Algorithm Walk-Through

This notebook will walkthrough the preprocessing steps as well as the ML algorithm training procedure used for the multi-dimensional, multi-output data.

Experiment Overview

Code

Packages

import sys
sys.path.insert(0, "/media/disk/erc/papers/2019_ML_OCN/ml4ocean/src")

# Standard packages
import numpy as np
import pandas as pd

# Datasets
from data.make_dataset import DataLoader, load_standard_data, load_high_dim_data, load_labels, get_data

# Experiments

# Features
from features.pca_features import transform_all, transform_individual
from features.analysis import get_stats
from sklearn.preprocessing import StandardScaler
from data.make_dataset import ValidationFloats
from features.build_features import run_input_preprocess, run_input_postprocess, run_output_preprocess, run_output_postprocess, run_split

# ML Models
from sklearn.model_selection import train_test_split
from models.baseline import train_rf_model
import statsmodels.api as smi

# Visualization
from visualization.visualize import plot_mo_stats, plot_geolocations, get_depth_labels
from sklearn.inspection import permutation_importance
import matplotlib.pyplot as plt
plt.style.use('seaborn-poster')

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

class DataParams:
    region = 'na'

class ProcessParams:
    n_components = 10
    valid_split = 0.2
    input_std = "before"
    pca_seed = 123
    bootstrap_seed = 123
    std_ouputs = False

def run_experiment():

    # Load Data
    dataset = get_data(DataParams)
    print("Input:\n", dataset['ytrain'].min().min(), dataset['ytrain'].max().max())

    # Run Inputs Preprocessing
    dataset = run_input_preprocess(ProcessParams, dataset)

    # Run Outputs Preprocessing
    # Train Test Split
    dataset = run_split(ProcessParams, dataset)




    # Run Inputs PostProcessing
    if ProcessParams.input_std == 'after':
        dataset = run_input_postprocess(ProcessParams, dataset)

    # Run Outputs Post Processing
    dataset = run_output_postprocess(ProcessParams, dataset)

    print("Output:\n", dataset['ytrain'].min().min(), dataset['ytrain'].max().max())
    return dataset

REGION = 'stg' # 'na'
REGION_FLOAT = 3902121 # 6901486
MODEL = 'lr'

class DataParams:
    region = REGION

class ProcessParams:
    n_components = 5
    valid_split = 0.2
    input_std = "before"
    pca_seed = 123
    bootstrap_seed = 123
    std_ouputs = True

dataset = run_experiment()

region = DataParams.region

print('Training:')
print(dataset['Xtrain'].shape, dataset['ytrain'].shape)
print('Validation:')
print(dataset['Xvalid'].shape, dataset['yvalid'].shape)
print('Testing:')
print(dataset['Xtest'].shape, dataset['ytest'].shape)

Input:
 3.9999998989515e-05 0.0042500000687937
Output:
 -5.28703036321553 8.299632921300223
Training:
(1082, 33) (1082, 276)
Validation:
(271, 33) (271, 276)
Testing:
(133, 33) (133, 276)

---

5 - Train ML Model

from models.baseline import (
    train_glm_model, 
    train_gp_model, 
    train_mlp_model, 
    train_lr_model, 
    train_mo_rf_model, 
    train_ridge_lr_model,
    train_stack_model
)

def save_model(model, save_name):
    # model path
    MODEL_PATH = "/media/disk/erc/papers/2019_ML_OCN/ml4ocean/models/control/"

    # save model
    from joblib import dump
    dump(model, MODEL_PATH + save_name + '.joblib')
    return None

def load_model(save_name):
    # model path
    MODEL_PATH = "/media/disk/erc/papers/2019_ML_OCN/ml4ocean/models/control/"

    # save model
    from joblib import load
    model = load(MODEL_PATH + save_name + '.joblib')
    return model

5.1 - Ridge Regression

rf_params = {
    "n_estimators": 100,
    "criterion": "mse",
    "n_jobs": -1,
    "random_state": 123,
    "warm_start": False,
    "verbose": 1,
}

model_name = MODEL

# train model
if model_name == 'lr':
    model = train_ridge_lr_model(dataset['Xtrain'], dataset['ytrain'])
elif model_name == 'rf':
     model = train_rf_model(dataset['Xtrain'], dataset['ytrain'], rf_params)
else:
    raise ValueError

# save model
save_model(model, f"{model_name}_{DataParams.region}")

5.3 - (3-Layer) MultiPerceptron Model

model_name = 'mlp'

# model params
mlp_kwargs = {
    "hidden_layer_sizes": (50, 100, 300, 276),
    "activation": 'relu',
    "solver": "adam",
    "batch_size": 100,
    "learning_rate": "adaptive",
    "max_iter": 1_000,
    "random_state": 123,
    "early_stopping": False,
    "verbose": 0,
    "validation_fraction": 0.1,
    "tol": 1e-7,
}


# train model
model = train_mlp_model(
    dataset['Xtrain'], 
    dataset['ytrain'], 
    params=mlp_kwargs
)

# save model
save_model(model, f"{model_name}_{DataParams.region}")

5.4 - MO Random Forest Model

model_name = 'morf'

# RF params
rf_params = {
    "n_estimators": 1_500,
    "criterion": "mse",
    "n_jobs": -1,
    "random_state": 123,
    "warm_start": False,
    "verbose": 1,
}

# train model
model = train_rf_model(dataset['Xtrain'], dataset['ytrain'], params=rf_params)

# save model
save_model(model, f"{model_name}_{DataParams.region}")

[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 28 concurrent workers.
[Parallel(n_jobs=-1)]: Done 144 tasks      | elapsed:    4.0s
[Parallel(n_jobs=-1)]: Done 394 tasks      | elapsed:   10.2s
[Parallel(n_jobs=-1)]: Done 744 tasks      | elapsed:   18.7s
[Parallel(n_jobs=-1)]: Done 1194 tasks      | elapsed:   29.5s
[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   36.8s finished

Training time: 37.416 secs.

5.2 - Stacked Model (LR + RF)

model_name = 'lr_rf'

# train model
model = train_stack_model(dataset['Xtrain'], dataset['ytrain'], order=('lr', 'rf'), verbose=0, n_jobs=-1)

# save model
save_model(model, f"{model_name}_{DataParams.region}")

5.5 - Gaussian Process (White, ARD, Periodic, Matern32 Kernels)

model_name = 'morf'

# GP params
gp_params = {
    "normalize_y": True,  # good standard practice 
    "random_state": 123,  # reproducibility
    "n_restarts_optimizer": 10,  # good practice (avoids local minima)
}

# train GP model
model = train_gp_model(dataset['Xtrain'], dataset['ytrain'], params=gp_params)

# save model
save_model(model, f"{model_name}_{DataParams.region}")

ypred = model.predict(dataset['Xtrain'])

min_lim = np.min(np.concatenate((ypred, dataset['ytrain'])))
max_lim = np.max(np.concatenate((ypred, dataset['ytrain'])))


plt.scatter(ypred, dataset['ytrain'], s=0.1)
plt.plot(np.linspace(min_lim, max_lim), np.linspace(min_lim, max_lim), color='black', zorder=3)
plt.xlim([min_lim, max_lim])
plt.ylim([min_lim, max_lim])

(-5.28703036321553, 8.299632921300223)

stat_mod = smi.OLS(ypred.ravel(), dataset['ytrain'].values.ravel())

res = stat_mod.fit()

print(res.summary())

                                 OLS Regression Results                                
=======================================================================================
Dep. Variable:                      y   R-squared (uncentered):                   0.524
Model:                            OLS   Adj. R-squared (uncentered):              0.524
Method:                 Least Squares   F-statistic:                          3.289e+05
Date:                Mon, 27 Apr 2020   Prob (F-statistic):                        0.00
Time:                        18:06:35   Log-Likelihood:                     -2.1329e+05
No. Observations:              298632   AIC:                                  4.266e+05
Df Residuals:                  298631   BIC:                                  4.266e+05
Df Model:                           1                                                  
Covariance Type:            nonrobust                                                  
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1             0.5187      0.001    573.502      0.000       0.517       0.520
==============================================================================
Omnibus:                    35947.047   Durbin-Watson:                   0.023
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           114627.120
Skew:                           0.625   Prob(JB):                         0.00
Kurtosis:                       5.766   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# each level
each_level = False

stats = get_stats(ypred, dataset['ytrain'], each_level=each_level)

stats

	mae	mse	rmse	r2
0	0.521483	0.475935	0.475935	0.524065

Then we can look at the statistics for each level.

