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QuAcc
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rework completed

This commit is contained in:
Lorenzo Volpi 2023-10-31 03:01:24 +01:00
parent 5f0ee9b780
commit f7c8f69351
11 changed files with 456 additions and 328 deletions
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1
.vscode/launch.json vendored
View File

@ -5,6 +5,7 @@
"version": "0.2.0",
"configurations": [
{
"name": "main",
"type": "python",

20
conf.yaml
View File

@ -2,17 +2,17 @@ debug_conf: &debug_conf
global:
METRICS:
- acc
DATASET_N_PREVS: 1
DATASET_N_PREVS: 5
DATASET_PREVS:
- 0.5
datasets:
- DATASET_NAME: rcv1
DATASET_TARGET: CCAT
- DATASET_NAME: imdb
plot_confs:
debug:
PLOT_ESTIMATORS:
# - mul_sld_bcts
- mul_sld_bcts
- mul_sld
- ref
- atc_mc
@ -23,11 +23,15 @@ test_conf: &test_conf
METRICS:
- acc
- f1
DATASET_N_PREVS: 3
DATASET_N_PREVS: 2
DATASET_PREVS:
- 0.5
- 0.1
datasets:
- DATASET_NAME: rcv1
DATASET_TARGET: CCAT
# - DATASET_NAME: rcv1
# DATASET_TARGET: CCAT
- DATASET_NAME: imdb
plot_confs:
best_vs_atc:
@ -100,4 +104,4 @@ main_conf: &main_conf
- atc_ne
- doc_feat
exec: *main_conf
exec: *test_conf

12
poetry.lock generated
View File

@ -443,6 +443,16 @@ files = [
{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
]
[[package]]
name = "logging"
version = "0.4.9.6"
description = "A logging module for Python"
optional = false
python-versions = "*"
files = [
{file = "logging-0.4.9.6.tar.gz", hash = "sha256:26f6b50773f085042d301085bd1bf5d9f3735704db9f37c1ce6d8b85c38f2417"},
]
[[package]]
name = "markupsafe"
version = "2.1.3"
@ -1261,4 +1271,4 @@ test = ["pytest", "pytest-cov"]
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "c98b7510ac055b667340b52e1b0b0777370e68d325d3149cb1fef42b6f1ec50a"
content-hash = "54d9922f6d48a46f554a6b350ce09d668a88755efb1fbf295f8f8a0a411bdef2"

1
pyproject.toml
View File

@ -11,6 +11,7 @@ quapy = "^0.1.7"
pandas = "^2.0.3"
jinja2 = "^3.1.2"
pyyaml = "^6.0.1"
logging = "^0.4.9.6"
[tool.poetry.scripts]
main = "quacc.main:main"

2
quacc/evaluation/baseline.py
View File

@ -86,6 +86,7 @@ def atc_mc(
protocol: AbstractStochasticSeededProtocol,
predict_method="predict_proba",
):
"""garg"""
c_model_predict = getattr(c_model, predict_method)
## Load ID validation data probs and labels
@ -124,6 +125,7 @@ def atc_ne(
protocol: AbstractStochasticSeededProtocol,
predict_method="predict_proba",
):
"""garg"""
c_model_predict = getattr(c_model, predict_method)
## Load ID validation data probs and labels

6
quacc/evaluation/comp.py
View File

@ -1,6 +1,6 @@
import multiprocessing
import time
import traceback
from traceback import print_exception as traceback
from typing import List
import pandas as pd
@ -11,11 +11,10 @@ from quacc.environment import env
from quacc.evaluation import baseline, method
from quacc.evaluation.report import CompReport, DatasetReport, EvaluationReport
from quacc.evaluation.worker import estimate_worker
from quacc.logging import Logger
from quacc.logger import Logger
pd.set_option("display.float_format", "{:.4f}".format)
qp.environ["SAMPLE_SIZE"] = env.SAMPLE_SIZE
log = Logger.logger()
class CompEstimator:
@ -43,6 +42,7 @@ CE = CompEstimator
def evaluate_comparison(
dataset: Dataset, estimators=["OUR_BIN_SLD", "OUR_MUL_SLD"]
) -> EvaluationReport:
log = Logger.logger()
# with multiprocessing.Pool(1) as pool:
with multiprocessing.Pool(len(estimators)) as pool:
dr = DatasetReport(dataset.name)

