From dfd5c5b32dbd5ea687ce443d4a2bb09c94060bf5 Mon Sep 17 00:00:00 2001 From: Cricial <2911646453@qq.com> Date: Sat, 18 Oct 2025 16:16:05 +0800 Subject: [PATCH] =?UTF-8?q?=E9=81=97=E4=BC=A0=E7=AE=97=E6=B3=95001?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .idea/dataSources.local.xml | 7 +- .idea/dataSources.xml | 4 +- ... 3ce7b935-0ff7-47a3-aaa8-91063c963644.xml} | 867 ++-- .../_src_/schema/iiabmdb_20250925.Yu75yQ.meta | 0 .idea/misc.xml | 3 + GA_Agent_0925/SQL_analysis_risk_ga.sql | 21 + .../__pycache__/controller_db.cpython-38.pyc | Bin 0 -> 9554 bytes .../__pycache__/creating.cpython-38.pyc | Bin 0 -> 1530 bytes .../__pycache__/evaluate_func.cpython-38.pyc | Bin 0 -> 8210 bytes GA_Agent_0925/__pycache__/orm.cpython-38.pyc | Bin 0 -> 3755 bytes GA_Agent_0925/best_result_with_industry.json | 136 + GA_Agent_0925/conf_experiment.yaml | 12 + GA_Agent_0925/config.json | 15 + GA_Agent_0925/controller_db.py | 393 ++ GA_Agent_0925/count_dcp.csv | 4571 +++++++++++++++++ GA_Agent_0925/count_firm.csv | 204 + GA_Agent_0925/count_firm_prod.csv | 266 + GA_Agent_0925/count_prod.csv | 73 + GA_Agent_0925/creating.py | 56 + GA_Agent_0925/evaluate_func.py | 327 ++ GA_Agent_0925/g_bom.pkl | Bin 0 -> 11122 bytes GA_Agent_0925/ga.py | 116 - GA_Agent_0925/main.py | 109 + GA_Agent_0925/orm.py | 117 + GA_Agent_0925/risk_firm_count.py | 23 - GA_Agent_0925/多功能.py | 211 + GA_Agent_0925/{ => 废案}/GA_random.py | 157 +- GA_Agent_0925/进度.py | 43 + __pycache__/controller_db.cpython-38.pyc | Bin 9166 -> 9124 bytes __pycache__/firm.cpython-38.pyc | Bin 11795 -> 11852 bytes __pycache__/my_model.cpython-38.pyc | Bin 26613 -> 26862 bytes __pycache__/orm.cpython-38.pyc | Bin 3626 -> 3688 bytes best_result_with_industry.json | 192 + cache/firm_network_1009.pkl | Bin 0 -> 154627 bytes conf_experiment.yaml | 4 +- controller_db.py | 21 +- count_firm.csv | 1 + count_firm_prod.csv | 1 + count_prod.csv | 1 + firm.py | 8 +- firm_network.pkl | Bin 154627 -> 195547 bytes input_data/firm_industry_relation.csv | 680 +-- input_data/input_firm_data/firm_amended.csv | 609 +-- input_data/input_firm_data/firms_devices.csv | 110 +- .../input_firm_data/firms_materials.csv | 196 +- input_data/input_firm_data/firms_products.csv | 680 +-- main.py | 7 +- my_model.py | 54 +- orm.py | 3 + 49 files changed, 8473 insertions(+), 1825 deletions(-) rename .idea/dataSources/{753dde79-ca3d-42e1-8fb4-13124f46b86d.xml => 3ce7b935-0ff7-47a3-aaa8-91063c963644.xml} (64%) rename .idea/dataSources/{753dde79-ca3d-42e1-8fb4-13124f46b86d => 3ce7b935-0ff7-47a3-aaa8-91063c963644}/storage_v2/_src_/schema/iiabmdb_20250925.Yu75yQ.meta (100%) create mode 100644 GA_Agent_0925/SQL_analysis_risk_ga.sql create mode 100644 GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc create mode 100644 GA_Agent_0925/__pycache__/creating.cpython-38.pyc create mode 100644 GA_Agent_0925/__pycache__/evaluate_func.cpython-38.pyc create mode 100644 GA_Agent_0925/__pycache__/orm.cpython-38.pyc create mode 100644 GA_Agent_0925/best_result_with_industry.json create mode 100644 GA_Agent_0925/conf_experiment.yaml create mode 100644 GA_Agent_0925/config.json create mode 100644 GA_Agent_0925/controller_db.py