In [1]:

    

import numpy as np
import pandas as pd

import sklearn.model_selection
from sklearn.metrics import log_loss

from sklearn.ensemble import RandomForestClassifier
from supervised.automl import AutoML


    

In [2]:

    

df = pd.read_csv('https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv', skipinitialspace=True)
x_cols = [c for c in df.columns if c != 'income']
X = df[x_cols]
y = df['income']


    

In [3]:

    

seed = 1234
X_train, X_test, y_train, y_test = \
    sklearn.model_selection.train_test_split(X, y, test_size = 0.3, random_state=seed)


    

In [4]:

    

X_train.head()


    

    
Out[4]:







  

    

      

      
age
      
workclass
      
fnlwgt
      
education
      
education-num
      
marital-status
      
occupation
      
relationship
      
race
      
sex
      
capital-gain
      
capital-loss
      
hours-per-week
      
native-country
    
  
  

    

      
29700
      
37
      
Private
      
34146
      
HS-grad
      
9
      
Married-civ-spouse
      
Craft-repair
      
Husband
      
White
      
Male
      
0
      
0
      
68
      
United-States
    
    

      
1529
      
37
      
Private
      
26898
      
HS-grad
      
9
      
Divorced
      
Exec-managerial
      
Unmarried
      
White
      
Female
      
0
      
0
      
12
      
United-States
    
    

      
27477
      
26
      
Private
      
190762
      
Some-college
      
10
      
Married-civ-spouse
      
Sales
      
Husband
      
White
      
Male
      
0
      
0
      
18
      
United-States
    
    

      
31950
      
35
      
Private
      
189092
      
Bachelors
      
13
      
Married-civ-spouse
      
Exec-managerial
      
Husband
      
White
      
Male
      
0
      
0
      
40
      
United-States
    
    

      
4732
      
23
      
Private
      
260019
      
7th-8th
      
4
      
Never-married
      
Farming-fishing
      
Unmarried
      
Other
      
Male
      
0
      
0
      
36
      
Mexico
    
  

In [ ]:

    




    

In [5]:

    

rf = RandomForestClassifier(n_estimators = 1000)
rf = rf.fit(X_train, y_train)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-9005dc76e109> in <module>
      1 rf = RandomForestClassifier(n_estimators = 1000)
----> 2 rf = rf.fit(X_train, y_train)

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
    248 
    249         # Validate or convert input data
--> 250         X = check_array(X, accept_sparse="csc", dtype=DTYPE)
    251         y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
    252         if sample_weight is not None:

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    525             try:
    526                 warnings.simplefilter('error', ComplexWarning)
--> 527                 array = np.asarray(array, dtype=dtype, order=order)
    528             except ComplexWarning:
    529                 raise ValueError("Complex data not supported\n"

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    536 
    537     """
--> 538     return array(a, dtype, copy=False, order=order)
    539 
    540 

ValueError: could not convert string to float: 'Private'

In [6]:

    

# The Random Forest does not work with categorical
# Let's convert them, with integer encoding


    

In [7]:

    

from sklearn.preprocessing import LabelEncoder

for column in ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex','native-country']:
    categorical_convert = LabelEncoder()
    X_train[column] = categorical_convert.fit_transform(X_train[column])
    X_test[column] = categorical_convert.transform(X_test[column])


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-a05c0e4307e4> in <module>
      3 for column in ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex','native-country']:
      4     categorical_convert = LabelEncoder()
----> 5     X_train[column] = categorical_convert.fit_transform(X_train[column])
      6     X_test[column] = categorical_convert.transform(X_test[column])

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in fit_transform(self, y)
    234         """
    235         y = column_or_1d(y, warn=True)
--> 236         self.classes_, y = _encode(y, encode=True)
    237         return y
    238 

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in _encode(values, uniques, encode)
    106     """
    107     if values.dtype == object:
--> 108         return _encode_python(values, uniques, encode)
    109     else:
    110         return _encode_numpy(values, uniques, encode)

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in _encode_python(values, uniques, encode)
     61     # only used in _encode below, see docstring there for details
     62     if uniques is None:
---> 63         uniques = sorted(set(values))
     64         uniques = np.array(uniques, dtype=values.dtype)
     65     if encode:

TypeError: '<' not supported between instances of 'float' and 'str'

In [8]:

    

# still something ?
# looks like we have missing values in the data


    

In [9]:

    

X_train[column].head(10)


    

    
Out[9]:





