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imanjraimanjra
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cleanup and restructure
1 parent 2731a4e commit 7c40323

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16 files changed

+28
-10643
lines changed

16 files changed

+28
-10643
lines changed

‎evaluation_panel/evaluation_panel/__init__.py renamed to ‎__init__.py

Lines changed: 25 additions & 40 deletions
Original file line numberDiff line numberDiff line change
@@ -1,14 +1,7 @@
1-
import fiftyone as fo
2-
import fiftyone.brain as fob
3-
import fiftyone.core.fields as fof
4-
import fiftyone.core.labels as fol
5-
import fiftyone.core.patches as fop
61
import fiftyone.operators as foo
72
import fiftyone.operators.types as types
8-
import fiftyone.zoo.models as fozm
93
import numpy as np
104
from fiftyone import ViewField as F
11-
from fiftyone.brain import Similarity
125

136

147
class EvaluationPanel(foo.Panel):
@@ -367,7 +360,7 @@ def _update_table_data(self, ctx):
367360
new_row = {"class": "All", "AP": int(results.mAP() * 1000) / 1000}
368361
mAP_list.append(new_row)
369362
ctx.panel.set_data("my_stack.mAP_evaluations", mAP_list)
370-
363+
371364
# Compare key DOES exist, update c_(table_name) instead
372365
else:
373366
c_eval = ctx.dataset.get_evaluation_info(compare_key).serialize()
@@ -512,9 +505,9 @@ def _update_plot_data(
512505
ctx,
513506
):
514507
# _update_plot_data is called in on_change_config
515-
# The function updates the DATA of all the plots in the panel,
508+
# The function updates the DATA of all the plots in the panel,
516509
# including histograms and confusion matrices# _update_plot_data is called in on_change_config
517-
# The function updates the DATA of all the plots in the panel,
510+
# The function updates the DATA of all the plots in the panel,
518511
# including histograms and confusion matrices
519512

520513
# Grab the basic info
@@ -629,25 +622,24 @@ def _update_plot_data(
629622
],
630623
)
631624

632-
#Calculate recall, precision, and f1. Dont forget to check for divide by 0!
625+
# Calculate recall, precision, and f1. Dont forget to check for divide by 0!
633626
tp = np.array(ctx.dataset.values(f"{eval_key}_tp"))
634627
fp = np.array(ctx.dataset.values(f"{eval_key}_fp"))
635628
fn = np.array(ctx.dataset.values(f"{eval_key}_fn"))
636629

637630
n = tp.astype(np.float64)
638-
d = (tp+fp).astype(np.float64)
639-
p = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
631+
d = (tp + fp).astype(np.float64)
632+
p = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
640633
p = np.nan_to_num(p, nan=0.0)
641634

642-
643635
n = tp.astype(np.float64)
644-
d = (tp+fn).astype(np.float64)
645-
r = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
636+
d = (tp + fn).astype(np.float64)
637+
r = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
646638
r = np.nan_to_num(r, nan=0.0)
647639

648640
n = (2 * (p * r)).astype(np.float64)
649-
d = (p+r).astype(np.float64)
650-
f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
641+
d = (p + r).astype(np.float64)
642+
f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
651643
f1 = np.nan_to_num(f1, nan=0.0)
652644

653645
p_left_edges, p_counts, p_widths = compute_histogram(p, 10)
@@ -736,7 +728,6 @@ def _update_plot_data(
736728

737729
conf = sum(conf_total) / len(conf_total)
738730

739-
740731
if tp + fp != 0:
741732
p = tp / (tp + fp)
742733
p = np.nan_to_num(p, nan=0.0)
@@ -899,7 +890,7 @@ def _update_plot_data(
899890
else:
900891
r = 0
901892
r = np.nan_to_num(r, nan=0.0)
902-
if p+r !=0:
893+
if p + r != 0:
903894
f1 = 2 * (p * r) / (p + r)
904895
else:
905896
f1 = 0
@@ -1093,35 +1084,33 @@ def _update_plot_data(
10931084
c_fn = np.array(ctx.dataset.values(f"{compare_key}_fn"))
10941085

10951086
n = tp.astype(np.float64)
1096-
d = (tp+fp).astype(np.float64)
1097-
p = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1087+
d = (tp + fp).astype(np.float64)
1088+
p = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
10981089
p = np.nan_to_num(p, nan=0.0)
10991090

1100-
11011091
n = tp.astype(np.float64)
1102-
d = (tp+fn).astype(np.float64)
1103-
r = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1092+
d = (tp + fn).astype(np.float64)
1093+
r = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
11041094
r = np.nan_to_num(r, nan=0.0)
11051095

11061096
n = (2 * (p * r)).astype(np.float64)
1107-
d = (p+r).astype(np.float64)
1108-
f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1097+
d = (p + r).astype(np.float64)
1098+
f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
11091099
f1 = np.nan_to_num(f1, nan=0.0)
11101100

11111101
n = c_tp.astype(np.float64)
1112-
d = (c_tp+c_fp).astype(np.float64)
1113-
c_p = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1102+
d = (c_tp + c_fp).astype(np.float64)
1103+
c_p = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
11141104
c_p = np.nan_to_num(c_p, nan=0.0)
11151105

1116-
11171106
n = c_tp.astype(np.float64)
1118-
d = (c_tp+c_fn).astype(np.float64)
1119-
c_r = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1107+
d = (c_tp + c_fn).astype(np.float64)
1108+
c_r = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
11201109
c_r = np.nan_to_num(r, nan=0.0)
11211110

11221111
n = (2 * (c_p * c_r)).astype(np.float64)
1123-
d = (c_p+c_r).astype(np.float64)
1124-
c_f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d!= 0)
1112+
d = (c_p + c_r).astype(np.float64)
1113+
c_f1 = np.divide(n, d, out=np.full_like(n, np.nan), where=d != 0)
11251114
c_f1 = np.nan_to_num(f1, nan=0.0)
11261115

11271116
p_left_edges, p_counts, p_widths = compute_histogram(p, 10)
@@ -1242,8 +1231,6 @@ def _update_plot_data(
12421231

12431232
conf = sum(conf_total) / len(conf_total)
12441233

1245-
1246-
12471234
if tp + fp != 0:
12481235
p = tp / (tp + fp)
12491236
p = np.nan_to_num(p, nan=0.0)
@@ -1316,7 +1303,6 @@ def _update_plot_data(
13161303
else:
13171304
c_f1 = 0
13181305

1319-
13201306
c_p_class_list.append(c_p)
13211307
c_r_class_list.append(c_r)
13221308
c_f1_class_list.append(c_f1)
@@ -1538,7 +1524,7 @@ def _update_plot_data(
15381524
else:
15391525
r = 0
15401526
r = np.nan_to_num(r, nan=0.0)
1541-
if p+r !=0:
1527+
if p + r != 0:
15421528
f1 = 2 * (p * r) / (p + r)
15431529
else:
15441530
f1 = 0
@@ -1720,7 +1706,6 @@ def _add_plots(self, ctx, stack):
17201706
# Start the ones that always appear regardless of model type and follow by eval_type
17211707
# specific ones afterwards
17221708

1723-
17241709
# Start by grabbing some basics
17251710
eval_key = ctx.panel.get_state("my_stack.menu.actions.eval_key")
17261711
compare_key = ctx.panel.get_state("my_stack.menu.actions.compare_key", None)
@@ -1744,7 +1729,7 @@ def _add_plots(self, ctx, stack):
17441729

17451730
# After the plot layout/config is defined, add the property to the stack with the
17461731
# appropriate on_call and on_selected calls
1747-
#TODO add on_selected
1732+
# TODO add on_selected
17481733
stack.add_property(
17491734
"confidence",
17501735
types.Property(

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