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tesi updated

This commit is contained in:
Lorenzo Volpi 2024-02-03 12:27:54 +01:00
parent 6afe3ddb31
commit feb9e0a59b
7 changed files with 173 additions and 24 deletions
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34
conf.yaml
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@ -72,13 +72,13 @@ test_conf: &test_conf
main:
confs: &main_confs
- DATASET_NAME: imdb
other_confs:
- DATASET_NAME: rcv1
DATASET_TARGET: CCAT
- DATASET_NAME: rcv1
DATASET_TARGET: GCAT
- DATASET_NAME: rcv1
DATASET_TARGET: MCAT
other_confs:
sld_lr_conf: &sld_lr_conf
@ -423,22 +423,34 @@ timing_conf: &timing_conf
- bin_kde_lr_a
- mul_kde_lr_a
- m3w_kde_lr_a
- doc
- atc_mc
- rca
- rca_star
- mandoline
- naive
N_JOBS: 1
PROTOCOL_REPEATS: 1
confs: *main_confs
timing_gs_conf: &timing_gs_conf
global:
METRICS:
- acc
- f1
OUT_DIR_NAME: output/timing_gs
DATASET_N_PREVS: 1
COMP_ESTIMATORS:
- bin_sld_lr_gs
- mul_sld_lr_gs
- m3w_sld_lr_gs
- bin_kde_lr_gs
- mul_kde_lr_gs
- m3w_kde_lr_gs
- doc
- atc_mc
- rca
- rca_star
- mandoline
N_JOBS: 1
PROTOCOL_N_PREVS: 1,
PROTOCOL_REPEATS: 1,
SAMPLE_SIZE: 1000,
N_JOBS: -1
PROTOCOL_REPEATS: 1
confs: *main_confs
exec: *baselines_conf
exec: *timing_gs_conf

8
copy_res.sh
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@ -1,9 +1,9 @@
#!/bin/bash
# scp -r andreaesuli@edge-nd1.isti.cnr.it:/home/andreaesuli/raid/lorenzo/output/kde_lr_gs ./output/
# scp -r andreaesuli@edge-nd1.isti.cnr.it:/home/andreaesuli/raid/lorenzo/output/baselines ./output/
scp -r andreaesuli@edge-nd1.isti.cnr.it:/home/andreaesuli/raid/lorenzo/output/cc_lr ./output/
# scp -r andreaesuli@edge-nd1.isti.cnr.it:/home/andreaesuli/raid/lorenzo/output/cc_lr ./output/
scp -r andreaesuli@edge-nd1.isti.cnr.it:/home/andreaesuli/raid/lorenzo/output/baselines ./output/
# scp -r ./output/kde_lr_gs volpi@ilona.isti.cnr.it:/home/volpi/tesi/output/
# scp -r ./output/baselines volpi@ilona.isti.cnr.it:/home/volpi/tesi/output/
scp -r ./output/cc_lr volpi@ilona.isti.cnr.it:/home/volpi/tesi/output/
# scp -r ./output/cc_lr volpi@ilona.isti.cnr.it:/home/volpi/tesi/output/
scp -r ./output/baselines volpi@ilona.isti.cnr.it:/home/volpi/tesi/output/

