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"""Implementation of ARFF parsers: via LIAC-ARFF and pandas."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import itertools
import re
from collections import OrderedDict
from collections.abc import Generator
from typing import List
import numpy as np
import scipy as sp
from ..externals import _arff
from ..externals._arff import ArffSparseDataType
from ..utils._chunking import chunk_generator, get_chunk_n_rows
from ..utils._optional_dependencies import check_pandas_support
from ..utils.fixes import pd_fillna
def _split_sparse_columns(
arff_data: ArffSparseDataType, include_columns: List
) -> ArffSparseDataType:
"""Obtains several columns from sparse ARFF representation. Additionally,
the column indices are re-labelled, given the columns that are not
included. (e.g., when including [1, 2, 3], the columns will be relabelled
to [0, 1, 2]).
Parameters
----------
arff_data : tuple
A tuple of three lists of equal size; first list indicating the value,
second the x coordinate and the third the y coordinate.
include_columns : list
A list of columns to include.
Returns
-------
arff_data_new : tuple
Subset of arff data with only the include columns indicated by the
include_columns argument.
"""
arff_data_new: ArffSparseDataType = (list(), list(), list())
reindexed_columns = {
column_idx: array_idx for array_idx, column_idx in enumerate(include_columns)
}
for val, row_idx, col_idx in zip(arff_data[0], arff_data[1], arff_data[2]):
if col_idx in include_columns:
arff_data_new[0].append(val)
arff_data_new[1].append(row_idx)
arff_data_new[2].append(reindexed_columns[col_idx])
return arff_data_new
def _sparse_data_to_array(
arff_data: ArffSparseDataType, include_columns: List
) -> np.ndarray:
# turns the sparse data back into an array (can't use toarray() function,
# as this does only work on numeric data)
num_obs = max(arff_data[1]) + 1
y_shape = (num_obs, len(include_columns))
reindexed_columns = {
column_idx: array_idx for array_idx, column_idx in enumerate(include_columns)
}
# TODO: improve for efficiency
y = np.empty(y_shape, dtype=np.float64)
for val, row_idx, col_idx in zip(arff_data[0], arff_data[1], arff_data[2]):
if col_idx in include_columns:
y[row_idx, reindexed_columns[col_idx]] = val
return y
def _post_process_frame(frame, feature_names, target_names):
"""Post process a dataframe to select the desired columns in `X` and `y`.
Parameters
----------
frame : dataframe
The dataframe to split into `X` and `y`.
feature_names : list of str
The list of feature names to populate `X`.
target_names : list of str
The list of target names to populate `y`.
Returns
-------
X : dataframe
The dataframe containing the features.
y : {series, dataframe} or None
The series or dataframe containing the target.
"""
X = frame[feature_names]
if len(target_names) >= 2:
y = frame[target_names]
elif len(target_names) == 1:
y = frame[target_names[0]]
else:
y = None
return X, y
def _liac_arff_parser(
gzip_file,
output_arrays_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape=None,
):
"""ARFF parser using the LIAC-ARFF library coded purely in Python.
This parser is quite slow but consumes a generator. Currently it is needed
to parse sparse datasets. For dense datasets, it is recommended to instead
use the pandas-based parser, although it does not always handles the
dtypes exactly the same.
Parameters
----------
gzip_file : GzipFile instance
The file compressed to be read.
output_arrays_type : {"numpy", "sparse", "pandas"}
The type of the arrays that will be returned. The possibilities ara:
- `"numpy"`: both `X` and `y` will be NumPy arrays;
- `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array;
- `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a
pandas Series or DataFrame.
columns_info : dict
The information provided by OpenML regarding the columns of the ARFF
file.
feature_names_to_select : list of str
A list of the feature names to be selected.
target_names_to_select : list of str
A list of the target names to be selected.
