv GridSearchCV took 5.59 seconds for 16 candidate parameter settings.
dashk-searchcv GridSearchCV took 5.96 seconds for 16 candidate parameter settings.
scikit-learn RandomizedSearchCV took 86.75 seconds for 250 candidates parameter settings.
scikit-learn RandomizedSearchCV with memory took 83.36 seconds for 250 candidates parameter settings.
dashk-searchcv RandomizedSearchCV took 75.33 seconds for 250 candidates parameter settings.

if __name__ == '__main__':
    from dask_searchcv import GridSearchCV, RandomizedSearchCV
    from distributed import Client
    from scipy import stats
    from shutil import rmtree
    from sklearn import model_selection as ms
    from sklearn.datasets import load_digits
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from tempfile import mkdtemp
    from time import time


    def get_clf(random_state, memory_dir=None):
        clf = Pipeline(
                [
                    ('scaler', StandardScaler()),
                    ('clf', LogisticRegression(random_state=random_state)),
                ],
                memory=memory_dir
        )
        return clf


    client = Client()

    # get some data
    digits = load_digits()
    X, y = digits.data, digits.target

    # use a full grid over all parameters
    param_grid = {
        "C": [1e-5, 1e-3, 1e-1, 1],
        "fit_intercept": [True, False],
        "penalty": ["l1", "l2"]
    }
    param_grid = dict([('clf__' + x, y) for x, y in param_grid.items()])

    # scikit-learn
    for i in range(3):
        sk_grid_search = ms.GridSearchCV(get_clf(i), param_grid=param_grid, n_jobs=-1)
        start = time()
        sk_grid_search.fit(X, y)
        print("scikit-learn GridSearchCV took %.2f seconds for %d candidate parameter settings."
              % (time() - start, len(sk_grid_search.cv_results_['params'])))
        del sk_grid_search

    # scikit-learn w/ memory
    for i in range(3):
        tmp_dir = mkdtemp()
        try:
            sk_grid_search_with_memory = ms.GridSearchCV(get_clf(i, tmp_dir), param_grid=param_grid, n_jobs=-1)
            start = time()
            sk_grid_search_with_memory.fit(X, y)

            print("scikit-learn GridSearchCV with memory took %.2f seconds for %d candidate parameter settings."
                  % (time() - start, len(sk_grid_search_with_memory.cv_results_['params'])))
            del sk_grid_search_with_memory
        finally:
            rmtree(tmp_dir)

    # dask
    for i in range(3):
        dk_grid_search = GridSearchCV(get_clf(i), param_grid=param_grid, n_jobs=-1)
        start = time()
        dk_grid_search.fit(X, y)
        print("dashk-searchcv GridSearchCV took %.2f seconds for %d candidate parameter settings."
              % (time() - start, len(dk_grid_search.cv_results_['params'])))
        del dk_grid_search

    param_dist = {
        "C": stats.beta(1, 3),
        "fit_intercept": [True, False],
        "penalty": ["l1", "l2"]
    }
    param_dist = dict([('clf__' + x, y) for x, y in param_dist.items()])

    n_iter_search = 250

    # scikit-learn randomized search
    sk_random_search = ms.RandomizedSearchCV(get_clf(42), param_distributions=param_dist, n_iter=n_iter_search, n_jobs=-1, random_state=42)
    start = time()
    sk_random_search.fit(X, y)
    print("scikit-learn RandomizedSearchCV took %.2f seconds for %d candidates"
          " parameter settings." % ((time() - start), n_iter_search))
    del sk_random_search

    # scikit-learn randomized search with memory
    tmp_dir = mkdtemp()
    try:
        sk_random_search_with_memory = ms.RandomizedSearchCV(
                get_clf(42, tmp_dir), param_distributions=param_dist, n_iter=n_iter_search, n_jobs=-1, random_state=42
        )
        start = time()
        sk_random_search_with_memory.fit(X, y)
        print("scikit-learn RandomizedSearchCV with memory took %.2f seconds for %d candidates"
              " parameter settings." % ((time() - start), n_iter_search))
        del sk_random_search_with_memory
    finally:
        rmtree(tmp_dir)

