def process(self, b: Evaluation) -> Evaluation:
a_nums = self.a.get_df().select_dtypes(include=[np.number])
b_nums = b.get_df().select_dtypes(include=[np.number])
diff = (b_nums - a_nums) / a_nums
difference_eval = Evaluation(diff)
return difference_eval
def test_filterbyindex_multindex(self):
""" tests if filtering by index works for multi-index dataframe """
# test dataframe
# {"group": "a", "key": "a", "value": 10},
# {"group": "a", "key": "b", "value": 5},
# {"group": "a", "key": "c", "value": 3},
# {"group": "b", "key": "d", "value": 100},
# {"group": "b", "key": "e", "value": 31}
idx_arrays = [["a", "a", "a", "b", "b"], ["a", "b", "c", "d", "e"]]
index = pd.MultiIndex.from_arrays(idx_arrays, names=("group", "key"))
df = pd.DataFrame({"value": [10, 5, 3, 100, 31]}, index=index)
eval1 = Evaluation(df, eval_id=10)
# filter by first index
pipeline = [FilterByIndex("group", "a")]
result = eval1.process(pipeline)
expected_index = pd.Index(["a", "b", "c"], name="key")
expected_df = pd.DataFrame({"value": [10, 5, 3]}, index=expected_index)
assert_frame_equal(result.get_df(), expected_df)
assert result.get_eval_id() == 10
# filter by second index
pipeline = [FilterByIndex("key", "a")]
result = eval1.process(pipeline)
expected_index = pd.Index(["a"], name="group")
expected_df = pd.DataFrame({"value": [10]}, index=expected_index)
assert_frame_equal(result.get_df(), expected_df)
assert result.get_eval_id() == 10
def test_evaluation_add_different_columns(self):
"""
Using the + magic method should return a new Evaluation that consists
of the concatenated Evaluations, even if columns don't match
"""
df1 = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result1 = Evaluation(df1)
df2 = pd.DataFrame([{"a": 5}, {"a": 7}])
result2 = Evaluation(df2)
sum_result = result1 + result2
expected = pd.DataFrame([{
"a": 1,
"b": 2
}, {
"a": 3,
"b": 4
}, {
"a": 5
}, {
"a": 7
}])
assert_frame_equal(sum_result.get_df(), expected)
assert sum_result.get_eval_id() is None
def test_evaluation_add(self):
"""
Using the + magic method should return a new Evaluation that consists
of the concatenated Evaluations.
"""
df1 = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result1 = Evaluation(df1)
df2 = pd.DataFrame([{"a": 5, "b": 6}, {"a": 7, "b": 8}])
result2 = Evaluation(df2)
sum_result = result1 + result2
expected = pd.DataFrame([{
"a": 1,
"b": 2
}, {
"a": 3,
"b": 4
}, {
"a": 5,
"b": 6
}, {
"a": 7,
"b": 8
}])
assert_frame_equal(sum_result.get_df(), expected)
assert sum_result.get_eval_id() is None
def test_evaluation_add_multiple(self):
"""
Using the sum() built-in function should return a new Evaluation
that consists of concatenated Evaluations
"""
eval1 = Evaluation(pd.DataFrame([{"a": 1, "b": 2}]))
eval2 = Evaluation(pd.DataFrame([{"a": 3, "b": 4}]))
eval3 = Evaluation(pd.DataFrame([{"a": 5, "b": 6}]))
eval4 = Evaluation(pd.DataFrame([{"a": 7, "b": 8}]))
sum_result = sum([eval1, eval2, eval3, eval4])
expected = pd.DataFrame([{
"a": 1,
"b": 2
}, {
"a": 3,
"b": 4
}, {
"a": 5,
"b": 6
}, {
"a": 7,
"b": 8
}])
assert_frame_equal(sum_result.get_df(), expected)
assert sum_result.get_eval_id() is None
def test_singletablevisualizer(self):
df = pd.DataFrame([
{
"a": 0.1,
"b": 0.2,
"c": 0.3