class PlotParams:
    model = MODEL
    region = REGION
#     validation_float = 

each_level = True

stats_df = get_stats(ypred, dataset['ytrain'], each_level=each_level)

stats_df.head()

	mae	mse	rmse	r2
0	0.529779	0.540059	0.734887	0.459941
1	0.519471	0.500952	0.707779	0.499048
2	0.520429	0.495406	0.703850	0.504594
3	0.522859	0.493923	0.702796	0.506077
4	0.520367	0.486153	0.697247	0.513847

statistic = 'r2'

plt.style.use('seaborn-talk')
plot_mo_stats(stats_df, stat='r2', save_name=f'{REGION}_train_{MODEL}')
plot_mo_stats(stats_df, stat='rmse', save_name=f'{REGION}_train_{MODEL}')

ypred = model.predict(dataset['Xvalid'])

def plot_scatter(df, plot_config):

    import seaborn as sns

    plt.figure(figsize=(10,10))
    g = sns.jointplot(x="Predictions", y="Test", data=df, kind='reg', scatter=False)
    pts = g.ax_joint.scatter(x='Predictions', y='Test', c='n_cycle', data=df, cmap='winter', s=10)
    plt.colorbar(pts)
    plt.tight_layout()
    plt.legend([fr'R$^2$: {plot_config['r2']:.3f}'], fontsize=20)
    plt.savefig(SAVE_PATH + f'stg_{plot_config["data"]}_m{plot_config["model_name"]}_f{plot_config["float_name"]}_time' + '.png')
    plt.show()
    pass

  File "<ipython-input-33-15e29e239bfd>", line 10
    plt.legend([fr'R$^2$: {plot_config['r2']:.3f}'], fontsize=20)
                                         ^
SyntaxError: invalid syntax

Get R2 Values

predictions = pd.DataFrame(ypred, columns=get_depth_labels())
validations = pd.DataFrame(dataset['yvalid'].values, columns=get_depth_labels())

# create time series
predictions = pd.melt(
    predictions, var_name="Depth", value_name="Predictions"
)
validations = pd.melt(validations, var_name="Depth", value_name="Labels")

y_scatter = pd.merge(predictions, validations)

y_scatter.head()

	Depth	Predictions	Labels
0	0	-6.099962	-5.750874
1	0	-6.099962	-7.024289
2	0	-6.099962	-6.105754
3	0	-6.099962	-5.722965
4	0	-6.099962	-7.581100

Plot Data

plot_config = dict()

plot_config['region'] = DataParams.region
plot_config['model'] = model_name
plot_config['float'] = 'test' 

# y_val_scat = get_scatter_validation(ypred, plot_config)

plot_config['r2'] = r2_score(y_scatter['Predictions'], y_scatter['Labels'])


plot_scatter(y_scatter.sample(5_000), plot_config)

<Figure size 720x1080 with 0 Axes>

each_level = True

stats_df = get_stats(ypred, dataset['yvalid'], each_level=each_level)

stats_df.head()

	mae	mse	rmse	r2
0	0.093128	0.023003	0.151669	0.709351
1	0.085847	0.018484	0.135957	0.757591
2	0.080686	0.014418	0.120076	0.798835
3	0.079242	0.013651	0.116836	0.804536
4	0.078813	0.013175	0.114781	0.804788

statistic = 'r2'
plt.style.use('seaborn-talk')
plot_mo_stats(stats_df, stat='r2', save_name='stg_test_rf')
plot_mo_stats(stats_df, stat='rmse', save_name='stg_test_rf')

predictions = pd.DataFrame(ypred, columns=get_depth_labels())
validations = pd.DataFrame(dataset['yvalid'].values, columns=get_depth_labels())

# create time series
predictions = pd.melt(
    predictions, var_name="Depth", value_name="Predictions"
)
validations = pd.melt(validations, var_name="Depth", value_name="Test")

y_scatter = pd.merge(predictions, validations)

y_scatter.head()

	Depth	Predictions	Test
0	0	-5.761626	-5.750874
1	0	-5.761626	-7.024289
2	0	-5.761626	-6.105754
3	0	-5.761626	-5.722965
4	0	-5.761626	-7.581100

y_scatter.sample(n=1_000, random_state=1).shape, y_scatter.shape

((1000, 3), (90302784, 3))

SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"
plot_config = dict()
plot_config['region'] = region
plot_config['r2'] = 0.982
plot_config['model'] = model_name
plot_config['float'] = 3902123
plot_config['data'] = 'Labels'

# y_val_scat = get_scatter_validation(ypred, plot_config)
plot_scatter(y_scatter.sample(n=5_000, random_state=1), plot_config)

<Figure size 720x720 with 0 Axes>

ypred.columns.values = depth_labels

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-162-316bf8ccbe3f> in <module>
----> 1 ypred.columns.values = depth_labels

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

ypred.shape

(572, 276)

ypred.shape, dataset['yvalid'].shape

((572, 276), (572, 276))

min_lim = np.min(np.concatenate((ypred, dataset['yvalid'])))
max_lim = np.max(np.concatenate((ypred, dataset['yvalid'])))


plt.scatter(ypred, dataset['yvalid'], s=0.1)
plt.plot(np.linspace(min_lim, max_lim), np.linspace(min_lim, max_lim), color='black', zorder=3)
plt.xlim([min_lim, max_lim])
plt.ylim([min_lim, max_lim])

(9.99999974737875e-06, 0.00908999983221292)

dataset['yvalid'].shape

(572, 276)

stat_mod = smi.OLS(ypred.ravel(), dataset['yvalid'].values.ravel())

res = stat_mod.fit()

print(res.summary())

                                 OLS Regression Results                                
=======================================================================================
Dep. Variable:                      y   R-squared (uncentered):                   0.851
Model:                            OLS   Adj. R-squared (uncentered):              0.851
Method:                 Least Squares   F-statistic:                          8.999e+05
Date:                Mon, 27 Jan 2020   Prob (F-statistic):                        0.00
Time:                        16:09:01   Log-Likelihood:                      1.0674e+06
No. Observations:              157872   AIC:                                 -2.135e+06
Df Residuals:                  157871   BIC:                                 -2.135e+06
Df Model:                           1                                                  
Covariance Type:            nonrobust                                                  
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1             0.8142      0.001    948.642      0.000       0.813       0.816
==============================================================================
Omnibus:                    97924.741   Durbin-Watson:                   0.056
Prob(Omnibus):                  0.000   Jarque-Bera (JB):         13955562.950
Skew:                          -2.018   Prob(JB):                         0.00
Kurtosis:                      48.883   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

((36708,), (74796,))

each_level = True

stats_df = get_stats(ypred.ravel(), dataset['ytest'].values.ravel(), each_level=each_level)

stats_df.head()

	mae	mse	rmse	r2
0	0.000041	2.374942e-08	0.000154	0.444236

statistic = 'r2'
plt.style.use('seaborn-talk')
plot_mo_stats(stats_df, stat='r2', save_name='stg_test_lr')
plot_mo_stats(stats_df, stat='rmse', save_name='stg_test_lr')

ypred = model.predict(dataset['Xvalid'])

[Parallel(n_jobs=28)]: Using backend ThreadingBackend with 28 concurrent workers.
[Parallel(n_jobs=28)]: Done 144 tasks      | elapsed:    0.1s
[Parallel(n_jobs=28)]: Done 394 tasks      | elapsed:    0.2s
[Parallel(n_jobs=28)]: Done 744 tasks      | elapsed:    0.4s
[Parallel(n_jobs=28)]: Done 1000 out of 1000 | elapsed:    0.5s finished

plot_config = dict()
plot_config['region'] = region
plot_config['float'] = 6901486
plot_config['model'] = model_name
plot_config['data'] = 'validation'

# dat = get_valid_floats(plot_config)
plot_profiles(ypred, plot_config)

plot_config = dict()
plot_config['region'] = region
plot_config['float'] = 6901486
plot_config['model'] = model_name
plot_config['data'] = 'yvalid'

# dat = get_valid_floats(plot_config)
plot_profiles(dataset['yvalid'], plot_config)

ypred = model.predict(dataset['Xtest'])

[Parallel(n_jobs=28)]: Using backend ThreadingBackend with 28 concurrent workers.
[Parallel(n_jobs=28)]: Done 144 tasks      | elapsed:    0.1s
[Parallel(n_jobs=28)]: Done 394 tasks      | elapsed:    0.1s
[Parallel(n_jobs=28)]: Done 744 tasks      | elapsed:    0.3s
[Parallel(n_jobs=28)]: Done 1000 out of 1000 | elapsed:    0.3s finished

def get_scatter_validation(df, df_test, plot_config):

    # initialize class
    valid_getter = ValidationFloats(plot_config['region'])

    # get validation floats
    valid_getter.get_validation_floats(plot_config['region'])

    # get timeseries
    df = valid_getter.get_validation_res(df_test, df, validation_float=plot_config['float'])

    return df


def df_2_xr(data):

    # create multiindex data
    data = data.set_index(['n_cycle', 'Depth'])

    # convert to xarray
    data = data.to_xarray()

    return data
def plot_profiles(xr_data, plot_config):

    import matplotlib.colors as colors

    # Convert to xarray
    SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"
    fig, ax = plt.subplots(figsize=(10,5))
    if plot_config['robust'] == True:
        xr_data.T.plot.pcolormesh(ax=ax, cmap='jet', cbar_kwargs={'label': ''}, robust=plot_config['robust'])
    else:
        xr_data.T.plot.pcolormesh(
            ax=ax, 
            cmap='jet', 
            cbar_kwargs={'label': ''}, 
            norm=colors.LogNorm(vmin=plot_config['vmin'], vmax=plot_config['vmax']), 
            vmin=plot_config['vmin'], 
            vmax=plot_config['vmax'], 
            robust=False
        )
    ax.set_xlabel('')
    ax.set_ylabel('')
#     ax.set_yticklabels(get_depth_labels())
    plt.tight_layout()
    fig.savefig(SAVE_PATH + f"{plot_config['region']}_y_{plot_config['data']}_heatmap_{plot_config['float']}_pred_{plot_config['model']}")
    pass