29700    Private
1529     Private
27477    Private
31950    Private
4732     Private
10858    Private
24518    Private
10035        NaN
1324     Private
26727    Private
Name: workclass, dtype: object

In [10]:

    

# lets fill with mean values


    

In [11]:

    

train_mode = X_train.mode().iloc[0]
X_train = X_train.fillna(train_mode)
X_test = X_test.fillna(train_mode)


    

In [12]:

    

# try to fit again


    

In [13]:

    

rf = RandomForestClassifier(n_estimators = 1000)
rf = rf.fit(X_train, y_train)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-9005dc76e109> in <module>
      1 rf = RandomForestClassifier(n_estimators = 1000)
----> 2 rf = rf.fit(X_train, y_train)

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
    248 
    249         # Validate or convert input data
--> 250         X = check_array(X, accept_sparse="csc", dtype=DTYPE)
    251         y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
    252         if sample_weight is not None:

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    525             try:
    526                 warnings.simplefilter('error', ComplexWarning)
--> 527                 array = np.asarray(array, dtype=dtype, order=order)
    528             except ComplexWarning:
    529                 raise ValueError("Complex data not supported\n"

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    536 
    537     """
--> 538     return array(a, dtype, copy=False, order=order)
    539 
    540 

ValueError: could not convert string to float: 'Private'

In [14]:

    

# oh, yes! still need to convert!


    

In [15]:

    

from sklearn.preprocessing import LabelEncoder

for column in ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex','native-country']:
    categorical_convert = LabelEncoder()
    X_train[column] = categorical_convert.fit_transform(X_train[column])
    X_test[column] = categorical_convert.transform(X_test[column])


    

    




---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in _encode_python(values, uniques, encode)
     67         try:
---> 68             encoded = np.array([table[v] for v in values])
     69         except KeyError as e:

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in <listcomp>(.0)
     67         try:
---> 68             encoded = np.array([table[v] for v in values])
     69         except KeyError as e:

KeyError: 'Holand-Netherlands'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-15-a05c0e4307e4> in <module>
      4     categorical_convert = LabelEncoder()
      5     X_train[column] = categorical_convert.fit_transform(X_train[column])
----> 6     X_test[column] = categorical_convert.transform(X_test[column])

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in transform(self, y)
    255             return np.array([])
    256 
--> 257         _, y = _encode(y, uniques=self.classes_, encode=True)
    258         return y
    259 

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in _encode(values, uniques, encode)
    106     """
    107     if values.dtype == object:
--> 108         return _encode_python(values, uniques, encode)
    109     else:
    110         return _encode_numpy(values, uniques, encode)

~/sandbox/rf/automl-rf/venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py in _encode_python(values, uniques, encode)
     69         except KeyError as e:
     70             raise ValueError("y contains previously unseen labels: %s"
---> 71                              % str(e))
     72         return uniques, encoded
     73     else:

ValueError: y contains previously unseen labels: 'Holand-Netherlands'

In [16]:

    

# what!?@^%$^%$^ ????????
# looks like that there are different labels between X_train and X_test ...
# which can happen in real life as well :) and will break your production model!
# what to do in this situation ?


    

In [ ]:

    




    

In [17]:

    

# we need to assign a new integer for this new label or remove this row from predictions
# let's hack it ...


    

In [18]:

    

column = 'native-country'
all_values = np.unique(list(X_test[column].values))
diff = np.setdiff1d(all_values, categorical_convert.classes_)
categorical_convert.classes_ = np.concatenate((categorical_convert.classes_, diff))
X_test[column] = categorical_convert.transform(X_test[column])


    

In [19]:

    

np.unique(X_train[column])


    

    
Out[19]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39])

In [20]:

    

np.unique(X_test[column])


    

    
Out[20]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40])

In [21]:

    

# can we train now?


    

In [22]:

    

rf = RandomForestClassifier(n_estimators = 1000)
rf = rf.fit(X_train, y_train)


    

In [23]:

    

# :), let's do predictions! and compute some metrics!


    

In [24]:

    

log_loss(y_test, rf.predict_proba(X_test)[:,1])


    

    
Out[24]:





0.338857591429531

In [25]:

    

# Please remember that for production you need to save:
# 1. values used for missing values inputation
# 2. encoders to properly convert categoricals to integers


    

In [ ]:

    




    

In [26]:

    

# and the AutoML way


    

In [27]:

    

# let's reread the data, to have raw data


    