691
quacc/evaluation/report.py
View File

@ -9,68 +9,48 @@ from quacc.environment import env
from quacc.utils import fmt_line_md
def _get_metric(metric: str):
return slice(None) if metric is None else metric
def _get_estimators(estimators: List[str], cols: np.ndarray):
return slice(None) if estimators is None else cols[np.in1d(cols, estimators)]
class EvaluationReport:
def __init__(self, name=None):
self._prevs = []
self._dict = {}
self.data: pd.DataFrame = None
self.fit_score = None
self.name = name if name is not None else "default"
def append_row(self, basep: np.ndarray | Tuple, **row):
bp = basep[1]
self._prevs.append(bp)
for k, v in row.items():
if k not in self._dict:
self._dict[k] = {}
if bp not in self._dict[k]:
self._dict[k][bp] = []
self._dict[k][bp] = np.append(self._dict[k][bp], [v])
_keys, _values = zip(*row.items())
# _keys = list(row.keys())
# _values = list(row.values())
if self.data is None:
_idx = 0
self.data = pd.DataFrame(
{k: [v] for k, v in row.items()},
index=pd.MultiIndex.from_tuples([(bp, _idx)]),
columns=_keys,
)
return
_idx = len(self.data.loc[(bp,), :]) if (bp,) in self.data.index else 0
not_in_data = np.setdiff1d(list(row.keys()), self.data.columns.unique(0))
self.data.loc[:, not_in_data] = np.nan
self.data.loc[(bp, _idx), :] = row
return
@property
def columns(self):
return self._dict.keys()
def columns(self) -> np.ndarray:
return self.data.columns.unique(0)
@property
def prevs(self):
return np.sort(np.unique([list(self._dict[_k].keys()) for _k in self._dict]))
# def group_by_prevs(self, metric: str = None, estimators: List[str] = None):
# if self._g_dict is None:
# self._g_prevs = []
# self._g_dict = {k: [] for k in self._dict.keys()}
# for col, vals in self._dict.items():
# col_grouped = {}
# for bp, v in zip(self._prevs, vals):
# if bp not in col_grouped:
# col_grouped[bp] = []
# col_grouped[bp].append(v)
# self._g_dict[col] = [
# vs
# for bp, vs in sorted(col_grouped.items(), key=lambda cg: cg[0][1])
# ]
# self._g_prevs = sorted(
# [(p0, p1) for [p0, p1] in np.unique(self._prevs, axis=0).tolist()],
# key=lambda bp: bp[1],
# )
# fg_dict = _filter_dict(self._g_dict, metric, estimators)
# return self._g_prevs, fg_dict
# def merge(self, other):
# if not all(v1 == v2 for v1, v2 in zip(self._prevs, other._prevs)):
# raise ValueError("other has not same base prevalences of self")
# inters_keys = set(self._dict.keys()).intersection(set(other._dict.keys()))
# if len(inters_keys) > 0:
# raise ValueError(f"self and other have matching keys {str(inters_keys)}.")
# report = EvaluationReport()
# report._prevs = self._prevs
# report._dict = self._dict | other._dict
# return report
return np.sort(self.data.index.unique(0))
class CompReport:
@ -82,22 +62,16 @@ class CompReport:
valid_prev=None,
times=None,
):
all_prevs = np.array([er.prevs for er in reports])
if not np.all(all_prevs == all_prevs[0, :], axis=0).all():
raise ValueError(
"Not all evaluation reports have the same base prevalences"
self._data = (
pd.concat(
[er.data for er in reports],
keys=[er.name for er in reports],
axis=1,
)
uq_names, name_c = np.unique([er.name for er in reports], return_counts=True)
if np.sum(name_c) > uq_names.shape[0]:
_matching = uq_names[[c > 1 for c in name_c]]
raise ValueError(
f"Evaluation reports have matching names: {_matching.tolist()}."
)
all_dicts = [{(k, er.name): v for k, v in er._dict.items()} for er in reports]
self._dict = {}
for d in all_dicts:
self._dict = self._dict | d
.swaplevel(0, 1, axis=1)
.sort_index(axis=1, level=0, sort_remaining=False)
.sort_index(axis=0, level=0)
)
self.fit_scores = {
