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_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_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 _get_targets(cls, data: d3m_dataframe, hyperparams: Hyperparams):
if not hyperparams['use_semantic_types']:
return data, list(data.columns), list(range(len(data.columns)))
metadata = data.metadata
def can_produce_column(column_index: int) -> bool:
accepted_semantic_types = set()
accepted_semantic_types.add("https://metadata.datadrivendiscovery.org/types/TrueTarget")
column_metadata = metadata.query((metadata_base.ALL_ELEMENTS, column_index))
semantic_types = set(column_metadata.get('semantic_types', []))
if len(semantic_types) == 0:
cls.logger.warning("No semantic types found in column metadata")
return False
# Making sure all accepted_semantic_types are available in semantic_types
if len(accepted_semantic_types - semantic_types) == 0:
return True
return False
target_column_indices, target_columns_not_to_produce = base_utils.get_columns_to_use(metadata,
use_columns=hyperparams[
'use_outputs_columns'],
exclude_columns=
hyperparams[
'exclude_outputs_columns'],
can_use_column=can_produce_column)
targets = []
if target_column_indices:
targets = data.select_columns(target_column_indices)
target_column_names = []
for idx in target_column_indices:
target_column_names.append(data.columns[idx])
return targets, target_column_names, target_column_indices
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
logger.debug(f"Running {__name__}")
# set values that only occur once to a special token
outputs = inputs.copy()
# determine columns to operate on
cols = distil_utils.get_operating_columns(
inputs, self.hyperparams["use_columns"], CATEGORICALS)
for c in cols:
vcs = pd.value_counts(list(inputs.iloc[:, c]))
singletons = set(vcs[vcs == 1].index)
if singletons:
mask = outputs.iloc[:, c].isin(singletons)
outputs.loc[mask, outputs.columns[c]] = SINGLETON_INDICATOR
logger.debug(f"\n{outputs}")
return base.CallResult(outputs)
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 _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 _produce_threaded(
self,
*,
index: int,
left_df_full: container.DataFrame, # type: ignore
left_dfs: typing.Sequence[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]
) -> typing.Tuple[int, base.CallResult[Outputs]]:
if left_dfs[index].empty:
return (index, None)
output = self._produce(
left_df_full = left_df_full,
left_df = left_dfs[index].reset_index(drop=True),
right_df = right_df.copy(),
join_types = join_types,
left_col = left_col,
right_col = right_col,
accuracy = accuracy,
absolute_accuracy = absolute_accuracy
)
return (index, output)
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
logger.debug(f"Running {__name__}")
# determine columns to operate on
cols = distil_utils.get_operating_columns(
inputs, self.hyperparams["use_columns"], CATEGORICALS)
logger.debug(f"Found {len(cols)} categorical columns to evaluate")
if len(cols) is 0:
return base.CallResult(inputs)
imputer = CategoricalImputer(
strategy=self.hyperparams["strategy"],
fill_value=self.hyperparams["fill_value"],
missing_values="",
tie_breaking="first",
)
outputs = inputs.copy()
failures: List[int] = []
for c in cols:
input_col = inputs.iloc[:, c]
try:
imputer.fit(input_col)
result = imputer.transform(input_col)
outputs.iloc[:, c] = result
except ValueError as e:
# value error gets thrown when all data is missing
if not self.hyperparams["error_on_empty"]:
failures.append(c)
else:
raise e
# for columns that failed using 'most_frequent' try again using 'constant'
if not self.hyperparams["error_on_empty"]:
imputer = CategoricalImputer(
strategy="constant",
fill_value=self.hyperparams["fill_value"],
missing_values="",
tie_breaking="first",
)
for f in failures:
outputs_col = outputs.iloc[:, f]
imputer.fit(outputs_col)
result = imputer.transform(outputs_col)
outputs.iloc[:, f] = result
logger.debug(f"\n{outputs}")
return base.CallResult(outputs)
def _generate_labels(self, inputs: container.DataFrame) -> None:
self._labels = {}
for col_idx, (label, col) in enumerate(inputs.iteritems()):