In [28]:

    

df = pd.read_csv('https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv', skipinitialspace=True)
x_cols = [c for c in df.columns if c != 'income']
X = df[x_cols]
y = df['income']
seed = 1234
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.3, random_state=seed)


    

In [ ]:

    




    

In [29]:

    

automl = AutoML(total_time_limit=60, algorithms=["RF"], train_ensemble=False)
automl.fit(X_train, y_train)


    

    






 
 










    




Learner RF final loss 0.31295985228245726 time 3.15 seconds
Learner RF final loss 0.3058324469987815 time 3.05 seconds
Learner RF final loss 0.3065861813563275 time 3.16 seconds
Learner RF final loss 0.305368676349097 time 3.2 seconds
Learner RF final loss 0.3094612291478825 time 3.16 seconds
Learner RF final loss 0.31307862961403815 time 3.17 seconds
Learner RF final loss 0.3056438384380418 time 3.28 seconds
Learner RF final loss 0.5094367132563837 time 3.3 seconds
Learner RF final loss 0.30783368905879144 time 3.15 seconds
Learner RF final loss 0.33719065961498546 time 3.26 seconds
Learner RF final loss 0.3072727392839799 time 3.19 seconds
Learner RF final loss 0.30193530622863163 time 3.21 seconds
Learner RF final loss 0.3078442875513561 time 3.05 seconds
Learner RF final loss 0.308193037515502 time 3.07 seconds
Learner RF final loss 0.3058324469987815 time 3.15 seconds
Learner RF final loss 0.3058324469987815 time 3.11 seconds

In [30]:

    

log_loss(y_test, automl.predict(X_test)['p_>50K'])


    

    
Out[30]:





0.3148709233696557

In [31]:

    

automl.get_leaderboard()


    

    
Out[31]:







  

    

      

      
uid
      
model_type
      
metric_type
      
metric_value
      
train_time
    
  
  

    

      
0
      
6bfa0d57-01c4-40d7-975a-374ec3b94f4d
      
RF
      
logloss
      
0.312960
      
3.149549
    
    

      
1
      
53edf435-bb04-4803-ba25-32228e433a48
      
RF
      
logloss
      
0.305832
      
3.045671
    
    

      
2
      
02ea275c-ebcf-4053-9a77-6336fd520f11
      
RF
      
logloss
      
0.306586
      
3.158201
    
    

      
3
      
e4ee51d7-db4e-4d60-ab1e-1129228cb29f
      
RF
      
logloss
      
0.305369
      
3.198720
    
    

      
4
      
ca2d8d92-7ca7-48c8-bd4c-ce627d3523cb
      
RF
      
logloss
      
0.309461
      
3.156306
    
    

      
5
      
744502ae-c48e-4b64-9341-c0acf05becdb
      
RF
      
logloss
      
0.313079
      
3.169226
    
    

      
6
      
0930b97d-e060-4908-bed6-a0f05d908f4e
      
RF
      
logloss
      
0.305644
      
3.276672
    
    

      
7
      
6cf5c1fd-b100-45b7-9010-d1a051a2b828
      
RF
      
logloss
      
0.509437
      
3.300742
    
    

      
8
      
a1683004-755d-4f35-9701-f07a1917871e
      
RF
      
logloss
      
0.307834
      
3.153975
    
    

      
9
      
daf12a37-596d-4339-981d-3bef87daf47a
      
RF
      
logloss
      
0.337191
      
3.257477
    
    

      
10
      
bdcc1c7c-aeeb-4c1a-a7a7-5fc7ea8ee0a8
      
RF
      
logloss
      
0.307273
      
3.185796
    
    

      
11
      
5884e228-617a-4a05-b5e5-76cda65f731b
      
RF
      
logloss
      
0.301935
      
3.214842
    
    

      
12
      
e478c956-579b-43c4-adeb-26a2bc9d73bb
      
RF
      
logloss
      
0.307844
      
3.046856
    
    

      
13
      
f0ba8511-bc71-4acf-b173-aea9ab5f65c9
      
RF
      
logloss
      
0.308193
      
3.072767
    
    

      
14
      
314dd81c-41f8-456c-b19f-8d181580f2b4
      
RF
      
logloss
      
0.305832
      
3.154421
    
    

      
15
      
fc64a7c4-3bfc-4660-ac58-2854a259015c
      
RF
      
logloss
      
0.305832
      
3.112666
    
  

In [32]:

    

automl.to_json()


