Spaces:
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File size: 2,981 Bytes
6ed031c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | # Copyright 2023-2024 The SapientML Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import logging
import threading
from contextvars import ContextVar
from pathlib import Path
from uuid import UUID
from sapientml import SapientML
from .utils import convert_int64
class GenerateCodeThread(threading.Thread):
def __init__(
self,
sml: SapientML,
config: dict,
log_handler: logging.Handler,
ctx_uuid: ContextVar[UUID],
uuid: UUID,
):
self.sml = sml
self.config = config
self.result = None
self.exception = None
self.log_handler = log_handler
self.ctx_uuid = ctx_uuid
self.uuid = uuid
threading.Thread.__init__(self)
def run(self):
try:
self.ctx_uuid.set(self.uuid)
fit_args = ["save_datasets_format", "csv_encoding", "ignore_columns", "output_dir", "test_data"]
self.sml.fit(self.config["training_dataframe"], **({k: v for k, v in self.config.items() if k in fit_args}))
output_dir = self.config["output_dir"]
if not Path(output_dir / "final_script_code_explainability.json").exists():
script_code_explainability = self.sml.generator._best_pipeline.pipeline_json
with open(output_dir / "script_code_explainability.json", "w") as f:
json.dump(script_code_explainability, f, ensure_ascii=False, indent=2)
candidates = self.sml.generator._candidate_scripts
elements = [t[0] for t in candidates]
for i in range(3):
# explainability =
with open(output_dir / f"{i+1}_script_code_explainability.json", "w") as f:
json.dump(elements[i].pipeline_json, f, ensure_ascii=False, indent=2)
if not Path(output_dir / ".skeleton.json").exists():
skeleton = self.sml.generator._best_pipeline.labels
with open(output_dir / ".skeleton.json", "w") as f:
json.dump(convert_int64(skeleton), f, ensure_ascii=False, indent=2)
except Exception as e:
self.exception = e
finally:
pass
def get_result(self):
return self.result
def get_exception(self):
return self.exception
def get_sml(self):
return self.sml
def trigger_cancel(self):
self.cancel_token.isTriggered = True
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