16416951177

Committed 21 Jul 2025 12:27PM UTC coverage: 90.447% (-0.6%) from 91.09%

Build # 16416951177

Build Type

push

github

Committed by

amarrerod

Commit Message

📌 Pins fcmaes to 1.6.11

Run Details

2064 of 2282 relevant lines covered (90.45%)

1.81 hits per line

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31.25

/digneapy/utils/save_data.py

#!/usr/bin/env python
# -*-coding:utf-8 -*-
"""
@File    :   save_data.py
@Time    :   2025/04/03 10:02:16
@Author  :   Alejandro Marrero
@Version :   1.0
@Contact :   amarrerd@ull.edu.es
@License :   (C)Copyright 2025, Alejandro Marrero
@Desc    :   None
"""

from collections.abc import Sequence
from typing import Literal, Optional

import pandas as pd

from digneapy.generators import GenResult


def save_results_to_files(
    filename_pattern: str,
    result: GenResult,
    solvers_names: Optional[Sequence[str]],
    features_names: Optional[Sequence[str]],
    vars_names: Optional[Sequence[str]],
    format: Literal["csv", "parquet"] = "parquet",
):
    """Saves the results of the generation to CSV files.
    Args:
        filename_pattern (str): Pattern for the filenames.
        result (GenResult): Result of the generation.
        solvers_names (Sequence[str]): Names of the solvers.
        features_names (Sequence[str]): Names of the features.
        vars_names (Sequence[str]): Names of the variables.
        format (Literal[str] = "csv" or "parquet"): Format to store the resulting instances file.
            Parquet is the most efficient for large datasets.
    """
    df = pd.DataFrame(
        [
            i.to_series(
                variables_names=vars_names,
                features_names=features_names,
                score_names=solvers_names,
            )
            for i in result.instances
        ]
    )
    if not df.empty:
        df.insert(0, "target", result.target)
        print(df.head())
    if format == "csv":
        df.to_csv(f"{filename_pattern}_instances.csv", index=False)
    elif format == "parquet":
        df.to_parquet(f"{filename_pattern}_instances.parquet", index=False)

    result.history.to_df().to_csv(f"{filename_pattern}_history.csv", index=False)
    if result.metrics is not None:
        result.metrics.to_csv(f"{filename_pattern}_archive_metrics.csv")

1	#!/usr/bin/env python
2	# --coding:utf-8 --
3	"""
4	@File : save_data.py
5	@Time : 2025/04/03 10:02:16
6	@Author : Alejandro Marrero
7	@Version : 1.0
8	@Contact : amarrerd@ull.edu.es
9	@License : (C)Copyright 2025, Alejandro Marrero
10	@Desc : None
11	"""
12
13	from collections.abc import Sequence	2✔
14	from typing import Literal, Optional	2✔
15
16	import pandas as pd	2✔
17
18	from digneapy.generators import GenResult	2✔
19
20
21	def save_results_to_files(	2✔
22	filename_pattern: str,
23	result: GenResult,
24	solvers_names: Optional[Sequence[str]],
25	features_names: Optional[Sequence[str]],
26	vars_names: Optional[Sequence[str]],
27	format: Literal["csv", "parquet"] = "parquet",
28	):
29	"""Saves the results of the generation to CSV files.
30	Args:
31	filename_pattern (str): Pattern for the filenames.
32	result (GenResult): Result of the generation.
33	solvers_names (Sequence[str]): Names of the solvers.
34	features_names (Sequence[str]): Names of the features.
35	vars_names (Sequence[str]): Names of the variables.
36	format (Literal[str] = "csv" or "parquet"): Format to store the resulting instances file.
37	Parquet is the most efficient for large datasets.
38	"""
39	df = pd.DataFrame(	×
40	[
41	i.to_series(
42	variables_names=vars_names,
43	features_names=features_names,
44	score_names=solvers_names,
45	)
46	for i in result.instances
47	]
48	)
49	if not df.empty:	×
50	df.insert(0, "target", result.target)	×
51	print(df.head())	×
52	if format == "csv":	×
53	df.to_csv(f"{filename_pattern}_instances.csv", index=False)	×
54	elif format == "parquet":	×
55	df.to_parquet(f"{filename_pattern}_instances.parquet", index=False)	×
56
57	result.history.to_df().to_csv(f"{filename_pattern}_history.csv", index=False)	×
58	if result.metrics is not None:	×
59	result.metrics.to_csv(f"{filename_pattern}_archive_metrics.csv")	×

DIGNEA / DIGNEApy / 16416951177

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