# Get all the unique data in the column and assign each element an int representation.
# We reserve 0 for unseen labels so we increment the encodings by one
unique_data = col.unique()
self._labels[col_idx] = {
label: encoded + 1
for encoded, label in enumerate(unique_data)
}
def _remap_graphs(
cls,
data: container.DataFrame) -> Tuple[container.DataFrame, int, int]:
assert data.shape[1] == 2
data = data.copy()
data.columns = ("user", "item")
uusers = np.unique([data.user, data.user])
user_lookup = dict(zip(uusers, range(len(uusers))))
data.user = data.user.apply(user_lookup.get)
uitems = np.unique(data.item)
item_lookup = dict(zip(uitems, range(len(uitems))))
data.item = data.item.apply(item_lookup.get)
n_users = len(uusers)
n_items = len(uitems)
return data, n_users, n_items
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
df = inputs.select_columns(
inputs.metadata.list_columns_with_semantic_types(
("http://schema.org/Float",)
)
)
df = df.to_numpy().reshape(
df.shape[0], 2048, self.hyperparams["height"], self.hyperparams["width"]
)
all_img_features = []
batch_size = self.hyperparams["batch_size"]
spatial_a = 2.0
spatial_b = 2.0
for i in range(math.ceil(df.shape[0] / batch_size)):
features = df[i * batch_size : (i + 1) * batch_size]
spatial_weight = features.sum(axis=1, keepdims=True)
z = (spatial_weight ** spatial_a).sum(axis=(2, 3), keepdims=True)
z = z ** (1.0 / spatial_a)
spatial_weight = (spatial_weight / z) ** (1.0 / spatial_b)
_, c, w, h = features.shape
nonzeros = (features != 0).astype(float).sum(axis=(2, 3)) / 1.0 / (
w * h
) + 1e-6
channel_weight = np.log(nonzeros.sum(axis=1, keepdims=True) / nonzeros)
features = features * spatial_weight
features = features.sum(axis=(2, 3))
features = features * channel_weight
all_img_features.append(features)
all_img_features = np.vstack(all_img_features)
col_names = [f"feat_{i}" for i in range(0, all_img_features.shape[1])]
feature_df = pd.DataFrame(all_img_features, columns=col_names)
outputs = container.DataFrame(feature_df.head(1), generate_metadata=True)
outputs.metadata = outputs.metadata.update(
(metadata_base.ALL_ELEMENTS,),
{"dimension": {"length": feature_df.shape[0]}},
)
outputs = outputs.append(feature_df.iloc[1:])
for idx in range(outputs.shape[1]):
outputs.metadata = outputs.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, idx), "http://schema.org/Float"
)
return base.CallResult(outputs)
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
logger.debug(f"Producing {__name__}")
if len(self._cols) == 0:
return base.CallResult(inputs)
# add the binary encoded columns and remove the source columns
outputs = inputs.copy()
encoded_cols = container.DataFrame()
encoded_cols_source = []
bin_idx = 0
for i, c in enumerate(self._cols):
categorical_inputs = outputs.iloc[:, c]
result = self._encoders[i].transform(categorical_inputs)
for j in range(result.shape[1]):
encoded_cols[(f"__binary_{bin_idx}")] = result[:, j]
encoded_cols_source.append(c)
bin_idx += 1
encoded_cols.metadata = encoded_cols.metadata.generate(encoded_cols)
for c in range(encoded_cols.shape[1]):
encoded_cols.metadata = encoded_cols.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, c), "http://schema.org/Integer"
)
encoded_cols.metadata = encoded_cols.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, c), self._attribute_semantic
)
col_dict = dict(
encoded_cols.metadata.query((metadata_base.ALL_ELEMENTS, c))
)
col_dict["source_column"] = outputs.metadata.query(
(metadata_base.ALL_ELEMENTS, encoded_cols_source[c])
)["name"]
encoded_cols.metadata = encoded_cols.metadata.update(
(metadata_base.ALL_ELEMENTS, c), col_dict
)
outputs = outputs.append_columns(encoded_cols)
outputs = outputs.remove_columns(self._cols)
logger.debug(f"\n{outputs}")
return base.CallResult(outputs)
def _is_unique_key(self, input_column: container.DataFrame) -> bool:
column_values = input_column.iloc[:, 0]