NA * 6901486 * 3902123

STG * 6901472 * 3902121

ypred_

array([[0.03241515, 0.03249627, 0.03277638, ..., 0.02021938, 0.02021231,
        0.02020567],
       [0.03258741, 0.03265431, 0.03296169, ..., 0.02002968, 0.02001938,
        0.02001338],
       [0.03256133, 0.03264498, 0.03292198, ..., 0.02000669, 0.01997411,
        0.01996073],
       ...,
       [0.03691499, 0.03649553, 0.036584  , ..., 0.02367047, 0.02366361,
        0.02364842],
       [0.03628594, 0.0360738 , 0.03612746, ..., 0.02339059, 0.0234753 ,
        0.02338352],
       [0.03632206, 0.03613055, 0.03616642, ..., 0.02347837, 0.02354185,
        0.0234447 ]])

ypred_ = dataset['out_post_trans'].inverse_transform(ypred)
ytest_ = dataset['out_post_trans'].inverse_transform(dataset['ytest'])

ypred_.shape

(133, 276)

plot_config = dict()
plot_config['region'] = DataParams.region
plot_config['r2'] = 0.9
plot_config['model'] = model_name
plot_config['float'] = 6901486
plot_config['data'] = 'Predictions'

y_val_scat = get_scatter_validation(ypred_, ytest_, plot_config)
y_val_scat.iloc[:, 2:].describe()

(133, 2) (133, 276)
0.0001116734674130233 6901472.0 0.0001300000003539031 6901472.0

	Predictions	Labels
count	29808.000000	29808.000000
mean	0.000296	0.000330
std	0.000128	0.000222
min	0.000171	0.000170
25%	0.000197	0.000220
50%	0.000228	0.000250
75%	0.000429	0.000466
max	0.000596	0.009760

y_val_scat.Predictions.min()

0.0001707489683609274

plot_config = dict()
plot_config['region'] = DataParams.region
plot_config['r2'] = 0.9
plot_config['model'] = model_name

plot_config['float'] = 6901486
plot_config['data'] = 'Predictions'
plot_config['robust'] = False


y_val_scat = get_scatter_validation(ypred_, ytest_, plot_config)
plot_config['vmin'] = np.minimum(y_val_scat.Predictions.min(), y_val_scat.Labels.min())
plot_config['vmax'] = np.maximum(y_val_scat.Predictions.max(), y_val_scat.Labels.max())
# print(y_val_scat.head())
# y_val_scat.iloc[:, 3:] = y_val_scat.iloc[:, 3:]
y_val_scat = df_2_xr(y_val_scat)
plot_profiles(y_val_scat.Predictions, plot_config)

(162, 2) (162, 276)
0.0001433435529995995 6901486.0 0.00016999999934341794 6901486.0

SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"


def plot_scatter(df, plot_config):

    import seaborn as sns

    plt.figure(figsize=(10,15))


    g = sns.jointplot(x="Predictions", y="Labels", data=df, kind='reg', scatter=False, truncate=True, logx=False)
#     g.plot_marginals(sns.distplot, hist=True, kde=True, color='blue')

    plt.legend([fr'R$^2$: {plot_config["r2"]:.3f}'], fontsize=20, loc='upper left', )
    plt.xlabel('')
    plt.ylabel('')
    pts = g.ax_joint.scatter(x='Predictions', y='Labels', c='Depth', data=df, cmap='winter', s=10)
    g.ax_joint.set_xscale('log')
    g.ax_joint.set_yscale('log')
#     g.ax_marg_x.set_xscale('log')
#     g.ax_marg_y.set_yscale('log')
    # get the mapping from the scatter
    PCM=g.ax_joint.get_children()[1]

    # Make New axis for the bar
    cbar_ax = g.fig.add_axes([1.01, .1, .02, .7])
    cb = plt.colorbar(PCM, cax=cbar_ax)
#     plt.colorbar(pts, orientation='horizontal')
#     cbar_ax = g.fig.add_axes([.85, .25, .05, .4])  # x, y, width, height
#     plt.colorbar(cax=cbar_ax)
#     outpathc = g.get_children()[3] 
#TODO -- don't assume PathCollection is 4th; at least check type

#     plt.colorbar(mappable=outpathc, cax=cbar_ax)

#     plt.tight_layout()

#     plt.show()
    g.savefig(SAVE_PATH + f'{plot_config["region"]}_m{plot_config["model"]}_f{plot_config["float"]}_depth' + '.png')
    plt.show()
    pass

y_val_scat.describe()

	n_cycle	Predictions	Labels
count	6900.000000	6900.000000	6900.000000
mean	13.840000	0.000222	0.000260
std	7.449996	0.000107	0.000105
min	1.000000	0.000112	0.000130
25%	8.000000	0.000141	0.000180
50%	14.000000	0.000165	0.000200
75%	20.000000	0.000335	0.000369
max	26.000000	0.000441	0.000520

ypred_ = dataset['out_post_trans'].inverse_transform(ypred)
ytest_ = dataset['out_post_trans'].inverse_transform(dataset['ytest'])

plot_config = dict()

plot_config['region'] = DataParams.region
plot_config['model'] = model_name
plot_config['float'] = 3902121 
plot_config['slope'] = 0.9773

y_val_scat = get_scatter_validation(ypred_, ytest_, plot_config)

plot_config['vmin'] = np.minimum(y_val_scat.Predictions.min(), y_val_scat.Labels.min())
plot_config['vmax'] = np.maximum(y_val_scat.Predictions.max(), y_val_scat.Labels.max())
plot_config['r2'] = r2_score(np.log10(y_val_scat['Predictions']), np.log10(y_val_scat['Labels']))
plot_config['mapd'] = np.median(np.abs((y_val_scat['Predictions']) - (y_val_scat['Labels'])) / (y_val_scat['Labels']))

y_val_scat['Predictions'] = y_val_scat['Predictions']
y_val_scat['Labels'] = y_val_scat['Labels']

(133, 2) (133, 276)
0.0001116734674130233 6901472.0 0.0001300000003539031 6901472.0

f"{plot_config['mapd']:.2%}"

'18.47%'

y_val_scat.to_csv(SAVE_PATH + 'stg_results.csv')

stat_mod = smi.OLS(np.log10(y_val_scat['Labels']), np.log10(y_val_scat['Predictions']))

res = stat_mod.fit()
r2_val = res.rsquared
print(res.summary())

                                 OLS Regression Results                                
=======================================================================================
Dep. Variable:                 Labels   R-squared (uncentered):                   1.000
Model:                            OLS   Adj. R-squared (uncentered):              1.000
Method:                 Least Squares   F-statistic:                          4.883e+07
Date:                Wed, 05 Feb 2020   Prob (F-statistic):                        0.00
Time:                        14:26:46   Log-Likelihood:                          11917.
No. Observations:                6900   AIC:                                 -2.383e+04
Df Residuals:                    6899   BIC:                                 -2.382e+04
Df Model:                           1                                                  
Covariance Type:            nonrobust                                                  
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Predictions     0.9773      0.000   6987.669      0.000       0.977       0.978
==============================================================================
Omnibus:                      165.078   Durbin-Watson:                   0.698
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              176.531
Skew:                          -0.389   Prob(JB):                     4.64e-39
Kurtosis:                       3.099   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

line = np.logspace(-4, -2, 100)

from matplotlib.offsetbox import AnchoredText

fig, ax = plt.subplots(figsize=(10,7))
y_val_scat.plot.scatter(ax=ax, x='Predictions', y='Labels', c='Depth', logx=True, logy=True, cmap='winter')
ax.plot(line, line, linewidth=5, color='black')

at = AnchoredText(f"R$^2$: {plot_config['r2']:.3f}\nSlope: {plot_config['slope']:.3f}\nMAPD: {plot_config['mapd']:.2%}",
                  prop=dict(size=15, fontsize=20), frameon=True,
                  loc='upper left',

                  )
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax.add_artist(at)
# ax.annotate([fr'R$^2$: {plot_config["r2"]:.3f}\n', xy=(-12, -12), xycoords='axes points',
#             size=14, ha='right', va='top',
#             bbox=dict(boxstyle='round', fc='w'))
ax.autoscale(enable=True, axis='both', tight=True)
ax.set_xlim(0.0001, 0.001)
ax.set_ylim(0.0001, 0.001)
ax.set_xlabel('')
ax.set_ylabel('')
# ax.grid(True, ls="-", color='gray')
ax.tick_params(axis='both', which='major', labelsize=20)
ax.tick_params(axis='both', which='minor', labelsize=12)
# plt.legend([fr'R$^2$: {plot_config["r2"]:.3f}'], fontsize=20, loc='upper left', )
plt.tight_layout()
fig.savefig(SAVE_PATH + f'{plot_config["region"]}_m{plot_config["model"]}_f{plot_config["float"]}_depth' + '.png')
plt.show()

fig, ax = plt.subplots()

ax.scatter(y_val_scat.Predictions, y_val_scat.Labels, c=y_val_scat.Depth)
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()

y_val_scat.head()

	n_cycle	Depth	Predictions	Labels
0	3.0	0	0.003238	0.003690
1	12.0	0	0.003148	0.003060
2	24.0	0	0.003298	0.003780
3	25.0	0	0.003612	0.003905
4	26.0	0	0.003370	0.003920

import seaborn as sns

mybins=np.logspace(0,np.log(100),100)