er.name: er.fit_score for er in reports if er.fit_score is not None
@ -107,177 +81,195 @@ class CompReport:
self.times = times
@property
def prevs(self):
return np.sort(np.unique([list(self._dict[_k].keys()) for _k in self._dict]))
def prevs(self) -> np.ndarray:
return np.sort(self._data.index.unique(0))
@property
def cprevs(self):
def np_prevs(self) -> np.ndarray:
return np.around([(1.0 - p, p) for p in self.prevs], decimals=2)
def data(self, metric: str = None, estimators: List[str] = None) -> dict:
f_dict = self._dict.copy()
if metric is not None:
f_dict = {(c0, c1): ls for ((c0, c1), ls) in f_dict.items() if c0 == metric}
if estimators is not None:
f_dict = {
(c0, c1): ls for ((c0, c1), ls) in f_dict.items() if c1 in estimators
}
if (metric, estimators) != (None, None):
f_dict = {c1: ls for ((c0, c1), ls) in f_dict.items()}
def data(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame:
_metric = _get_metric(metric)
_estimators = _get_estimators(estimators, self._data.columns.unique(1))
f_data: pd.DataFrame = self._data.copy().loc[:, (_metric, _estimators)]
return f_dict
if len(f_data.columns.unique(0)) == 1:
f_data = f_data.droplevel(level=0, axis=1)
def group_by_shift(self, metric: str = None, estimators: List[str] = None):
f_dict = self.data(metric=metric, estimators=estimators)
shift_prevs = np.around(
np.absolute(self.prevs - self.train_prev[1]), decimals=2
return f_data
def shift_data(
self, metric: str = None, estimators: List[str] = None
) -> pd.DataFrame:
shift_idx_0 = np.around(
np.abs(
self._data.index.get_level_values(0).to_numpy() - self.train_prev[1]
),
decimals=2,
)
shift_dict = {col: {sp: [] for sp in shift_prevs} for col in f_dict.keys()}
for col, vals in f_dict.items():
for sp, bp in zip(shift_prevs, self.prevs):
shift_dict[col][sp] = np.concatenate(
[shift_dict[col][sp], f_dict[col][bp]]
)
return np.sort(np.unique(shift_prevs)), shift_dict
shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype="<i4")
for _id in np.unique(shift_idx_0):
_wh = np.where(shift_idx_0 == _id)[0]
shift_idx_1[_wh] = np.arange(_wh.shape[0], dtype="<i4")
def avg_by_prevs(self, metric: str = None, estimators: List[str] = None):
shift_data = self._data.copy()
shift_data.index = pd.MultiIndex.from_arrays([shift_idx_0, shift_idx_1])
shift_data.sort_index(axis=0, level=0)
_metric = _get_metric(metric)
_estimators = _get_estimators(estimators, shift_data.columns.unique(1))
shift_data: pd.DataFrame = shift_data.loc[:, (_metric, _estimators)]
if len(shift_data.columns.unique(0)) == 1:
shift_data = shift_data.droplevel(level=0, axis=1)
return shift_data
def avg_by_prevs(
self, metric: str = None, estimators: List[str] = None
) -> pd.DataFrame:
f_dict = self.data(metric=metric, estimators=estimators)
return {
col: np.array([np.mean(vals[bp]) for bp in self.prevs])
for col, vals in f_dict.items()
}
return f_dict.groupby(level=0).mean()
def stdev_by_prevs(self, metric: str = None, estimators: List[str] = None):
def stdev_by_prevs(
self, metric: str = None, estimators: List[str] = None
) -> pd.DataFrame:
f_dict = self.data(metric=metric, estimators=estimators)
return {
col: np.array([np.std(vals[bp]) for bp in self.prevs])
for col, vals in f_dict.items()
}
return f_dict.groupby(level=0).std()
def avg_all(self, metric: str = None, estimators: List[str] = None):
f_dict = self.data(metric=metric, estimators=estimators)
return {
col: [np.mean(np.concatenate(list(vals.values())))]
for col, vals in f_dict.items()
}
def get_dataframe(self, metric="acc", estimators=None):
avg_dict = self.avg_by_prevs(metric=metric, estimators=estimators)