    

    
Out[32]:





{'best_model': {'uid': '5884e228-617a-4a05-b5e5-76cda65f731b',
  'algorithm_short_name': 'RF',
  'framework_file': '5884e228-617a-4a05-b5e5-76cda65f731b.framework',
  'framework_file_path': '/tmp/5884e228-617a-4a05-b5e5-76cda65f731b.framework',
  'preprocessing': [{'missing_values': [{'fill_method': 'na_fill_median',
      'fill_params': {'workclass': 'Private',
       'occupation': 'Prof-specialty',
       'native-country': 'United-States'}}],
    'categorical': [{'convert_method': 'categorical_to_int',
      'convert_params': {'workclass': {'Federal-gov': 0,
        'Local-gov': 1,
        'Never-worked': 2,
        'Private': 3,
        'Self-emp-inc': 4,
        'Self-emp-not-inc': 5,
        'State-gov': 6,
        'Without-pay': 7},
       'education': {'10th': 0,
        '11th': 1,
        '12th': 2,
        '1st-4th': 3,
        '5th-6th': 4,
        '7th-8th': 5,
        '9th': 6,
        'Assoc-acdm': 7,
        'Assoc-voc': 8,
        'Bachelors': 9,
        'Doctorate': 10,
        'HS-grad': 11,
        'Masters': 12,
        'Preschool': 13,
        'Prof-school': 14,
        'Some-college': 15},
       'marital-status': {'Divorced': 0,
        'Married-AF-spouse': 1,
        'Married-civ-spouse': 2,
        'Married-spouse-absent': 3,
        'Never-married': 4,
        'Separated': 5,
        'Widowed': 6},
       'occupation': {'Adm-clerical': 0,
        'Armed-Forces': 1,
        'Craft-repair': 2,
        'Exec-managerial': 3,
        'Farming-fishing': 4,
        'Handlers-cleaners': 5,
        'Machine-op-inspct': 6,
        'Other-service': 7,
        'Priv-house-serv': 8,
        'Prof-specialty': 9,
        'Protective-serv': 10,
        'Sales': 11,
        'Tech-support': 12,
        'Transport-moving': 13},
       'relationship': {'Husband': 0,
        'Not-in-family': 1,
        'Other-relative': 2,
        'Own-child': 3,
        'Unmarried': 4,
        'Wife': 5},
       'race': {'Amer-Indian-Eskimo': 0,
        'Asian-Pac-Islander': 1,
        'Black': 2,
        'Other': 3,
        'White': 4},
       'sex': {'Female': 0, 'Male': 1},
       'native-country': {'Cambodia': 0,
        'Canada': 1,
        'China': 2,
        'Columbia': 3,
        'Cuba': 4,
        'Dominican-Republic': 5,
        'Ecuador': 6,
        'El-Salvador': 7,
        'England': 8,
        'France': 9,
        'Germany': 10,
        'Greece': 11,
        'Guatemala': 12,
        'Haiti': 13,
        'Honduras': 14,
        'Hong': 15,
        'Hungary': 16,
        'India': 17,
        'Iran': 18,
        'Ireland': 19,
        'Italy': 20,
        'Jamaica': 21,
        'Japan': 22,
        'Laos': 23,
        'Mexico': 24,
        'Nicaragua': 25,
        'Outlying-US(Guam-USVI-etc)': 26,
        'Peru': 27,
        'Philippines': 28,
        'Poland': 29,
        'Portugal': 30,
        'Puerto-Rico': 31,
        'Scotland': 32,
        'South': 33,
        'Taiwan': 34,
        'Thailand': 35,
        'Trinadad&Tobago': 36,
        'United-States': 37,
        'Vietnam': 38,
        'Yugoslavia': 39}}}],
    'categorical_y': {'<=50K': 0, '>50K': 1}},
   {'missing_values': [{'fill_method': 'na_fill_median',
      'fill_params': {'workclass': 'Private',
       'occupation': 'Prof-specialty',
       'native-country': 'United-States'}}],
    'categorical': [{'convert_method': 'categorical_to_int',
      'convert_params': {'workclass': {'Federal-gov': 0,
        'Local-gov': 1,
        'Never-worked': 2,
        'Private': 3,
        'Self-emp-inc': 4,
        'Self-emp-not-inc': 5,
        'State-gov': 6,
        'Without-pay': 7},
       'education': {'10th': 0,
        '11th': 1,
        '12th': 2,
        '1st-4th': 3,
