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Merge pull request #570 from HyunjunA/master
Update react and validateDataset.py to make errors on uploads more user-friendly
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"""~This file is part of the Aliro library~ | ||
Copyright (C) 2023 Epistasis Lab, | ||
Center for Artificial Intelligence Research and Education (CAIRE), | ||
Department of Computational Biomedicine (CBM), | ||
Cedars-Sinai Medical Center. | ||
Aliro is maintained by: | ||
- Hyunjun Choi (hyunjun.choi@cshs.org) | ||
- Miguel Hernandez (miguel.e.hernandez@cshs.org) | ||
- Nick Matsumoto (nicholas.matsumoto@cshs.org) | ||
- Jay Moran (jay.moran@cshs.org) | ||
- and many other generous open source contributors | ||
This program is free software: you can redistribute it and/or modify | ||
it under the terms of the GNU General Public License as published by | ||
the Free Software Foundation, either version 3 of the License, or | ||
(at your option) any later version. | ||
This program is distributed in the hope that it will be useful, | ||
but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
GNU General Public License for more details. | ||
You should have received a copy of the GNU General Public License | ||
along with this program. If not, see <https://www.gnu.org/licenses/>. | ||
(Autogenerated header, do not modify) | ||
""" | ||
import argparse | ||
import sys | ||
import simplejson | ||
from sklearn.utils import check_X_y, check_array | ||
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder | ||
from sklearn.compose import ColumnTransformer | ||
import os | ||
import os.path | ||
import pandas as pd | ||
import numpy as np | ||
import logging | ||
import requests | ||
import time | ||
import traceback | ||
from io import StringIO | ||
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logger = logging.getLogger(__name__) | ||
logger.addHandler(logging.StreamHandler()) | ||
logger.setLevel(logging.INFO) | ||
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MIN_ROWS = 10 | ||
MIN_COLS = 2 | ||
MIN_ROW_PER_CLASS = 2 | ||
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def validate_data_from_server(file_id, prediction_type, target_field, categories=None, ordinals=None, **kwargs): | ||
# Read the data set into memory | ||
raw_data = get_file_from_server(file_id) | ||
df = pd.read_csv(StringIO(raw_data), sep=None, engine='python', **kwargs) | ||
return validate_data(df, prediction_type, target_field, categories, ordinals) | ||
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def validate_data_from_filepath(file_id, prediction_type, target_field, categories=None, ordinals=None, **kwargs): | ||
# Read the data set into memory | ||
df = pd.read_csv(file_id, sep=None, engine='python', **kwargs) | ||
return validate_data(df, prediction_type, target_field, categories, ordinals) | ||
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def encode_data(df, target_column, categories, ordinals, encoding_strategy="OneHotEncoder"): | ||
''' | ||
use OneHotEncoder or OrdinalEncoder to convert categorical features | ||
See skl_utils | ||
''' | ||
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# check that categorical and ordinal columns can be encoded | ||
if categories or ordinals: | ||
transformers = [] | ||
if categories: | ||
if encoding_strategy == "OneHotEncoder": | ||
transformers.append( | ||
("categorical_encoder", OneHotEncoder(), categories)) | ||
elif encoding_strategy == "OrdinalEncoder": | ||
transformers.append( | ||
("categorical_encoder", OrdinalEncoder(), categories)) | ||
if ordinals: | ||
ordinal_features = sorted(list(ordinals.keys())) | ||
ordinal_map = [ordinals[k] for k in ordinal_features] | ||
transformers.append(("ordinalencoder", | ||
OrdinalEncoder(categories=ordinal_map), | ||
ordinal_features)) | ||
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ct = ColumnTransformer( | ||
transformers=transformers, | ||
remainder='passthrough', | ||
sparse_threshold=0 | ||
) | ||
return ct.fit_transform(df) | ||
else: | ||
return df | ||
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def validate_data(df, prediction_type="classification", target_column=None, categories=None, ordinals=None): | ||
''' | ||
Check that a datafile is valid | ||
@return tuple | ||
boolean - validation result | ||
string - message | ||
''' | ||
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if prediction_type not in ["classification", "regression"]: | ||
logger.warn(f"Invalid prediction type: '{prediction_type}'") | ||
return False, f"Invalid prediction type: '{prediction_type}'" | ||
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num_df = df | ||
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# dimension validation | ||
if df.shape[0] < MIN_ROWS: | ||
logger.warn("Dataset has dimensions {}, classification datasets must have at least {} rows.".format( | ||
df.shape, MIN_ROWS)) | ||
return False, "Dataset has dimensions {}, classification datasets must have at least {} rows.".format(df.shape, MIN_ROWS) | ||
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if df.shape[1] < MIN_COLS: | ||
logger.warn("Dataset has dimensions {}, classification datasets must have at least {} columns.".format( | ||
df.shape, MIN_COLS)) | ||
return False, "Dataset has dimensions {}, classification datasets must have at least {} columns.".format(df.shape, MIN_COLS) | ||
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# target column validation | ||
if (target_column != None): | ||
if not (target_column in df.columns): | ||
logger.warn("Target column '" + target_column + "' not in data") | ||
return False, "Target column '" + target_column + "' not in data" | ||
if categories and target_column in categories: | ||
logger.warn("Target column '" + target_column + | ||
"' cannot be a categorical feature") | ||
return False, "Target column '" + target_column + "' cannot be a categorical feature" | ||
if ordinals and target_column in ordinals: | ||
logger.warn("Target column '" + target_column + | ||
"' cannot be an ordinal feature") | ||
return False, "Target column '" + target_column + "' cannot be an ordinal feature" | ||
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# check that cat columns can be encoded | ||
if categories or ordinals: | ||
try: | ||
encode_data(df, target_column, categories, | ||