all_dict = self.avg_all(metric=metric, estimators=estimators)
for col in avg_dict.keys():
avg_dict[col] = np.append(avg_dict[col], all_dict[col])
return pd.DataFrame(
avg_dict,
index=self.prevs.tolist() + ["tot"],
columns=avg_dict.keys(),
)
def table(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame:
f_data = self.data(metric=metric, estimators=estimators)
avg_p = f_data.groupby(level=0).mean()
avg_p.loc["avg", :] = f_data.mean()
return avg_p
def get_plots(
self,
modes=["delta", "diagonal", "shift"],
metric="acc",
estimators=None,
conf="default",
stdev=False,
) -> Path:
pps = []
for mode in modes:
pp = []
if mode == "delta":
f_dict = self.avg_by_prevs(metric=metric, estimators=estimators)
_pp0 = plot.plot_delta(
self.cprevs,
f_dict,
metric=metric,
name=conf,
train_prev=self.train_prev,
fit_scores=self.fit_scores,
)
pp = [(mode, _pp0)]
if stdev:
fs_dict = self.stdev_by_prevs(metric=metric, estimators=estimators)
_pp1 = plot.plot_delta(
self.cprevs,
f_dict,
metric=metric,
name=conf,
train_prev=self.train_prev,
fit_scores=self.fit_scores,
stdevs=fs_dict,
)
pp.append((f"{mode}_stdev", _pp1))
elif mode == "diagonal":
f_dict = {
col: np.concatenate([vals[bp] for bp in self.prevs])
for col, vals in self.data(
metric=metric + "_score", estimators=estimators
).items()
}
reference = f_dict["ref"]
f_dict = {k: v for k, v in f_dict.items() if k != "ref"}
_pp0 = plot.plot_diagonal(
reference,
f_dict,
metric=metric,
name=conf,
train_prev=self.train_prev,
)
pp = [(mode, _pp0)]
self, mode="delta", metric="acc", estimators=None, conf="default", stdev=False
) -> List[Tuple[str, Path]]:
if mode == "delta":
avg_data = self.avg_by_prevs(metric=metric, estimators=estimators)
return plot.plot_delta(
base_prevs=self.np_prevs,
columns=avg_data.columns.to_numpy(),
data=avg_data.T.to_numpy(),
metric=metric,
name=conf,
train_prev=self.train_prev,
)
elif mode == "delta_stdev":
avg_data = self.avg_by_prevs(metric=metric, estimators=estimators)
st_data = self.stdev_by_prevs(metric=metric, estimators=estimators)
return plot.plot_delta(
base_prevs=self.np_prevs,
columns=avg_data.columns.to_numpy(),
data=avg_data.T.to_numpy(),
metric=metric,
name=conf,
train_prev=self.train_prev,
stdevs=st_data.T.to_numpy(),
)
elif mode == "diagonal":
f_data = self.data(metric=metric + "_score", estimators=estimators)
ref: pd.Series = f_data.loc[:, "ref"]
f_data.drop(columns=["ref"], inplace=True)
return plot.plot_diagonal(
reference=ref.to_numpy(),
columns=f_data.columns.to_numpy(),
data=f_data.T.to_numpy(),
metric=metric,
name=conf,
train_prev=self.train_prev,
)
elif mode == "shift":
shift_data = (
self.shift_data(metric=metric, estimators=estimators)
.groupby(level=0)
.mean()
)
shift_prevs = np.around(
[(1.0 - p, p) for p in np.sort(shift_data.index.unique(0))],
decimals=2,
)
return plot.plot_shift(
shift_prevs=shift_prevs,
columns=shift_data.columns.to_numpy(),
data=shift_data.T.to_numpy(),
metric=metric,
name=conf,
train_prev=self.train_prev,
)
elif mode == "shift":
s_prevs, s_dict = self.group_by_shift(
metric=metric, estimators=estimators
)
_pp0 = plot.plot_shift(
np.around([(1.0 - p, p) for p in s_prevs], decimals=2),
{
col: np.array([np.mean(vals[sp]) for sp in s_prevs])
for col, vals in s_dict.items()
},
metric=metric,
name=conf,
train_prev=self.train_prev,
fit_scores=self.fit_scores,
)
pp = [(mode, _pp0)]
pps.extend(pp)
return pps
def to_md(self, conf="default", metric="acc", estimators=None, stdev=False):
def to_md(self, conf="default", metric="acc", estimators=None, stdev=False) -> str:
res = f"## {int(np.around(self.train_prev, decimals=2)[1]*100)}% positives\n"
