|
1
|
|
|
# Author: Simon Blanke |
|
2
|
|
|
# Email: [email protected] |
|
3
|
|
|
# License: MIT License |
|
4
|
|
|
|
|
5
|
|
|
import numpy as np |
|
6
|
|
|
import matplotlib.pyplot as plt |
|
7
|
|
|
import plotly.express as px |
|
8
|
|
|
|
|
9
|
|
|
try: |
|
10
|
|
|
from progress_io import ProgressIO |
|
11
|
|
|
except: |
|
12
|
|
|
from .progress_io import ProgressIO |
|
13
|
|
|
|
|
14
|
|
|
|
|
15
|
|
|
color_scale = px.colors.sequential.Jet |
|
16
|
|
|
|
|
17
|
|
|
|
|
18
|
|
|
class StreamlitBackend: |
|
19
|
|
|
def __init__(self, search_ids): |
|
20
|
|
|
self.search_ids = search_ids |
|
21
|
|
|
self.search_id_dict = {} |
|
22
|
|
|
|
|
23
|
|
|
_io_ = ProgressIO("./") |
|
24
|
|
|
|
|
25
|
|
|
for search_id in search_ids: |
|
26
|
|
|
self.search_id_dict[search_id] = {} |
|
27
|
|
|
|
|
28
|
|
|
self.search_id_dict[search_id]["prog_d"] = _io_.load_progress(search_id) |
|
29
|
|
|
self.search_id_dict[search_id]["filt_f"] = _io_.load_filter(search_id) |
|
30
|
|
|
|
|
31
|
|
|
def get_progress_data(self, search_id): |
|
32
|
|
|
progress_data = self.search_id_dict[search_id]["prog_d"] |
|
33
|
|
|
if progress_data is None: |
|
34
|
|
|
return |
|
35
|
|
|
|
|
36
|
|
|
return progress_data[~progress_data.isin([np.nan, np.inf, -np.inf]).any(1)] |
|
37
|
|
|
|
|
38
|
|
|
def pyplot(self, progress_data, search_id): |
|
39
|
|
|
nth_iter = progress_data["nth_iter"] |
|
40
|
|
|
score_best = progress_data["score_best"] |
|
41
|
|
|
nth_process = list(progress_data["nth_process"]) |
|
42
|
|
|
|
|
43
|
|
|
if np.all(nth_process == nth_process[0]): |
|
44
|
|
|
fig, ax = plt.subplots() |
|
45
|
|
|
plt.plot(nth_iter, score_best) |
|
46
|
|
|
else: |
|
47
|
|
|
fig, ax = plt.subplots() |
|
48
|
|
|
ax.set_xlabel("nth iteration") |
|
49
|
|
|
ax.set_ylabel("score") |
|
50
|
|
|
|
|
51
|
|
|
for i in np.unique(nth_process): |
|
52
|
|
|
nth_iter_p = nth_iter[nth_process == i] |
|
53
|
|
|
score_best_p = score_best[nth_process == i] |
|
54
|
|
|
plt.plot(nth_iter_p, score_best_p, label=str(i) + ". process") |
|
55
|
|
|
plt.legend() |
|
56
|
|
|
|
|
57
|
|
|
return fig |
|
58
|
|
|
|
|
59
|
|
|
def filter_data(self, df, filter_df): |
|
60
|
|
|
prog_data_columns = list(df.columns) |
|
61
|
|
|
|
|
62
|
|
|
if len(df) > 1: |
|
63
|
|
|
for column in prog_data_columns: |
|
64
|
|
|
if column not in list(filter_df["parameter"]): |
|
65
|
|
|
continue |
|
66
|
|
|
|
|
67
|
|
|
filter_ = filter_df[filter_df["parameter"] == column] |
|
68
|
|
|
lower, upper = ( |
|
69
|
|
|
filter_["lower bound"].values[0], |
|
70
|
|
|
filter_["upper bound"].values[0], |
|
71
|
|
|
) |
|
72
|
|
|
|
|
73
|
|
|
col_data = df[column] |
|
74
|
|
|
|
|
75
|
|
|
if lower == "lower": |
|
76
|
|
|
lower = np.min(col_data) |
|
77
|
|
|
else: |
|
78
|
|
|
lower = float(lower) |
|
79
|
|
|
|
|
80
|
|
|
if upper == "upper": |
|
81
|
|
|
upper = np.max(col_data) |
|
82
|
|
|
else: |
|
83
|
|
|
upper = float(upper) |
|
84
|
|
|
|
|
85
|
|
|
df = df[(df[column] >= lower) & (df[column] <= upper)] |
|
86
|
|
|
|
|
87
|
|
|
return df |
|
88
|
|
|
|
|
89
|
|
|
def plotly(self, progress_data, search_id): |
|
90
|
|
|
filter_df = self.search_id_dict[search_id]["filt_f"] |
|
91
|
|
|
|
|
92
|
|
|
progress_data.drop( |
|
93
|
|
|
["nth_iter", "score_best", "nth_process"], axis=1, inplace=True |
|
94
|
|
|
) |
|
95
|
|
|
|
|
96
|
|
|
if filter_df is not None: |
|
97
|
|
|
progress_data = self.filter_data(progress_data, filter_df) |
|
98
|
|
|
|
|
99
|
|
|
# remove score |
|
100
|
|
|
prog_data_columns = list(progress_data.columns) |
|
101
|
|
|
prog_data_columns.remove("score") |
|
102
|
|
|
|
|
103
|
|
|
fig = px.parallel_coordinates( |
|
104
|
|
|
progress_data, |
|
105
|
|
|
dimensions=prog_data_columns, |
|
106
|
|
|
color="score", |
|
107
|
|
|
color_continuous_scale=color_scale, |
|
108
|
|
|
) |
|
109
|
|
|
fig.update_layout(autosize=False, width=1200, height=540) |
|
110
|
|
|
|
|
111
|
|
|
return fig |
|
112
|
|
|
|
|
113
|
|
|
def create_plots(self, search_id): |
|
114
|
|
|
progress_data = self.get_progress_data(search_id) |
|
115
|
|
|
if progress_data is None: |
|
116
|
|
|
return None, None |
|
117
|
|
|
|
|
118
|
|
|
pyplot_fig = self.pyplot(progress_data, search_id) |
|
119
|
|
|
plotly_fig = self.plotly(progress_data, search_id) |
|
120
|
|
|
|
|
121
|
|
|
return pyplot_fig, plotly_fig |
|
122
|
|
|
|
SimonBlanke /
Hyperactive
