def _split_aggregated(self, df: container.DataFrame,
split_col_names: list) -> container.DataFrame:
lengths = [len(df.loc[0, col_name]) for col_name in split_col_names]
for idx, col_name in enumerate(split_col_names):
if self._sorted_pipe_ids:
if len(self._sorted_pipe_ids) == lengths[idx]:
extend_col_names = [
"{}_{}".format(col_name, i)
for i in self._sorted_pipe_ids
]
else:
raise ValueError(
"Unique number of pipeline ids not equal to the number of aggregated values"
)
else:
extend_col_names = [
"{}_{}".format(col_name, i) for i in range(lengths[idx])
]
extends = container.DataFrame(df.loc[:, col_name].values.tolist(),
columns=extend_col_names)
df = common_utils.horizontal_concat(left=df, right=extends)
origin_metadata = dict(
df.metadata.query(
(mbase.ALL_ELEMENTS, df.columns.get_loc(col_name))))
for name in extend_col_names:
col_idx = df.columns.get_loc(name)
origin_metadata["name"] = name
df.metadata = df.metadata.update((mbase.ALL_ELEMENTS, col_idx),
origin_metadata)
return df
def _update_metadata_dimension(
df: container.DataFrame) -> container.DataFrame:
old_metadata = dict(df.metadata.query(()))
old_metadata["dimension"] = dict(old_metadata["dimension"])
old_metadata["dimension"]["length"] = df.shape[0]
df.metadata = df.metadata.update((), old_metadata)
return df
def _update_type_info(self, semantic_types: Sequence[str],
outputs: container.DataFrame,
i: int) -> container.DataFrame:
# update the structural / df type from the semantic type
if "http://schema.org/Integer" in semantic_types:
outputs.metadata = outputs.metadata.update_column(
i, {"structural_type": int})
outputs.iloc[:, i] = pd.to_numeric(outputs.iloc[:, i])
elif "http://schema.org/Float" in semantic_types:
outputs.metadata = outputs.metadata.update_column(
i, {"structural_type": float})
outputs.iloc[:, i] = pd.to_numeric(outputs.iloc[:, i])
elif "http://schema.org/Boolean" in semantic_types:
outputs.metadata = outputs.metadata.update_column(
i, {"structural_type": bool})
outputs.iloc[:, i] = outputs.iloc[:, i].astype("bool")
return outputs
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
start = time.time()
logger.debug(f"Producing {__name__}")
cols = self._get_columns(inputs.metadata)
# outputs = container.DataFrame(generate_metadata=False)
outputs = [None] * inputs.shape[1]
parsing_semantics = self.hyperparams["parsing_semantics"]
def fromstring(x: str) -> np.ndarray:
# if column isn't a string, we'll just pass it through assuming it doesn't need to be parsed
if type(x) is not str:
return x
return np.fromstring(x, dtype=float, sep=",")
for col_index in range(len(inputs.columns)):
if col_index in cols:
column_metadata = inputs.metadata.query(
(metadata_base.ALL_ELEMENTS, col_index)
)
semantic_types = column_metadata.get("semantic_types", [])
desired_semantics = set(semantic_types).intersection(parsing_semantics)
if desired_semantics:
if (
"https://metadata.datadrivendiscovery.org/types/FloatVector"
in desired_semantics
):
outputs[col_index] = inputs.iloc[:, col_index].apply(
fromstring, convert_dtype=False
)
if outputs[col_index].shape[0] > 0:
inputs.metadata = inputs.metadata.update_column(
col_index,
{"structural_type": type(outputs[col_index][0])},
)
elif "http://schema.org/DateTime" in desired_semantics:
outputs[col_index] = inputs.iloc[:, col_index].apply(
utils.parse_datetime_to_float,
fuzzy=self.hyperparams["fuzzy_time_parsing"],
convert_dtype=False,
)
inputs.metadata = inputs.metadata.update_column(
col_index, {"structural_type": float}
)
elif (
"https://metadata.datadrivendiscovery.org/types/CategoricalData"
in desired_semantics
):
# need to make sure if a categorical type is a numeric string, convert it
if inputs[inputs.columns[col_index]][0].isnumeric():
outputs[col_index] = pd.to_numeric(
inputs.iloc[:, col_index],
errors=self.hyperparams["error_handling"],
)
if outputs[col_index].shape[0] > 0:
updated_type = type(outputs[col_index][0].item())
inputs.metadata = inputs.metadata.update_column(
col_index, {"structural_type": updated_type}
)
else:
# if it's categorical but not numerical, ensure the string stays
outputs[col_index] = inputs.iloc[:, col_index]
else:
outputs[col_index] = pd.to_numeric(
inputs.iloc[:, col_index],
errors=self.hyperparams["error_handling"],
)
