def test_replace_function_calls_2():
"""Ensure export utils' replace_function_calls generates no exceptions"""
tpot_obj = TPOT()
for prim in tpot_obj._pset.primitives[pd.DataFrame]:
simple_pipeline = ['result1']
simple_pipeline.append(prim.name)
for arg in prim.args:
simple_pipeline.append(tpot_obj._pset.terminals[arg][0].value)
replace_function_calls([simple_pipeline])
def test_replace_function_calls_2():
"""Ensure export utils' replace_function_calls generates no exceptions"""
tpot_obj = TPOT()
for prim in tpot_obj._pset.primitives[pd.DataFrame]:
simple_pipeline = ['result1']
simple_pipeline.append(prim.name)
for arg in prim.args:
simple_pipeline.append(tpot_obj._pset.terminals[arg][0].value)
replace_function_calls([simple_pipeline])
def test_replace_function_calls():
"""Ensure export utils' replace_function_calls outputs as expected"""
reference_code = """
result1 = tpot_data.copy()
# Use Scikit-learn's SelectKBest for feature selection
training_features = result1.loc[training_indices].drop('class', axis=1)
training_class_vals = result1.loc[training_indices, 'class'].values
if len(training_features.columns.values) == 0:
result1 = result1.copy()
else:
selector = SelectKBest(f_classif, k=min(26, len(training_features.columns)))
selector.fit(training_features.values, training_class_vals)
mask = selector.get_support(True)
mask_cols = list(training_features.iloc[:, mask].columns) + ['class']
result1 = result1[mask_cols]
# Perform classification with a decision tree classifier
dtc2 = DecisionTreeClassifier(min_weight_fraction_leaf=0.1)
dtc2.fit(result1.loc[training_indices].drop('class', axis=1).values, result1.loc[training_indices, 'class'].values)
result2 = result1.copy()
result2['dtc2-classification'] = dtc2.predict(result2.drop('class', axis=1).values)
"""
pipeline = [['result1', '_select_kbest', 'input_df', '26'],
['result2', '_decision_tree', 'result1', '0.1']]
exported_code = replace_function_calls(pipeline)
assert reference_code == exported_code
def test_replace_function_calls():
"""Ensure export utils' replace_function_calls outputs as expected"""
reference_code = """
result1 = tpot_data.copy()
# Use Scikit-learn's SelectKBest for feature selection
training_features = result1.loc[training_indices].drop('class', axis=1)
training_class_vals = result1.loc[training_indices, 'class'].values
if len(training_features.columns.values) == 0:
result1 = result1.copy()
else:
selector = SelectKBest(f_classif, k=min(26, len(training_features.columns)))
selector.fit(training_features.values, training_class_vals)
mask = selector.get_support(True)
mask_cols = list(training_features.iloc[:, mask].columns) + ['class']
result1 = result1[mask_cols]
# Perform classification with a decision tree classifier
dtc2 = DecisionTreeClassifier(min_weight_fraction_leaf=0.1)
dtc2.fit(result1.loc[training_indices].drop('class', axis=1).values, result1.loc[training_indices, 'class'].values)
result2 = result1.copy()
result2['dtc2-classification'] = dtc2.predict(result2.drop('class', axis=1).values)
"""
pipeline = [['result1', '_select_kbest', 'input_df', '26'],
['result2', '_decision_tree', 'result1', '0.1']]
exported_code = replace_function_calls(pipeline)
assert reference_code == exported_code