},
{
"a": 0.4,
"b": 0.5,
"c": 0.6
},
{
"a": 0.7,
"b": 0.8,
"c": 0.9
},
])
eval1 = Evaluation(df)
cmap = sns.diverging_palette(180, 0, s=75, l=75, sep=100, as_cmap=True)
styled_eval = eval1.process([ColorMapStyle(cmap)])
vis = SingleTableVisualizer(eval1,
styled_eval,
column_order=["a", "b", "c"])
vis.get_visualization() # should not fail
with pytest.raises(KeyError):
vis = SingleTableVisualizer(eval1, styled_eval, column_order=["d"])
vis.get_visualization()
def test_addnormalizedcolumn(self):
""" Test whether normalized column is added """
df = pd.DataFrame([
{
"group": "a",
"value": 10
},
{
"group": "a",
"value": 5
},
{
"group": "a",
"value": 3
},
{
"group": "b",
"value": 100
},
{
"group": "b",
"value": 31
},
])
eval1 = Evaluation(df, eval_id=10)
pipeline = [AddNormalizedColumn("group", "value", "normalized")]
result = eval1.process(pipeline)
expected = pd.DataFrame([
{
"group": "a",
"value": 10,
"normalized": 1.0
},
{
"group": "a",
"value": 5,
"normalized": 0.5
},
{
"group": "a",
"value": 3,
"normalized": 0.3
},
{
"group": "b",
"value": 100,
"normalized": 1.0
},
{
"group": "b",
"value": 31,
"normalized": 0.31
},
])
assert_frame_equal(result.get_df(), expected)
assert result.get_eval_id() == 10
def test_evaluation_process_empty(self):
"""
Calling process() with an empty list of processors should return the
input DF without any changes.
"""
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result = Evaluation(df).process([]).get_df()
self.assertTrue(result.equals(df))
def test_normalize(self):
"""
Test whether all values are normalized
"""
df = pd.DataFrame(data=[
{
"group": "b",
"value": -50
},
{
"group": "b",
"value": 50
},
{
"group": "b",
"value": 100
},
{
"group": "a",
"value": 0
},
{
"group": "a",
"value": 10
},
])
eval1 = Evaluation(df, eval_id=10)
normalize_direction = {"value": Direction.MINIMIZE}
pipeline = [Normalize(normalize_direction)]
result = eval1.process(pipeline)
expected = pd.DataFrame(data=[
{
"group": "b",
"value": 0.25
},
{
"group": "b",
"value": 0.75
},
{
"group": "b",
"value": 1.0
},
{
"group": "a",
"value": 0.5
},
{
"group": "a",
"value": 0.55
},
], )
assert_frame_equal(result.get_df(), expected)
assert result.get_eval_id() == 10
def test_normalize_negated(self):
"""
Test whether all values are normalized
"""
df = pd.DataFrame(data=[
{
"group": "b",
"value": -50
},
{
"group": "b",
"value": 50
},
{
"group": "b",
"value": 100
},
{
"group": "a",
"value": 0
},
{
"group": "a",
"value": 10
},
])
eval1 = Evaluation(df)
normalize_direction = {"value": -1}
pipeline = [Normalize(normalize_direction)]
result = eval1.process(pipeline).get_df()
expected = pd.DataFrame(data=[
{
"group": "b",
"value": 0.75
},
{
"group": "b",
"value": 0.25
},
{
"group": "b",
"value": 0
},
{
"group": "a",
"value": 0.5
},
{
"group": "a",
"value": 0.45
},
], )
assert_frame_equal(result, expected)
def process(self, input_eval: Evaluation):
old_df = input_eval.get_df()
if isinstance(old_df.index, pd.MultiIndex):
new_df = old_df.xs(self.index_value, level=self.index_name)
elif isinstance(old_df.index, pd.Index):
# slicing instead of indexing to maintain shape
new_df = old_df.loc[self.index_value:self.index_value]
else:
raise ValueError("Incompatible dataframe index.")