        '5th-6th': 4,
        '7th-8th': 5,
        '9th': 6,
        'Assoc-acdm': 7,
        'Assoc-voc': 8,
        'Bachelors': 9,
        'Doctorate': 10,
        'HS-grad': 11,
        'Masters': 12,
        'Preschool': 13,
        'Prof-school': 14,
        'Some-college': 15},
       'marital-status': {'Divorced': 0,
        'Married-AF-spouse': 1,
        'Married-civ-spouse': 2,
        'Married-spouse-absent': 3,
        'Never-married': 4,
        'Separated': 5,
        'Widowed': 6},
       'occupation': {'Adm-clerical': 0,
        'Armed-Forces': 1,
        'Craft-repair': 2,
        'Exec-managerial': 3,
        'Farming-fishing': 4,
        'Handlers-cleaners': 5,
        'Machine-op-inspct': 6,
        'Other-service': 7,
        'Priv-house-serv': 8,
        'Prof-specialty': 9,
        'Protective-serv': 10,
        'Sales': 11,
        'Tech-support': 12,
        'Transport-moving': 13},
       'relationship': {'Husband': 0,
        'Not-in-family': 1,
        'Other-relative': 2,
        'Own-child': 3,
        'Unmarried': 4,
        'Wife': 5},
       'race': {'Amer-Indian-Eskimo': 0,
        'Asian-Pac-Islander': 1,
        'Black': 2,
        'Other': 3,
        'White': 4},
       'sex': {'Female': 0, 'Male': 1},
       'native-country': {'Cambodia': 0,
        'Canada': 1,
        'China': 2,
        'Columbia': 3,
        'Cuba': 4,
        'Dominican-Republic': 5,
        'Ecuador': 6,
        'El-Salvador': 7,
        'England': 8,
        'France': 9,
        'Germany': 10,
        'Greece': 11,
        'Guatemala': 12,
        'Haiti': 13,
        'Honduras': 14,
        'Hong': 15,
        'Hungary': 16,
        'India': 17,
        'Iran': 18,
        'Ireland': 19,
        'Italy': 20,
        'Jamaica': 21,
        'Japan': 22,
        'Laos': 23,
        'Mexico': 24,
        'Nicaragua': 25,
        'Outlying-US(Guam-USVI-etc)': 26,
        'Peru': 27,
        'Philippines': 28,
        'Poland': 29,
        'Portugal': 30,
        'Puerto-Rico': 31,
        'Scotland': 32,
        'South': 33,
        'Taiwan': 34,
        'Thailand': 35,
        'Trinadad&Tobago': 36,
        'United-States': 37,
        'Vietnam': 38,
        'Yugoslavia': 39}}}],
    'categorical_y': {'<=50K': 0, '>50K': 1}},
   {'missing_values': [{'fill_method': 'na_fill_median',
      'fill_params': {'workclass': 'Private',
       'occupation': 'Prof-specialty',
       'native-country': 'United-States'}}],
    'categorical': [{'convert_method': 'categorical_to_int',
      'convert_params': {'workclass': {'Federal-gov': 0,
        'Local-gov': 1,
        'Never-worked': 2,
        'Private': 3,
        'Self-emp-inc': 4,
        'Self-emp-not-inc': 5,
        'State-gov': 6,
        'Without-pay': 7},
       'education': {'10th': 0,
        '11th': 1,
        '12th': 2,
        '1st-4th': 3,
        '5th-6th': 4,
        '7th-8th': 5,
        '9th': 6,
        'Assoc-acdm': 7,
        'Assoc-voc': 8,
        'Bachelors': 9,
        'Doctorate': 10,
        'HS-grad': 11,
        'Masters': 12,
        'Preschool': 13,
        'Prof-school': 14,
        'Some-college': 15},
       'marital-status': {'Divorced': 0,
        'Married-AF-spouse': 1,
        'Married-civ-spouse': 2,
        'Married-spouse-absent': 3,
        'Never-married': 4,
        'Separated': 5,
        'Widowed': 6},
       'occupation': {'Adm-clerical': 0,
        'Armed-Forces': 1,
        'Craft-repair': 2,
        'Exec-managerial': 3,
        'Farming-fishing': 4,
        'Handlers-cleaners': 5,
        'Machine-op-inspct': 6,
        'Other-service': 7,
        'Priv-house-serv': 8,
        'Prof-specialty': 9,
        'Protective-serv': 10,
        'Sales': 11,