    # dask randomized search
    dk_random_search = RandomizedSearchCV(get_clf(42), param_distributions=param_dist, n_iter=n_iter_search, n_jobs=-1, random_state=42)
    start = time()
    dk_random_search.fit(X, y)
    print("dashk-searchcv RandomizedSearchCV took %.2f seconds for %d candidates"
          " parameter settings." % ((time() - start), n_iter_search))
    del dk_random_search

bokeh                     0.12.10                  py36_0    conda-forge
boto3                     1.4.7            py36h4cc92d5_0
botocore                  1.7.20           py36h085fff1_0
bumpversion               0.5.3                     <pip>
certifi                   2017.7.27.1      py36h8b7b77e_0
click                     6.7                      py36_0    conda-forge
cloudpickle               0.4.0                    py36_0    conda-forge
dask                      0.15.4                     py_0    conda-forge
dask-core                 0.15.4                     py_0    conda-forge
dask-glm                  0.1.0                         0    conda-forge
dask-ml                   0.3.1                    py36_0    conda-forge
dask-searchcv             0.1.0                      py_0    conda-forge
distributed               1.19.3                   py36_0    conda-forge
docutils                  0.14             py36hb0f60f5_0
heapdict                  1.0.0                    py36_0    conda-forge
intel-openmp              2018.0.0             h15fc484_7
jinja2                    2.9.6                    py36_0    conda-forge
jmespath                  0.9.3            py36hd3948f9_0
joblib                    0.11                     py36_0
libedit                   3.1                  heed3624_0
libffi                    3.2.1                h4deb6c0_3
libgcc-ng                 7.2.0                h7cc24e2_2
libgfortran               3.0.0                         1
libgfortran-ng            7.2.0                h9f7466a_2
libstdcxx-ng              7.2.0                h7a57d05_2
locket                    0.2.0                    py36_1    conda-forge
markupsafe                1.0                      py36_0    conda-forge
mkl                       2018.0.0             hb491cac_4
msgpack-python            0.4.8                    py36_0    conda-forge
multipledispatch          0.4.9                    py36_0    conda-forge
ncurses                   6.0                  h06874d7_1
nose                      1.3.7            py36hcdf7029_2
numpy                     1.13.3           py36ha12f23b_0
openssl                   1.0.2l                        0
pandas                    0.20.3           py36h842e28d_2
partd                     0.3.8                    py36_0    conda-forge
patsy                     0.4.1            py36ha3be15e_0
pip                       9.0.1            py36h8ec8b28_3
psutil                    5.4.0                    py36_0    conda-forge
python                    3.6.3                hcad60d5_0
python-dateutil           2.6.1            py36h88d3b88_1
pytz                      2017.2           py36hc2ccc2a_1
pyyaml                    3.12                     py36_1    conda-forge
readline                  7.0                  hac23ff0_3
s3fs                      0.1.2                    py36_0
s3transfer                0.1.10           py36h0257dcc_1
scikit-learn              0.19.1           py36h7aa7ec6_0
scipy                     0.19.1           py36h9976243_3
setuptools                36.5.0           py36he42e2e1_0
six                       1.10.0           py36hcac75e4_1
sortedcontainers          1.5.7                    py36_0    conda-forge
sqlite                    3.20.1               h6d8b0f3_1
statsmodels               0.8.0            py36h8533d0b_0
tblib                     1.3.2                    py36_0    conda-forge
tk                        8.6.7                h5979e9b_1
toolz                     0.8.2                     <pip>
toolz                     0.8.2                      py_2    conda-forge
tornado                   4.5.2                    py36_0    conda-forge
wheel                     0.29.0           py36he7f4e38_1
xz                        5.2.3                         0
yaml                      0.1.6                         0    conda-forge
zict                      0.1.3                      py_0    conda-forge
zlib                      1.2.11                        0

Microsoft Windows [Version 10.0.16299.19]
(c) 2017 Microsoft Corporation. All rights reserved.

VBoxManage.exe --version
5.1.24r117012

$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 14.04 LTS
Release:        14.04
Codename:       trusty