# Define plot
fig, ax = plt.subplots()
# g= sns.jointplot(x="Predictions", y="Labels", data=y_val_scat, kind='reg', scatter=False, logx=False)
g = sns.scatter(
    ax=ax,
    x="Predictions", 
    y="Labels", 
    data=y_val_scat.iloc[:100,:], 
    c='Depth'
#     xlim=[plot_config['vmin'], plot_config['vmax']],
#     ylim=[plot_config['vmin'], plot_config['vmax']]
)
# g = sns.JointGrid(x="Predictions", y="Labels", data=y_val_scat,)
# g.plot_marginals(sns.distplot, hist=True, kde=False, color='blue')
# g.plot_joint(g.ax_joint.scatter,)
# g.plot
# ax = g.ax_joint
# plt.xscale('log')
ax.set_xscale('log')
ax.set_yscale('log')
# g.ax_joint.set_yscale('log')
# ax.set_yscale('log')
# g.ax_marg_x.set_xscale('log')
# g.ax_marg_y.set_yscale('log')

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-188-230c1ce2d14a> in <module>
      6 fig, ax = plt.subplots()
      7 # g= sns.jointplot(x="Predictions", y="Labels", data=y_val_scat, kind='reg', scatter=False, logx=False)
----> 8 g = sns.scatter(
      9     ax=ax,
     10     x="Predictions",

AttributeError: module 'seaborn' has no attribute 'scatter'

plot_config = dict()

plot_config['region'] = DataParams.region
plot_config['model'] = model_name
plot_config['float'] = 3902121 

y_val_scat = get_scatter_validation(ypred_, ytest_, plot_config)
plot_config['r2'] = r2_score(y_val_scat['Predictions'], y_val_scat['Labels'])
# y_val_scat.iloc[:, 2:] = np.exp(y_val_scat.iloc[:, 2:])
# plot_config['r2'] = r2_score(y_val_scat['Predictions'], y_val_scat['Labels'])


plot_scatter(y_val_scat, plot_config)

(133, 2) (133, 276)
0.0001116734674130233 6901472.0 0.0001300000003539031 6901472.0

<Figure size 720x1080 with 0 Axes>

y_val_scat.iloc[:, 2:].describe()

	Predictions	Labels
count	6900.000000	6900.000000
mean	0.025229	0.027252
std	0.005063	0.004608
min	0.019215	0.020526
25%	0.021274	0.023642
50%	0.022744	0.024749
75%	0.030956	0.032292
max	0.034900	0.037478

from sklearn.metrics import mean_squared_error, r2_score

mean_squared_error(y_val_scat['Predictions'], y_val_scat['Labels'])
r2_score(y_val_scat['Predictions'], y_val_scat['Labels'])

0.5673513484557717

NA * 6901486 * 3902123

STG * 6901472 * 3902121

ypred = model.predict(dataset['Xtest'])

[Parallel(n_jobs=28)]: Using backend ThreadingBackend with 28 concurrent workers.
[Parallel(n_jobs=28)]: Done 144 tasks      | elapsed:    0.1s
[Parallel(n_jobs=28)]: Done 394 tasks      | elapsed:    0.1s
[Parallel(n_jobs=28)]: Done 744 tasks      | elapsed:    0.3s
[Parallel(n_jobs=28)]: Done 1000 out of 1000 | elapsed:    0.3s finished

SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"
# g = sns.JointGrid('total_bill', 'tip', data,xlim=[1,100],ylim=[0.01,100])
# g.plot_marginals(sns.distplot, hist=True, kde=True, color='blue',bins=mybins)
# g.plot_joint(plt.scatter, color='black', edgecolor='black')
# ax = g.ax_joint
# ax.set_xscale('log')
# ax.set_yscale('log')
# g.ax_marg_x.set_xscale('log')
# g.ax_marg_y.set_yscale('log')

def plot_scatter(df, plot_config):

    import seaborn as sns

    plt.figure(figsize=(10,15))

    # Plot Joint Grid
    g = sns.JointGrid(x="Predictions", y="Labels", data=df,)
    ax = g.ax_joint
    ax.set_xscale('log')
    ax.set_yscale('log')

    # Plot Marginals
    g.plot_marginals(sns.distplot, hist=True, kde=True, color='blue', bins=50)

    # Plot Regression

    # Plot scatter points

#     pts = g.ax_joint.scatter(x='Predictions', y='Labels', c='Depth', data=df, cmap='winter', s=10)
#     pts.set_xscale('log')
#     g = sns.jointplot(x="Predictions", y="Labels", data=df, kind='reg', scatter=False,, logx=True)
# #     g.plot_marginals(sns.distplot, hist=True, kde=True, color='blue')

#     plt.legend([fr'R$^2$: {plot_config["r2"]:.3f}'], fontsize=20, loc='upper left', )
#     plt.xlabel('')
#     plt.ylabel('')
#     pts = g.ax_joint.scatter(x='Predictions', y='Labels', c='Depth', data=df, cmap='winter', s=10)
#     g.ax_joint.set_xscale('log')
#     g.ax_joint.set_yscale('log')
# #     g.ax_marg_x.set_xscale('log')
# #     g.ax_marg_y.set_yscale('log')
#     # get the mapping from the scatter
#     PCM=g.ax_joint.get_children()[1]

#     # Make New axis for the bar
#     cbar_ax = g.fig.add_axes([1.01, .1, .02, .7])
#     cb = plt.colorbar(PCM, cax=cbar_ax)
# #     plt.colorbar(pts, orientation='horizontal')
# #     cbar_ax = g.fig.add_axes([.85, .25, .05, .4])  # x, y, width, height
# #     plt.colorbar(cax=cbar_ax)
# #     outpathc = g.get_children()[3] 
# #TODO -- don't assume PathCollection is 4th; at least check type

# #     plt.colorbar(mappable=outpathc, cax=cbar_ax)

# #     plt.tight_layout()

# #     plt.show()
#     g.savefig(SAVE_PATH + f'{plot_config["region"]}_m{plot_config["model"]}_f{plot_config["float"]}_depth' + '.png')
#     plt.show()
    pass

plot_config = dict()

plot_config['region'] = DataParams.region
plot_config['model'] = model_name
plot_config['float'] = 6901472 

y_val_scat = get_scatter_validation(ypred_, ytest_, plot_config)
plot_config['r2'] = r2_score(y_val_scat['Predictions'], y_val_scat['Labels'])
# y_val_scat.iloc[:, 2:] = np.exp(y_val_scat.iloc[:, 2:])
# plot_config['r2'] = r2_score(y_val_scat['Predictions'], y_val_scat['Labels'])


plot_scatter(y_val_scat, plot_config)
# plt.savefig(SAVE_PATH + 'test.png')

(133, 2) (133, 276)
0.0192152700873465 6901472.0 0.02052612843666904 6901472.0

<Figure size 720x1080 with 0 Axes>

plot_config = dict()
plot_config['region'] = DataParams.region
plot_config['float'] = 6901486 
plot_config['model'] = 'rf'
plot_config['data'] = 'labels'

dat = get_valid_floats(ypred, plot_config)
dat

ypred.shape, dat.shape

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-7-0a6aac7348a7> in <module>
----> 1 ypred.shape, dat.shape

NameError: name 'dat' is not defined

def get_valid_floats(ypred, plot_config):
    dat = load_labels(plot_config['region'], training=False)
#     print(dat.shape, ypred.shape)
    if plot_config['data'] == 'prediction':
        dat.iloc[:, 2:] = ypred
    elif plot_config['data'] == 'labels':
        dat.iloc[:, 2:] = np.log(dat.iloc[:, 2:])
    else:
        raise ValueError()
#     print(dat.shape)
    float_data = dat[dat['wmo'] == plot_config['float']].drop(labels=['wmo'], axis=1)
#     print(float_data.shape)
    y_labels = float_data['n_cycle']

    float_data = float_data.iloc[:, 1:]

    float_data.index = y_labels
    float_data.columns = get_depth_labels()
    return float_data

def plot_profiles(data, plot_config):
    SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"
    fig, ax = plt.subplots(figsize=(10,5))

    pts = ax.pcolormesh(data.T, cmap='jet')
#     ax.set_yticklabels(get_depth_labels())
    plt.colorbar(pts)
    plt.gca().invert_yaxis()
    plt.savefig(SAVE_PATH + f"{plot_config['region']}_y_{plot_config['data']}_heatmap_{plot_config['float']}_pred_{plot_config['model']}")

# dat.head(), dat.tail()
SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"

dat.head()