res += fmt_line_md(f"train: {str(self.train_prev)}")
res += fmt_line_md(f"validation: {str(self.valid_prev)}")
for k, v in self.times.items():
res += fmt_line_md(f"{k}: {v:.3f}s")
res += "\n"
res += (
self.get_dataframe(metric=metric, estimators=estimators).to_html() + "\n\n"
)
plot_modes = ["delta", "diagonal", "shift"]
for mode, op in self.get_plots(
modes=plot_modes,
metric=metric,
estimators=estimators,
conf=conf,
stdev=stdev,
):
res += self.table(metric=metric, estimators=estimators).to_html() + "\n\n"
plot_modes = np.array(["delta", "diagonal", "shift"], dtype="object")
whd = np.where(plot_modes == "delta")[0]
if len(whd) > 0:
plot_modes = np.insert(plot_modes, whd + 1, "delta_stdev")
for mode in plot_modes:
op = self.get_plots(
mode=mode,
metric=metric,
estimators=estimators,
conf=conf,
stdev=stdev,
)
res += f"![plot_{mode}]({op.relative_to(env.OUT_DIR).as_posix()})\n"
return res
class DatasetReport:
def __init__(self, name):
def __init__(self, name, crs=None):
self.name = name
self._dict = None
self.crs: List[CompReport] = []
self.crs: List[CompReport] = [] if crs is None else crs
@property
def cprevs(self):
return np.around([(1.0 - p, p) for p in self.prevs], decimals=2)
def data(self, metric: str = None, estimators: str = None) -> pd.DataFrame:
def _cr_train_prev(cr: CompReport):
return cr.train_prev[1]
def _cr_data(cr: CompReport):
return cr.data(metric, estimators)
_crs_sorted = sorted(
[(_cr_train_prev(cr), _cr_data(cr)) for cr in self.crs],
key=lambda cr: len(cr[1].columns),
reverse=True,
)
_crs_train, _crs_data = zip(*_crs_sorted)
_data = pd.concat(_crs_data, axis=0, keys=_crs_train)
_data = _data.sort_index(axis=0, level=0)
return _data
def shift_data(self, metric: str = None, estimators: str = None) -> pd.DataFrame:
_shift_data: pd.DataFrame = pd.concat(
sorted(
[cr.shift_data(metric, estimators) for cr in self.crs],
key=lambda d: len(d.columns),
reverse=True,
),
axis=0,
)
shift_idx_0 = _shift_data.index.get_level_values(0)
shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype="<i4")
for _id in np.unique(shift_idx_0):
_wh = np.where(shift_idx_0 == _id)[0]
shift_idx_1[_wh] = np.arange(_wh.shape[0])
_shift_data.index = pd.MultiIndex.from_arrays([shift_idx_0, shift_idx_1])
_shift_data = _shift_data.sort_index(axis=0, level=0)
return _shift_data
def add(self, cr: CompReport):
if cr is None:
@ -285,57 +277,11 @@ class DatasetReport:
self.crs.append(cr)
if self._dict is None:
self.prevs = cr.prevs
self._dict = {
col: {bp: vals[bp] for bp in self.prevs}
for col, vals in cr.data().items()
}
self.s_prevs, self.s_dict = cr.group_by_shift()
self.fit_scores = {k: [score] for k, score in cr.fit_scores.items()}
def __add__(self, cr: CompReport):
if cr is None:
return
cr_dict = cr.data()
both_prevs = np.array([self.prevs, cr.prevs])
if not np.all(both_prevs == both_prevs[0, :]).all():
raise ValueError("Comp report has incompatible base prevalences")
for col, vals in cr_dict.items():
if col not in self._dict:
self._dict[col] = {}
for bp in self.prevs:
if bp not in self._dict[col]:
self._dict[col][bp] = []
self._dict[col][bp] = np.concatenate(
[self._dict[col][bp], cr_dict[col][bp]]
)
cr_s_prevs, cr_s_dict = cr.group_by_shift()
self.s_prevs = np.sort(np.unique(np.concatenate([self.s_prevs, cr_s_prevs])))
for col, vals in cr_s_dict.items():
if col not in self.s_dict:
self.s_dict[col] = {}
for sp in cr_s_prevs:
if sp not in self.s_dict[col]:
self.s_dict[col][sp] = []
self.s_dict[col][sp] = np.concatenate(
[self.s_dict[col][sp], cr_s_dict[col][sp]]
)
for sp in self.s_prevs:
for col, vals in self.s_dict.items():