Returns
-------
X : {ndarray, sparse matrix, dataframe}
The data matrix.
y : {ndarray, dataframe, series}
The target.
frame : dataframe or None
A dataframe containing both `X` and `y`. `None` if
`output_array_type != "pandas"`.
categories : list of str or None
The names of the features that are categorical. `None` if
`output_array_type == "pandas"`.
"""
def _io_to_generator(gzip_file):
for line in gzip_file:
yield line.decode("utf-8")
stream = _io_to_generator(gzip_file)
# find which type (dense or sparse) ARFF type we will have to deal with
return_type = _arff.COO if output_arrays_type == "sparse" else _arff.DENSE_GEN
# we should not let LIAC-ARFF to encode the nominal attributes with NumPy
# arrays to have only numerical values.
encode_nominal = not (output_arrays_type == "pandas")
arff_container = _arff.load(
stream, return_type=return_type, encode_nominal=encode_nominal
)
columns_to_select = feature_names_to_select + target_names_to_select
categories = {
name: cat
for name, cat in arff_container["attributes"]
if isinstance(cat, list) and name in columns_to_select
}
if output_arrays_type == "pandas":
pd = check_pandas_support("fetch_openml with as_frame=True")
columns_info = OrderedDict(arff_container["attributes"])
columns_names = list(columns_info.keys())
# calculate chunksize
first_row = next(arff_container["data"])
first_df = pd.DataFrame([first_row], columns=columns_names, copy=False)
row_bytes = first_df.memory_usage(deep=True).sum()
chunksize = get_chunk_n_rows(row_bytes)
# read arff data with chunks
columns_to_keep = [col for col in columns_names if col in columns_to_select]
dfs = [first_df[columns_to_keep]]
for data in chunk_generator(arff_container["data"], chunksize):
dfs.append(
pd.DataFrame(data, columns=columns_names, copy=False)[columns_to_keep]
)
# dfs[0] contains only one row, which may not have enough data to infer to
# column's dtype. Here we use `dfs[1]` to configure the dtype in dfs[0]
if len(dfs) >= 2:
dfs[0] = dfs[0].astype(dfs[1].dtypes)
# liac-arff parser does not depend on NumPy and uses None to represent
# missing values. To be consistent with the pandas parser, we replace
# None with np.nan.
frame = pd.concat(dfs, ignore_index=True)
frame = pd_fillna(pd, frame)
del dfs, first_df
# cast the columns frame
dtypes = {}
for name in frame.columns:
column_dtype = openml_columns_info[name]["data_type"]
if column_dtype.lower() == "integer":
# Use a pandas extension array instead of np.int64 to be able
# to support missing values.
dtypes[name] = "Int64"
elif column_dtype.lower() == "nominal":
dtypes[name] = "category"
else:
dtypes[name] = frame.dtypes[name]
frame = frame.astype(dtypes)
X, y = _post_process_frame(
frame, feature_names_to_select, target_names_to_select
)
else:
arff_data = arff_container["data"]
feature_indices_to_select = [
int(openml_columns_info[col_name]["index"])
for col_name in feature_names_to_select
]
target_indices_to_select = [
int(openml_columns_info[col_name]["index"])
for col_name in target_names_to_select
]
if isinstance(arff_data, Generator):
if shape is None:
raise ValueError(
"shape must be provided when arr['data'] is a Generator"
)
if shape[0] == -1:
count = -1
else:
count = shape[0] * shape[1]
data = np.fromiter(
itertools.chain.from_iterable(arff_data),
dtype="float64",
count=count,
)
data = data.reshape(*shape)
X = data[:, feature_indices_to_select]
y = data[:, target_indices_to_select]
elif isinstance(arff_data, tuple):
arff_data_X = _split_sparse_columns(arff_data, feature_indices_to_select)
num_obs = max(arff_data[1]) + 1
X_shape = (num_obs, len(feature_indices_to_select))
X = sp.sparse.coo_matrix(
(arff_data_X[0], (arff_data_X[1], arff_data_X[2])),
shape=X_shape,
dtype=np.float64,
)
X = X.tocsr()
y = _sparse_data_to_array(arff_data, target_indices_to_select)
else:
# This should never happen
raise ValueError(
f"Unexpected type for data obtained from arff: {type(arff_data)}"
)
is_classification = {
col_name in categories for col_name in target_names_to_select
}
if not is_classification:
# No target
pass