	0	-2	-4	-6	-8	-10	-12	-14	-16	-18	...	-955	-960	-965	-970	-975	-980	-985	-990	-995	-1000
n_cycle
3	-5.602129	-5.596723	-5.666487	-5.726028	-5.578031	-5.578031	-5.588670	-5.607564	-5.605975	-5.588670	...	-8.278119	-8.292591	-8.294050	-8.294050	-8.336261	-8.355925	-8.355925	-8.355925	-8.355925	-8.355925
12	-5.789340	-5.795898	-5.754024	-5.732182	-5.675195	-5.675195	-5.697710	-5.747734	-5.710807	-5.678115	...	-8.111728	-8.180721	-8.055558	-8.047190	-8.145630	-8.145630	-7.013116	-7.583904	-8.254829	-8.254829
24	-5.578031	-5.611659	-5.626821	-5.528360	-5.639400	-5.537082	-5.528989	-5.518964	-5.518964	-5.554501	...	-8.111728	-8.111728	-8.111728	-8.111728	-8.178537	-8.294050	-8.294050	-8.294050	-8.294050	-8.294050
25	-5.545497	-5.539116	-5.509038	-5.489478	-5.421616	-5.375198	-5.375198	-5.381699	-5.408132	-5.521461	...	-7.902008	-7.902008	-7.902008	-7.902008	-7.902008	-7.902008	-7.902008	-7.902008	-7.939878	-8.254829
26	-5.541664	-5.570126	-5.572754	-5.511511	-5.444500	-5.551920	-5.524680	-5.489478	-5.527870	-5.521461	...	-8.016787	-8.035445	-8.069152	-8.129363	-8.145630	-8.145630	-8.164797	-8.180721	-8.180721	-8.188104

5 rows × 276 columns

plot_config = dict()
plot_config['region'] = DataParams.region
plot_config['float'] = 6901486 
plot_config['model'] = 'rf'
plot_config['data'] = 'labels'

dat = get_valid_floats(ypred, plot_config)
dat

plot_profiles(dat, plot_config)

plt.imshow(ypred.T)

<matplotlib.image.AxesImage at 0x7f3210539710>

from data.make_dataset import load_labels, get_depth_labels

# initialize class
valid_getter = ValidationFloats('stg')

# get validation floats
valid_getter.get_validation_floats('stg')

[6901472, 3902121]

dat = load_labels('stg', training=False)

dat.iloc[:, 2:] = ypred

dat.iloc[:, 2:].shape, ypred.shape

((133, 276), (133, 276))

dat.shape, ypred.shape

((133, 278), (133, 276))

ypred.shape

(133, 276)

plt.imshow(dat.iloc[:, 2:].T)

<matplotlib.image.AxesImage at 0x7f3217e30a20>

# floatname = 6901486 # 6901486 #3902123

float1 = dat[dat['wmo'] == floatname].drop(labels=['wmo'], axis=1)

y_labels = float1['n_cycle']

float1 = float1.iloc[:, 1:]

x_labels = get_depth_labels()

plt.imshow(float1)

<matplotlib.image.AxesImage at 0x7f3218241b70>

def get_valid_floats():
    dat = load_labels('stg', training=False)

[6901472, 3902121]

NA * 6901486 * 3902123

STG [6901472, 3902121]

plot_config = dict()
plot_config['region'] = region
plot_config['float'] = 6901472
plot_config['model'] = model_name
plot_config['data'] = 'label'

dat = get_valid_floats(plot_config)
plot_profiles(dat, plot_config)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-84-3fab012d283c> in <module>
      5 plot_config['data'] = 'label'
      6 
----> 7 dat = get_valid_floats(plot_config)
      8 plot_profiles(dat, plot_config)

<ipython-input-82-88bfde9434d6> in get_valid_floats(plot_config)
      7         pass
      8     else:
----> 9         raise ValueError()
     10 
     11     float_data = dat[dat['wmo'] == plot_config['float']].drop(labels=['wmo'], axis=1)

ValueError: 

plot_config = dict()
plot_config['region'] = region
plot_config['float'] = 3902123
plot_config['model'] = model_name
plot_config['data'] = 'prediction'

dat = get_valid_floats(plot_config)
plot_profile_predicted(dat, plot_config)

def get_scatter_validation(df, plot_config):

    # initialize class
    valid_getter = ValidationFloats(plot_config['region'])

    # get validation floats
    valid_getter.get_validation_floats(plot_config['region'])

    # get timeseries
    df = valid_getter.get_validation_res(ypred, dataset['ytest'], 1)

    return df

def plot_scatter(df, plot_config):

    import seaborn as sns

    plt.figure(figsize=(10,10))
    g = sns.jointplot(x="Predictions", y="Test", data=df, kind='reg', scatter=False)
    pts = g.ax_joint.scatter(x='Predictions', y='Test', c='Depth', data=df, cmap='winter', s=10)
    plt.colorbar(pts)
    plt.tight_layout()
#     plt.axes().set_aspect('equal', 'datalim')
    plt.ylabel(plot_config['data'])
    plt.legend([fr"R$^2$: {plot_config['r2']:.3f}"], fontsize=20)
    plt.savefig(SAVE_PATH + f'{plot_config["region"]}_{plot_config["data"]}_m{plot_config["model"]}_f{plot_config["float"]}_time' + '.png')
    plt.show()
    pass

y_val_scat.head()

	n_cycle	Depth	Predictions	Test
0	17.0	0	-7.070274	-7.347102
1	19.0	0	-7.013116	-7.350406
2	20.0	0	-6.897805	-7.353594
3	21.0	0	-6.938214	-7.357767
4	22.0	0	-7.082109	-7.354769

df_multi = y_val_scat.set_index(['n_cycle', 'Depth'])
dat_xr = df_multi.to_xarray()

dat_xr.Test.T.plot()

<matplotlib.collections.QuadMesh at 0x7f6844551898>

import xarray as xr

df_multi

df_multi.shape

(7728, 2)

y_val

plot_config = dict()
plot_config['region'] = DataParams.region
plot_config['r2'] = 0.9
plot_config['model'] = model_name
plot_config['float'] = 3902123
plot_config['data'] = 'Labels'

y_val_scat = get_scatter_validation(ypred, plot_config)
# plot_scatter(y_val_scat, plot_config)

(162, 2) (162, 276)

# initialize class
valid_getter = ValidationFloats('stg')

# get validation floats
valid_getter.get_validation_floats('stg')

# get timeseries
y_validation = valid_getter.get_validation_res(ypred, dataset['ytest'], 1)

y_validation.head()

(162, 2) (162, 276)

	n_cycle	Depth	Predictions	Test
0	17.0	0	-0.924080	-0.122721
1	19.0	0	-0.847228	-0.153329
2	20.0	0	-0.692187	-0.267439
3	21.0	0	-0.746520	-0.236638
4	22.0	0	-0.939992	-0.152829

stat_mod = smi.OLS(y_validation['Predictions'].ravel(), y_validation['Test'].ravel())

res = stat_mod.fit()
r2_val = res.rsquared
print(res.summary())

                                 OLS Regression Results                                
=======================================================================================
Dep. Variable:                      y   R-squared (uncentered):                   0.020
Model:                            OLS   Adj. R-squared (uncentered):              0.020
Method:                 Least Squares   F-statistic:                              156.6
Date:                Fri, 31 Jan 2020   Prob (F-statistic):                    1.40e-35
Time:                        16:30:04   Log-Likelihood:                         -8775.1
No. Observations:                7728   AIC:                                  1.755e+04
Df Residuals:                    7727   BIC:                                  1.756e+04
Df Model:                           1                                                  
Covariance Type:            nonrobust                                                  
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1             0.1043      0.008     12.513      0.000       0.088       0.121
==============================================================================
Omnibus:                       31.674   Durbin-Watson:                   0.506
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               32.028
Skew:                          -0.157   Prob(JB):                     1.11e-07
Kurtosis:                       2.975   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

model_name = 'rf'
ifloat = 3902123 # 6901486 #3902123

def plot_scatter(df, plot_config):

    import seaborn as sns

    plt.figure(figsize=(10,10))
    g = sns.jointplot(x="Predictions", y="Test", data=df, kind='reg', scatter=False)
    pts = g.ax_joint.scatter(x='Predictions', y='Test', c='n_cycle', data=df, cmap='winter', s=10)
    plt.colorbar(pts)
    plt.tight_layout()
    plt.legend([fr'R$^2$: {plot_config['r2']:.3f}'], fontsize=20)
    plt.savefig(SAVE_PATH + f'stg_{plot_config["data"]}_m{plot_config["model_name"]}_f{plot_config["float_name"]}_time' + '.png')
    plt.show()
    pass

plot_scatter(y_validation)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-109-8bbbf5da5681> in <module>
----> 1 plot_scatter(y_validation)

NameError: name 'y_validation' is not defined

plt.figure(figsize=(10,10))
g = sns.jointplot(x="Predictions", y="Test", data=y_validation, kind='reg', scatter=False)
pts = g.ax_joint.scatter(x='Predictions', y='Test', c='n_cycle', data=y_validation, cmap='winter', s=10)
plt.colorbar(pts)
plt.tight_layout()
plt.legend([fr'R$^2$: {r2_val:.3f}'], fontsize=20)
plt.savefig(SAVE_PATH + f'stg_valid_m{model_name}_f{ifloat}_time' + '.png')
plt.show()