if sp not in vals:
vals[sp] = []
for k, score in cr.fit_scores.items():
if k not in self.fit_scores:
self.fit_scores[k] = []
self.fit_scores[k].append(score)
def __add__(self, cr: CompReport):
self.add(cr)
return self
return DatasetReport(self.name, crs=self.crs + [cr])
def __iadd__(self, cr: CompReport):
self.add(cr)
@ -346,70 +292,51 @@ class DatasetReport:
for cr in self.crs:
res += f"{cr.to_md(conf, metric=metric, estimators=estimators, stdev=stdev)}\n\n"
f_dict = {
c1: v
for ((c0, c1), v) in self._dict.items()
if c0 == metric and c1 in estimators
}
s_avg_dict = {
col: np.array([np.mean(vals[sp]) for sp in self.s_prevs])
for col, vals in {
c1: v
for ((c0, c1), v) in self.s_dict.items()
if c0 == metric and c1 in estimators
}.items()
}
avg_dict = {
col: np.array([np.mean(vals[bp]) for bp in self.prevs])
for col, vals in f_dict.items()
}
if stdev:
stdev_dict = {
col: np.array([np.std(vals[bp]) for bp in self.prevs])
for col, vals in f_dict.items()
}
all_dict = {
col: [np.mean(np.concatenate(list(vals.values())))]
for col, vals in f_dict.items()
}
df = pd.DataFrame(
{col: np.append(avg_dict[col], val) for col, val in all_dict.items()},
index=self.prevs.tolist() + ["tot"],
columns=all_dict.keys(),
)
_data = self.data(metric=metric, estimators=estimators)
_shift_data = self.shift_data(metric=metric, estimators=estimators)
avg_x_test = _data.groupby(level=1).mean()
prevs_x_test = np.sort(avg_x_test.index.unique(0))
stdev_x_test = _data.groupby(level=1).std() if stdev else None
avg_x_test_tbl = _data.groupby(level=1).mean()
avg_x_test_tbl.loc["avg", :] = _data.mean()
avg_x_shift = _shift_data.groupby(level=0).mean()
prevs_x_shift = np.sort(avg_x_shift.index.unique(0))
res += "## avg\n"
res += df.to_html() + "\n\n"
res += avg_x_test_tbl.to_html() + "\n\n"
delta_op = plot.plot_delta(
np.around([(1.0 - p, p) for p in self.prevs], decimals=2),
avg_dict,
base_prevs=np.around([(1.0 - p, p) for p in prevs_x_test], decimals=2),
columns=avg_x_test.columns.to_numpy(),
data=avg_x_test.T.to_numpy(),
metric=metric,
name=conf,
train_prev=None,
fit_scores={k: np.mean(vals) for k, vals in self.fit_scores.items()},
)
res += f"![plot_delta]({delta_op.relative_to(env.OUT_DIR).as_posix()})\n"
if stdev:
delta_stdev_op = plot.plot_delta(
np.around([(1.0 - p, p) for p in self.prevs], decimals=2),
avg_dict,
base_prevs=np.around([(1.0 - p, p) for p in prevs_x_test], decimals=2),
columns=avg_x_test.columns.to_numpy(),
data=avg_x_test.T.to_numpy(),
metric=metric,
name=conf,
train_prev=None,
fit_scores={k: np.mean(vals) for k, vals in self.fit_scores.items()},
stdevs=stdev_dict,
stdevs=stdev_x_test.T.to_numpy(),
)
res += f"![plot_delta_stdev]({delta_stdev_op.relative_to(env.OUT_DIR).as_posix()})\n"
shift_op = plot.plot_shift(
np.around([(1.0 - p, p) for p in self.s_prevs], decimals=2),
s_avg_dict,
shift_prevs=np.around([(1.0 - p, p) for p in prevs_x_shift], decimals=2),
columns=avg_x_shift.columns.to_numpy(),
data=avg_x_shift.T.to_numpy(),
metric=metric,
name=conf,
train_prev=None,
fit_scores={k: np.mean(vals) for k, vals in self.fit_scores.items()},
)
res += f"![plot_shift]({shift_op.relative_to(env.OUT_DIR).as_posix()})\n"
@ -417,3 +344,175 @@ class DatasetReport:
def __iter__(self):
return (cr for cr in self.crs)
def __test():
df = None
print(f"{df is None = }")
if df is None:
bp = 0.75
idx = 0
d = {"a": 0.0, "b": 0.1}
df = pd.DataFrame(
d,
index=pd.MultiIndex.from_tuples([(bp, idx)]),
columns=d.keys(),
)
print(df)
print("-" * 100)
bp = 0.75
idx = len(df.loc[bp, :])
df.loc[(bp, idx), :] = {"a": 0.2, "b": 0.3}
print(df)
print("-" * 100)
