In [4]:

    

from __future__ import print_function
import numpy as np
from random import shuffle
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
from keras.models import Sequential, Model
from keras.utils import plot_model
from keras.layers import Dense, LSTM, GRU, Flatten, Input, Reshape, TimeDistributed, Bidirectional, Dense, Dropout, \
    Activation, Flatten, Conv1D, MaxPooling1D, GlobalAveragePooling1D, AveragePooling1D, concatenate, BatchNormalization
from keras.initializers import lecun_normal, glorot_normal
from keras.regularizers import l1, l1_l2, l2
from keras import metrics
from keras.optimizers import adam, rmsprop
import pandas as pd
import scipy.io as sio
from keras.callbacks import CSVLogger, TerminateOnNaN
import os
import csv
import json
import scattergro_utils as sg_utils
import sklearn.preprocessing
import h5py
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error, explained_variance_score, \
    r2_score
from sklearn.kernel_ridge import KernelRidge
import time

#https://github.com/fchollet/keras/issues/1671 how to set weights 

if __name__ == "__main__":
    #aux_reg_regressor = RandomForestRegressor(n_estimators=5,criterion='mse',n_jobs=2,warm_start=True)
    #aux_reg_regressor = Ridge()
    #aux_reg_regressor = LinearRegression()
    #aux_reg_regressor = KernelRidge(alpha=1,kernel='polynomial',gamma=1.0e-3,)
    aux_reg_regressor = ExtraTreesRegressor(n_estimators=5,criterion='mse',n_jobs=2,warm_start=True)
    print(type(aux_reg_regressor))
    #print(str(type(aux_reg_regressor)))
    print(aux_reg_regressor.__class__)
    print(aux_reg_regressor.base_estimator)


    

    




<class 'sklearn.ensemble.forest.ExtraTreesRegressor'>
<class 'sklearn.ensemble.forest.ExtraTreesRegressor'>
ExtraTreeRegressor(criterion='mse', max_depth=None, max_features='auto',
          max_leaf_nodes=None, min_impurity_decrease=0.0,
          min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,
          min_weight_fraction_leaf=0.0, random_state=None,
          splitter='random')

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