        'Tech-support': 12,
        'Transport-moving': 13},
       'relationship': {'Husband': 0,
        'Not-in-family': 1,
        'Other-relative': 2,
        'Own-child': 3,
        'Unmarried': 4,
        'Wife': 5},
       'race': {'Amer-Indian-Eskimo': 0,
        'Asian-Pac-Islander': 1,
        'Black': 2,
        'Other': 3,
        'White': 4},
       'sex': {'Female': 0, 'Male': 1},
       'native-country': {'Cambodia': 0,
        'Canada': 1,
        'China': 2,
        'Columbia': 3,
        'Cuba': 4,
        'Dominican-Republic': 5,
        'Ecuador': 6,
        'El-Salvador': 7,
        'England': 8,
        'France': 9,
        'Germany': 10,
        'Greece': 11,
        'Guatemala': 12,
        'Haiti': 13,
        'Honduras': 14,
        'Hong': 15,
        'Hungary': 16,
        'India': 17,
        'Iran': 18,
        'Ireland': 19,
        'Italy': 20,
        'Jamaica': 21,
        'Japan': 22,
        'Laos': 23,
        'Mexico': 24,
        'Nicaragua': 25,
        'Outlying-US(Guam-USVI-etc)': 26,
        'Peru': 27,
        'Philippines': 28,
        'Poland': 29,
        'Portugal': 30,
        'Puerto-Rico': 31,
        'Scotland': 32,
        'South': 33,
        'Taiwan': 34,
        'Thailand': 35,
        'Trinadad&Tobago': 36,
        'United-States': 37,
        'Vietnam': 38,
        'Yugoslavia': 39}}}],
    'categorical_y': {'<=50K': 0, '>50K': 1}},
   {'missing_values': [{'fill_method': 'na_fill_median',
      'fill_params': {'workclass': 'Private',
       'occupation': 'Prof-specialty',
       'native-country': 'United-States'}}],
    'categorical': [{'convert_method': 'categorical_to_int',
      'convert_params': {'workclass': {'Federal-gov': 0,
        'Local-gov': 1,
        'Never-worked': 2,
        'Private': 3,
        'Self-emp-inc': 4,
        'Self-emp-not-inc': 5,
        'State-gov': 6,
        'Without-pay': 7},
       'education': {'10th': 0,
        '11th': 1,
        '12th': 2,
        '1st-4th': 3,
        '5th-6th': 4,
        '7th-8th': 5,
        '9th': 6,
        'Assoc-acdm': 7,
        'Assoc-voc': 8,
        'Bachelors': 9,
        'Doctorate': 10,
        'HS-grad': 11,
        'Masters': 12,
        'Preschool': 13,
        'Prof-school': 14,
        'Some-college': 15},
       'marital-status': {'Divorced': 0,
        'Married-AF-spouse': 1,
        'Married-civ-spouse': 2,
        'Married-spouse-absent': 3,
        'Never-married': 4,
        'Separated': 5,
        'Widowed': 6},
       'occupation': {'Adm-clerical': 0,
        'Armed-Forces': 1,
        'Craft-repair': 2,
        'Exec-managerial': 3,
        'Farming-fishing': 4,
        'Handlers-cleaners': 5,
        'Machine-op-inspct': 6,
        'Other-service': 7,
        'Priv-house-serv': 8,
        'Prof-specialty': 9,
        'Protective-serv': 10,
        'Sales': 11,
        'Tech-support': 12,
        'Transport-moving': 13},
       'relationship': {'Husband': 0,
        'Not-in-family': 1,
        'Other-relative': 2,
        'Own-child': 3,
        'Unmarried': 4,
        'Wife': 5},
       'race': {'Amer-Indian-Eskimo': 0,
        'Asian-Pac-Islander': 1,
        'Black': 2,
        'Other': 3,
        'White': 4},
       'sex': {'Female': 0, 'Male': 1},
       'native-country': {'Cambodia': 0,
        'Canada': 1,
        'China': 2,
        'Columbia': 3,
        'Cuba': 4,
        'Dominican-Republic': 5,
        'Ecuador': 6,
        'El-Salvador': 7,
        'England': 8,
        'France': 9,
        'Germany': 10,
        'Greece': 11,
        'Guatemala': 12,
        'Haiti': 13,
        'Honduras': 14,
        'Hong': 15,
        'Hungary': 16,
        'India': 17,
        'Iran': 18,
        'Ireland': 19,
