In [1]:

    

%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd

plt.style.use('ggplot')


    

In [19]:

    

from sklearn.datasets import load_boston
boston = load_boston()


    

In [21]:

    

boston.keys()


    

    
Out[21]:





dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])

In [22]:

    

feature_df = pd.DataFrame(data=boston['data'], columns=boston['feature_names'])


    

In [23]:

    

target = pd.Series(data=boston['target'], name='target')


    

In [24]:

    

_ = target.hist()


    

In [25]:

    

feature_df.head()


    

    
Out[25]:







  

    

      

      
CRIM
      
ZN
      
INDUS
      
CHAS
      
NOX
      
RM
      
AGE
      
DIS
      
RAD
      
TAX
      
PTRATIO
      
B
      
LSTAT
    
  
  

    

      
0
      
0.00632
      
18.0
      
2.31
      
0.0
      
0.538
      
6.575
      
65.2
      
4.0900
      
1.0
      
296.0
      
15.3
      
396.90
      
4.98
    
    

      
1
      
0.02731
      
0.0
      
7.07
      
0.0
      
0.469
      
6.421
      
78.9
      
4.9671
      
2.0
      
242.0
      
17.8
      
396.90
      
9.14
    
    

      
2
      
0.02729
      
0.0
      
7.07
      
0.0
      
0.469
      
7.185
      
61.1
      
4.9671
      
2.0
      
242.0
      
17.8
      
392.83
      
4.03
    
    

      
3
      
0.03237
      
0.0
      
2.18
      
0.0
      
0.458
      
6.998
      
45.8
      
6.0622
      
3.0
      
222.0
      
18.7
      
394.63
      
2.94
    
    

      
4
      
0.06905
      
0.0
      
2.18
      
0.0
      
0.458
      
7.147
      
54.2
      
6.0622
      
3.0
      
222.0
      
18.7
      
396.90
      
5.33
    
  

In [33]:

    

from tpot import TPOTRegressor


regressor = TPOTRegressor(generations=5, 
                          population_size=50, 
                          verbosity=2, 
                          n_jobs=4,
                          random_state=42, 
                          template='Regressor')

regressor.fit(feature_df.values, target.values)


    

    






 
 










    




Generation 1 - Current best internal CV score: -20.164673087731806
Generation 2 - Current best internal CV score: -20.164673087731806
Generation 3 - Current best internal CV score: -18.810523582154456
Generation 4 - Current best internal CV score: -18.810523582154456
Generation 5 - Current best internal CV score: -18.810523582154456

Best pipeline: GradientBoostingRegressor(input_matrix, alpha=0.9, learning_rate=0.1, loss=lad, max_depth=7, max_features=0.3, min_samples_leaf=10, min_samples_split=3, n_estimators=100, subsample=0.7500000000000001)






    
Out[33]:





TPOTRegressor(config_dict=None, crossover_rate=0.1, cv=5,
              disable_update_check=False, early_stop=None, generations=5,
              max_eval_time_mins=5, max_time_mins=None, memory=None,
              mutation_rate=0.9, n_jobs=4, offspring_size=None,
              periodic_checkpoint_folder=None, population_size=50,
              random_state=42, scoring=None, subsample=1.0,
              template='Regressor', use_dask=False, verbosity=2,
              warm_start=False)

In [36]:

    

from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import train_test_split


    

In [35]:

    

m = regressor.export()
print(m)


    

    




import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split

# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
            train_test_split(features, tpot_data['target'], random_state=42)

# Average CV score on the training set was: -18.810523582154456
exported_pipeline = GradientBoostingRegressor(alpha=0.9, learning_rate=0.1, loss="lad", max_depth=7, max_features=0.3, min_samples_leaf=10, min_samples_split=3, n_estimators=100, subsample=0.7500000000000001)
# Fix random state in exported estimator
if hasattr(exported_pipeline, 'random_state'):
    setattr(exported_pipeline, 'random_state', 42)

exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)

In [37]:

    

exported_pipeline = GradientBoostingRegressor(
    alpha=0.9, learning_rate=0.1, 
    loss="lad", max_depth=7, max_features=0.3, min_samples_leaf=10, min_samples_split=3, n_estimators=100, subsample=0.7500000000000001)


exported_pipeline.fit(feature_df.values, target.values)
results = exported_pipeline.predict(feature_df.values)


    

In [41]:

    

values = pd.DataFrame(data=np.column_stack([results, target.values]), columns=['predicted', 'actual'])
values.plot(kind='scatter', x='actual', y='predicted')


    

    
Out[41]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fe9656eaf50>