bp = 0.90
idx = len(df.loc[bp, :]) if bp in df.index else 0
df.loc[(bp, idx), :] = {"a": 0.2, "b": 0.3}
print(df)
print("-" * 100)
bp = 0.90
idx = len(df.loc[bp, :]) if bp in df.index else 0
d = {"a": 0.2, "v": 0.3, "e": 0.4}
notin = np.setdiff1d(list(d.keys()), df.columns)
df.loc[:, notin] = np.nan
df.loc[(bp, idx), :] = d
print(df)
print("-" * 100)
bp = 0.90
idx = len(df.loc[bp, :]) if bp in df.index else 0
d = {"a": 0.3, "v": 0.4, "e": 0.5}
notin = np.setdiff1d(list(d.keys()), df.columns)
print(f"{notin = }")
df.loc[:, notin] = np.nan
df.loc[(bp, idx), :] = d
print(df)
print("-" * 100)
print(f"{np.sort(np.unique(df.index.get_level_values(0))) = }")
print("-" * 100)
print(f"{df.loc[(0.75, ),:] = }\n")
print(f"{df.loc[(slice(None), 1),:] = }")
print("-" * 100)
print(f"{(0.75, ) in df.index = }")
print(f"{(0.7, ) in df.index = }")
print("-" * 100)
df1 = pd.DataFrame(
{
"a": np.linspace(0.0, 1.0, 6),
"b": np.linspace(1.0, 2.0, 6),
"e": np.linspace(2.0, 3.0, 6),
"v": np.linspace(0.0, 1.0, 6),
},
index=pd.MultiIndex.from_product([[0.75, 0.9], [0, 1, 2]]),
columns=["a", "b", "e", "v"],
)
df2 = (
pd.concat([df, df1], keys=["a", "b"], axis=1)
.swaplevel(0, 1, axis=1)
.sort_index(axis=1, level=0)
)
df3 = pd.concat([df1, df], keys=["b", "a"], axis=1)
print(df)
print(df1)
print(df2)
print(df3)
df = df3
print("-" * 100)
print(df.loc[:, ("b", ["e", "v"])])
print(df.loc[:, (slice(None), ["e", "v"])])
print(df.loc[:, ("b", slice(None))])
print(df.loc[:, ("b", slice(None))].droplevel(level=0, axis=1))
print(df.loc[:, (slice(None), ["e", "v"])].droplevel(level=0, axis=1))
print(len(df.loc[:, ("b", slice(None))].columns.unique(0)))
print("-" * 100)
idx_0 = np.around(np.abs(df.index.get_level_values(0).to_numpy() - 0.8), decimals=2)
midx = pd.MultiIndex.from_arrays([idx_0, df.index.get_level_values(1)])
print(midx)
dfs = df.copy()
dfs.index = midx
print(df)
print(dfs)
print("-" * 100)
df.loc[(0.85, 0), :] = np.linspace(0, 1, 8)
df.loc[(0.85, 1), :] = np.linspace(0, 1, 8)
df.loc[(0.85, 2), :] = np.linspace(0, 1, 8)
idx_0 = np.around(np.abs(df.index.get_level_values(0).to_numpy() - 0.8), decimals=2)
print(np.where(idx_0 == 0.05))
idx_1 = np.empty(shape=idx_0.shape, dtype="<i4")
print(idx_1)
for _id in np.unique(idx_0):
wh = np.where(idx_0 == _id)[0]
idx_1[wh] = np.arange(wh.shape[0])
midx = pd.MultiIndex.from_arrays([idx_0, idx_1])
dfs = df.copy()
dfs.index = midx
dfs.sort_index(level=0, axis=0, inplace=True)
print(df)
print(dfs)
print("-" * 100)
print(np.sort(dfs.index.unique(0)))
print("-" * 100)
print(df.groupby(level=0).mean())
print(dfs.groupby(level=0).mean())
print("-" * 100)
s = df.mean(axis=0)
dfa = df.groupby(level=0).mean()
dfa.loc["avg", :] = s
print(dfa)
print("-" * 100)
print(df)
dfn = df.loc[:, (slice(None), slice(None))]
print(dfn)
print(f"{df is dfn = }")
print("-" * 100)
a = np.array(["abc", "bcd", "cde", "bcd"], dtype="object")
print(a)
whb = np.where(a == "bcd")[0]
if len(whb) > 0:
a = np.insert(a, whb + 1, "pippo")
print(a)
print("-" * 100)
dff: pd.DataFrame = df.loc[:, ("a",)]
print(dff.to_dict(orient="list"))
dff = dff.drop(columns=["v"])
print(dff)
s: pd.Series = dff.loc[:, "e"]
print(s)
print(s.to_numpy())
print(type(s.to_numpy()))
print("-" * 100)
df3 = pd.concat([df, df], axis=0, keys=[0.5, 0.3]).sort_index(axis=0, level=0)
print(df3)
df3n = pd.concat([df, df], axis=0).sort_index(axis=0, level=0)
print(df3n)
df = df3
print("-" * 100)
print(df.groupby(level=1).mean(), df.groupby(level=1).count())
print("-" * 100)
print(df)
for ls in df.T.to_numpy():
print(ls)
print("-" * 100)
if __name__ == "__main__":
__test()