        'Italy': 20,
        'Jamaica': 21,
        'Japan': 22,
        'Laos': 23,
        'Mexico': 24,
        'Nicaragua': 25,
        'Outlying-US(Guam-USVI-etc)': 26,
        'Peru': 27,
        'Philippines': 28,
        'Poland': 29,
        'Portugal': 30,
        'Puerto-Rico': 31,
        'Scotland': 32,
        'South': 33,
        'Taiwan': 34,
        'Thailand': 35,
        'Trinadad&Tobago': 36,
        'United-States': 37,
        'Vietnam': 38,
        'Yugoslavia': 39}}}],
    'categorical_y': {'<=50K': 0, '>50K': 1}},
   {'missing_values': [{'fill_method': 'na_fill_median',
      'fill_params': {'workclass': 'Private',
       'occupation': 'Prof-specialty',
       'native-country': 'United-States'}}],
    'categorical': [{'convert_method': 'categorical_to_int',
      'convert_params': {'workclass': {'Federal-gov': 0,
        'Local-gov': 1,
        'Never-worked': 2,
        'Private': 3,
        'Self-emp-inc': 4,
        'Self-emp-not-inc': 5,
        'State-gov': 6,
        'Without-pay': 7},
       'education': {'10th': 0,
        '11th': 1,
        '12th': 2,
        '1st-4th': 3,
        '5th-6th': 4,
        '7th-8th': 5,
        '9th': 6,
        'Assoc-acdm': 7,
        'Assoc-voc': 8,
        'Bachelors': 9,
        'Doctorate': 10,
        'HS-grad': 11,
        'Masters': 12,
        'Preschool': 13,
        'Prof-school': 14,
        'Some-college': 15},
       'marital-status': {'Divorced': 0,
        'Married-AF-spouse': 1,
        'Married-civ-spouse': 2,
        'Married-spouse-absent': 3,
        'Never-married': 4,
        'Separated': 5,
        'Widowed': 6},
       'occupation': {'Adm-clerical': 0,
        'Armed-Forces': 1,
        'Craft-repair': 2,
        'Exec-managerial': 3,
        'Farming-fishing': 4,
        'Handlers-cleaners': 5,
        'Machine-op-inspct': 6,
        'Other-service': 7,
        'Priv-house-serv': 8,
        'Prof-specialty': 9,
        'Protective-serv': 10,
        'Sales': 11,
        'Tech-support': 12,
        'Transport-moving': 13},
       'relationship': {'Husband': 0,
        'Not-in-family': 1,
        'Other-relative': 2,
        'Own-child': 3,
        'Unmarried': 4,
        'Wife': 5},
       'race': {'Amer-Indian-Eskimo': 0,
        'Asian-Pac-Islander': 1,
        'Black': 2,
        'Other': 3,
        'White': 4},
       'sex': {'Female': 0, 'Male': 1},
       'native-country': {'Cambodia': 0,
        'Canada': 1,
        'China': 2,
        'Columbia': 3,
        'Cuba': 4,
        'Dominican-Republic': 5,
        'Ecuador': 6,
        'El-Salvador': 7,
        'England': 8,
        'France': 9,
        'Germany': 10,
        'Greece': 11,
        'Guatemala': 12,
        'Haiti': 13,
        'Honduras': 14,
        'Hong': 15,
        'Hungary': 16,
        'India': 17,
        'Iran': 18,
        'Ireland': 19,
        'Italy': 20,
        'Jamaica': 21,
        'Japan': 22,
        'Laos': 23,
        'Mexico': 24,
        'Nicaragua': 25,
        'Outlying-US(Guam-USVI-etc)': 26,
        'Peru': 27,
        'Philippines': 28,
        'Poland': 29,
        'Portugal': 30,
        'Puerto-Rico': 31,
        'Scotland': 32,
        'South': 33,
        'Taiwan': 34,
        'Thailand': 35,
        'Trinadad&Tobago': 36,
        'United-States': 37,
        'Vietnam': 38,
        'Yugoslavia': 39}}}],
    'categorical_y': {'<=50K': 0, '>50K': 1}}],
  'learners': [{'library_version': '0.20.3',
    'algorithm_name': 'Random Forest',
    'algorithm_short_name': 'RF',
    'uid': '7b624da6-bca5-4887-9a76-f89cfea3c919',
    'model_file': '7b624da6-bca5-4887-9a76-f89cfea3c919.rf.model',
    'model_file_path': '/tmp/7b624da6-bca5-4887-9a76-f89cfea3c919.rf.model',
