def test_taxon_as_sample_name(self, get_testing_data):
"""Feature table has sample called Taxon"""
table = get_testing_data.table
table["Taxon"] = [np.random.randint(1, 10) for x in range(6)]
table["Taxon"] = table["Taxon"].astype(np.float64)
num_df, denom_df = filter_and_join_taxonomy(
table,
get_testing_data.taxonomy,
"Char",
"saur",
)
# test that num/denom df accurately extract table values for Taxon
num_features = ["F3", "F4", "F5"]
denom_features = ["F0", "F1", "F2"]
table_num_taxon_filt = table.loc[num_features]
table_denom_taxon_filt = table.loc[denom_features]
num_df_taxon_filt = num_df.loc[num_features]
denom_df_taxon_filt = denom_df.loc[denom_features]
_check_dataframe_equality(
num_df_taxon_filt,
table_num_taxon_filt,
)
_check_dataframe_equality(
denom_df_taxon_filt,
table_denom_taxon_filt,
)
def _check_missing_features_dfs(self, table, taxonomy):
"""Auxillary function to test missing features
test_taxonomy_missing_features and test_feat_table_missing_features
are essentially the same code, only differing in whether a feature
is removed from the table or the taxonomy.
"""
num_df, denom_df = filter_and_join_taxonomy(
table,
taxonomy,
"Char",
"saur",
)
assert "F5" not in num_df.index
num_features = ["F3", "F4"]
denom_features = ["F0", "F1", "F2"]
sample_order = ["S{}".format(i) for i in range(5)]
table_num_taxon_filt = pd.DataFrame(table.loc[num_features])
table_denom_taxon_filt = pd.DataFrame(table.loc[denom_features])
num_df_taxon_filt = num_df.loc[num_features]
denom_df_taxon_filt = denom_df.loc[denom_features]
# set operations in Qarcoal change order of samples
# want to make sure order is the same when comparing
table_num_taxon_filt = table_num_taxon_filt[sample_order]
table_denom_taxon_filt = table_denom_taxon_filt[sample_order]
num_df_taxon_filt = num_df_taxon_filt[sample_order]
denom_df_taxon_filt = denom_df_taxon_filt[sample_order]
# test that num/denom df accurately extract values from table
assert table_num_taxon_filt.equals(num_df_taxon_filt)
assert table_denom_taxon_filt.equals(denom_df_taxon_filt)
def test_no_common_samples(self):
"""No samples with both numerator and denominator features.
-----------------------------------------------
| S0 S1 S2 S3 S4 |
| F0/Charmander 0 1 10 0 0 |
| F1/Charmeleon 0 2 15 0 0 |
| F2/Charizard 0 4 7 0 0 |
| F3/Bulbasaur 5 0 0 0 6 |
| F4/Ivysaur 7 0 0 0 4 |
| F5/Vensaur 9 0 0 0 2 |
-----------------------------------------------
"""
samps = ["S{}".format(i) for i in range(5)]
feats = ["F{}".format(i) for i in range(6)]
s0 = [0, 0, 0, 5, 7, 9]
s1 = [1, 2, 4, 0, 0, 0]
s2 = [10, 15, 7, 0, 0, 0]
s3 = [0, 0, 0, 0, 0, 0]
s4 = [0, 0, 0, 6, 4, 2]
mat = np.matrix([s0, s1, s2, s3, s4]).T
table = biom.table.Table(mat, feats, samps).to_dataframe()
tax_labels = [
"Charmander",
"Charmeleon",
"Charizard",
"Bulbasaur",
"Ivysaur",
"Venusaur",
]
confidence = ["0.99"] * 6
taxonomy = pd.DataFrame([feats, tax_labels, confidence]).T
taxonomy.columns = ["feature-id", "Taxon", "Confidence"]
taxonomy.set_index("feature-id", inplace=True, drop=True)
with pytest.raises(ValueError) as excinfo:
filter_and_join_taxonomy(table, taxonomy, "Char", "saur")
assert ("No samples contain both numerator and denominator features!"
== str(excinfo.value))
def test_overlapping_columns(self, get_testing_data):
"""Feature table and taxonomy have overlapping column(s)
This test is basically making sure that Qarcoal drops all columns
except Taxon in the taxonomy table. If there are overlapping columns
between the feature table and the taxonomy table (i.e. a sample named
Confidence), want to ensure that the feature table columns are retained
without causing Pandas join errors.
"""
table = get_testing_data.table
taxonomy = get_testing_data.taxonomy
taxonomy["Overlap1"] = [np.random.randint(1, 10) for x in range(6)]
table["Overlap1"] = [np.random.randint(1, 10) for x in range(6)]
taxonomy["Overlap2"] = [np.random.randint(1, 10) for x in range(6)]
table["Overlap2"] = [np.random.randint(1, 10) for x in range(6)]
table = table.apply(lambda x: x.astype(np.float64))
num_df, denom_df = filter_and_join_taxonomy(
table,
taxonomy,
"Char",
"saur",
)
num_features = ["F3", "F4", "F5"]
denom_features = ["F0", "F1", "F2"]
table_num_taxon_filt = table.loc[num_features]
table_denom_taxon_filt = table.loc[denom_features]
num_df_taxon_filt = num_df.loc[num_features]
denom_df_taxon_filt = denom_df.loc[denom_features]
# test that num/denom df accurately extract table values for
# Overlap1 and Overlap2
_check_dataframe_equality(
table_num_taxon_filt,
num_df_taxon_filt,
)
_check_dataframe_equality(
table_denom_taxon_filt,
denom_df_taxon_filt,
)