# There should be at least one row. This prevents a degenerate case
# where we would mark a column of no rows as a unique key column.
# (Otherwise we also get division by zero below.)
if not len(column_values):
return False
# Here we look at every value as-is. Even empty strings and other missing/nan values.
if any(input_column.duplicated()):
return False
return True
def combine(self, prediction_groups: typing.Dict,
inputs: container.DataFrame):
all_results = []
only_one = 'only_one_time_series' in prediction_groups
for i, row in inputs.iterrows():
# date = pd.Timestamp(et.time_indicator.get_datetime(row))
date = self.time_indicator.get_datetime(row)
if only_one:
key = 'only_one_time_series'
else:
key = []
for x in self.categorical_indices:
key.append(row.iloc[x])
key = tuple(key)
predictions = prediction_groups[key]
all_results.append(predictions.loc[date, 0])
return np.array(all_results).T
def produce(self,
*,
inputs: Input,
timeout: float = None,
iterations: int = None) -> CallResult[Output]:
columns_list_to_fold = self._mapping.get('foldable_columns', [])
if len(columns_list_to_fold) == 0:
return CallResult(inputs, True, 1)
if inputs.shape[0] > 20000:
return CallResult(inputs, True, 1)
self._column_names = list(inputs) if inputs is not None else []
df = None
for columns_to_fold in columns_list_to_fold:
df = self._fold_columns(inputs, columns_to_fold)
cols_to_drop = list()
for col_idx, col_name in enumerate(inputs.columns):
if col_name not in df.columns:
cols_to_drop.append(col_idx)
inputs = utils.remove_columns(inputs, cols_to_drop)
new_df = inputs[0:0]
for col_name in new_df.columns:
new_df.loc[:, col_name] = df.loc[:, col_name]
extends = {}
for col_name in df.columns:
if col_name not in new_df.columns:
extends[col_name] = df.loc[:, col_name].tolist()
if extends:
extends_df = d3m_DataFrame.from_dict(extends)
extends_df.index = new_df.index.copy()
new_df = utils.append_columns(new_df, extends_df)
new_df = self._update_type(new_df, list(extends.keys()))
old_metadata = dict(new_df.metadata.query(()))
old_metadata["dimension"] = dict(old_metadata["dimension"])
old_metadata["dimension"]["length"] = new_df.shape[0]
new_df.metadata = new_df.metadata.update((), old_metadata)
return CallResult(new_df, True,
1) if new_df is not None else CallResult(
inputs, True, 1)
def update_type(extends, df_origin):
extends_df = pd.DataFrame.from_dict(extends)
extends_df = d3m_DataFrame(extends_df, generate_metadata=True)
if extends != {}:
extends_df.index = df_origin.index.copy()
new_df = d3m_DataFrame.append_columns(df_origin, extends_df)
indices = list()
for key in extends:
indices.append(new_df.columns.get_loc(key))
for idx in indices:
old_metadata = dict(new_df.metadata.query((mbase.ALL_ELEMENTS, idx)))
numerics = pd.to_numeric(new_df.iloc[:, idx], errors='coerce')
length = numerics.shape[0]
nans = numerics.isnull().sum()
if nans / length > 0.9:
if HelperFunction.is_categorical(new_df.iloc[:, idx]):
old_metadata['semantic_types'] = (
"https://metadata.datadrivendiscovery.org/types/CategoricalData",
)
else:
old_metadata['semantic_types'] = ("http://schema.org/Text", )
else:
intcheck = (numerics % 1) == 0
if np.sum(intcheck) / length > 0.9:
old_metadata['semantic_types'] = (
"http://schema.org/Integer", )
else:
old_metadata['semantic_types'] = ("http://schema.org/Float", )
old_metadata['semantic_types'] += (
"https://metadata.datadrivendiscovery.org/types/Attribute", )
new_df.metadata = new_df.metadata.update((mbase.ALL_ELEMENTS, idx),
old_metadata)
return new_df
