def test_prepare_data_exception_mismatch_columns_numpy(self):
clf = PMMLBaseEstimator(pmml=StringIO("""
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<DataDictionary>
<DataField name="Class" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
<DataField name="test1" optype="continuous" dataType="double"/>
</DataDictionary>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
</PMML>
"""))
X = pd.DataFrame(data=[[1, 2], [3, 4], [5, 6]], columns=["test1", "test2"])
with self.assertRaises(Exception) as cm:
clf._prepare_data(np.asanyarray(X))
assert str(cm.exception) == "The number of features in provided data does not match expected number of features " \
"in the PMML. Provide pandas.Dataframe, or provide data matching the DataFields in " \
"the PMML document."
def test_prepare_data_exception_mismatch_columns_pandas(self):
clf = PMMLBaseEstimator(pmml=StringIO("""
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<DataDictionary>
<DataField name="Class" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
<DataField name="test1" optype="continuous" dataType="double"/>
<DataField name="test2" optype="continuous" dataType="double"/>
</DataDictionary>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
</PMML>
"""))
X = pd.DataFrame(data=[[1, 2], [3, 4], [5, 6]], columns=["Test_1", "Test_2"])
with self.assertRaises(Exception) as cm:
clf._prepare_data(X)
assert str(cm.exception) == "The features in the input data do not match features expected by the PMML model."
def test_prepare_data_removes_unused_columns(self):
clf = PMMLBaseEstimator(pmml=StringIO("""
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<DataDictionary>
<DataField name="Class" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
<DataField name="test1" optype="continuous" dataType="double"/>
</DataDictionary>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
</PMML>
"""))
X = pd.DataFrame(data=[[1, 2], [3, 4], [5, 6]], columns=["test1", "test2"])
result = clf._prepare_data(X)
assert list(X.columns) == ["test1", "test2"]
assert list(result.columns) == ["test1"]
