def test_get_property_valid_df_name_2(self):
# cm.del_catalog()
self.assertEqual(cm.get_catalog_len(), 0)
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C = read_csv_metadata(path_c, ltable=A, rtable=B)
self.assertEqual(cm.get_property(C, 'key'), '_id')
self.assertEqual(cm.get_property(C, 'fk_ltable'), 'ltable_ID')
self.assertEqual(cm.get_property(C, 'fk_rtable'), 'rtable_ID')
self.assertEqual(cm.get_property(C, 'ltable').equals(A), True)
self.assertEqual(cm.get_property(C, 'rtable').equals(B), True)
def eval_matches(X, gold_label_attr, predicted_label_attr):
if not isinstance(X, pd.DataFrame):
logger.error('The input table is not of type dataframe')
raise AssertionError('The input is not of type dataframe')
if not isinstance(gold_label_attr, six.string_types):
logger.error('The input gold_label_attr is not of type string')
raise AssertionError('The input gold_label_attr is not of type string')
if not isinstance(predicted_label_attr, six.string_types):
logger.error('The input predicted_label_attr is not of type string')
raise AssertionError(
'The input predicted_label_attr is not of type string')
if not check_attrs_present(X, gold_label_attr):
logger.error('The gold_label_attr is not present in the input table')
raise AssertionError(
'The gold_label_attr is not present in the input table')
if not check_attrs_present(X, predicted_label_attr):
logger.error(
'The predicted_label_attr is not present in the input table')
raise AssertionError(
'The predicted_label_attr is not present in the input table')
Y = X.reset_index(drop=False, inplace=False)
g = Y[gold_label_attr]
# if isinstance(g, pd.DataFrame):
# g = g.T
# assert len(g) == 1, 'Error: Column is picked as dataframe and the num rows > 1'
# g = g.iloc[0]
p = Y[predicted_label_attr]
# if isinstance(p, pd.DataFrame):
# p = p.T
# assert len(p) == 1, 'Error: Column is picked as dataframe and the num rows > 1'
# p = p.iloc[0]
# get false label (0) indices
gf = g[g == 0].index.values
pf = p[p == 0].index.values
# get true label (1) indices
gt = g[g == 1].index.values
pt = p[p == 1].index.values
# get false positive indices
fp_indices = list(set(gf).intersection(pt))
# get true positive indices
tp_indices = list(set(gt).intersection(pt))
# get false negative indices
fn_indices = list(set(gt).intersection(pf))
# get true negative indices
tn_indices = list(set(gf).intersection(pf))
n_tp = float(len(tp_indices))
n_fp = float(len(fp_indices))
n_fn = float(len(fn_indices))
n_tn = float(len(tn_indices))
prec_num = n_tp
prec_den = n_tp + n_fp
rec_num = n_tp
rec_den = n_tp + n_fn
if prec_den == 0.0:
precision = 0.0
else:
precision = prec_num / prec_den
if rec_den == 0.0:
recall = 0.0
else:
recall = rec_num / rec_den
if precision == 0.0 and recall == 0.0:
f1 = 0.0
else:
f1 = (2.0 * precision * recall) / (precision + recall)
fk_ltable, fk_rtable = cm.get_property(X, 'fk_ltable'), cm.get_property(
X, 'fk_rtable')
Y.set_index([fk_ltable, fk_rtable], drop=False, inplace=True)
false_pos_ls = list(Y.ix[fp_indices].index.values)
false_neg_ls = list(Y.ix[fn_indices].index.values)
ret_dict = OrderedDict()
ret_dict['prec_numerator'] = prec_num
ret_dict['prec_denominator'] = prec_den
ret_dict['precision'] = precision
ret_dict['recall_numerator'] = rec_num
ret_dict['recall_denominator'] = rec_den
ret_dict['recall'] = recall
ret_dict['f1'] = f1
ret_dict['pred_pos_num'] = n_tp + n_fp
ret_dict['false_pos_num'] = n_fp
ret_dict['false_pos_ls'] = false_pos_ls
ret_dict['pred_neg_num'] = n_fn + n_tn
ret_dict['false_neg_num'] = n_fn
ret_dict['false_neg_ls'] = false_neg_ls
return ret_dict
def test_valid_path_wi_metadata_unknownprop(self):
cm.del_catalog()
p = os.sep.join([io_datasets_path, 'InvalidMetadata1.csv'])
IM = read_csv_metadata(p)
self.assertEqual(cm.is_dfinfo_present(IM), True)
self.assertEqual(cm.get_property(IM, 'key1'), 'ID')
def test_set_property_valid_df_name_value(self):
# cm.del_catalog()
df = pd.read_csv(path_a)
cm.set_property(df, 'key', 'ID')
self.assertEqual(cm.get_property(df, 'key'), 'ID')