25
qcdash/app.py
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@ -13,7 +13,7 @@ from dash import Dash, Input, Output, State, callback, ctx, dash_table, dcc, htm
from dash.dash_table.Format import Align, Format, Scheme
from quacc import plot
from quacc.evaluation.estimators import CE
from quacc.evaluation.estimators import CE, _renames
from quacc.evaluation.report import CompReport, DatasetReport
from quacc.evaluation.stats import wilcoxon
@ -26,6 +26,23 @@ def _get_prev_str(prev: np.ndarray):
return str(tuple(np.around(prev, decimals=2)))
def rename_estimators(estimators, rev=False):
_rnm = _renames
if rev:
_rnm = {v: k for k, v in _renames.items()}
new_estimators = []
for c in estimators:
nc = c
for old, new in _rnm.items():
if c.startswith(old):
nc = new + c[len(old) :]
new_estimators.append(nc)
return new_estimators
def get_datasets(root: str | Path) -> List[DatasetReport]:
def load_dataset(dataset):
dataset = Path(dataset)
@ -153,7 +170,7 @@ def get_DataTable(df, mode):
columns = {
c: dict(
id=c,
name=_index_name[mode] if c == "index" else c,
name=_index_name[mode] if c == "index" else rename_estimators([c])[0],
type="numeric",
format=columns_format,
)
@ -412,12 +429,13 @@ def update_estimators(href, dataset, metric, curr_estimators, root):
old_estimators = json.loads(old_estimators)
except JSONDecodeError:
old_estimators = []
old_estimators = rename_estimators(old_estimators, rev=True)
valid_estimators: np.ndarray = dr.data(metric=metric).columns.unique(0).to_numpy()
new_estimators = valid_estimators[
np.isin(valid_estimators, old_estimators)
].tolist()
valid_estimators = CE.name.sort(valid_estimators.tolist())
return valid_estimators, new_estimators
return rename_estimators(valid_estimators), rename_estimators(new_estimators)
@callback(
@ -473,6 +491,7 @@ def update_content(dataset, metric, estimators, view, mode, root):
quote_via=quote,
)
dr = get_dr(root, dataset)
estimators = rename_estimators(estimators, rev=True)
match mode:
case m if m.endswith("table"):
df = get_table(

30
quacc/evaluation/estimators.py
View File

@ -78,3 +78,33 @@ class CompEstimator:
CE = CompEstimator()
_renames = {
"bin_sld_lr": "(2x2)_SLD_LR",
"mul_sld_lr": "(1x4)_SLD_LR",
"m3w_sld_lr": "(1x3)_SLD_LR",
"d_bin_sld_lr": "d_(2x2)_SLD_LR",
"d_mul_sld_lr": "d_(1x4)_SLD_LR",
"d_m3w_sld_lr": "d_(1x3)_SLD_LR",
"d_bin_sld_rbf": "(2x2)_SLD_RBF",
"d_mul_sld_rbf": "(1x4)_SLD_RBF",
"d_m3w_sld_rbf": "(1x3)_SLD_RBF",
"sld_lr": "SLD_LR",
"bin_kde_lr": "(2x2)_KDEy_LR",
"mul_kde_lr": "(1x4)_KDEy_LR",
"m3w_kde_lr": "(1x3)_KDEy_LR",
"d_bin_kde_lr": "d_(2x2)_KDEy_LR",
"d_mul_kde_lr": "d_(1x4)_KDEy_LR",
"d_m3w_kde_lr": "d_(1x3)_KDEy_LR",
"bin_cc_lr": "(2x2)_CC_LR",
"mul_cc_lr": "(1x4)_CC_LR",
"m3w_cc_lr": "(1x3)_CC_LR",
"kde_lr": "KDEy_LR",
"cc_lr": "CC_LR",
"atc_mc": "ATC",
"doc": "DoC",
"mandoline": "Mandoline",
"rca": "RCA",
"rca_star": "RCA*",
"naive": "Naive",
}

29
quacc/plot/plot.py
View File

@ -39,8 +39,16 @@ def plot_delta(
else:
title = f"{_base_title}_{name}_avg_{avg}_{metric}"
x_label = f"{'test' if avg is None or avg == 'train' else 'train'} prevalence"
y_label = f"{metric} error"
if avg is None or avg == "train":
x_label = "Test Prevalence"
else:
x_label = "Train Prevalence"
if metric == "acc":
y_label = "Prediction Error for Vanilla Accuracy"
elif metric == "f1":
y_label = "Prediction Error for F1"
else:
y_label = f"{metric} error"
fig = backend.plot_delta(
base_prevs,
columns,
@ -81,8 +89,12 @@ def plot_diagonal(
else:
title = f"diagonal_{name}_{metric}"
x_label = f"true {metric}"
y_label = f"estim. {metric}"
if metric == "acc":
x_label = "True Vanilla Accuracy"
y_label = "Estimated Vanilla Accuracy"
else:
x_label = f"true {metric}"
y_label = f"estim. {metric}"
fig = backend.plot_diagonal(
reference,
columns,
@ -123,8 +135,13 @@ def plot_shift(
else:
title = f"shift_{name}_avg_{metric}"
x_label = "dataset shift"
y_label = f"{metric} error"
x_label = "Amount of Prior Probability Shift"
if metric == "acc":
y_label = "Prediction Error for Vanilla Accuracy"
elif metric == "f1":
y_label = "Prediction Error for F1"
else:
y_label = f"{metric} error"
fig = backend.plot_shift(
shift_prevs,
columns,