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
logger.debug(f"Producing {__name__}")
if len(self._cols) == 0:
return base.CallResult(inputs)
# encode using the previously identified categorical columns
input_cols = inputs.iloc[:, self._cols]
from itertools import zip_longest
encoded_cols = container.DataFrame()
for i in self._cols:
col_name = inputs.columns[i]
col = container.DataFrame.from_records(
zip_longest(*inputs[col_name].values)).T
col.columns = [f"{col_name}_{x}" for x in range(len(col.columns))]
encoded_cols = pd.concat([encoded_cols, col], axis=1)
# append the encoding columns and generate metadata
outputs = inputs.copy()
encoded_cols.metadata = encoded_cols.metadata.generate(encoded_cols)
for c in range(encoded_cols.shape[1]):
encoded_cols.metadata = encoded_cols.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, c), "http://schema.org/Float")
outputs = outputs.append_columns(encoded_cols)
# drop the source columns
outputs = outputs.remove_columns(self._cols)
logger.debug(f"\n{outputs}")
return base.CallResult(outputs)
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 calculate_score(ground_truth: container.DataFrame, prediction: container.DataFrame,
performance_metrics: typing.List[typing.Dict],
task_type, regression_metric: set):
"""
static method used to calculate the score based on given predictions and metric tpyes
Parameters
---------
ground_truth: the ground truth of target
prediction: the predicted results of target
performance_metrics: the metehod to calculate the score
task_type: the task type of the problem
"""
result_metrics = []
target_amount = 0
if prediction is not None:
prediction = graph_problem_conversion(task_type, prediction)
for metric_description in performance_metrics:
metricDesc = metric_description['metric']
params: typing.Dict = metric_description.get('params', {})
if params:
metric: problem.PerformanceMetric = metricDesc.get_class()(**params)
else:
metric = metricDesc.get_class()
# updated for d3m v2019.5.8: we need to instantiate the metric class first if it was not done yet
if type(metric) is AbstractMetaclass:
metric = metric()
# special design for objectDetectionAP
if metric_description["metric"] == problem.PerformanceMetric.OBJECT_DETECTION_AVERAGE_PRECISION:
if ground_truth is not None and prediction is not None:
# training_image_name_column = ground_truth.iloc[:,
# ground_truth.shape[1] - 2]
# prediction.insert(loc=0, column='image_name',
# value=training_image_name_column)
ground_truth_to_send = ground_truth.iloc[:, ground_truth.shape[1] - 2: ground_truth.shape[1]]
prediction_to_send = prediction# .iloc[:, prediction.shape[1] - 2: prediction.shape[1]]
if prediction_to_send['d3mIndex'].dtype.name != ground_truth_to_send['d3mIndex'].dtype.name:
ground_truth_to_send = ground_truth_to_send['d3mIndex'].astype(str)
prediction_to_send = prediction_to_send['d3mIndex'].astype(str)
# truth = ground_truth_to_send.astype(str).values.tolist()
# predictions = prediction_to_send.astype(str).values.tolist()
value = metric.score(ground_truth_to_send, prediction_to_send)
result_metrics.append({
'column_name': ground_truth.columns[-1],
'metric': metric_description['metric'],
'value': value
})
return result_metrics
# END special design for objectDetectionAP
do_regression_mode = metric_description["metric"] in regression_metric
try:
# generate the metrics for training results
if ground_truth is not None and prediction is not None:
if "d3mIndex" not in ground_truth.columns:
raise NotSupportedError("No d3mIndex found for ground truth!")