return Evaluation(new_df, input_eval.get_eval_id())
def test_evaluation_get_df_equality(self):
"""
get_df() should return a dataframe with values equal to constructor
argument
"""
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result = Evaluation(df).get_df()
self.assertIsInstance(result, pd.DataFrame)
self.assertTrue(result.equals(df))
def test_comparetofirst_dir_subset(self):
""" Test if CompareToFirst works with different direction and subset"""
df = pd.DataFrame([
{
"a": 1,
"b": 5
},
{
"a": 2,
"b": 4
},
{
"a": 3,
"b": 3
},
{
"a": 4,
"b": 2
},
{
"a": 5,
"b": 1
},
])
eval1 = Evaluation(df, eval_id=20)
direction = {"a": Direction.MINIMIZE}
pipeline = [CompareToFirst(direction)]
eval1 = eval1.process(pipeline)
expected_df = pd.DataFrame([
{
"a": 1,
"a.relative": 1.0 / 1
},
{
"a": 2,
"a.relative": 1.0 / 2
},
{
"a": 3,
"a.relative": 1.0 / 3
},
{
"a": 4,
"a.relative": 1.0 / 4
},
{
"a": 5,
"a.relative": 1.0 / 5
},
])
assert_frame_equal(eval1.get_df(), expected_df)
assert eval1.get_eval_id() == 20
def test_evaluation_get_df_defensive_copy(self):
"""
get_df() should return a copy of the constructor argument to prevent
caller from mutating the dataframe
"""
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result = Evaluation(df).get_df()
result["a"][0] = 5 # mutate result
self.assertFalse(result.equals(df))
def test_standardizetypes(self):
""" Test whether types are standardized """
types = {"a": float}
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
eval1 = Evaluation(df, eval_id=10)
assert eval1.get_df().dtypes["a"] == int
pipeline = [StandardizeTypes(types)]
result = eval1.process(pipeline)
assert result.get_df().dtypes["a"] == float
assert result.get_eval_id() == 10
def get_evaluation(self) -> Evaluation:
"""
Returns an Evaluation that represents the fetched data.
"""
data = self._download()
preprocessed_df = self._preprocess(data)
return Evaluation(preprocessed_df, eval_id=self._abs_eval_id)
def test_debugvisualizer(self):
"""Test for raised exceptions when valid inputs"""
df = pd.DataFrame([
{
"a": 0.1,
"b": 0.2,
"c": 0.3
},
{
"a": 0.4,
"b": 0.5,
"c": 0.6
},
{
"a": 0.7,
"b": 0.8,
"c": 0.9
},
])
eval1 = Evaluation(df)
vis = DebugVisualizer(eval1, column_order=["a", "b", "c"])
vis.get_visualization() # should not fail
with pytest.raises(KeyError):
vis = DebugVisualizer(eval1, column_order=["d"])
vis.get_visualization()
def test_evaluation_get_copy(self):
"""
get_copy() should return a deep copy of Evaluation
"""
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
eval1 = Evaluation(df, eval_id=1000)
result = eval1.get_copy()
assert result.get_eval_id() == 1000
assert_frame_equal(result.get_df(), df)
# change original
df.iloc[0]["a"] = 0
# assert that copy has not changed
assert result.get_df().iloc[0]["a"] == 1
def test_evaluation_process_single(self):
"""
Calling process() with one MinusOne should return a new df that
subtract one from every input value, but not affect the input dataframe.
"""
pipeline = [MinusOne()]
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
result = Evaluation(df).process(pipeline).get_df()
expected = pd.DataFrame([{"a": 0, "b": 1}, {"a": 2, "b": 3}])
# check if input eval has been altered
self.assertFalse(result.equals(df))
self.assertFalse(df.equals(expected))
# check if output eval has correct values
self.assertTrue(result.equals(expected))
def process(self, input_eval: Evaluation) -> Evaluation:
input_df = input_eval.get_df()
try:
# if int, we might need to convert to float first
# (e.g. int(float("6.0")))
if int in self.types.values():
# create dictionary replacing int with float
pre_df_types = {
k: (v if v != int else float) for k, v in self.types.items()
}
input_df = input_df.astype(pre_df_types)
new_df = input_df.astype(self.types)
except KeyError:
raise KeyError("A key in the types parameter does not exist in the evaluation.")
