def _join_string_col(cls, left_df: container.DataFrame, left_col: str,
right_df: container.DataFrame, right_col: str,
accuracy: float) -> pd.DataFrame:
# use d3mIndex from left col if present
right_df = right_df.drop(columns='d3mIndex')
# pre-compute fuzzy matches
left_keys = left_df[left_col].unique()
right_keys = right_df[right_col].unique()
matches: typing.Dict[str, typing.Optional[str]] = {}
for left_key in left_keys:
matches[left_key] = cls._string_fuzzy_match(
left_key, right_keys, accuracy * 100)
# look up pre-computed fuzzy match for each element in the left column
left_df.index = left_df[left_col].map(lambda key: matches[key])
# make the right col the right dataframe index
right_df = right_df.set_index(right_col)
# inner join on the left / right indices
joined = container.DataFrame(
left_df.join(right_df, lsuffix='_1', rsuffix='_2', how='inner'))
# sort on the d3m index if there, otherwise use the joined column
if 'd3mIndex' in joined:
joined = joined.sort_values(by=['d3mIndex'])
else:
joined = joined.sort_values(by=[left_col])
joined = joined.reset_index(drop=True)
return joined
def _join_numeric_col(cls, left_df: container.DataFrame, left_col: str,
right_df: container.DataFrame, right_col: str,
accuracy: float) -> pd.DataFrame:
# use d3mIndex from left col if present
right_df = right_df.drop(columns='d3mIndex')
# fuzzy match each of the left join col against the right join col value and save the results as the left
# dataframe index
right_df[right_col] = pd.to_numeric(right_df[right_col])
choices = right_df[right_col].unique()
left_df[left_col] = pd.to_numeric(left_df[left_col])
left_df.index = left_df[left_col]. \
map(lambda x: cls._numeric_fuzzy_match(x, choices, accuracy))
# make the right col the right dataframe index
right_df = right_df.set_index(right_col)
# inner join on the left / right indices
joined = container.DataFrame(
left_df.join(right_df, lsuffix='_1', rsuffix='_2', how='inner'))
# sort on the d3m index if there, otherwise use the joined column
if 'd3mIndex' in joined:
joined = joined.sort_values(by=['d3mIndex'])
else:
joined = joined.sort_values(by=[left_col])
joined = joined.reset_index(drop=True)
return joined
def _join_datetime_col(cls,
left_df: container.DataFrame,
left_col: str,
right_df: container.DataFrame,
right_col: str,
accuracy: float) -> pd.DataFrame:
# use d3mIndex from left col if present
right_df = right_df.drop(columns='d3mIndex')
# compute a tolerance delta for time matching based on a percentage of the minimum left/right time
# range
choices = np.array([np.datetime64(parser.parse(dt)) for dt in right_df[right_col].unique()])
left_keys = np.array([np.datetime64(parser.parse(dt)) for dt in left_df[left_col].values])
time_tolerance = (1.0 - accuracy) * cls._compute_time_range(left_keys, choices)
left_df.index = np.array([cls._datetime_fuzzy_match(dt, choices, time_tolerance) for dt in left_keys])
# make the right col the right dataframe index
right_df = right_df.set_index(right_col)
# inner join on the left / right indices
joined = container.DataFrame(left_df.join(right_df, lsuffix='_1', rsuffix='_2', how='inner'))
# sort on the d3m index if there, otherwise use the joined column
if 'd3mIndex' in joined:
joined = joined.sort_values(by=['d3mIndex'])
else:
joined = joined.sort_values(by=[left_col])
joined = joined.reset_index(drop=True)
return joined
