Amltk 搜索空间解析器示例
AMLTK (https://github.com/automl/amltk) 提供了一个用于开发AutoML系统的框架。该系统的一个组件是搜索空间定义。
TPOT2 提供了一个名为 tpot2.utils.tpot2_parser 的函数，可以将 AMLTK API 中定义的搜索空间转换为 TPOT2 使用的搜索空间类。这允许用户定义一个搜索空间，供两种算法使用，从而促进更好的比较。以下是一些在 AMLTK 中定义的搜索空间的示例，以及如何在 TPOT2 中使用它们。
注意：此功能仍处于实验阶段，TPOT2 尚未完全支持 AMLTK API 中的所有功能。（例如，基于分类与数值的自动拆分功能，使用 amltk.pipeline.Split 在解析器中尚未实现。）
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from sklearn.compose import make_column_selector
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import SVC
from amltk.pipeline import Choice, Component, Sequential, Split
import tpot2
from sklearn.preprocessing import FunctionTransformer
from sklearn.compose import make_column_transformer
import tpot2
import numpy as np
import sklearn
import sklearn.datasets
import pandas as pd
# create dummy pandas dataset with both categorical and numerical columns
X, y = sklearn.datasets.make_classification(n_samples=100, n_features=5, n_informative=3, n_classes=2, random_state=42)
X = pd.DataFrame(X, columns=[f"num_{i}" for i in range(5)])
# add 5 categorical columns
for i in range(5):
X[f"cat_{i}"] = np.random.choice(["A", "B", "C"], size=100)
y = y.flatten()
# train test split
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.5)
# TODO: implement support for this condition
# select_categories = make_column_selector(dtype_include=object)
# select_numerical = make_column_selector(dtype_include=np.number)
# split_imputation = Split(
# {
# "categories": [SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(drop="first")],
# "numerics": Component(SimpleImputer, space={"strategy": ["mean", "median"]}),
# },
# config={"categories": select_categories, "numerics": select_numerical}, #not yet supported
# name="feature_preprocessing",
# )
# split_imputation
select_categories = make_column_selector(dtype_include=object)
select_numerical = make_column_selector(dtype_include=np.number)
cat_selector = make_column_transformer(("passthrough", select_categories))
num_selector = make_column_transformer(("passthrough", select_numerical))
split_imputation = Split(
{
"categories": [cat_selector,SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(drop="first", sparse_output=False)],
"numerics": [num_selector, Component(SimpleImputer, space={"strategy": ["mean", "median"]})],
},
name="split_imputation",
)
split_imputation
from sklearn.compose import make_column_selector
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import SVC
from amltk.pipeline import Choice, Component, Sequential, Split
import tpot2
from sklearn.preprocessing import FunctionTransformer
from sklearn.compose import make_column_transformer
import tpot2
import numpy as np
import sklearn
import sklearn.datasets
import pandas as pd
# create dummy pandas dataset with both categorical and numerical columns
X, y = sklearn.datasets.make_classification(n_samples=100, n_features=5, n_informative=3, n_classes=2, random_state=42)
X = pd.DataFrame(X, columns=[f"num_{i}" for i in range(5)])
# add 5 categorical columns
for i in range(5):
X[f"cat_{i}"] = np.random.choice(["A", "B", "C"], size=100)
y = y.flatten()
# train test split
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.5)
# TODO: implement support for this condition
# select_categories = make_column_selector(dtype_include=object)
# select_numerical = make_column_selector(dtype_include=np.number)
# split_imputation = Split(
# {
# "categories": [SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(drop="first")],
# "numerics": Component(SimpleImputer, space={"strategy": ["mean", "median"]}),
# },
# config={"categories": select_categories, "numerics": select_numerical}, #not yet supported
# name="feature_preprocessing",
# )
# split_imputation
select_categories = make_column_selector(dtype_include=object)
