Streamlit: Using cache not for performance but for repeatability
I came up with using random module for random selection from initial item of multi selectbox.
But my first implementation like below did not work well.
import random import streamlit as st def genRandIntWithoutCache(): return random.randint(0, 3) if __name__ == '__main__': color_list = ['yellow', 'red', 'blue', 'green'] # Does not work well rand_index_01 = genRandIntWithoutCache() st.write(rand_index_01) options = st.multiselect( 'What are your favorite colors?', color_list, color_list[rand_index_01], key="01" ) st.write(options)
If you reloaded this page. you might see strange scene. You selected any items but every your action does not keep its state. This was caused by the feature of streamlit like being re-excecuted all of your code.
And then. our next step is to use "Cache" with decorator of it. The code below works well because of its caching. This makes its generating random number work only one time.
import random import streamlit as st from streamlit import caching @st.cache def genRandInt(): return random.randint(0, 3) def genRandIntWithoutCache(): return random.randint(0, 3) if __name__ == '__main__': color_list = ['yellow', 'red', 'blue', 'green'] # Does not work well rand_index_01 = genRandIntWithoutCache() st.write(rand_index_01) options = st.multiselect( 'What are your favorite colors?', color_list, color_list[rand_index_01], key="01" ) st.write(options) # Works well rand_index_02 = genRandInt() st.write(rand_index_02) options = st.multiselect( 'What are your favorite colors?', color_list, color_list[rand_index_02], key="02" ) st.write(options)
Yes!! It works well. But is it really good?? In this time. user is only me. But in production. user will be many. If that random generator works only one time,this cause only one number generating. So next step,we add one step which make us possible to control the timing of doing cache and we do not need to use caching when generating random number.
import random import streamlit as st from streamlit import caching @st.cache def genRandInt(): return random.randint(0, 3) def genRandIntWithoutCache(): return random.randint(0, 3) if __name__ == '__main__': color_list = ['yellow', 'red', 'blue', 'green'] # Does not work well rand_index_01 = genRandIntWithoutCache() st.write(rand_index_01) options = st.multiselect( 'What are your favorite colors?', color_list, color_list[rand_index_01], key="01" ) st.write(options) # Works well form = st.form(key="form") rand_index_02 = genRandIntWithoutCache() st.write(rand_index_02) options = form.multiselect( 'What are your favorite colors?', color_list, color_list[rand_index_02], key="02" ) st.write(options) # To control cache submitted = form.form_submit_button("Submit") if submitted: caching.clear_cache()
Testing numpy array
I tried to test below. But 4th assertion could not be passed.
import numpy as np def test__sigmoid(): assert round(sigmoid(-5), 7) == (0.0066929) assert round(sigmoid(0), 5) == (0.5000000) assert round(sigmoid(5), 5) == (0.99331) assert (sigmoid([4, 5, 6])).round(5) == np.array([0.98201, 0.99331, 0.99753])
The reason of its failure assertion line returns array with boolean like below.
sigmoid([4, 5, 6]).round(5) == np.array([0.98201, 0.99331, 0.99753]) # array([ True, True, True])
Then I found a testing module in numpy.
Then I revised test code like below and it worked well.
def test__sigmoid(): assert round(sigmoid(-5), 7) == (0.0066929) assert round(sigmoid(0), 5) == (0.5000000) assert round(sigmoid(5), 5) == (0.99331) np.testing.assert_almost_equal(sigmoid([4, 5, 6]).round(5), np.array([0.98201, 0.99331, 0.99753]))
But if you unnoticed about decimal. this test will be failed.
Sort multi edge nodes with networkx in python
import
import networkx as nx import numpy as np
Create multi graph
G = nx.MultiGraph()
G.add_nodes_from(["A", "B", "C", "D", "E", "F"])
G.add_edges_from([("A", "B"), ("B", "C"), ("B", "D"), ("C", "D"), ("A", "E"), ("C", "E"), ("C", "F"), ("C", "F"), ("F", "F")])
- this graph has multi edge in between node C and F
- in this case, we want to visualize its multi edges in matplotlib
Approach1. Convert data structure G to Adjacency matrix
- "Adjacency matrix" is the way to describe network data structure by using matrix.
- It is described as below
A = (nx.adjacency_matrix(G)).todense() // output of A matrix([[0., 1., 0., 0., 1., 0.], [1., 0., 1., 1., 0., 0.], [0., 1., 0., 1., 1., 2.], [0., 1., 1., 0., 0., 0.], [1., 0., 1., 0., 0., 0.], [0., 0., 2., 0., 0., 1.]])