<Figure size 720x720 with 0 Axes>

plt.figure(figsize=(10,10))
g = sns.jointplot(x="Predictions", y="Test", data=y_validation, kind='reg', scatter=False)
pts = g.ax_joint.scatter(x='Predictions', y='Test', c='Depth', data=y_validation, cmap='winter', s=10)
plt.colorbar(pts)
plt.tight_layout()
plt.legend([fr'R$^2$: {r2_val:.3f}'], fontsize=20)
plt.savefig(SAVE_PATH + f'stg_valid_m{model_name}_f{ifloat}_depth' + '.png')
plt.show()

<Figure size 720x720 with 0 Axes>

SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"

r2_val = res.rsquared
slope = res.params[0]

min_lim = y_validation[['Predictions', 'Test']].min().min()
max_lim = y_validation[['Predictions', 'Test']].max().max()

plt.style.use('seaborn-talk')
fig, ax = plt.subplots()
y_validation.plot.scatter(ax=ax, x='Predictions', y='Test', c='n_cycle', s=25)
ax.plot(np.linspace(min_lim, max_lim), np.linspace(min_lim, max_lim), color='black', zorder=3)
plt.xlim([min_lim, max_lim])
plt.ylim([min_lim, max_lim])
plt.tight_layout()
plt.legend([fr'R$^2$: {r2_val:.3f}'], fontsize=20)
plt.savefig(SAVE_PATH + f'na_valid_m{model_name}_f{ifloat}_t' + '.png')
plt.show()

SAVE_PATH = "/home/emmanuel/figures/ml4ocn/"

xlim = y_validation[['Predictions']].min()
max_lim = y_validation[['Predictions', 'Test']].max()

plt.style.use('seaborn-talk')
fig, ax = plt.subplots()
y_validation.plot.scatter(ax=ax, x='Predictions', y='Test', c='Depth', s=25)
ax.plot(np.linspace(min_lim, max_lim), r2_val * np.linspace(min_lim, max_lim), color='black', zorder=3)
plt.xlim([min_lim, max_lim])
# plt.ylim([r2_val * min_lim, r2_val * max_lim])
plt.tight_layout()
plt.legend([fr'R$^2$: {r2_val:.3f}'], fontsize=20)
plt.savefig(SAVE_PATH + f'na_valid_m{model_name}_f{ifloat}_d' + '.png')
plt.show()

# each level
each_level = False

stats = get_stats(ypred, dataset['ytest'], each_level=each_level)

stats

	mae	mse	rmse	r2
0	0.0002	2.580603e-07	2.580603e-07	-0.53396

each_level = True

stats_df = get_stats(ypred, dataset['ytest'], each_level=each_level)

stats_df.head()

	mae	mse	rmse	r2
0	0.001149	0.000002	0.001466	-0.076087
1	0.001200	0.000002	0.001544	-0.081775
2	0.001238	0.000003	0.001606	-0.085894
3	0.001290	0.000003	0.001673	-0.091362
4	0.001343	0.000003	0.001753	-0.099165

statistic = 'r2'
plt.style.use('seaborn-talk')
plot_mo_stats(stats_df, stat='r2', save_name='valid_mlp')
plot_mo_stats(stats_df, stat='rmse', save_name='valid_mlp')

---

7 - Post Analysis

7.1 - Feature Importance

For some algorithms, we can calculate the feature importance based on the inputs dimensions. This is true for the random forest model.

rf_model.

{'bootstrap': True,
 'ccp_alpha': 0.0,
 'criterion': 'mse',
 'max_depth': None,
 'max_features': 'auto',
 'max_leaf_nodes': None,
 'max_samples': None,
 'min_impurity_decrease': 0.0,
 'min_impurity_split': None,
 'min_samples_leaf': 1,
 'min_samples_split': 2,
 'min_weight_fraction_leaf': 0.0,
 'n_estimators': 1500,
 'n_jobs': -1,
 'oob_score': False,
 'random_state': 123,
 'verbose': 0,
 'warm_start': False}

tree_feature_importances = \
    rf_model.feature_importances_

feature_names = np.asarray(new_columns) #np.concatenate((core_columns.values, np.array(pca_columns)))

assert feature_names.shape[0] == tree_feature_importances.shape[0], "Shapes don't match"

sorted_idx = tree_feature_importances.argsort()

SAVE_PATH = "/media/disk/erc/papers/2019_ML_OCN/ml4ocean/reports/figures/"
save_name = 'na'
y_ticks = np.arange(0, len(feature_names))
plt.style.use(['seaborn-talk'])
fig, ax = plt.subplots(figsize=(10,10))
ax.barh(y_ticks, tree_feature_importances[sorted_idx], zorder=3, height=0.8)
ax.set_yticklabels(feature_names[sorted_idx])
ax.set_yticks(y_ticks)
# ax.set_title("Random Forest Feature Importances (MDI)")
ax.tick_params(axis="both", which="major", labelsize=20)
ax.tick_params(axis="both", which="minor", labelsize=20)
ax.grid(alpha=0.6, color='gray', zorder=0)
plt.tight_layout()
plt.show()
# fig.savefig(SAVE_PATH + f"fi_{save_name}.png")

7.2 - Permutation Plot

perm_result_test = permutation_importance(
    model, 
    dataset['Xtest'], 
    dataset['ytest'], 
    n_repeats=10,
    random_state=42, 
    n_jobs=1
)

sorted_idx = perm_result_test.importances_mean.argsort()

# feature_names = np.asarray(new_columns) 

fig, ax = plt.subplots(figsize=(10,10))
plt.style.use(['seaborn-talk'])
ax.boxplot(
    perm_result_test.importances[sorted_idx].T,
    vert=False, 
#     labels=feature_names[sorted_idx]
)
ax.set_title("Permutation Importances (Test set)")
fig.tight_layout()
plt.show()

perm_result_train = permutation_importance(
    rf_model, 
    Xtrain, 
    ytrain, 
    n_repeats=10,
    random_state=42, 
    n_jobs=1
)

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.9s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.5s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.2s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.6s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.9s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.6s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.3s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.7s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.9s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.4s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.1s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.5s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.6s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.4s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.4s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.9s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.4s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.1s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.5s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.3s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.0s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.9s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.6s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.0s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.6s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.0s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.6s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.0s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.2s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.2s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.4s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.1s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.5s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.4s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.8s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.3s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    2.0s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.4s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.7s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.6s
[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    1.1s
[Parallel(n_jobs=16)]: Done 1218 tasks      | elapsed:    1.8s
[Parallel(n_jobs=16)]: Done 1500 out of 1500 | elapsed:    2.1s finished
/home/emmanuel/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
  "multioutput='uniform_average').", FutureWarning)
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.3s
[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.7s

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-38-bd4b09c39570> in <module>
      5     n_repeats=10,
      6     random_state=42,
----> 7     n_jobs=1
      8 )

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/inspection/_permutation_importance.py in permutation_importance(estimator, X, y, scoring, n_repeats, n_jobs, random_state)
    116     scores = Parallel(n_jobs=n_jobs)(delayed(_calculate_permutation_scores)(
    117         estimator, X, y, col_idx, random_seed, n_repeats, scorer
--> 118     ) for col_idx in range(X.shape[1]))
    119 
    120     importances = baseline_score - np.array(scores)

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in __call__(self, iterable)
   1005                 self._iterating = self._original_iterator is not None
   1006 
-> 1007             while self.dispatch_one_batch(iterator):
   1008                 pass
   1009 

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    833                 return False
    834             else:
--> 835                 self._dispatch(tasks)
    836                 return True
    837 

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in _dispatch(self, batch)
    752         with self._lock:
    753             job_idx = len(self._jobs)
--> 754             job = self._backend.apply_async(batch, callback=cb)
    755             # A job can complete so quickly than its callback is
    756             # called before we get here, causing self._jobs to

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    207     def apply_async(self, func, callback=None):
    208         """Schedule a func to be run"""
--> 209         result = ImmediateResult(func)
    210         if callback:
    211             callback(result)

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    588         # Don't delay the application, to avoid keeping the input
    589         # arguments in memory
--> 590         self.results = batch()
    591 
    592     def get(self):

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in __call__(self)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in <listcomp>(.0)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/inspection/_permutation_importance.py in _calculate_permutation_scores(estimator, X, y, col_idx, random_state, n_repeats, scorer)
     32         else:
     33             X_permuted[:, col_idx] = X_permuted[shuffling_idx, col_idx]
---> 34         feature_score = scorer(estimator, X_permuted, y)
     35         scores[n_round] = feature_score
     36 

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/metrics/_scorer.py in _passthrough_scorer(estimator, *args, **kwargs)
    369 def _passthrough_scorer(estimator, *args, **kwargs):
    370     """Function that wraps estimator.score"""
--> 371     return estimator.score(*args, **kwargs)
    372 
    373 

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
    420         from .metrics import r2_score
    421         from .metrics._regression import _check_reg_targets
--> 422         y_pred = self.predict(X)
    423         # XXX: Remove the check in 0.23
    424         y_type, _, _, _ = _check_reg_targets(y, y_pred, None)

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/sklearn/ensemble/_forest.py in predict(self, X)
    780                  **_joblib_parallel_args(require="sharedmem"))(
    781             delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
--> 782             for e in self.estimators_)
    783 
    784         y_hat /= len(self.estimators_)

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in __call__(self, iterable)
   1015 
   1016             with self._backend.retrieval_context():
-> 1017                 self.retrieve()
   1018             # Make sure that we get a last message telling us we are done
   1019             elapsed_time = time.time() - self._start_time

