def test_predict_proba2():
"""Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float)"""
tpot_obj = TPOTClassifier()
pipeline_string = (
'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(
pipeline_string, 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)
result = tpot_obj.predict_proba(testing_features)
rows = result.shape[0]
columns = result.shape[1]
try:
for i in range(rows):
for j in range(columns):
float_range(result[i][j])
assert True
except Exception:
assert False
def test_imputer_in_export():
"""Assert that TPOT exports a pipeline with an imputation step if imputation was used in fit()."""
tpot_obj = TPOTClassifier(
random_state=42,
population_size=1,
offspring_size=2,
generations=1,
verbosity=0,
config_dict='TPOT light'
)
features_with_nan = np.copy(training_features)
features_with_nan[0][0] = float('nan')
tpot_obj.fit(features_with_nan, training_target)
# use fixed pipeline since the random.seed() performs differently in python 2.* and 3.*
pipeline_string = (
'KNeighborsClassifier('
'input_matrix, '
'KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1, '
'KNeighborsClassifier__weights=uniform'
')'
)
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
export_code = export_pipeline(tpot_obj._optimized_pipeline, tpot_obj.operators, tpot_obj._pset, tpot_obj._imputed)
expected_code = """import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
try:
from sklearn.impute import SimpleImputer as Imputer
except ImportError:
from sklearn.preprocessing import Imputer
# 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)
imputer = Imputer(strategy="median")
imputer.fit(training_features)
training_features = imputer.transform(training_features)
testing_features = imputer.transform(testing_features)
exported_pipeline = KNeighborsClassifier(n_neighbors=10, p=1, weights="uniform")
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert_equal(export_code, expected_code)
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(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)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(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)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0], )
def test_imputer_in_export():
"""Assert that TPOT exports a pipeline with an imputation step if imputation was used in fit()."""
tpot_obj = TPOTClassifier(
random_state=42,
population_size=1,
offspring_size=2,
generations=1,
verbosity=0,
config_dict='TPOT light'
)
features_with_nan = np.copy(training_features)
features_with_nan[0][0] = float('nan')
tpot_obj.fit(features_with_nan, training_target)
# use fixed pipeline since the random.seed() performs differently in python 2.* and 3.*
pipeline_string = (
'KNeighborsClassifier('
'input_matrix, '
'KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1, '
'KNeighborsClassifier__weights=uniform'
')'
)
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
export_code = export_pipeline(tpot_obj._optimized_pipeline, tpot_obj.operators, tpot_obj._pset, tpot_obj._imputed)
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.preprocessing import Imputer
# 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)
imputer = Imputer(strategy="median")
imputer.fit(training_features)
training_features = imputer.transform(training_features)
testing_features = imputer.transform(testing_features)
exported_pipeline = KNeighborsClassifier(n_neighbors=10, p=1, weights="uniform")
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
"""
assert_equal(export_code, expected_code)
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)
def test_export():
"""Assert that TPOT's export function throws a RuntimeError when no optimized pipeline exists."""
tpot_obj = TPOTClassifier()
assert_raises(RuntimeError, tpot_obj.export, "test_export.py")
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)
tpot_obj._optimized_pipeline = pipeline
tpot_obj.export("test_export.py")
assert path.isfile("test_export.py")
remove("test_export.py") # clean up exported file
def test_export():
"""Assert that TPOT's export function throws a RuntimeError when no optimized pipeline exists."""
tpot_obj = TPOTClassifier()
assert_raises(RuntimeError, tpot_obj.export, "test_export.py")
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)
tpot_obj._optimized_pipeline = pipeline
tpot_obj.export("test_export.py")
assert path.isfile("test_export.py")
remove("test_export.py") # clean up exported file
def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fix pipeline"""
tpot_obj = TPOTClassifier()
known_score = 0.977777777778 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
pipeline_string= ('KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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 fix pipeline"""
tpot_obj = TPOTClassifier()
known_score = 0.977777777778 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
pipeline_string= ('KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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_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_predict_proba2():
"""Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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)
result = tpot_obj.predict_proba(testing_features)
rows = result.shape[0]
columns = result.shape[1]
try:
for i in range(rows):
for j in range(columns):
float_range(result[i][j])
assert True
except Exception:
assert False