return Evaluation(new_df, input_eval.get_eval_id())
def test_reindex(self):
""" Test whether the dataframe was reindexed """
df = pd.DataFrame([
{
"group": "a",
"key": "a",
"value": 10
},
{
"group": "a",
"key": "b",
"value": 5
},
{
"group": "a",
"key": "c",
"value": 3
},
{
"group": "b",
"key": "d",
"value": 100
},
{
"group": "b",
"key": "e",
"value": 31
},
])
eval1 = Evaluation(df, eval_id=10)
pipeline = [Reindex(["value"])]
result = eval1.process(pipeline)
expected_index = pd.Index([10, 5, 3, 100, 31], name="value")
assert_index_equal(result.get_df().index, expected_index)
assert result.get_eval_id() == 10
pipeline = [Reindex(["group", "key"])]
result = eval1.process(pipeline)
arrays = [["a", "a", "a", "b", "b"], ["a", "b", "c", "d", "e"]]
expected_index = pd.MultiIndex.from_arrays(arrays,
names=("group", "key"))
assert_index_equal(result.get_df().index, expected_index)
assert result.get_eval_id() == 10
def test_aggregate(self):
""" Test aggregate processor with custom aggregator functions """
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": 5
},
{
"a": 1,
"b": 2,
"c": 4
},
{
"a": 3,
"b": 3,
"c": 3
},
{
"a": 4,
"b": 4,
"c": 2
},
{
"a": 5,
"b": 5,
"c": 1
},
])
eval1 = Evaluation(df, eval_id=20)
pipeline = [Aggregate(lambda x: x.sum())]
result = eval1.process(pipeline)
expected_df = pd.DataFrame([{"a": 14, "b": 15, "c": 15}])
assert_frame_equal(result.get_df(), expected_df)
assert eval1.get_eval_id() == 20
pipeline2 = [Aggregate(lambda x: x.product())]
result2 = eval1.process(pipeline2)
expected_df2 = pd.DataFrame([{"a": 60, "b": 120, "c": 120}])
assert_frame_equal(result2.get_df(), expected_df2)
assert result2.get_eval_id() == 20
def test_geomean_aggregate(self):
""" Test built-in geomean aggregator """
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": 5
},
{
"a": 1,
"b": 2,
"c": 4
},
{
"a": 3,
"b": 3,
"c": 3
},
{
"a": 4,
"b": 4,
"c": 2
},
{
"a": 5,
"b": 5,
"c": 1
},
])
eval1 = Evaluation(df, eval_id=20)
pipeline = [GeomeanAggregate()]
eval1 = eval1.process(pipeline)
expected_a = (1 * 1 * 3 * 4 * 5)**(1 / 5)
expected_b = expected_c = (1 * 2 * 3 * 4 * 5)**(1 / 5)
expected_df = pd.DataFrame([{
"a": expected_a,
"b": expected_b,
"c": expected_c
}])
assert_frame_equal(eval1.get_df(), expected_df)
assert eval1.get_eval_id() == 20
def test_minusone(self):
""" Test whether all values are correctly changed """
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
eval1 = Evaluation(df, eval_id=10)
result = eval1.get_df()["a"]
expected = pd.Series([1, 2, 3, 4, 5], name="a")
assert_series_equal(result, expected)
pipeline = [MinusOne()]
result_processed = eval1.process(pipeline)
result_df = result_processed.get_df()["a"]
expected_df = pd.Series([0, 1, 2, 3, 4], name="a")
assert_series_equal(result_df, expected_df)
assert result_processed.get_eval_id() == 10
def test_relativediff(self):
"""
Test if difference is correct
"""
a = pd.DataFrame(data=[
{
"a": 2,
"b": 2
},
{
"a": 5,
"b": 10
},
])
b = pd.DataFrame(data=[
{
"a": 4,
"b": 1
},
{
"a": 20,
"b": 1
},
])
a_eval = Evaluation(a)
b_eval = Evaluation(b)
diff = b_eval.process([RelativeDiff(a_eval)])
