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# ----------------------------------------------------------------------------
# Copyright (c) 2017-2023, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import os
import tarfile
import json
import pandas as pd
import numpy as np
import qiime2
import qiime2.plugin.model as model
import sklearn
import joblib
from sklearn.pipeline import Pipeline
from .plugin_setup import plugin
from ._format import (SampleEstimatorDirFmt, JSONFormat, BooleanSeriesFormat,
ImportanceFormat, PredictionsFormat, PickleFormat,
ProbabilitiesFormat)
def _read_dataframe(fh):
# Using `dtype=object` and `set_index` to avoid type casting/inference
# of any columns or the index.
df = pd.read_csv(fh, sep='\t', header=0, dtype='str')
df.set_index(df.columns[0], drop=True, append=False, inplace=True)
df.index.name = 'id'
return df
@plugin.register_transformer
def _1(data: pd.Series) -> (BooleanSeriesFormat):
ff = BooleanSeriesFormat()
with ff.open() as fh:
data.to_csv(fh, sep='\t', header=True)
return ff
@plugin.register_transformer
def _2(ff: BooleanSeriesFormat) -> (pd.Series):
with ff.open() as fh:
df = _read_dataframe(fh)
return df.iloc[:, 0]
@plugin.register_transformer
def _3(ff: BooleanSeriesFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh))
@plugin.register_transformer
def _4(data: pd.Series) -> (PredictionsFormat):
ff = PredictionsFormat()
with ff.open() as fh:
data.to_csv(fh, sep='\t', header=True)
return ff
@plugin.register_transformer
def _5(ff: PredictionsFormat) -> (pd.Series):
with ff.open() as fh:
df = _read_dataframe(fh)
return pd.to_numeric(df.iloc[:, 0], errors='ignore')
@plugin.register_transformer
def _6(ff: PredictionsFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh).apply(
lambda x: pd.to_numeric(x, errors='ignore')))
@plugin.register_transformer
def _7(data: pd.DataFrame) -> (ImportanceFormat):
ff = ImportanceFormat()
with ff.open() as fh:
data.to_csv(fh, sep='\t', header=True, na_rep=np.nan)
return ff
@plugin.register_transformer
def _8(ff: ImportanceFormat) -> (pd.DataFrame):
with ff.open() as fh:
return _read_dataframe(fh).apply(
lambda x: pd.to_numeric(x, errors='raise'))
@plugin.register_transformer
def _9(ff: ImportanceFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh).apply(
lambda x: pd.to_numeric(x, errors='raise')))
@plugin.register_transformer
def _10(data: pd.DataFrame) -> (ProbabilitiesFormat):
ff = ProbabilitiesFormat()
with ff.open() as fh:
data.to_csv(fh, sep='\t', na_rep=np.nan, header=True)
return ff
@plugin.register_transformer
def _11(ff: ProbabilitiesFormat) -> (pd.DataFrame):
with ff.open() as fh:
return _read_dataframe(fh).apply(
lambda x: pd.to_numeric(x, errors='raise'))
@plugin.register_transformer
def _12(ff: ProbabilitiesFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh).apply(
lambda x: pd.to_numeric(x, errors='raise')))
@plugin.register_transformer
def _a(dirfmt: SampleEstimatorDirFmt) -> Pipeline:
sklearn_version = dirfmt.version_info.view(dict)['sklearn-version']
if sklearn_version != sklearn.__version__:
raise ValueError('The scikit-learn version (%s) used to generate this'
' artifact does not match the current version'
' of scikit-learn installed (%s). Please retrain your'
' classifier for your current deployment to prevent'
' data-corruption errors.'
% (sklearn_version, sklearn.__version__))
sklearn_pipeline = dirfmt.sklearn_pipeline.view(PickleFormat)
with tarfile.open(str(sklearn_pipeline)) as tar:
tmpdir = model.DirectoryFormat()
dirname = str(tmpdir)
def is_within_directory(directory, target):
abs_directory = os.path.abspath(directory)
abs_target = os.path.abspath(target)
prefix = os.path.commonprefix([abs_directory, abs_target])
return prefix == abs_directory
def safe_extract(tar, path=".", members=None, *, numeric_owner=False):
for member in tar.getmembers():
member_path = os.path.join(path, member.name)
if not is_within_directory(path, member_path):
raise Exception("Attempted Path Traversal in Tar File")
tar.extractall(path, members, numeric_owner=numeric_owner)
safe_extract(tar, dirname)
pipeline = joblib.load(os.path.join(dirname, 'sklearn_pipeline.pkl'))
for fn in tar.getnames():
os.unlink(os.path.join(dirname, fn))
return pipeline
@plugin.register_transformer
def _b(data: Pipeline) -> SampleEstimatorDirFmt:
sklearn_pipeline = PickleFormat()
with tarfile.open(str(sklearn_pipeline), 'w') as tar:
tmpdir = model.DirectoryFormat()
pf = os.path.join(str(tmpdir), 'sklearn_pipeline.pkl')
for fn in joblib.dump(data, pf):
tar.add(fn, os.path.basename(fn))
os.unlink(fn)
dirfmt = SampleEstimatorDirFmt()
dirfmt.version_info.write_data(
{'sklearn-version': sklearn.__version__}, dict)
dirfmt.sklearn_pipeline.write_data(sklearn_pipeline, PickleFormat)
return dirfmt
@plugin.register_transformer
def _d(fmt: JSONFormat) -> dict:
with fmt.open() as fh:
return json.load(fh)
@plugin.register_transformer
def _e(data: dict) -> JSONFormat:
result = JSONFormat()
with result.open() as fh:
json.dump(data, fh)
return result
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