4
quacc/evaluation/worker.py
View File

@ -1,10 +1,11 @@
import time
from traceback import print_exception as traceback
import quapy as qp
from quapy.protocol import APP
from sklearn.linear_model import LogisticRegression
from quacc.logging import SubLogger
from quacc.logger import SubLogger
def estimate_worker(_estimate, train, validation, test, _env=None, q=None):
@ -26,6 +27,7 @@ def estimate_worker(_estimate, train, validation, test, _env=None, q=None):
result = _estimate(model, validation, protocol)
except Exception as e:
log.warning(f"Method {_estimate.__name__} failed. Exception: {e}")
# traceback(e)
return {
"name": _estimate.__name__,
"result": None,

2
quacc/logging.py → quacc/logger.py
View File

@ -102,7 +102,7 @@ class SubLogger:
rh.setLevel(logging.DEBUG)
rh.setFormatter(
logging.Formatter(
fmt="%(asctime)s| %(levelname)s: %(message)s",
fmt="%(asctime)s| %(levelname)s:\t%(message)s",
datefmt="%d/%m/%y %H:%M:%S",
)
)

8
quacc/main.py
View File

@ -1,14 +1,12 @@
import traceback
from sys import platform
from traceback import print_exception as traceback
import quacc.evaluation.comp as comp
from quacc.dataset import Dataset
from quacc.environment import env
from quacc.logging import Logger
from quacc.logger import Logger
from quacc.utils import create_dataser_dir
log = Logger.logger()
def toast():
if platform == "win32":
@ -18,6 +16,7 @@ def toast():
def estimate_comparison():
log = Logger.logger()
for conf in env.get_confs():
create_dataser_dir(conf, update=env.DATASET_DIR_UPDATE)
dataset = Dataset(
@ -49,6 +48,7 @@ def estimate_comparison():
def main():
log = Logger.logger()
try:
estimate_comparison()
except Exception as e:

37
quacc/plot.py
View File

@ -19,7 +19,8 @@ def _get_markers(n: int):
def plot_delta(
base_prevs,
dict_vals,
columns,
data,
*,
stdevs=None,
pos_class=1,
@ -40,14 +41,14 @@ def plot_delta(
ax.set_aspect("auto")
ax.grid()
NUM_COLORS = len(dict_vals)
NUM_COLORS = len(data)
cm = plt.get_cmap("tab10")
if NUM_COLORS > 10:
cm = plt.get_cmap("tab20")
cy = cycler(color=[cm(i) for i in range(NUM_COLORS)])
base_prevs = base_prevs[:, pos_class]
for (method, deltas), _cy in zip(dict_vals.items(), cy):
for method, deltas, _cy in zip(columns, data, cy):
ax.plot(
base_prevs,
deltas,
@ -59,11 +60,17 @@ def plot_delta(
zorder=2,
)
if stdevs is not None:
stdev = stdevs[method]
_col_idx = np.where(columns == method)[0]
stdev = stdevs[_col_idx].flatten()
nn_idx = np.intersect1d(
np.where(deltas != np.nan)[0],
np.where(stdev != np.nan)[0],
)
_bps, _ds, _st = base_prevs[nn_idx], deltas[nn_idx], stdev[nn_idx]
ax.fill_between(
base_prevs,
deltas - stdev,
deltas + stdev,
_bps,
_ds - _st,
_ds + _st,
color=_cy["color"],
alpha=0.25,
)
@ -88,7 +95,8 @@ def plot_delta(
def plot_diagonal(
reference,
dict_vals,
columns,
data,
*,
pos_class=1,
metric="acc",
@ -107,7 +115,7 @@ def plot_diagonal(
ax.grid()
ax.set_aspect("equal")
NUM_COLORS = len(dict_vals)
NUM_COLORS = len(data)
cm = plt.get_cmap("tab10")
if NUM_COLORS > 10:
cm = plt.get_cmap("tab20")
@ -120,7 +128,7 @@ def plot_diagonal(
x_ticks = np.unique(reference)
x_ticks.sort()
for (_, deltas), _cy in zip(dict_vals.items(), cy):
for deltas, _cy in zip(data, cy):
ax.plot(
reference,
deltas,
@ -137,7 +145,7 @@ def plot_diagonal(
_lims = np.array([f(ls) for f, ls in zip([np.min, np.max], _alims)])
ax.set(xlim=tuple(_lims), ylim=tuple(_lims))
for (method, deltas), _cy in zip(dict_vals.items(), cy):
for method, deltas, _cy in zip(columns, data, cy):
slope, interc = np.polyfit(reference, deltas, 1)
y_lr = np.array([slope * x + interc for x in _lims])
ax.plot(
@ -171,7 +179,8 @@ def plot_diagonal(
def plot_shift(
shift_prevs,
shift_dict,
columns,
data,
*,
pos_class=1,
metric="acc",
@ -190,14 +199,14 @@ def plot_shift(
ax.set_aspect("auto")
ax.grid()
NUM_COLORS = len(shift_dict)
NUM_COLORS = len(data)
cm = plt.get_cmap("tab10")
if NUM_COLORS > 10:
cm = plt.get_cmap("tab20")
cy = cycler(color=[cm(i) for i in range(NUM_COLORS)])
shift_prevs = shift_prevs[:, pos_class]
for (method, shifts), _cy in zip(shift_dict.items(), cy):
for method, shifts, _cy in zip(columns, data, cy):
ax.plot(
shift_prevs,
shifts,