~/.conda/envs/ml4ocn/lib/python3.6/site-packages/joblib/parallel.py in retrieve(self)
    907             try:
    908                 if getattr(self._backend, 'supports_timeout', False):
--> 909                     self._output.extend(job.get(timeout=self.timeout))
    910                 else:
    911                     self._output.extend(job.get())

~/.conda/envs/ml4ocn/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    636 
    637     def get(self, timeout=None):
--> 638         self.wait(timeout)
    639         if not self.ready():
    640             raise TimeoutError

~/.conda/envs/ml4ocn/lib/python3.6/multiprocessing/pool.py in wait(self, timeout)
    633 
    634     def wait(self, timeout=None):
--> 635         self._event.wait(timeout)
    636 
    637     def get(self, timeout=None):

~/.conda/envs/ml4ocn/lib/python3.6/threading.py in wait(self, timeout)
    549             signaled = self._flag
    550             if not signaled:
--> 551                 signaled = self._cond.wait(timeout)
    552             return signaled
    553 

~/.conda/envs/ml4ocn/lib/python3.6/threading.py in wait(self, timeout)
    293         try:    # restore state no matter what (e.g., KeyboardInterrupt)
    294             if timeout is None:
--> 295                 waiter.acquire()
    296                 gotit = True
    297             else:

KeyboardInterrupt: 

sorted_idx = perm_result_train.importances_mean.argsort()

plt.style.use(['seaborn-talk'])
fig, ax = plt.subplots()
ax.boxplot(
    perm_result_train.importances[sorted_idx].T,
    vert=False, 
#     labels=feature_names[sorted_idx]
)
ax.set_title("Permutation Importances (Train set)")
fig.tight_layout()
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-40-b4fd4a447e9a> in <module>
      2 fig, ax = plt.subplots()
      3 ax.boxplot(
----> 4     perm_result_train.importances[sorted_idx].T,
      5     vert=False,
      6 #     labels=feature_names[sorted_idx]

NameError: name 'perm_result_train' is not defined

Load Data

X_core = load_standard_data('STG', training=True)

# Testing Data
X_core_te = load_standard_data('STG', training=False)


X_core_te = X_core_te.iloc[:, 2:]

X_core.shape, X_core_te.shape

((1353, 11), (133, 11))

X_temp, X_dens, X_sal, X_spicy = load_high_dim_data('STG', training=True)

# add prefix (Training/Validation)
X_temp = X_temp.add_prefix('temp_')
X_dens = X_dens.add_prefix('dens_')
X_sal = X_sal.add_prefix('sal_')
X_spicy = X_spicy.add_prefix('spice_')

# 
X_temp_te, X_dens_te, X_sal_te, X_spicy_te = load_high_dim_data('STG', training=False)


# Subset
X_temp_te = X_temp_te.iloc[:, 2:] 
X_dens_te = X_dens_te.iloc[:, 2:] 
X_sal_te= X_sal_te.iloc[:, 2:]
X_spicy_te = X_spicy_te.iloc[:, 2:]

# add prefix (Test)
X_temp_te = X_temp_te.add_prefix('temp_')
X_dens_te = X_dens_te.add_prefix('dens_')
X_sal_te = X_sal_te.add_prefix('sal_')
X_spicy_te = X_spicy_te.add_prefix('spice_')

y = load_labels('STG', training=True)

yte = load_labels('STG', training=False)

yte = yte.iloc[:, 2:]

# Training Data
Xtr = pd.concat([
    X_core, 
    X_temp, 
    X_dens, 
    X_sal, 
    X_spicy
], axis=1)



# Testing Data
Xte = pd.concat([
    X_core_te, 
    X_temp_te, 
    X_dens_te, 
    X_sal_te, 
    X_spicy_te
], axis=1)

Tre Transformations

# new columns columns
temp_columns = X_temp.columns.values
dens_columns = X_dens.columns.values
sal_columns = X_sal.columns.values
spicy_columns = X_spicy.columns.values
core_columns = ['sla', "PAR","RHO_WN_412","RHO_WN_443","RHO_WN_490","RHO_WN_555","RHO_WN_670","MLD"]
time_columns = ['doy']
loc_columns = ['lat', 'lon']

n_components = 5
times = ['doy']

new_columns = [
    *["doy_cos", "doy_sin"], 
    *['x', 'y', 'z',],
    *[f"temperature_pc{icomponent+1}" for icomponent in range(n_components)],
    *[f"density_pc{icomponent+1}" for icomponent in range(n_components)],
    *[f"salinity_pc{icomponent+1}" for icomponent in range(n_components)],
    *[f"spicy_pc{icomponent+1}" for icomponent in range(n_components)],
    *core_columns, 
]
seed = 123
# define transfomer
X_transformer = ColumnTransformer(
    [   ("time", CycleTransform(times), time_columns),
        ("space", GeoCartTransform(), loc_columns),
        ('temp', PCA(n_components=n_components, random_state=seed), temp_columns),
        ('dens', PCA(n_components=n_components, random_state=seed), dens_columns),
        ('sal', PCA(n_components=n_components, random_state=seed), sal_columns),
        ('spice', PCA(n_components=n_components, random_state=seed), spicy_columns),
#         ('core', StandardScaler(with_mean=True, with_std=True), core_columns)
    ],
    remainder='passthrough'
)
# fit transform to data
X_transformer.fit(Xtr)

# transform data
Xtrain = X_transformer.fit_transform(Xtr)
Xtest = X_transformer.transform(Xte)

ytrain = np.log(y)
ytest = np.log(yte)

ytrain.shape, ytest.shape

((1353, 276), (133, 276))

Train-Test Split

Xtrain, Xvalid, ytrain, yvalid =  train_test_split(
        Xtrain, ytrain,
        train_size=.8, random_state=1
    )

Post Transformations

X_transformer = StandardScaler(with_mean=True, with_std=True)

# fit transform to data
X_transformer.fit(Xtrain)

# transform data
Xtrain = X_transformer.fit_transform(Xtrain)
Xvalid = X_transformer.transform(Xvalid)
Xtest = X_transformer.transform(Xtest)

output_transformer = StandardScaler(with_mean=True, with_std=False)

ytrain = output_transformer.fit_transform(ytrain)
yvalid = output_transformer.fit_transform(yvalid)
ytest = output_transformer.transform(ytest)

Train ML Model

model = train_mlp_model(Xtrain, ytrain, verbose=1, valid=0.1)