23
quacc/plot/plotly.py
View File

@ -5,6 +5,7 @@ import numpy as np
import plotly
import plotly.graph_objects as go
from quacc.evaluation.estimators import _renames
from quacc.plot.base import BasePlot
@ -50,6 +51,7 @@ class PlotlyPlot(BasePlot):
def __init__(self, theme=None):
self.theme = PlotlyPlot.__themes[theme]
self.rename = True
def hex_to_rgb(self, hex: str, t: float | None = None):
hex = hex.lstrip("#")
@ -85,6 +87,24 @@ class PlotlyPlot(BasePlot):
def save_fig(self, fig, base_path, title) -> Path:
return None
def rename_plots(
self,
columns,
):
if not self.rename:
return columns
new_columns = []
for c in columns:
nc = c
for old, new in _renames.items():
if c.startswith(old):
nc = new + c[len(old) :]
new_columns.append(nc)
return np.array(new_columns)
def plot_delta(
self,
base_prevs,
@ -102,6 +122,7 @@ class PlotlyPlot(BasePlot):
if isinstance(base_prevs[0], float):
base_prevs = np.around([(1 - bp, bp) for bp in base_prevs], decimals=4)
x = [str(tuple(bp)) for bp in base_prevs]
columns = self.rename_plots(columns)
line_colors = self.get_colors(len(columns))
for name, delta in zip(columns, data):
color = next(line_colors)
@ -150,6 +171,7 @@ class PlotlyPlot(BasePlot):
) -> go.Figure:
fig = go.Figure()
x = reference
columns = self.rename_plots(columns)
line_colors = self.get_colors(len(columns))
_edges = (np.min([np.min(x), np.min(data)]), np.max([np.max(x), np.max(data)]))
@ -211,6 +233,7 @@ class PlotlyPlot(BasePlot):
fig = go.Figure()
# x = shift_prevs[:, pos_class]
x = shift_prevs
columns = self.rename_plots(columns)
line_colors = self.get_colors(len(columns))
for name, delta in zip(columns, data):
col_idx = (columns == name).nonzero()[0][0]

48
selected_gs.py Normal file
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@ -0,0 +1,48 @@
import numpy as np
from quacc.evaluation.report import DatasetReport
datasets = [
"imdb/imdb.pickle",
"rcv1_CCAT/rcv1_CCAT.pickle",
"rcv1_GCAT/rcv1_GCAT.pickle",
"rcv1_MCAT/rcv1_MCAT.pickle",
]
gs = {
"sld_lr_gs": [
"bin_sld_lr_gs",
"mul_sld_lr_gs",
"m3w_sld_lr_gs",
],
"kde_lr_gs": [
"bin_kde_lr_gs",
"mul_kde_lr_gs",
"m3w_kde_lr_gs",
],
}
for dst in datasets:
dr = DatasetReport.unpickle("output/main/" + dst)
print(f"{dst}\n")
for name, methods in gs.items():
print(f"{name}")
sel_methods = [
{k: v for k, v in cr.fit_scores.items() if k in methods} for cr in dr.crs
]
best_methods = [
list(ms.keys())[np.argmin(list(ms.values()))] for ms in sel_methods
]
m_cnt = []
for m in methods:
m_cnt.append((np.array(best_methods) == m).nonzero()[0].shape[0])
m_cnt = np.array(m_cnt)
m_freq = m_cnt / len(best_methods)
for n in methods:
print(n, end="\t")
print()
for v in m_freq:
print(f"{v*100:.2f}", end="\t")
print("\n\n")