select_numerical = make_column_selector(dtype_include=np.number)
cat_selector = make_column_transformer(("passthrough", select_categories))
num_selector = make_column_transformer(("passthrough", select_numerical))
split_imputation = Split(
{
"categories": [cat_selector,SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(drop="first", sparse_output=False)],
"numerics": [num_selector, Component(SimpleImputer, space={"strategy": ["mean", "median"]})],
},
name="split_imputation",
)
split_imputation
╭─ Split(split_imputation) ───────────────────────────────────────────────────────────────────────────────────────╮ │ ╭─ Sequential(categories) ───────────────────────────╮ ╭─ Sequential(numerics) ───────────────────────────────╮ │ │ │ ╭─ Fixed(ColumnTransformer) ─────────────────────╮ │ │ ╭─ Fixed(ColumnTransformer) ───────────────────────╮ │ │ │ │ │ item ColumnTransformer(transformers=[('passth… │ │ │ │ item ColumnTransformer(transformers=[('passthro… │ │ │ │ │ │ 'passthrough', │ │ │ │ 'passthrough', │ │ │ │ │ │ <sklear… │ │ │ │ <sklearn.… │ │ │ │ │ │ object at 0x7d354d946290>)]) │ │ │ │ object at 0x7d34edf94fa0>)]) │ │ │ │ │ ╰────────────────────────────────────────────────╯ │ │ ╰──────────────────────────────────────────────────╯ │ │ │ │ ↓ │ │ ↓ │ │ │ │ ╭─ Fixed(SimpleImputer) ─────────────────────────╮ │ │ ╭─ Component(SimpleImputer) ─────────────╮ │ │ │ │ │ item SimpleImputer(fill_value='missing', │ │ │ │ item class SimpleImputer(...) │ │ │ │ │ │ strategy='constant') │ │ │ │ space {'strategy': ['mean', 'median']} │ │ │ │ │ ╰────────────────────────────────────────────────╯ │ │ ╰────────────────────────────────────────╯ │ │ │ │ ↓ │ ╰──────────────────────────────────────────────────────╯ │ │ │ ╭─ Fixed(OneHotEncoder) ─────────────────────────╮ │ │ │ │ │ item OneHotEncoder(drop='first', │ │ │ │ │ │ sparse_output=False) │ │ │ │ │ ╰────────────────────────────────────────────────╯ │ │ │ ╰────────────────────────────────────────────────────╯ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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from tpot2.builtin_modules import Passthrough, ZeroCount
from sklearn.preprocessing import PolynomialFeatures
from sklearn.decomposition import PCA
from sklearn.feature_selection import VarianceThreshold, SelectKBest
selectors = Choice(
Component(VarianceThreshold, space={"threshold": (0.1,1)}),
Component(SelectKBest, space={"k": (1, 10)}),
name="selectors",
)
transformers = Split(
{
"passthrough": Passthrough(),
"polynomial": Component(PolynomialFeatures, space={"degree": [2, 3]}),
"zerocount" : ZeroCount(),
},
# config={"categories": select_categories, "numerics": select_numerical},
name="transformers",
)
pipeline = (
Sequential(name="my_pipeline")
>> split_imputation
# >> Component(SimpleImputer, space={"strategy": ["mean", "median"]}) # Choose either mean or median
>> selectors
>> transformers
>> Choice(
# Our pipeline can choose between two different estimators
Component(
RandomForestClassifier,
space={"n_estimators": (10, 100), "criterion": ["gini", "log_loss"]},
config={"max_depth": 3},
),
Component(SVC, space={"kernel": ["linear", "rbf", "poly"]}),
name="estimator",
)
)
# Display the amltk Pipeline
pipeline
from tpot2.builtin_modules import Passthrough, ZeroCount
from sklearn.preprocessing import PolynomialFeatures
from sklearn.decomposition import PCA
from sklearn.feature_selection import VarianceThreshold, SelectKBest
selectors = Choice(
Component(VarianceThreshold, space={"threshold": (0.1,1)}),