    'params': {'model_type': 'RF',
     'seed': 4,
     'criterion': 'entropy',
     'max_features': 0.3,
     'min_samples_split': 50,
     'min_samples_leaf': 5}},
   {'library_version': '0.20.3',
    'algorithm_name': 'Random Forest',
    'algorithm_short_name': 'RF',
    'uid': '6d65af76-06cf-41c1-962e-aad5904c0567',
    'model_file': '6d65af76-06cf-41c1-962e-aad5904c0567.rf.model',
    'model_file_path': '/tmp/6d65af76-06cf-41c1-962e-aad5904c0567.rf.model',
    'params': {'model_type': 'RF',
     'seed': 4,
     'criterion': 'entropy',
     'max_features': 0.3,
     'min_samples_split': 50,
     'min_samples_leaf': 5}},
   {'library_version': '0.20.3',
    'algorithm_name': 'Random Forest',
    'algorithm_short_name': 'RF',
    'uid': 'a0c6fa09-846a-40d6-8d36-13965a0c76cf',
    'model_file': 'a0c6fa09-846a-40d6-8d36-13965a0c76cf.rf.model',
    'model_file_path': '/tmp/a0c6fa09-846a-40d6-8d36-13965a0c76cf.rf.model',
    'params': {'model_type': 'RF',
     'seed': 4,
     'criterion': 'entropy',
     'max_features': 0.3,
     'min_samples_split': 50,
     'min_samples_leaf': 5}},
   {'library_version': '0.20.3',
    'algorithm_name': 'Random Forest',
    'algorithm_short_name': 'RF',
    'uid': '574a3899-01f0-4d93-97b8-f86e7401164b',
    'model_file': '574a3899-01f0-4d93-97b8-f86e7401164b.rf.model',
    'model_file_path': '/tmp/574a3899-01f0-4d93-97b8-f86e7401164b.rf.model',
    'params': {'model_type': 'RF',
     'seed': 4,
     'criterion': 'entropy',
     'max_features': 0.3,
     'min_samples_split': 50,
     'min_samples_leaf': 5}},
   {'library_version': '0.20.3',
    'algorithm_name': 'Random Forest',
    'algorithm_short_name': 'RF',
    'uid': '5274e0ae-85d2-4018-993d-2346365d31b8',
    'model_file': '5274e0ae-85d2-4018-993d-2346365d31b8.rf.model',
    'model_file_path': '/tmp/5274e0ae-85d2-4018-993d-2346365d31b8.rf.model',
    'params': {'model_type': 'RF',
     'seed': 4,
     'criterion': 'entropy',
     'max_features': 0.3,
     'min_samples_split': 50,
     'min_samples_leaf': 5}}],
  'params': {'additional': {'trees_in_step': 10,
    'train_cant_improve_limit': 5,
    'max_steps': 500,
    'max_rows_limit': None,
    'max_cols_limit': None},
   'preprocessing': {'columns_preprocessing': {'workclass': ['na_fill_median',
      'categorical_to_int'],
     'education': ['categorical_to_int'],
     'marital-status': ['categorical_to_int'],
     'occupation': ['na_fill_median', 'categorical_to_int'],
     'relationship': ['categorical_to_int'],
     'race': ['categorical_to_int'],
     'sex': ['categorical_to_int'],
     'native-country': ['na_fill_median', 'categorical_to_int']},
    'target_preprocessing': ['na_exclude', 'categorical_to_int']},
   'validation': {'validation_type': 'kfold', 'k_folds': 5, 'shuffle': True},
   'learner': {'model_type': 'RF',
    'seed': 4,
    'criterion': 'entropy',
    'max_features': 0.3,
    'min_samples_split': 50,
    'min_samples_leaf': 5}}},
 'threshold': 0.3342849477117008}

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [33]:

    

automl._best_model.learners[0].model


    

    
Out[33]:





RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',
            max_depth=None, max_features=0.3, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=5, min_samples_split=50,
            min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=-1,
            oob_score=False, random_state=4, verbose=0, warm_start=True)

In [ ]:

    




    

In [ ]:

    




    

In [35]:

    

import matplotlib.pyplot as plt
var_imp = automl._best_model.learners[0].model.feature_importances_
plt.barh(X_train.columns, var_imp)


    

    
Out[35]:





<BarContainer object of 14 artists>






    

In [ ]: