def test_export_random_ind():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""
tpot_obj = TPOTClassifier(random_state=39, config_dict="TPOT light")
tpot_obj._fit_init()
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
# 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=39)
exported_pipeline = BernoulliNB(alpha=1.0, fit_prior=False)
# Fix random state in exported estimator
if hasattr(exported_pipeline, 'random_state'):
setattr(exported_pipeline, 'random_state', 39)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
exported_code = export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset, random_state=tpot_obj.random_state)
assert expected_code == exported_code
def test_pipeline_score_save():
"""Assert that the TPOTClassifier can generate a scored pipeline export correctly."""
tpot_obj = TPOTClassifier()
tpot_obj._fit_init()
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline_string = (
'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
)
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
expected_code = """import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
# NOTE: Make sure that the class 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).values
training_features, testing_features, training_target, testing_target = \\
train_test_split(features, tpot_data['target'].values, random_state=None)
# Average CV score on the training set was:0.929813743
exported_pipeline = make_pipeline(
SelectPercentile(score_func=f_classif, percentile=20),
DecisionTreeClassifier(criterion="gini", max_depth=8, min_samples_leaf=5, min_samples_split=5)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert_equal(expected_code, export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset, pipeline_score=0.929813743))
def test_random_ind_2():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 45"""
tpot_obj = TPOTClassifier(random_state=45)
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from tpot.built_in_operators import ZeroCount
# 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)
exported_pipeline = make_pipeline(
ZeroCount(),
LogisticRegression(C=0.0001, dual=False, penalty="l2")
)
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
"""
assert expected_code == export_pipeline(pipeline, tpot_obj.operators,
tpot_obj._pset)
def test_pipeline_score_save():
"""Assert that the TPOTClassifier can generate a scored pipeline export correctly."""
tpot_obj = TPOTClassifier()
tpot_obj._fit_init()
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline_string = (
'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
)
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
expected_code = """import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
# 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=None)
# Average CV score on the training set was: 0.929813743
exported_pipeline = make_pipeline(
SelectPercentile(score_func=f_classif, percentile=20),
DecisionTreeClassifier(criterion="gini", max_depth=8, min_samples_leaf=5, min_samples_split=5)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert_equal(expected_code, export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset, pipeline_score=0.929813743))
def test_export_random_ind():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""
tpot_obj = TPOTClassifier(random_state=39)
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
# NOTE: Make sure that the class 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).values
training_features, testing_features, training_target, testing_target = \\
train_test_split(features, tpot_data['target'].values, random_state=42)
exported_pipeline = make_pipeline(
SelectPercentile(score_func=f_classif, percentile=65),
DecisionTreeClassifier(criterion="gini", max_depth=7, min_samples_leaf=4, min_samples_split=18)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
print(export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset))
assert expected_code == export_pipeline(pipeline, tpot_obj.operators,
tpot_obj._pset)
def test_export_random_ind():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""
tpot_obj = TPOTClassifier(random_state=39)
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
# NOTE: Make sure that the class 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).values
training_features, testing_features, training_target, testing_target = \\
train_test_split(features, tpot_data['target'].values, random_state=42)
exported_pipeline = make_pipeline(
SelectPercentile(score_func=f_classif, percentile=65),
DecisionTreeClassifier(criterion="gini", max_depth=7, min_samples_leaf=4, min_samples_split=18)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert expected_code == export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset)
def test_random_ind_2():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 45"""
tpot_obj = TPOTClassifier(random_state=45)
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from tpot.built_in_operators import ZeroCount
# 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)
exported_pipeline = make_pipeline(
ZeroCount(),
LogisticRegression(C=0.0001, dual=False, penalty="l2")
)
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
"""
assert expected_code == export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset)
def test_gp_new_generation():
"""Assert that the gp_generation count gets incremented when _gp_new_generation is called"""
tpot_obj = TPOTClassifier()
tpot_obj._pbar = tqdm(total=1, disable=True)
assert tpot_obj._gp_generation == 0
# Since _gp_new_generation is a decorator, and we dont want to run a full
# fit(), decorate a dummy function and then call the dummy function.
@_gp_new_generation
def dummy_function(self, foo):
pass
dummy_function(tpot_obj, None)
assert tpot_obj._gp_generation == 1
def test_gp_new_generation():
"""Assert that the gp_generation count gets incremented when _gp_new_generation is called"""
tpot_obj = TPOTClassifier()
tpot_obj._pbar = tqdm(total=1, disable=True)
assert tpot_obj._gp_generation == 0
# Since _gp_new_generation is a decorator, and we dont want to run a full
# fit(), decorate a dummy function and then call the dummy function.
@_gp_new_generation
def dummy_function(self, foo):
pass
dummy_function(tpot_obj, None)
assert tpot_obj._gp_generation == 1
def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline"""
tpot_obj = TPOTClassifier()
tpot_obj._pbar = tqdm(total=1, disable=True)
known_score = 0.986318199045 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('RandomForestClassifier(input_matrix)', tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
# Get score from TPOT
score = tpot_obj.score(testing_features, testing_classes)
# http://stackoverflow.com/questions/5595425/
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
assert isclose(known_score, score)
def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline"""
tpot_obj = TPOTClassifier()
tpot_obj._pbar = tqdm(total=1, disable=True)
known_score = 0.986318199045 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('RandomForestClassifier(input_matrix)', tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(
expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
# Get score from TPOT
score = tpot_obj.score(testing_features, testing_classes)
# http://stackoverflow.com/questions/5595425/
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
assert isclose(known_score, score)
def test_export_random_ind():
"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""
tpot_obj = TPOTClassifier(random_state=39, config_dict="TPOT light")
tpot_obj._fit_init()
tpot_obj._pbar = tqdm(total=1, disable=True)
pipeline = tpot_obj._toolbox.individual()
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
# NOTE: Make sure that the class 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).values
training_features, testing_features, training_target, testing_target = \\
train_test_split(features, tpot_data['target'].values, random_state=39)
exported_pipeline = BernoulliNB(alpha=1.0, fit_prior=False)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert expected_code == export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset, random_state=tpot_obj.random_state)