Component(SelectKBest, space={"k": (1, 10)}),
name="selectors",
)
transformers = Split(
{
"passthrough": Passthrough(),
"polynomial": Component(PolynomialFeatures, space={"degree": [2, 3]}),
"zerocount" : ZeroCount(),
},
# config={"categories": select_categories, "numerics": select_numerical},
name="transformers",
)
pipeline = (
Sequential(name="my_pipeline")
>> split_imputation
# >> Component(SimpleImputer, space={"strategy": ["mean", "median"]}) # Choose either mean or median
>> selectors
>> transformers
>> Choice(
# Our pipeline can choose between two different estimators
Component(
RandomForestClassifier,
space={"n_estimators": (10, 100), "criterion": ["gini", "log_loss"]},
config={"max_depth": 3},
),
Component(SVC, space={"kernel": ["linear", "rbf", "poly"]}),
name="estimator",
)
)
# Display the amltk Pipeline
pipeline
╭─ Sequential(my_pipeline) ───────────────────────────────────────────────────────────────────────────────────────╮ │ ╭─ Split(split_imputation) ───────────────────────────────────────────────────────────────────────────────────╮ │ │ │ ╭─ Sequential(categories) ─────────────────────────╮ ╭─ Sequential(numerics) ─────────────────────────────╮ │ │ │ │ │ ╭─ Fixed(ColumnTransformer) ───────────────────╮ │ │ ╭─ Fixed(ColumnTransformer) ─────────────────────╮ │ │ │ │ │ │ │ item ColumnTransformer(transformers=[('pass… │ │ │ │ item ColumnTransformer(transformers=[('passth… │ │ │ │ │ │ │ │ 'passthrough', │ │ │ │ 'passthrough', │ │ │ │ │ │ │ │ <skle… │ │ │ │ <sklear… │ │ │ │ │ │ │ │ object at 0x7d354d946290>)]) │ │ │ │ object at 0x7d34edf94fa0>)]) │ │ │ │ │ │ │ ╰──────────────────────────────────────────────╯ │ │ ╰────────────────────────────────────────────────╯ │ │ │ │ │ │ ↓ │ │ ↓ │ │ │ │ │ │ ╭─ Fixed(SimpleImputer) ───────────────────────╮ │ │ ╭─ Component(SimpleImputer) ─────────────╮ │ │ │ │ │ │ │ item SimpleImputer(fill_value='missing', │ │ │ │ item class SimpleImputer(...) │ │ │ │ │ │ │ │ strategy='constant') │ │ │ │ space {'strategy': ['mean', 'median']} │ │ │ │ │ │ │ ╰──────────────────────────────────────────────╯ │ │ ╰────────────────────────────────────────╯ │ │ │ │ │ │ ↓ │ ╰────────────────────────────────────────────────────╯ │ │ │ │ │ ╭─ Fixed(OneHotEncoder) ───────────────────────╮ │ │ │ │ │ │ │ item OneHotEncoder(drop='first', │ │ │ │ │ │ │ │ sparse_output=False) │ │ │ │ │ │ │ ╰──────────────────────────────────────────────╯ │ │ │ │ │ ╰──────────────────────────────────────────────────╯ │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ ↓ │ │ ╭─ Choice(selectors) ─────────────────────────────────────────────────────╮ │ │ │ ╭─ Component(SelectKBest) ─────╮ ╭─ Component(VarianceThreshold) ─────╮ │ │ │ │ │ item class SelectKBest(...) │ │ item class VarianceThreshold(...) │ │ │ │ │ │ space {'k': (1, 10)} │ │ space {'threshold': (0.1, 1)} │ │ │ │ │ ╰──────────────────────────────╯ ╰────────────────────────────────────╯ │ │ │ ╰─────────────────────────────────────────────────────────────────────────╯ │ │ ↓ │ │ ╭─ Split(transformers) ─────────────────────────────────────────────────────────────────────────────────╮ │ │ │ ╭─ Sequential(passthrough) ─╮ ╭─ Sequential(polynomial) ────────────────╮ ╭─ Sequential(zerocount) ─╮ │ │ │ │ │ ╭─ Fixed(Passthrough) ─╮ │ │ ╭─ Component(PolynomialFeatures) ─────╮ │ │ ╭─ Fixed(ZeroCount) ─╮ │ │ │ │ │ │ │ item Passthrough() │ │ │ │ item class PolynomialFeatures(...) │ │ │ │ item ZeroCount() │ │ │ │ │ │ │ ╰──────────────────────╯ │ │ │ space {'degree': [2, 3]} │ │ │ ╰────────────────────╯ │ │ │ │ │ ╰───────────────────────────╯ │ ╰─────────────────────────────────────╯ │ ╰─────────────────────────╯ │ │ │ │ ╰─────────────────────────────────────────╯ │ │ │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ ↓ │ │ ╭─ Choice(estimator) ─────────────────────────────────────────────────────────────────────────────────────────╮ │ │ │ ╭─ Component(RandomForestClassifier) ──────────╮ ╭─ Component(SVC) ────────────────────────────╮ │ │ │ │ │ item class RandomForestClassifier(...) │ │ item class SVC(...) │ │ │ │ │ │ config {'max_depth': 3} │ │ space {'kernel': ['linear', 'rbf', 'poly']} │ │ │ │ │ │ space { │ ╰─────────────────────────────────────────────╯ │ │ │ │ │ 'n_estimators': (10, 100), │ │ │ │ │ │ 'criterion': [ │ │ │ │ │ │ 'gini', │ │ │ │ │ │ 'log_loss' │ │ │ │ │ │ ] │ │ │ │ │ │ } │ │ │ │ │ ╰──────────────────────────────────────────────╯ │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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#convert to tpot search space
tpot_search_space = tpot2.utils.tpot2_parser(pipeline)
# sample a pipeline from the tpot search space
tpot_search_space.generate().export_pipeline()
#convert to tpot search space
tpot_search_space = tpot2.utils.tpot2_parser(pipeline)
# sample a pipeline from the tpot search space
tpot_search_space.generate().export_pipeline()
Out[3]:
Pipeline(steps=[('featureunion-1',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d354d946290>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencode...
VarianceThreshold(threshold=0.6738938110936)),
('featureunion-2',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures(degree=3))])),
('pipeline-3',
Pipeline(steps=[('zerocount',
ZeroCount())]))])),
('randomforestclassifier',
RandomForestClassifier(n_estimators=16))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('featureunion-1',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d354d946290>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencode...
VarianceThreshold(threshold=0.6738938110936)),
('featureunion-2',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures(degree=3))])),
('pipeline-3',
Pipeline(steps=[('zerocount',
ZeroCount())]))])),
('randomforestclassifier',
RandomForestClassifier(n_estimators=16))])FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d354d946290>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencoder',
OneHotEncoder(drop='first',
sparse_output=False))])),
('pipeline-2',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34edf94fa0>)])),
('simpleimputer',
SimpleImputer(strategy='median'))]))])ColumnTransformer(transformers=[('passthrough', 'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d354d946290>)])<sklearn.compose._column_transformer.make_column_selector object at 0x7d354d946290>
passthrough
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder(drop='first', sparse_output=False)
ColumnTransformer(transformers=[('passthrough', 'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34edf94fa0>)])<sklearn.compose._column_transformer.make_column_selector object at 0x7d34edf94fa0>
passthrough
SimpleImputer(strategy='median')
VarianceThreshold(threshold=0.6738938110936)
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures(degree=3))])),
('pipeline-3',
Pipeline(steps=[('zerocount', ZeroCount())]))])Passthrough()
PolynomialFeatures(degree=3)
ZeroCount()
RandomForestClassifier(n_estimators=16)
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est = tpot2.TPOTEstimator(
scorers = ["roc_auc"],
scorers_weights = [1],
classification = True,
cv = 5,
search_space = tpot_search_space, #converted search space goes here
population_size= 10,
generations = 2,
max_eval_time_mins = 60*5,
verbose = 5,
n_jobs=10,
)
est.fit(X_train, y_train)