# Update structural type to reflect the results of the to_numeric call. We can't rely on the semantic type because
# error coersion may result in a type becoming a float due to the presence of NaN.
if outputs[col_index].shape[0] > 0:
updated_type = type(outputs[col_index][0].item())
inputs.metadata = inputs.metadata.update_column(
col_index, {"structural_type": updated_type}
)
else:
# columns without specified semantics need to be concatenated
outputs[col_index] = inputs.iloc[:, col_index]
else:
# columns not specified still need to be concatenated
outputs[col_index] = inputs.iloc[:, col_index]
outputs = container.DataFrame(pd.concat(outputs, axis=1))
outputs.metadata = inputs.metadata
end = time.time()
logger.debug(f"Produce {__name__} completed in {end - start} ms")
return base.CallResult(outputs)
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None) -> base.CallResult[container.DataFrame]:
cols = ["idx", "name", "rank"]
# Make sure the target column is of a valid type and return no ranked features if it isn't.
target_idx = self.hyperparams["target_col_index"]
if not self._can_use_column(inputs.metadata, target_idx):
return base.CallResult(container.DataFrame(data={}, columns=cols))
# check if target is discrete or continuous
semantic_types = inputs.metadata.query_column(
target_idx)["semantic_types"]
discrete = len(set(semantic_types).intersection(
self._discrete_types)) > 0
# make a copy of the inputs and clean out any missing data
feature_df = inputs.copy()
if self.hyperparams["sub_sample"]:
sub_sample_size = (self.hyperparams["sub_sample_size"]
if self.hyperparams["sub_sample_size"] <
inputs.shape[0] else inputs.shape[0])
rows = random.sample_without_replacement(inputs.shape[0],
sub_sample_size)
feature_df = feature_df.iloc[rows, :]
# makes sure that if an entire column is NA, we remove that column, so as to not remove ALL rows
cols_to_drop = feature_df.columns[feature_df.isna().sum() ==
feature_df.shape[0]]
feature_df.drop(columns=cols_to_drop, inplace=True)
feature_df.dropna(inplace=True)
# split out the target feature
target_df = feature_df.iloc[:,
feature_df.columns.
get_loc(inputs.columns[target_idx])]
# drop features that are not compatible with ranking
feature_indices = set(
inputs.metadata.list_columns_with_semantic_types(
self._semantic_types))
role_indices = set(
inputs.metadata.list_columns_with_semantic_types(self._roles))
feature_indices = feature_indices.intersection(role_indices)
feature_indices.remove(target_idx)
for categ_ind in inputs.metadata.list_columns_with_semantic_types(
("https://metadata.datadrivendiscovery.org/types/CategoricalData",
)):
if categ_ind in feature_indices:
if (np.unique(inputs[inputs.columns[categ_ind]]).shape[0] ==
inputs.shape[0]):
feature_indices.remove(categ_ind)
elif (inputs.metadata.query(
(metadata_base.ALL_ELEMENTS,
categ_ind))["structural_type"] == str):
feature_df[inputs.columns[categ_ind]] = pd.to_numeric(
feature_df[inputs.columns[categ_ind]])
text_indices = inputs.metadata.list_columns_with_semantic_types(
self._text_semantic)
tfv = TfidfVectorizer(max_features=20)
column_to_text_features = {}
text_feature_indices = []
for text_index in text_indices:
if (text_index not in feature_indices
and text_index in role_indices
and text_index != target_idx):
word_features = tfv.fit_transform(
feature_df[inputs.columns[text_index]])
if issparse(word_features):
column_to_text_features[inputs.columns[
text_index]] = pd.DataFrame.sparse.from_spmatrix(
word_features)
else:
column_to_text_features[
inputs.columns[text_index]] = word_features
text_feature_indices.append(text_index)
text_feature_indices = set(text_feature_indices)
# return an empty result if all features were incompatible
numeric_features = len(feature_indices) > 0
if not numeric_features and len(column_to_text_features) == 0:
return base.CallResult(container.DataFrame(data={}, columns=cols))
all_indices = set(range(0, inputs.shape[1]))
skipped_indices = all_indices.difference(
feature_indices.union(text_feature_indices))
# remove columns that were dropped
feature_indices = feature_indices - set(
[inputs.columns.get_loc(c) for c in cols_to_drop])
for i, v in enumerate(skipped_indices):
feature_df.drop(inputs.columns[v], axis=1, inplace=True)
# figure out the discrete and continuous feature indices and create an array
# that flags them
feature_columns = inputs.columns[list(feature_indices)]
numeric_data = feature_df[feature_columns]
discrete_indices = inputs.metadata.list_columns_with_semantic_types(
self._discrete_types)
discrete_flags = [False] * numeric_data.shape[1]
for v in discrete_indices:
col_name = inputs.columns[v]
if col_name in numeric_data:
# only mark columns with a least 1 duplicate value as discrete when predicting
# a continuous target - there's a check in the bowels of MI code that will throw
# an exception otherwise
if numeric_data[col_name].duplicated().any() and not discrete:
col_idx = numeric_data.columns.get_loc(col_name)
discrete_flags[col_idx] = True
target_np = target_df.values
# compute mutual information for discrete or continuous target
ranked_features_np = np.empty([0])
text_ranked_features_np = np.empty((len(column_to_text_features), ))
if discrete:
if numeric_features:
ranked_features_np = mutual_info_classif(
numeric_data.values,
target_np,
discrete_features=discrete_flags,
n_neighbors=self.hyperparams["k"],
random_state=self._random_seed,
)
for i, column in enumerate(column_to_text_features):
text_rankings = mutual_info_classif(
column_to_text_features[column],
target_np,
discrete_features=[False] *
column_to_text_features[column].shape[1],
n_neighbors=self.hyperparams["k"],
random_state=self._random_seed,
)
sum_text_rank = np.sum(text_rankings)
text_ranked_features_np[i] = sum_text_rank
else:
if numeric_features:
ranked_features_np = mutual_info_regression(
numeric_data.values,
target_np,
discrete_features=discrete_flags,
n_neighbors=self.hyperparams["k"],
random_state=self._random_seed,
)
for i, column in enumerate(column_to_text_features):
text_rankings = mutual_info_regression(
column_to_text_features[column],
target_np,
discrete_features=[False] *
column_to_text_features[column].shape[1],
n_neighbors=self.hyperparams["k"],
random_state=self._random_seed,
)
sum_text_rank = np.sum(text_rankings)
text_ranked_features_np[i] = sum_text_rank
ranked_features_np, target_entropy = self._normalize(
ranked_features_np,
feature_df[feature_columns],
target_np,
discrete,
discrete_flags,
)
text_ranked_features_np = self._normalize_text(
text_ranked_features_np, column_to_text_features, target_entropy)
if self.hyperparams["return_as_metadata"]:
ranked_features_np = np.append(ranked_features_np,
text_ranked_features_np)
for i, f in enumerate(feature_indices.union(text_feature_indices)):
column_metadata = inputs.metadata.query(
(metadata_base.ALL_ELEMENTS, f))
rank_dict = dict(column_metadata)
rank_dict["rank"] = ranked_features_np[i]
inputs.metadata = inputs.metadata.update(
(metadata_base.ALL_ELEMENTS, f),
FrozenOrderedDict(rank_dict.items()),
)
return base.CallResult(inputs)
# merge back into a single list of col idx / rank value tuples
data: typing.List[typing.Tuple[int, str, float]] = []
data = self._append_rank_info(inputs, data, ranked_features_np,
feature_df[feature_columns])
data = self._append_rank_info(
inputs,
data,
text_ranked_features_np,
feature_df[inputs.columns[list(text_feature_indices)]],
)
# wrap as a D3M container - metadata should be auto generated
results = container.DataFrame(data=data,
columns=cols,
generate_metadata=True)
results = results.sort_values(by=["rank"],
ascending=False).reset_index(drop=True)
return base.CallResult(results)