else:
ground_truth_amount = len(ground_truth.columns) - 1
if "d3mIndex" not in prediction.columns:
# for the condition that ground_truth have index but
# prediction don't have
target_amount = len(prediction.columns)
prediction.insert(0,'d3mIndex' ,ground_truth['d3mIndex'].copy())
else:
target_amount = len(prediction.columns) - 1
if prediction['d3mIndex'].dtype.name != ground_truth['d3mIndex'].dtype.name:
ground_truth['d3mIndex'] = ground_truth['d3mIndex'].astype(str).copy()
prediction['d3mIndex'] = prediction['d3mIndex'].astype(str).copy()
if not (ground_truth_amount == target_amount):
_logger.error("Ground truth's amount and prediction's amount does not match")
_logger.error('predicition columns :' + str(prediction.columns))
_logger.error('Ground truth columns:' + str(ground_truth.columns))
raise ValueError("Ground truth's amount and prediction's amount does not match")
# from runtime import ForkedPdb
# ForkedPdb().set_trace()
if do_regression_mode:
# regression mode require the targets must be float
for each_column in range(-target_amount, 0, 1):
prediction.iloc[:,each_column] = prediction.iloc[:,each_column].astype(float).copy()
# update 2019.4.12, now d3m v2019.4.4 have new metric function, we have to change like this
ground_truth_d3m_index_column_index = ground_truth.columns.tolist().index("d3mIndex")
prediction_d3m_index_column_index = prediction.columns.tolist().index("d3mIndex")
for each_column in range(-target_amount, 0, 1):
result_metrics.append({
'column_name': ground_truth.columns[each_column],
'metric': metric_description['metric'],
'value': metric.score(truth=ground_truth.iloc[:,[ground_truth_d3m_index_column_index,each_column]],
predictions=prediction.iloc[:,[prediction_d3m_index_column_index,each_column]])
})
elif ground_truth is None:
raise NotSupportedError("Metric calculation failed because ground truth is None!")
elif prediction is not None:
raise NotSupportedError("Metric calculation failed because prediction is None!")
except Exception:
traceback.print_exc()
raise NotSupportedError('[ERROR] metric calculation failed')
# END for loop
if len(result_metrics) > target_amount:
_logger.warning("[WARN] Training metrics's amount is larger than target amount.")
# return the training and test metrics
return result_metrics
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
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)
B = np.random.randn(r, n)
X_org = np.append(X_org, np.dot(A, B), axis=1)
# mask a fraction of entries randomly
X_incomplete = X_org.copy()
m, n = X_org.shape
for i in range(n):
idx = np.random.choice(m, int(np.round(m * missing_rate)), replace=False)
X_incomplete[idx, i] = np.nan
# recover the missing entries
# hp= hrmc_sf.Hyperparams.defaults()
hp = high_rank_imputer.Hyperparams({
'd': 0,
'alpha': 1,
'beta': 1,
'tol': 1e-4,
'maxiter': 500
})
# if d=0, d will be automatically estimated; otherwise (d>=1), the value of d will be applied
sf = high_rank_imputer.HighRankImputer(hyperparams=hp)
df_incomplete = DataFrame(X_incomplete.T)
# the missing entries in the matrix must be noted by NaN
df_recovered = sf.produce(inputs=df_incomplete).value
X_recovered = df_recovered.values.T
# compute the recovery error (relative mean squared error, within [0,1], the smaller the better)
RMSE = np.square(X_recovered - X_org).sum() / np.square(X_org).sum()
print("RMSE:", RMSE)
def produce(
self,
*,
inputs: container.DataFrame,
timeout: float = None,
iterations: int = None,
) -> base.CallResult[container.DataFrame]:
logger.debug(f"Producing {__name__}")
# fallthrough if there's nothing to do
if len(self._cols) == 0 or self._encoder is None:
return base.CallResult(inputs)
# map encoded cols to source column names
feature_names = self._encoder.get_feature_names()
encoded_cols_source = []
# feature names are xA_YY where A is the source column index and YY is the value
for name in feature_names:
# take the first part of the name (xA) and remove the x
encoded_feature_index = int(name.split("_")[0][1:])
feature_index = self._cols[encoded_feature_index]
encoded_cols_source.append(
inputs.metadata.query((metadata_base.ALL_ELEMENTS, feature_index))[
"name"
]
)
# encode using the previously identified categorical columns
input_cols = inputs.iloc[:, self._cols]
