def test_generate_import_code():
"""Ensure export utils' generate_import_code outputs as expected"""
reference_code = """\
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import RandomizedPCA
from sklearn.feature_selection import VarianceThreshold
from sklearn.linear_model import LogisticRegression
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')
training_indices, testing_indices = train_test_split(tpot_data.index, stratify = tpot_data['class'].values, train_size=0.75, test_size=0.25)
"""
pipeline = [['result1', '_variance_threshold', 'input_df', '100.0'],
['result2', '_pca', 'input_df', '66', '34'],
['result3', '_combine_dfs', 'result2', 'result1'],
['result4', '_logistic_regression', 'result3', '0.12030075187969924', '0', 'True']]
import_code = generate_import_code(pipeline)
assert reference_code == import_code
def test_generate_import_code_2():
"""Ensure export utils' generate_import_code outputs as expected when using multiple classes from the same module"""
reference_code = """\
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import FastICA, RandomizedPCA
from sklearn.linear_model import LogisticRegression
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')
training_indices, testing_indices = train_test_split(tpot_data.index, stratify = tpot_data['class'].values, train_size=0.75, test_size=0.25)
"""
pipeline = [['result1', '_fast_ica', 'input_df', '5', '0.1'],
['result2', '_pca', 'input_df', '66', '34'],
['result3', '_combine_dfs', 'result2', 'result1'],
[
'result4', '_logistic_regression', 'result3',
'0.12030075187969924', '0', 'True'
]]
import_code = generate_import_code(pipeline)
assert reference_code == import_code
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline."""
pipeline = creator.Individual.from_string('GaussianNB(RobustScaler(input_matrix))', tpot_obj._pset)
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
"""
assert expected_code == generate_import_code(pipeline, tpot_obj.operators)
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline."""
pipeline = creator.Individual.from_string('GaussianNB(RobustScaler(input_matrix))', tpot_obj._pset)
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
"""
assert expected_code == generate_import_code(pipeline, tpot_obj.operators)
def write_pipes(name, tpot):
"""Write TPOT pipelines out to subdirectories."""
import_codes = generate_import_code(tpot._optimized_pipeline,
tpot.operators)
pipeline_codes = generate_export_pipeline_code(
expr_to_tree(tpot._optimized_pipeline, tpot._pset), tpot.operators)
pipe_text = import_codes.replace(
'import numpy as np\nimport pandas as pd\n',
'from sklearn.preprocessing import FunctionTransformer\nfrom copy import copy\n'
)
if tpot._imputed: # add impute code when there is missing data
pipe_text += import_impute + make_func + impute_text
else:
pipe_text += make_func
pipe_text += '\texported_pipeline = ' + pipeline_codes + "\n\treturn({'train_feat': training_features, 'test_feat': testing_features, 'pipe': exported_pipeline})"
f = open(name + '.py', 'w')
f.write(pipe_text)
f.close()
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline"""
tpot_obj = TPOTClassifier()
pipeline = creator.Individual.from_string('GaussianNB(RobustScaler(input_matrix))', tpot_obj._pset)
expected_code = """import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \\
train_test_split(features, tpot_data['class'], random_state=42)
"""
assert expected_code == generate_import_code(pipeline, tpot_obj.operators)
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline"""
tpot_obj = TPOTClassifier()
pipeline = creator.Individual.from_string('GaussianNB(RobustScaler(input_matrix))', tpot_obj._pset)
expected_code = """import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \\
train_test_split(features, tpot_data['class'], random_state=42)
"""
assert expected_code == generate_import_code(pipeline, tpot_obj.operators)
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline"""
tpot_obj = TPOT()
pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(SelectKBest(input_matrix, 7), 0.5)', tpot_obj._pset)
expected_code = """import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
from sklearn.tree import DecisionTreeClassifier
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \\
train_test_split(features, tpot_data['class'], random_state=42)
"""
assert expected_code == generate_import_code(pipeline)
def test_generate_import_code():
"""Assert that generate_import_code() returns the correct set of dependancies for a given pipeline"""
tpot_obj = TPOT()
pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(SelectKBest(input_matrix, 7), 0.5)', tpot_obj._pset)
expected_code = """import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
from sklearn.tree import DecisionTreeClassifier
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \\
train_test_split(features, tpot_data['class'], random_state=42)
"""
assert expected_code == generate_import_code(pipeline)
def test_generate_import_code_2():
"""Assert that generate_import_code() returns the correct set of dependancies and dependancies are importable."""
pipeline_string = (
'KNeighborsClassifier(CombineDFs('
'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, '
'DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5), ZeroCount(input_matrix))'
'KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform')
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
import_code = generate_import_code(pipeline, tpot_obj.operators)
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.tree import DecisionTreeClassifier
from tpot.builtins import StackingEstimator, ZeroCount
"""
exec(import_code) # should not raise error
assert expected_code == import_code
def test_generate_import_code_2():
"""Assert that generate_import_code() returns the correct set of dependancies and dependancies are importable."""
pipeline_string = (
'KNeighborsClassifier(CombineDFs('
'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, '
'DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5), ZeroCount(input_matrix))'
'KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform'
)
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
import_code = generate_import_code(pipeline, tpot_obj.operators)
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.tree import DecisionTreeClassifier
from tpot.builtins import StackingEstimator, ZeroCount
"""
exec(import_code) # should not raise error
assert expected_code == import_code