- next, we want to sort its matrix with the filter.
- The filter serves some nodes which have multi edges.
- How to generate the filter
- Flatten A to one dimension array or list
- get index number from it by sorting
- get node value from origin node list by using above index number
node_list = list(G) A_flatten = np.ravel(A) idx_sorted_list = np.where(A_flatten >= 2)[0] # return value is tuple [node_list[(I % len(G.nodes))] for I in idx_sorted_list] // ['F', 'C']
Then after, let's try to apply this way to existed dataset. In this time, we use zachary's karate club graph.
import networkx as nx import numpy as np import matplotlib.pyplot as plt def get_multi_edge_nodes(G, threshold): node_list = list(G) A = nx.adjacency_matrix(G) A_flatten = np.ravel(A.todense()) idx_sorted_list = np.where(A_flatten >= threshold)[0] # return value is tuple node_multi_edge_list = [node_list[(I % len(G.nodes))] for I in idx_sorted_list] return set(node_multi_edge_list) def rand_edges(node_num, edge_num): def randint(low, high, size): return np.random.randint(low, high, size) return [(s, t) for s, t in zip(randint(0, node_num, edge_num), randint(0, node_num, edge_num))] G_01 = nx.karate_club_graph() G_02 = nx.MultiGraph() G_02.add_edges_from(G_01.edges) G_02.add_edges_from(rand_edges(len(G_02), 20)) ret1 = get_multi_edge_nodes(G_01, 2) ret2 = get_multi_edge_nodes(G_02, 2) G_02_sub = G_02.subgraph(ret2) fig, (ax1, ax2) = plt.subplots(1, 2) nx.draw_networkx(G_01, ax=ax1) nx.draw_networkx(G_02_sub, ax=ax2) plt.savefig('test.png')
Streamlitでインタラクティブな地図にデータを可視化する
この記事は何か
- Streamlitを使ってインタラクティブなデータ可視化を地図上で試みたい人のためのハウツーを記載
- 本記事で使用するコードのレポジトリは↓
完成イメージ
どんなコードか
まずダッシュボードのタイトルを設定
st.title("Garbage Distribution in Tochigi")
データを生成（デモなのでランダムで）
data= generate_data()
地図上にデータをプロット
st.write(pdk.Deck(
map_style="mapbox://styles/mapbox/light-v9",
initial_view_state={
"latitude": PARK[0][0],
"longitude": PARK[0][1],
"zoom": 11,
"pitch": 50,
},
layers=[
pdk.Layer(
"HexagonLayer",
data=data,
get_position=["lon", "lat"],
radius=100,
elevation_scale=4,
elevation_range=[0, 1000],
pickable=True,
extruded=True,
),
],
))
棒グラフも表示
st.altair_chart(alt.Chart(data)
.mark_bar().encode(
x=alt.X("day:Q", scale=alt.Scale(nice=False)),
y=alt.Y("q:Q"),
tooltip=['day', 'q']
), use_container_width=True)
チェックボックスで生データ表示を切り替え
if st.checkbox("Show raw data", False): st.write(data)
所感
- Streamlitを使った部分の実装については30分もかからなかったのでめちゃお手軽
- クラウド上へのデプロイ手法を身につけとけば企画〜実装のサイクルがめちゃ早まる
- pydeckについての理解はほとんど必要なかった
- APIについての理解が深まればもっと柔軟な設定ができそう
- AltairのAPIは多少理解しておかないとStreamlitのAPIだけだと使いづらいかな・・・
Python&Rの時系列予測ライブラリ Prophetとは
「ネットワーク思考のすすめーネットセントリック時代の組織戦略」を読んだ
本書の問い
- 環境変化に役立つ普遍的な原理とは
- 解法としてのネットワーク理論、社会システム理論
他研究との違い
- シミュレーションによる機械的な結論等の器物的なネットワーク分析ではない
- 既存の成果を批判的に摂取しながら道の領域を開拓
- 最新のシステム論の知見も織り込む
大事なこと
- 繋がり方と適度なリワイヤリング
- 本書の言葉を借りると「ご近所付き合い」と「遠距離恋愛」
- ネットセントリック
- バランスの取れたリワイヤリング
- サッカーはネットセントリック
- 実社会応用としての四川トヨタのサプライチェーン改革、トヨタ自主研究会
- ソーシャルキャピタル、「社会的習慣、習律」
初感
- 後半の社会システム論は難解でかなり飛ばし気味に読んだ
- 中盤までは納得感もあり、うまく言語化されている