Iteration 1, loss = 0.04404014
Iteration 2, loss = 0.04233716
Iteration 3, loss = 0.03999879
Iteration 4, loss = 0.03553489
Iteration 5, loss = 0.02922904
Iteration 6, loss = 0.02270823
Iteration 7, loss = 0.01774804
Iteration 8, loss = 0.01455149
Iteration 9, loss = 0.01275994
Iteration 10, loss = 0.01139479
Iteration 11, loss = 0.01041543
Iteration 12, loss = 0.00956958
Iteration 13, loss = 0.00893924
Iteration 14, loss = 0.00837105
Iteration 15, loss = 0.00784597
Iteration 16, loss = 0.00745897
Iteration 17, loss = 0.00710062
Iteration 18, loss = 0.00689064
Iteration 19, loss = 0.00659195
Iteration 20, loss = 0.00644984
Iteration 21, loss = 0.00620834
Iteration 22, loss = 0.00599103
Iteration 23, loss = 0.00585093
Iteration 24, loss = 0.00571502
Iteration 25, loss = 0.00557472
Iteration 26, loss = 0.00544984
Iteration 27, loss = 0.00532607
Iteration 28, loss = 0.00522278
Iteration 29, loss = 0.00508456
Iteration 30, loss = 0.00503143
Iteration 31, loss = 0.00501642
Iteration 32, loss = 0.00492858
Iteration 33, loss = 0.00483732
Iteration 34, loss = 0.00476257
Iteration 35, loss = 0.00471380
Iteration 36, loss = 0.00463958
Iteration 37, loss = 0.00454218
Iteration 38, loss = 0.00448899
Iteration 39, loss = 0.00441734
Iteration 40, loss = 0.00432583
Iteration 41, loss = 0.00428757
Iteration 42, loss = 0.00424003
Iteration 43, loss = 0.00421163
Iteration 44, loss = 0.00419324
Iteration 45, loss = 0.00413517
Iteration 46, loss = 0.00409957
Iteration 47, loss = 0.00404558
Iteration 48, loss = 0.00408237
Iteration 49, loss = 0.00397557
Iteration 50, loss = 0.00390968
Iteration 51, loss = 0.00390060
Iteration 52, loss = 0.00386618
Iteration 53, loss = 0.00382930
Iteration 54, loss = 0.00378482
Iteration 55, loss = 0.00374815
Iteration 56, loss = 0.00369985
Iteration 57, loss = 0.00370369
Iteration 58, loss = 0.00370657
Iteration 59, loss = 0.00364967
Iteration 60, loss = 0.00361953
Iteration 61, loss = 0.00363740
Iteration 62, loss = 0.00363886
Iteration 63, loss = 0.00364014
Iteration 64, loss = 0.00357380
Iteration 65, loss = 0.00354915
Iteration 66, loss = 0.00354744
Iteration 67, loss = 0.00356916
Iteration 68, loss = 0.00348918
Iteration 69, loss = 0.00345298
Iteration 70, loss = 0.00341895
Iteration 71, loss = 0.00338562
Iteration 72, loss = 0.00337161
Iteration 73, loss = 0.00333775
Iteration 74, loss = 0.00330174
Iteration 75, loss = 0.00328011
Iteration 76, loss = 0.00328366
Iteration 77, loss = 0.00327779
Iteration 78, loss = 0.00324590
Iteration 79, loss = 0.00322532
Iteration 80, loss = 0.00321512
Iteration 81, loss = 0.00322540
Iteration 82, loss = 0.00323972
Iteration 83, loss = 0.00319478
Iteration 84, loss = 0.00318867
Iteration 85, loss = 0.00315817
Iteration 86, loss = 0.00316153
Iteration 87, loss = 0.00317412
Iteration 88, loss = 0.00314881
Iteration 89, loss = 0.00316765
Iteration 90, loss = 0.00323642
Iteration 91, loss = 0.00315456
Iteration 92, loss = 0.00311508
Iteration 93, loss = 0.00310767
Iteration 94, loss = 0.00310070
Iteration 95, loss = 0.00311034
Iteration 96, loss = 0.00306191
Iteration 97, loss = 0.00303219
Iteration 98, loss = 0.00301453
Iteration 99, loss = 0.00300814
Iteration 100, loss = 0.00301214
Iteration 101, loss = 0.00296582
Iteration 102, loss = 0.00295890
Iteration 103, loss = 0.00294944
Iteration 104, loss = 0.00295457
Iteration 105, loss = 0.00295732
Iteration 106, loss = 0.00295811
Iteration 107, loss = 0.00291975
Iteration 108, loss = 0.00291308
Iteration 109, loss = 0.00293052
Iteration 110, loss = 0.00290656
Iteration 111, loss = 0.00291328
Iteration 112, loss = 0.00289352
Iteration 113, loss = 0.00288698
Iteration 114, loss = 0.00285006
Iteration 115, loss = 0.00286369
Iteration 116, loss = 0.00286365
Iteration 117, loss = 0.00285693
Iteration 118, loss = 0.00284021
Iteration 119, loss = 0.00280960
Iteration 120, loss = 0.00282471
Iteration 121, loss = 0.00282692
Iteration 122, loss = 0.00281811
Iteration 123, loss = 0.00280976
Iteration 124, loss = 0.00281109
Iteration 125, loss = 0.00278118
Iteration 126, loss = 0.00277009
Iteration 127, loss = 0.00277372
Iteration 128, loss = 0.00276675
Iteration 129, loss = 0.00273699
Iteration 130, loss = 0.00274575
Iteration 131, loss = 0.00277771
Iteration 132, loss = 0.00277319
Iteration 133, loss = 0.00276945
Iteration 134, loss = 0.00271861
Iteration 135, loss = 0.00270733
Iteration 136, loss = 0.00269192
Iteration 137, loss = 0.00268628
Iteration 138, loss = 0.00267595
Iteration 139, loss = 0.00267008
Iteration 140, loss = 0.00267025
Iteration 141, loss = 0.00266669
Iteration 142, loss = 0.00264064
Iteration 143, loss = 0.00266217
Iteration 144, loss = 0.00264794
Iteration 145, loss = 0.00263791
Iteration 146, loss = 0.00262205
Iteration 147, loss = 0.00261012
Iteration 148, loss = 0.00261269
Iteration 149, loss = 0.00260257
Iteration 150, loss = 0.00261959
Iteration 151, loss = 0.00260983
Iteration 152, loss = 0.00260160
Iteration 153, loss = 0.00259984
Iteration 154, loss = 0.00258859
Iteration 155, loss = 0.00260201
Iteration 156, loss = 0.00259312
Iteration 157, loss = 0.00258840
Iteration 158, loss = 0.00258678
Iteration 159, loss = 0.00257915
Iteration 160, loss = 0.00257172
Iteration 161, loss = 0.00255456
Iteration 162, loss = 0.00255829
Iteration 163, loss = 0.00256354
Iteration 164, loss = 0.00257048
Iteration 165, loss = 0.00256765
Iteration 166, loss = 0.00256455
Iteration 167, loss = 0.00256505
Iteration 168, loss = 0.00255623
Iteration 169, loss = 0.00253190
Iteration 170, loss = 0.00251063
Iteration 171, loss = 0.00250799
Iteration 172, loss = 0.00249209
Iteration 173, loss = 0.00248792
Iteration 174, loss = 0.00246772
Iteration 175, loss = 0.00246227
Iteration 176, loss = 0.00247650
Iteration 177, loss = 0.00247792
Iteration 178, loss = 0.00246150
Iteration 179, loss = 0.00245814
Iteration 180, loss = 0.00246831
Iteration 181, loss = 0.00245703
Iteration 182, loss = 0.00245560
Iteration 183, loss = 0.00244056
Iteration 184, loss = 0.00244849
Iteration 185, loss = 0.00246202
Iteration 186, loss = 0.00246362
Iteration 187, loss = 0.00247775
Iteration 188, loss = 0.00250301
Iteration 189, loss = 0.00253404
Iteration 190, loss = 0.00249942
Iteration 191, loss = 0.00247626
Iteration 192, loss = 0.00243972
Iteration 193, loss = 0.00241549
Iteration 194, loss = 0.00240424
Iteration 195, loss = 0.00240119
Iteration 196, loss = 0.00240227
Iteration 197, loss = 0.00241523
Iteration 198, loss = 0.00242200
Iteration 199, loss = 0.00243402
Iteration 200, loss = 0.00244168
Iteration 201, loss = 0.00244610
Iteration 202, loss = 0.00244003
Iteration 203, loss = 0.00241704
Iteration 204, loss = 0.00238488
Iteration 205, loss = 0.00236594
Iteration 206, loss = 0.00238378
Iteration 207, loss = 0.00239985
Iteration 208, loss = 0.00237313
Iteration 209, loss = 0.00236799
Iteration 210, loss = 0.00235266
Iteration 211, loss = 0.00235649
Iteration 212, loss = 0.00236750
Iteration 213, loss = 0.00237051
Iteration 214, loss = 0.00238517
Iteration 215, loss = 0.00238750
Iteration 216, loss = 0.00237986
Iteration 217, loss = 0.00237983
Iteration 218, loss = 0.00236825
Iteration 219, loss = 0.00236512
Iteration 220, loss = 0.00238062
Iteration 221, loss = 0.00239693
Training loss did not improve more than tol=0.000010 for 10 consecutive epochs. Stopping.
Training time: 7.193 secs.

Test ML Model

ypred = model.predict(Xtest)
# each level
each_level = False

stats = get_stats(ypred, ytest, each_level=each_level)

stats

	mae	mse	rmse	r2
0	0.076336	0.011636	0.011636	0.812559

each_level = True

stats_df = get_stats(ypred, ytest, each_level=each_level)

stats_df.head()

	mae	mse	rmse	r2
0	0.088556	0.020310	0.142513	0.717452
1	0.081911	0.015445	0.124277	0.762677
2	0.076586	0.011129	0.105496	0.814074
3	0.081023	0.013181	0.114808	0.789395
4	0.073182	0.009919	0.099596	0.820807

statistic = 'r2'
plt.style.use('seaborn-talk')
plot_mo_stats(
    stats_df, 
    stat='r2', 
    save_name='stg_test',
    color='red'
)

plot_mo_stats(stats_df, stat='mae', save_name='stg_test', color='red')
plot_mo_stats(stats_df, stat='mse', save_name='stg_test', color='red')
plot_mo_stats(stats_df, stat='rmse', save_name='stg_test', color='red')

Post Analysis

tree_feature_importances = \
    rf_model.feature_importances_

feature_names = np.asarray(new_columns) #np.concatenate((core_columns.values, np.array(pca_columns)))

assert feature_names.shape[0] == tree_feature_importances.shape[0], "Shapes don't match"

sorted_idx = tree_feature_importances.argsort()

SAVE_PATH = "/media/disk/erc/papers/2019_ML_OCN/ml4ocean/reports/figures/"
save_name = 'stg'
y_ticks = np.arange(0, len(feature_names))
plt.style.use(['seaborn-talk'])
fig, ax = plt.subplots(figsize=(10,10))
ax.barh(y_ticks, tree_feature_importances[sorted_idx], zorder=3, height=0.8, color='red')
ax.set_yticklabels(feature_names[sorted_idx])
ax.set_yticks(y_ticks)
# ax.set_title("Random Forest Feature Importances (MDI)")
ax.tick_params(axis="both", which="major", labelsize=20)
ax.tick_params(axis="both", which="minor", labelsize=20)
ax.grid(alpha=0.6, color='gray', zorder=0)
plt.tight_layout()
plt.show()
fig.savefig(SAVE_PATH + f"fi_{save_name}.png")

depths = 276
first = [*range(1, 250)]
d1 = first[::2]

second = [*range(251,1000)]
d2 = second[::5]

len(d1), len(d2)

(125, 150)

plt.plot(-np.concatenate((d1,d2)))

[<matplotlib.lines.Line2D at 0x7f8ad6785a90>]