ordinals, "OneHotEncoder") | ||
encode_data(df, target_column, categories, | ||
ordinals, "OrdinalEncoder") | ||
except Exception as e: | ||
logger.warn("encode_data() failed, " + str(e)) | ||
return False, "encode_data() failed, " + str(e) | ||
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if categories: | ||
num_df = num_df.drop(columns=categories) | ||
if ordinals: | ||
num_df = num_df.drop(columns=list(ordinals.keys())) | ||
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# check only check if target is specified | ||
if target_column: | ||
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# classification | ||
if (prediction_type == "classification"): | ||
# target column of classification problem does not need to be numeric | ||
num_df = num_df.drop(columns=target_column, axis=1) | ||
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# Check rows per class | ||
counts = df.groupby(target_column).count() | ||
fails_validation = counts[counts[counts.columns[1]] | ||
< MIN_ROW_PER_CLASS] | ||
if (not fails_validation.empty): | ||
msg = "Classification datasets must have at least 2 rows per class, class(es) '{}' have only 1 row.".format( | ||
list(fails_validation.index.values)) | ||
logger.warn(msg) | ||
return False, msg | ||
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# check that non-cat feature columns contain only numeric data | ||
if (len(num_df.columns)) > 0: | ||
try: | ||
check_array(num_df, dtype=np.float64, | ||
order="C", force_all_finite=True) | ||
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except Exception as e: | ||
logger.warn("sklearn.check_array() validation " + str(e)) | ||
return False, "sklearn.check_array() validation " + str(e) | ||
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# check t | ||
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return True, None | ||
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def get_file_from_server(file_id): | ||
''' | ||
Retrieve a file from the main Aliro server | ||
''' | ||
apiPath = 'http://' + os.environ['LAB_HOST'] + ':' + os.environ['LAB_PORT'] | ||
path = apiPath + "/api/v1/files/" + file_id | ||
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logger.debug("retrieving file:" + file_id) | ||
logger.debug("api path: " + path) | ||
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res = None | ||
try: | ||
res = requests.request('GET', path, timeout=15) | ||
except: | ||
logger.error("Unexpected error in get_file_from_server for path 'GET: " + | ||
str(path) + "': " + str(sys.exc_info()[0])) | ||
raise | ||
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if res.status_code != requests.codes.ok: | ||
msg = "Request GET status_code not ok, path: '" + \ | ||
str(path) + "'' status code: '" + str(res.status_code) + \ | ||
"'' response text: " + str(res.text) | ||
logger.error(msg) | ||
raise RuntimeError(msg) | ||
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logger.info("File retrieved, file_id: '" + file_id + | ||
"', path: '" + path + "', status_code: " + str(res.status_code)) | ||
return res.text | ||
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def main(): | ||
meta_features_all = [] | ||
parser = argparse.ArgumentParser( | ||
description="Validate a dataset", add_help=False) | ||
parser.add_argument('INPUT_FILE', type=str, help='Filepath or fileId.') | ||
parser.add_argument('-target', action='store', dest='TARGET', type=str, default='class', | ||
help='Name of target column', required=False) | ||
parser.add_argument('-identifier_type', action='store', dest='IDENTIFIER_TYPE', type=str, choices=['filepath', 'fileid'], default='filepath', | ||
help='Name of target column') | ||
parser.add_argument('-categorical_features', action='store', dest='JSON_CATEGORIES', type=str, required=False, default=None, | ||
help='JSON list of categorical features') | ||
parser.add_argument('-ordinal_features', action='store', dest='JSON_ORDINALS', type=str, required=False, default=None, | ||
help='JSON dict of ordianl features and possible values') | ||
parser.add_argument('-prediction_type', action='store', dest='PREDICTION_TYPE', type=str, choices=['classification', 'regression'], default="classification", | ||
help="Classification or regression problem") | ||
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args = parser.parse_args() | ||
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# set up the file logger | ||
logpath = os.path.join(os.environ['PROJECT_ROOT'], "target/logs") | ||
if not os.path.exists(logpath): | ||
os.makedirs(logpath) | ||
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formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s') | ||
fhandler = logging.FileHandler( | ||
os.path.join(logpath, 'validateDataset.log')) | ||
fhandler.setFormatter(formatter) | ||
logger.addHandler(fhandler) | ||
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success = None | ||
errorMessage = None | ||
meta_json = None | ||
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categories = None | ||
ordinals = None | ||
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try: | ||
if args.JSON_CATEGORIES: | ||
categories = simplejson.loads(args.JSON_CATEGORIES) | ||
if args.JSON_ORDINALS: | ||
ordinals = simplejson.loads(args.JSON_ORDINALS) | ||
prediction_type = args.PREDICTION_TYPE | ||
# print("categories: ") | ||
# print(categories) | ||
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if (args.IDENTIFIER_TYPE == 'filepath'): | ||
success, errorMessage = validate_data_from_filepath( | ||
args.INPUT_FILE, prediction_type, args.TARGET, categories, ordinals) | ||
else: | ||
success, errorMessage = validate_data_from_server( | ||
args.INPUT_FILE, prediction_type, args.TARGET, categories, ordinals) | ||
meta_json = simplejson.dumps( | ||
{"success": success, "errorMessage": errorMessage}, ignore_nan=True) # , ensure_ascii=False) | ||
except Exception as e: | ||
logger.error(traceback.format_exc()) | ||
meta_json = simplejson.dumps( | ||
{"success": False, "errorMessage": "Exception: " + repr(e)}, ignore_nan=True) # , ensure_ascii=False) | ||
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print(meta_json) | ||
sys.stdout.flush() | ||
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if __name__ == '__main__': | ||
main() |
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