result = self._encoder.transform(input_cols)
# append the encoding columns and generate metadata
outputs = inputs.copy()
encoded_cols: container.DataFrame = container.DataFrame()
for i in range(result.shape[1]):
encoded_cols[f"__onehot_{str(i)}"] = result[:, i]
encoded_cols.metadata = encoded_cols.metadata.generate(encoded_cols)
for c in range(encoded_cols.shape[1]):
encoded_cols.metadata = encoded_cols.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, c), "http://schema.org/Float"
)
encoded_cols.metadata = encoded_cols.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, c), self._attribute_semantic
)
col_dict = dict(
encoded_cols.metadata.query((metadata_base.ALL_ELEMENTS, c))
)
col_dict["source_column"] = encoded_cols_source[c]
encoded_cols.metadata = encoded_cols.metadata.update(
(metadata_base.ALL_ELEMENTS, c), col_dict
)
outputs = outputs.append_columns(encoded_cols)
# drop the source columns
outputs = outputs.remove_columns(self._cols)
logger.debug(f"\n{outputs}")
return base.CallResult(outputs)
def _detect_text(cls, X: container.DataFrame, thresh: int = 8) -> bool:
""" returns true if median entry has more than `thresh` tokens"""
X = X[X.notnull()]
n_toks = X.apply(lambda xx: len(str(xx).split(" "))).values
return np.median(n_toks) >= thresh
def _convert_lists(dataframe: container.DataFrame) -> container.DataFrame:
# convert colum contents to numpy array of values similar to what extract semantic types would do
for index, row in dataframe.iterrows():
row["bravo"] = container.ndarray([int(i) for i in row["bravo"].split(",")])
return dataframe
def _encode_labels(self,
inputs: container.DataFrame) -> container.DataFrame:
for col_idx, (label, col) in enumerate(inputs.iteritems()):
encodes = [self._labels[col_idx].get(label, 0) for label in col]
inputs.iloc[:, col_idx] = encodes
return inputs
def combine_columns(
inputs: container.DataFrame,
column_indices: typing.Sequence[int],
columns_list: typing.Sequence[container.DataFrame],
*,
return_result: str,
add_index_columns: bool,
) -> container.DataFrame:
"""
Method which appends existing columns, replaces them, or creates new result from them, based on
``return_result`` argument, which can be ``append``, ``replace``, or ``new``.
``add_index_columns`` controls if when creating a new result, primary index columns should be added
if they are not already among columns.
``inputs`` is a DataFrame for which we are appending on replacing columns, or if we are creating new result,
from where a primary index column can be taken.
``column_indices`` controls which columns in ``inputs`` were used to create ``columns_list``,
and which columns should be replaced when replacing them.
``columns_list`` is a list of DataFrames representing all together new columns. The reason it is a list is
to make it easier to operate per-column when preparing ``columns_list`` and not have to concat them all
together unnecessarily.
Top-level metadata in ``columns_list`` is ignored, except when creating new result.
In that case top-level metadata from the first element in the list is used.
When ``column_indices`` columns are being replaced with ``columns_list``, existing metadata in ``column_indices``
columns is not preserved but replaced with metadata in ``columns_list``. Ideally, metadata for ``columns_list``
has been constructed by copying source metadata from ``column_indices`` columns and modifying it as
necessary to adapt it to new columns. But ``columns_list`` also can have completely new metadata, if this
is more reasonable, but it should be understood that in this case when replacing ``column_indices``
columns, any custom additional metadata on those columns will be lost.
``column_indices`` and ``columns_list`` do not have to match in number of columns. Columns are first
replaced in order for matching indices and columns. If then there are more ``column_indices`` than
``columns_list``, additional ``column_indices`` columns are removed. If there are more ``columns_list`` than
``column_indices`` columns, then additional ``columns_list`` are inserted after the last replaced column.
If ``column_indices`` is empty, then the replacing behavior is equivalent to appending.