def test_get_property_df_notin_catalog(self):
# cm.del_catalog()
A = pd.read_csv(path_a)
cm.get_property(A, 'key')
def test_get_property_invalid_path_1(self):
# cm.del_catalog()
A = read_csv_metadata(path_a)
cm.get_property(A, None)
def test_get_property_invalid_df_1(self):
cm.get_property(10, 'key')
def test_get_fk_rtable_valid(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b)
C = read_csv_metadata(path_c, ltable=A, rtable=B)
self.assertEqual(cm.get_fk_rtable(C), cm.get_property(C, 'fk_rtable'))
self.assertEqual(cm.get_fk_rtable(C), 'rtable_ID')
def test_get_property_valid_df_name_1(self):
# cm.del_catalog()
df = read_csv_metadata(path_a)
self.assertEqual(cm.get_property(df, 'key'), 'ID')
def test_valid_path_wi_metadata_unknownprop(self):
cm.del_catalog()
p = os.sep.join([io_datasets_path, 'InvalidMetadata1.csv'])
IM = read_csv_metadata(p)
self.assertEqual(cm.is_dfinfo_present(IM), True)
self.assertEqual(cm.get_property(IM, 'key1'), 'ID')
def eval_matches(X, gold_label_attr, predicted_label_attr):
if not isinstance(X, pd.DataFrame):
logger.error('The input table is not of type dataframe')
raise AssertionError('The input is not of type dataframe')
if not isinstance(gold_label_attr, six.string_types):
logger.error('The input gold_label_attr is not of type string')
raise AssertionError('The input gold_label_attr is not of type string')
if not isinstance(predicted_label_attr, six.string_types):
logger.error('The input predicted_label_attr is not of type string')
raise AssertionError('The input predicted_label_attr is not of type string')
if not check_attrs_present(X, gold_label_attr):
logger.error('The gold_label_attr is not present in the input table')
raise AssertionError('The gold_label_attr is not present in the input table')
if not check_attrs_present(X, predicted_label_attr):
logger.error('The predicted_label_attr is not present in the input table')
raise AssertionError('The predicted_label_attr is not present in the input table')
Y = X.reset_index(drop=False, inplace=False)
g = Y[gold_label_attr]
# if isinstance(g, pd.DataFrame):
# g = g.T
# assert len(g) == 1, 'Error: Column is picked as dataframe and the num rows > 1'
# g = g.iloc[0]
p = Y[predicted_label_attr]
# if isinstance(p, pd.DataFrame):
# p = p.T
# assert len(p) == 1, 'Error: Column is picked as dataframe and the num rows > 1'
# p = p.iloc[0]
# get false label (0) indices
gf = g[g == 0].index.values
pf = p[p == 0].index.values
# get true label (1) indices
gt = g[g == 1].index.values
pt = p[p == 1].index.values
# get false positive indices
fp_indices = list(set(gf).intersection(pt))
# get true positive indices
tp_indices = list(set(gt).intersection(pt))
# get false negative indices
fn_indices = list(set(gt).intersection(pf))
# get true negative indices
tn_indices = list(set(gf).intersection(pf))
n_tp = float(len(tp_indices))
n_fp = float(len(fp_indices))
n_fn = float(len(fn_indices))
n_tn = float(len(tn_indices))
prec_num = n_tp
prec_den = n_tp + n_fp
rec_num = n_tp
rec_den = n_tp + n_fn
if prec_den == 0.0:
precision = 0.0
else:
precision = prec_num / prec_den
if rec_den == 0.0:
recall = 0.0
else:
recall = rec_num / rec_den
if precision == 0.0 and recall == 0.0:
f1 = 0.0
else:
f1 = (2.0 * precision * recall) / (precision + recall)
fk_ltable, fk_rtable = cm.get_property(X, 'fk_ltable'), cm.get_property(X, 'fk_rtable')
Y.set_index([fk_ltable, fk_rtable], drop=False, inplace=True)
false_pos_ls = list(Y.ix[fp_indices].index.values)
false_neg_ls = list(Y.ix[fn_indices].index.values)
ret_dict = OrderedDict()
ret_dict['prec_numerator'] = prec_num
ret_dict['prec_denominator'] = prec_den
ret_dict['precision'] = precision
ret_dict['recall_numerator'] = rec_num
ret_dict['recall_denominator'] = rec_den
ret_dict['recall'] = recall
ret_dict['f1'] = f1
ret_dict['pred_pos_num'] = n_tp + n_fp
ret_dict['false_pos_num'] = n_fp
ret_dict['false_pos_ls'] = false_pos_ls
ret_dict['pred_neg_num'] = n_fn + n_tn
ret_dict['false_neg_num'] = n_fn
ret_dict['false_neg_ls'] = false_neg_ls
return ret_dict