In [1]:

!pip install 'liac-arff>=2.4.0'

Requirement already satisfied: liac-arff>=2.4.0 in /home/hirzel/python3.6venv/lib/python3.6/site-packages (2.4.0)

In [2]:

import lale.datasets.openml
import pandas as pd
(train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(
    'credit-g', 'classification', preprocess=False)
pd.concat([train_y.tail(), train_X.tail()], axis=1)

Out[2]:

	class	checking_status	duration	credit_history	purpose	credit_amount	savings_status	employment	installment_commitment	personal_status	...	residence_since	property_magnitude	age	other_payment_plans	housing	existing_credits	job	num_dependents	own_telephone	foreign_worker
835	bad	<0	12.0	no credits/all paid	new car	1082.0	<100	1<=X<4	4.0	male single	...	4.0	car	48.0	bank	own	2.0	skilled	1.0	none	yes
192	bad	0<=X<200	27.0	existing paid	business	3915.0	<100	1<=X<4	4.0	male single	...	2.0	car	36.0	none	own	1.0	skilled	2.0	yes	yes
629	good	no checking	9.0	existing paid	education	3832.0	no known savings	>=7	1.0	male single	...	4.0	real estate	64.0	none	own	1.0	unskilled resident	1.0	none	yes
559	bad	0<=X<200	18.0	critical/other existing credit	furniture/equipment	1928.0	<100	<1	2.0	male single	...	2.0	real estate	31.0	none	own	2.0	unskilled resident	1.0	none	yes
684	good	0<=X<200	36.0	delayed previously	business	9857.0	100<=X<500	4<=X<7	1.0	male single	...	3.0	life insurance	31.0	none	own	2.0	unskilled resident	2.0	yes	yes

5 rows × 21 columns

In [3]:

from sklearn.preprocessing import Normalizer as Norm
from lale.lib.lale import NoOp
from sklearn.preprocessing import OneHotEncoder as OneHot
from sklearn.linear_model import LogisticRegression as LR
from xgboost import XGBClassifier as XGBoost
from sklearn.svm import LinearSVC
from sklearn.compose import ColumnTransformer
from lale.operators import make_pipeline
lale.wrap_imported_operators()

In [4]:

import numpy as np
num_cols = [col for col in train_X.columns
            if np.issubdtype(train_X.dtypes[col], np.number)]
cat_cols = [col for col in train_X.columns if col not in num_cols]

In [5]:

pipeline_trainable = ColumnTransformer(
    transformers=[
        ('num_tfm', Norm(norm='l1'), num_cols),
        ('cat_tfm', OneHot(), cat_cols)]) >> LR()
pipeline_trainable.visualize()

In [6]:

%%time
pipeline_trained = pipeline_trainable.fit(train_X, train_y)

CPU times: user 1 s, sys: 188 ms, total: 1.19 s
Wall time: 1.06 s

In [7]:

import sklearn.metrics
predictions = pipeline_trained.predict(test_X)
print(f'accuracy {sklearn.metrics.accuracy_score(test_y, predictions):.1%}')

accuracy 71.2%

In [8]:

pipeline_planned = make_pipeline(
    ColumnTransformer(transformers=[
        ('num_tfm', Norm | NoOp, num_cols),
        ('cat_tfm', OneHot, cat_cols)]),
    LR | LinearSVC(dual=False)| XGBoost)
pipeline_planned.visualize()

In [9]:

%%time
from lale.lib.lale import Hyperopt
pipeline_trained = pipeline_planned.auto_configure(
    train_X, train_y, Hyperopt, cv=3, max_evals=5)

100%|█████████| 5/5 [00:52<00:00, 10.58s/trial, best loss: -0.7507273649370062]
CPU times: user 55.7 s, sys: 3.28 s, total: 59 s
Wall time: 54.6 s

In [10]:

pipeline_trained.visualize()
pipeline_trained.pretty_print(ipython_display=True, show_imports=False)

norm = Norm(norm="l1")
column_transformer = ColumnTransformer(
    transformers=[
        (
            "num_tfm",
            norm,
            [
                "duration", "credit_amount", "installment_commitment",
                "residence_since", "age", "existing_credits", "num_dependents",
            ],
        ),
        (
            "cat_tfm",
            OneHot(),
            [
                "checking_status", "credit_history", "purpose",
                "savings_status", "employment", "personal_status",
                "other_parties", "property_magnitude", "other_payment_plans",
                "housing", "job", "own_telephone", "foreign_worker",
            ],
        ),
    ]
)
linear_svc = LinearSVC(
    dual=False,
    C=16757.615906506046,
    fit_intercept=False,
    tol=0.0006905134087360421,
)
pipeline = column_transformer >> linear_svc

In [11]:

predictions = pipeline_trained.predict(test_X)
print(f'accuracy {sklearn.metrics.accuracy_score(test_y, predictions):.1%}')

accuracy 72.1%