elif all(is_classification):
y = np.hstack(
[
np.take(
np.asarray(categories.pop(col_name), dtype="O"),
y[:, i : i + 1].astype(int, copy=False),
)
for i, col_name in enumerate(target_names_to_select)
]
)
elif any(is_classification):
raise ValueError(
"Mix of nominal and non-nominal targets is not currently supported"
)
# reshape y back to 1-D array, if there is only 1 target column;
# back to None if there are not target columns
if y.shape[1] == 1:
y = y.reshape((-1,))
elif y.shape[1] == 0:
y = None
if output_arrays_type == "pandas":
return X, y, frame, None
return X, y, None, categories
def _pandas_arff_parser(
gzip_file,
output_arrays_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
read_csv_kwargs=None,
):
"""ARFF parser using `pandas.read_csv`.
This parser uses the metadata fetched directly from OpenML and skips the metadata
headers of ARFF file itself. The data is loaded as a CSV file.
Parameters
----------
gzip_file : GzipFile instance
The GZip compressed file with the ARFF formatted payload.
output_arrays_type : {"numpy", "sparse", "pandas"}
The type of the arrays that will be returned. The possibilities are:
- `"numpy"`: both `X` and `y` will be NumPy arrays;
- `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array;
- `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a
pandas Series or DataFrame.
openml_columns_info : dict
The information provided by OpenML regarding the columns of the ARFF
file.
feature_names_to_select : list of str
A list of the feature names to be selected to build `X`.
target_names_to_select : list of str
A list of the target names to be selected to build `y`.
read_csv_kwargs : dict, default=None
Keyword arguments to pass to `pandas.read_csv`. It allows to overwrite
the default options.
Returns
-------
X : {ndarray, sparse matrix, dataframe}
The data matrix.
y : {ndarray, dataframe, series}
The target.
frame : dataframe or None
A dataframe containing both `X` and `y`. `None` if
`output_array_type != "pandas"`.
categories : list of str or None
The names of the features that are categorical. `None` if
`output_array_type == "pandas"`.
"""
import pandas as pd
# read the file until the data section to skip the ARFF metadata headers
for line in gzip_file:
if line.decode("utf-8").lower().startswith("@data"):
break
dtypes = {}
for name in openml_columns_info:
column_dtype = openml_columns_info[name]["data_type"]
if column_dtype.lower() == "integer":
# Use Int64 to infer missing values from data
# XXX: this line is not covered by our tests. Is this really needed?
dtypes[name] = "Int64"
elif column_dtype.lower() == "nominal":
dtypes[name] = "category"
# since we will not pass `names` when reading the ARFF file, we need to translate
# `dtypes` from column names to column indices to pass to `pandas.read_csv`
dtypes_positional = {
col_idx: dtypes[name]
for col_idx, name in enumerate(openml_columns_info)
if name in dtypes
}
default_read_csv_kwargs = {
"header": None,
"index_col": False, # always force pandas to not use the first column as index
"na_values": ["?"], # missing values are represented by `?`
"keep_default_na": False, # only `?` is a missing value given the ARFF specs
"comment": "%", # skip line starting by `%` since they are comments
"quotechar": '"', # delimiter to use for quoted strings
"skipinitialspace": True, # skip spaces after delimiter to follow ARFF specs
"escapechar": "\\",
"dtype": dtypes_positional,
}
read_csv_kwargs = {**default_read_csv_kwargs, **(read_csv_kwargs or {})}
frame = pd.read_csv(gzip_file, **read_csv_kwargs)
try:
# Setting the columns while reading the file will select the N first columns
# and not raise a ParserError. Instead, we set the columns after reading the
# file and raise a ParserError if the number of columns does not match the
# number of columns in the metadata given by OpenML.
frame.columns = [name for name in openml_columns_info]
except ValueError as exc:
raise pd.errors.ParserError(
"The number of columns provided by OpenML does not match the number of "
"columns inferred by pandas when reading the file."