"""
if return_result == 'append':
outputs = inputs
for columns in columns_list:
outputs = outputs.append_columns(columns)
elif return_result == 'replace':
if not column_indices:
return combine_columns(inputs,
column_indices,
columns_list,
return_result='append',
add_index_columns=add_index_columns)
# Compute the difference in "columns"
to_be_added = list(
numpy.setdiff1d(numpy.arange(len(inputs.columns)), column_indices))
columns_replaced = 0
if len(to_be_added) < len(column_indices):
# More efficient to concatenate than replace one-by-one
outputs = pandas.concat(columns_list, axis=1)
outputs = container.DataFrame(data=outputs,
generate_metadata=False)
indices = range(columns_list[0].shape[1])
outputs.metadata = inputs.metadata.select_columns(
columns=list(indices))
c = 0
for columns in columns_list:
columns_length = columns.shape[1]
if c == 0:
outputs.metadata = outputs.metadata.replace_columns(
columns.metadata, list(indices))
else:
outputs.metadata = outputs.metadata.append_columns(
columns.metadata)
c += 1
for col in to_be_added:
insert_index = col.item()
if insert_index > outputs.shape[1]:
insert_index = outputs.shape[1]
outputs = outputs.insert_columns(
inputs.select_columns([col.item()]), insert_index)
outputs.metadata = outputs.metadata.compact(['structural_type'])
else:
# We copy here and disable copying inside "replace_columns" to copy only once.
# We have to copy because "replace_columns" is modifying data in-place.
outputs = copy.copy(inputs)
for columns in columns_list:
columns_length = columns.shape[1]
if columns_replaced < len(column_indices):
# It is OK if the slice of "column_indices" is shorter than "columns", Only those columns
# listed in the slice will be replaced and others appended after the last replaced column.
outputs = outputs.replace_columns(
columns,
column_indices[columns_replaced:columns_replaced +
columns_length],
copy=False)
else:
# We insert the rest of columns after the last columns we replaced. We know that "column_indices"
# is non-empty and that the last item of "column_indices" points ot the last column we replaced
# for those listed in "column_indices". We replaced more columns though, so we have to add the
# difference, and then add 1 to insert after the last column.
outputs = outputs.insert_columns(
columns, column_indices[-1] +
(columns_replaced - len(column_indices)) + 1)
columns_replaced += columns_length
if columns_replaced < len(column_indices):
outputs = outputs.remove_columns(
column_indices[columns_replaced:len(column_indices)])
elif return_result == 'new':
if not any(columns.shape[1] for columns in columns_list):
raise ValueError("No columns produced.")
outputs = columns_list[0]
for columns in columns_list[1:]:
outputs = outputs.append_columns(columns)
if add_index_columns:
inputs_index_columns = inputs.metadata.get_index_columns()
outputs_index_columns = outputs.metadata.get_index_columns()
if inputs_index_columns and not outputs_index_columns:
# Add index columns at the beginning.
outputs = inputs.select_columns(
inputs_index_columns).append_columns(
outputs, use_right_metadata=True)
else:
raise exceptions.InvalidArgumentValueError(
"\"return_result\" has an invalid value: {return_result}".format(
return_result=return_result))
return outputs
from d3m.container import DataFrame
from pyglrm_d3m.huber_pca import HuberPCA
A = DataFrame([[1, 2, 3, 4], [2, 4, 6, 8], [4, 5, 6, 7]])
model = HuberPCA(hyperparams={'k': 2}) #create a class for Huber PCA
model.set_training_data(inputs=A)
model.fit()
#get parameter
parameter = model.get_params()
print("Initial parameter (Y): {}".format(parameter['Y'].values))
#modify parameter
print(
"Now we change the (0,0) entry of Y to 0, and set the modified Y as parameter of the Huber PCA class."
)
parameter['Y'].values[0, 0] = 0
model.set_params(params={'Y': parameter['Y']})
#check if parameter has been modified
parameter = model.get_params()
print("Modified parameter (Y): {}".format(parameter['Y'].values))
from pyglrm_d3m.huber_pca import HuberPCA
from d3m.container import DataFrame
A = DataFrame([[1, 2, 3, 4], [2, 4, 6, 8], [4, 5, 6, 7]],
generate_metadata=True)
model = HuberPCA(hyperparams={'k': 2}) #create a class for Huber PCA
model.set_training_data(inputs=A)
model.fit()
a_new = DataFrame([[6, 7, 8, 9]]) #initialize a new row to be tested
x = model.produce(
inputs=a_new).value.values #get the latent representation of a_new
print(x)
