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
def _produce(
self,
*,
left_df_full: container.DataFrame, # type: ignore
left_df: container.DataFrame, # type: ignore
right_df: container.DataFrame, # type: ignore
join_types: typing.Sequence[str],
left_col: typing.Sequence[int],
right_col: typing.Sequence[int],
accuracy: typing.Sequence[float],
absolute_accuracy: typing.Sequence[bool]
) -> base.CallResult[Outputs]:
# cycle through the columns to join the dataframes
right_cols_to_drop = []
new_left_cols = []
new_right_cols = []
for col_index in range(len(left_col)):
# depending on the joining type, make a new dataframe that has columns we will want to merge on
# keep track of which columns we will want to drop later on
if len(self._STRING_JOIN_TYPES.intersection(join_types[col_index])) > 0:
new_left_df = self._create_string_merge_cols(
left_df,
left_col[col_index],
right_df,
right_col[col_index],
accuracy[col_index],
col_index,
)
left_df[new_left_df.columns] = new_left_df
right_name = "righty_string" + str(col_index)
right_df.rename(
columns={right_col[col_index]: right_name}, inplace=True
)
new_left_cols += list(new_left_df.columns)
new_right_cols.append(right_name)
elif len(self._NUMERIC_JOIN_TYPES.intersection(join_types[col_index])) > 0:
new_left_df = self._create_numeric_merge_cols(
left_df,
left_col[col_index],
right_df,
right_col[col_index],
accuracy[col_index],
col_index,
absolute_accuracy[col_index],
)
left_df[new_left_df.columns] = new_left_df
right_name = "righty_numeric" + str(col_index)
right_df.rename(
columns={right_col[col_index]: right_name}, inplace=True
)
new_left_cols += list(new_left_df.columns)
new_right_cols.append(right_name)
elif len(self._GEO_JOIN_TYPES.intersection(join_types[col_index])) > 0:
new_left_df, new_right_df = self._create_geo_vector_merging_cols(
left_df,
left_col[col_index],
right_df,
right_col[col_index],
accuracy[col_index],
col_index,
absolute_accuracy[col_index],
)
left_df[new_left_df.columns] = new_left_df
right_df[new_right_df.columns] = new_right_df
new_left_cols += list(new_left_df.columns)
new_right_cols += list(new_right_df.columns)
right_cols_to_drop.append(right_col[col_index])
elif len(self._VECTOR_JOIN_TYPES.intersection(join_types[col_index])) > 0:
new_left_df, new_right_df = self._create_vector_merging_cols(
left_df,
left_col[col_index],
right_df,
right_col[col_index],
accuracy[col_index],
col_index,
absolute_accuracy[col_index],
)
left_df[new_left_df.columns] = new_left_df
right_df[new_right_df.columns] = new_right_df
new_left_cols += list(new_left_df.columns)
new_right_cols += list(new_right_df.columns)
right_cols_to_drop.append(right_col[col_index])
elif len(self._DATETIME_JOIN_TYPES.intersection(join_types[col_index])) > 0:
tolerance = self._compute_datetime_tolerance(left_df_full, left_col[col_index], right_df, right_col[col_index], accuracy[col_index])
new_left_df, new_right_df = self._create_datetime_merge_cols(
left_df,
left_col[col_index],
right_df,
right_col[col_index],
tolerance,
col_index,
)
left_df[new_left_df.columns] = new_left_df
right_df[new_right_df.columns] = new_right_df
new_left_cols += list(new_left_df.columns)
new_right_cols += list(new_right_df.columns)
right_cols_to_drop.append(right_col[col_index])
else:
raise exceptions.InvalidArgumentValueError(
"join not surpported on type " + str(join_types[col_index])
)
if "d3mIndex" in right_df.columns:
right_cols_to_drop.append("d3mIndex")
right_df.drop(columns=right_cols_to_drop, inplace=True)
joined = pd.merge(
left_df,
right_df,
how=self.hyperparams["join_type"],
left_on=new_left_cols,
right_on=new_right_cols,
suffixes=["_left", "_right"],
)
# don't want to keep columns that were created specifically for merging
# also, inner merge keeps the right column we merge on, we want to remove it
joined.drop(columns=new_left_cols + new_right_cols, inplace=True)
return joined