result = diff.get_df()
expected = pd.DataFrame(data=[
{
"a": 1.0,
"b": -0.5
},
{
"a": 3.0,
"b": -0.9
},
])
assert_frame_equal(expected, result)
def test_colormapstyle_process(self):
cmap = sns.diverging_palette(180, 0, s=75, l=75, sep=100, as_cmap=True)
df = pd.DataFrame([
{
"a": 0.1,
"b": 0.2,
"c": 0.3
},
{
"a": 0.4,
"b": 0.5,
"c": 0.6
},
{
"a": 0.7,
"b": 0.8,
"c": 0.9
},
])
eval1 = Evaluation(df)
style = ColorMapStyle(cmap)
pipeline = [style]
result = eval1.process(pipeline).get_df()
expected = pd.DataFrame([
{
"a": "background-color: #8ed8d0",
"b": "background-color: #bde8e3",
"c": "background-color: #eaf7f6",
},
{
"a": "background-color: #f2f2f2",
"b": "background-color: #f2f2f2",
"c": "background-color: #f2f2f2",
},
{
"a": "background-color: #fbe8eb",
"b": "background-color: #f7d1d9",
"c": "background-color: #f3bac5",
},
])
assert_frame_equal(result, expected)
def test_cleanduplicates_sorting(self):
"""
Test by sorting before removing duplicate.
"""
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": 5
},
{
"a": 1,
"b": 2,
"c": 4
},
{
"a": 3,
"b": 3,
"c": 3
},
{
"a": 4,
"b": 4,
"c": 2
},
{
"a": 5,
"b": 5,
"c": 1
},
])
eval1 = Evaluation(df)
pipeline = [CleanDuplicates(["a"], ["c"])]
result = eval1.process(pipeline).get_df() # will remove idx 1
expected = df.drop(1)
assert_frame_equal(result, expected, check_like=True)
pipeline = [CleanDuplicates(["a"], ["c"], reverse_sort=True)]
result = eval1.process(pipeline).get_df() # will remove idx 0
expected = df.drop(0).sort_index(level=0, ascending=False)
assert_frame_equal(result, expected, check_like=True)
def test_cleanduplicates_no_duplicates(self):
""" Test for evaluation that has no duplicates in specified column """
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": 5
},
{
"a": 1,
"b": 2,
"c": 4
},
{
"a": 3,
"b": 3,
"c": 3
},
{
"a": 4,
"b": 4,
"c": 2
},
{
"a": 5,
"b": 5,
"c": 1
},
])
eval1 = Evaluation(df, eval_id=10)
# test no duplicates
pipeline = [CleanDuplicates(["b"])]
result = eval1.process(pipeline)
assert_frame_equal(result.get_df(), df, check_like=True)
assert result.get_eval_id() == 10
def test_aggregate_exclude_nonnumeric(self):
""" Check if aggregate processor excludes fields that are non-numeric """
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": "a"
},
{
"a": 1,
"b": 2,
"c": "b"
},
{
"a": 3,
"b": 3,
"c": "c"
},
{
"a": 4,
"b": 4,
"c": "d"
},
{
"a": 5,
"b": 5,
"c": "e"
},
])
eval1 = Evaluation(df, eval_id=20)
pipeline = [Aggregate(lambda x: x.sum())]
result = eval1.process(pipeline)
expected_df = pd.DataFrame([{"a": 14, "b": 15}])
assert_frame_equal(result.get_df(), expected_df)
assert eval1.get_eval_id() == 20
def test_cleanduplicates_multi_col(self):
"""
Test for evaluation that doesn't have duplicates when comparing
more than one column
"""
df = pd.DataFrame([
{
"a": 1,
"b": 1,
"c": 5
},
{
"a": 1,
"b": 2,
"c": 4
},
{
"a": 3,
"b": 3,
"c": 3
},
{
"a": 4,
"b": 4,
"c": 2
},
{
"a": 5,
"b": 5,
"c": 1
},
])
eval1 = Evaluation(df)
pipeline = [CleanDuplicates(["a", "b"])]
result2 = eval1.process(pipeline).get_df()
assert_frame_equal(result2, df, check_like=True)