est = tpot2.TPOTEstimator(
scorers = ["roc_auc"],
scorers_weights = [1],
classification = True,
cv = 5,
search_space = tpot_search_space, #converted search space goes here
population_size= 10,
generations = 2,
max_eval_time_mins = 60*5,
verbose = 5,
n_jobs=10,
)
est.fit(X_train, y_train)
Generation: 50%|█████ | 1/2 [00:02<00:02, 2.60s/it]
Generation: 1 Best roc_auc_score score: 0.976
Generation: 100%|██████████| 2/2 [00:03<00:00, 1.57s/it]
2024-09-09 17:25:40,301 - distributed.scheduler - ERROR - Removing worker 'tcp://127.0.0.1:39897' caused the cluster to lose scattered data, which can't be recovered: {'ndarray-3f2f44921e6e9cc40ef07cfcd8ae90fb', 'DataFrame-5551f84174fd651642ff10eb71e30b22'} (stimulus_id='handle-worker-cleanup-1725927940.3010821')
Generation: 2 Best roc_auc_score score: 0.984
Out[4]:
TPOTEstimator(classification=True, generations=2, max_eval_time_mins=300,
n_jobs=10, population_size=10, scorers=['roc_auc'],
scorers_weights=[1],
search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x7d34ec1efbb0>,
verbose=5)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
TPOTEstimator(classification=True, generations=2, max_eval_time_mins=300,
n_jobs=10, population_size=10, scorers=['roc_auc'],
scorers_weights=[1],
search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x7d34ec1efbb0>,
verbose=5)In [5]:
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est.fitted_pipeline_
est.fitted_pipeline_
Out[5]:
Pipeline(steps=[('featureunion-1',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencode...
VarianceThreshold(threshold=0.1557560591318)),
('featureunion-2',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures())])),
('pipeline-3',
Pipeline(steps=[('zerocount',
ZeroCount())]))])),
('randomforestclassifier',
RandomForestClassifier(criterion='log_loss',
n_estimators=80))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('featureunion-1',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencode...
VarianceThreshold(threshold=0.1557560591318)),
('featureunion-2',
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures())])),
('pipeline-3',
Pipeline(steps=[('zerocount',
ZeroCount())]))])),
('randomforestclassifier',
RandomForestClassifier(criterion='log_loss',
n_estimators=80))])FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),
('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencoder',
OneHotEncoder(drop='first',
sparse_output=False))])),
('pipeline-2',
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('passthrough',
'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307d30>)])),
('simpleimputer',
SimpleImputer(strategy='median'))]))])ColumnTransformer(transformers=[('passthrough', 'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>
passthrough
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder(drop='first', sparse_output=False)
ColumnTransformer(transformers=[('passthrough', 'passthrough',
<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307d30>)])<sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307d30>
passthrough
SimpleImputer(strategy='median')
VarianceThreshold(threshold=0.1557560591318)
FeatureUnion(transformer_list=[('pipeline-1',
Pipeline(steps=[('passthrough',
Passthrough())])),
('pipeline-2',
Pipeline(steps=[('polynomialfeatures',
PolynomialFeatures())])),
('pipeline-3',
Pipeline(steps=[('zerocount', ZeroCount())]))])Passthrough()
PolynomialFeatures()
ZeroCount()
RandomForestClassifier(criterion='log_loss', n_estimators=80)
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est.predict(X_test)
est.predict(X_test)
Out[6]:
array([1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,
1, 0, 0, 0, 0, 0])