) from exc
columns_to_select = feature_names_to_select + target_names_to_select
columns_to_keep = [col for col in frame.columns if col in columns_to_select]
frame = frame[columns_to_keep]
# `pd.read_csv` automatically handles double quotes for quoting non-numeric
# CSV cell values. Contrary to LIAC-ARFF, `pd.read_csv` cannot be configured to
# consider either single quotes and double quotes as valid quoting chars at
# the same time since this case does not occur in regular (non-ARFF) CSV files.
# To mimic the behavior of LIAC-ARFF parser, we manually strip single quotes
# on categories as a post-processing steps if needed.
#
# Note however that we intentionally do not attempt to do this kind of manual
# post-processing of (non-categorical) string-typed columns because we cannot
# resolve the ambiguity of the case of CSV cell with nesting quoting such as
# `"'some string value'"` with pandas.
single_quote_pattern = re.compile(r"^'(?P<contents>.*)'$")
def strip_single_quotes(input_string):
match = re.search(single_quote_pattern, input_string)
if match is None:
return input_string
return match.group("contents")
categorical_columns = [
name
for name, dtype in frame.dtypes.items()
if isinstance(dtype, pd.CategoricalDtype)
]
for col in categorical_columns:
frame[col] = frame[col].cat.rename_categories(strip_single_quotes)
X, y = _post_process_frame(frame, feature_names_to_select, target_names_to_select)
if output_arrays_type == "pandas":
return X, y, frame, None
else:
X, y = X.to_numpy(), y.to_numpy()
categories = {
name: dtype.categories.tolist()
for name, dtype in frame.dtypes.items()
if isinstance(dtype, pd.CategoricalDtype)
}
return X, y, None, categories
def load_arff_from_gzip_file(
gzip_file,
parser,
output_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape=None,
read_csv_kwargs=None,
):
"""Load a compressed ARFF file using a given parser.
Parameters
----------
gzip_file : GzipFile instance
The file compressed to be read.
parser : {"pandas", "liac-arff"}
The parser used to parse the ARFF file. "pandas" is recommended
but only supports loading dense datasets.
output_type : {"numpy", "sparse", "pandas"}
The type of the arrays that will be returned. The possibilities ara:
- `"numpy"`: both `X` and `y` will be NumPy arrays;
- `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array;
- `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a
pandas Series or DataFrame.
openml_columns_info : dict
The information provided by OpenML regarding the columns of the ARFF
file.
feature_names_to_select : list of str
A list of the feature names to be selected.
target_names_to_select : list of str
A list of the target names to be selected.
read_csv_kwargs : dict, default=None
Keyword arguments to pass to `pandas.read_csv`. It allows to overwrite
the default options.
Returns
-------
X : {ndarray, sparse matrix, dataframe}
The data matrix.
y : {ndarray, dataframe, series}
The target.
frame : dataframe or None
A dataframe containing both `X` and `y`. `None` if
`output_array_type != "pandas"`.
categories : list of str or None
The names of the features that are categorical. `None` if
`output_array_type == "pandas"`.
"""
if parser == "liac-arff":
return _liac_arff_parser(
gzip_file,
output_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape,
)
elif parser == "pandas":
return _pandas_arff_parser(
gzip_file,
output_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
read_csv_kwargs,
)
else:
raise ValueError(
f"Unknown parser: '{parser}'. Should be 'liac-arff' or 'pandas'."
)
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