create mode 100644 GA_Agent_0925/count_dcp.csv create mode 100644 GA_Agent_0925/count_firm.csv create mode 100644 GA_Agent_0925/count_firm_prod.csv create mode 100644 GA_Agent_0925/count_prod.csv create mode 100644 GA_Agent_0925/creating.py create mode 100644 GA_Agent_0925/evaluate_func.py create mode 100644 GA_Agent_0925/g_bom.pkl delete mode 100644 GA_Agent_0925/ga.py create mode 100644 GA_Agent_0925/main.py create mode 100644 GA_Agent_0925/orm.py delete mode 100644 GA_Agent_0925/risk_firm_count.py create mode 100644 GA_Agent_0925/多功能.py rename GA_Agent_0925/{ => 废案}/GA_random.py (86%) create mode 100644 GA_Agent_0925/进度.py create mode 100644 best_result_with_industry.json create mode 100644 cache/firm_network_1009.pkl create mode 100644 count_firm.csv create mode 100644 count_firm_prod.csv create mode 100644 count_prod.csv diff --git a/.idea/dataSources.local.xml b/.idea/dataSources.local.xml index c5eb274..068a6fe 100644 --- a/.idea/dataSources.local.xml +++ b/.idea/dataSources.local.xml @@ -1,7 +1,7 @@ - + #@ ` @@ -11,7 +11,10 @@ iiabm_user - + + + + diff --git a/.idea/dataSources.xml b/.idea/dataSources.xml index 4c7bbe4..f57d0d4 100644 --- a/.idea/dataSources.xml +++ b/.idea/dataSources.xml @@ -1,11 +1,11 @@ - + mysql_aurora.aws_wrapper true software.amazon.jdbc.Driver - jdbc:aws-wrapper:mysql://localhost:3306/iiabmdb_20250925 + jdbc:aws-wrapper:mysql://localhost:3306 $ProjectFileDir$ diff --git a/.idea/dataSources/753dde79-ca3d-42e1-8fb4-13124f46b86d.xml b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml similarity index 64% rename from .idea/dataSources/753dde79-ca3d-42e1-8fb4-13124f46b86d.xml rename to .idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml index 7f9d481..3410ade 100644 --- a/.idea/dataSources/753dde79-ca3d-42e1-8fb4-13124f46b86d.xml +++ b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml @@ -1,5 +1,5 @@ - + lower/lower @@ -61,950 +61,949 @@ iiabmdb_20250926|schema||iiabm_user|localhost|TRIGGER|G iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G 8.0.36 - - armscii8 - - + armscii8 1 - - ascii + + armscii8 - + ascii 1 - - big5 + + ascii - + big5 1 + + big5 + binary 1 - - cp1250 - - - cp1250 - - - cp1250 - - + cp1250 1 + + cp1250 + + + cp1250 + + + cp1250 + cp1250 - + cp1251 - + cp1251 - + + cp1251 + + cp1251 1 - + cp1251 - - cp1251 - - - cp1256 - - + cp1256 1 - + + cp1256 + + cp1257 - + + cp1257 + + cp1257 1 - - cp1257 - - - cp850 - - + cp850 1 - - cp852 + + cp850 - + cp852 1 - - cp866 + + cp852 - + cp866 1 - - cp932 + + cp866 - + cp932 1 - - dec8 + + cp932 - + dec8 1 - - eucjpms + + dec8 - + eucjpms 1 - - euckr + + eucjpms - + euckr 1 - - gb18030 + + euckr - + gb18030 1 + + gb18030 + gb18030 - - gb2312 - - + gb2312 1 - - gbk + + gb2312 - + gbk 1 - - geostd8 + + gbk - + geostd8 1 - - greek + + geostd8 - + greek 1 - - hebrew + + greek - + hebrew 1 - - hp8 + + hebrew - + hp8 1 - - keybcs2 + + hp8 - + keybcs2 1 - - koi8r + + keybcs2 - + koi8r 1 - - koi8u + + koi8r - + koi8u 1 - + + koi8u + + latin1 - - latin1 - - - latin1 - - - latin1 - - - latin1 - - - latin1 - - - latin1 - - + latin1 1 - + + latin1 + + + latin1 + + + latin1 + + + latin1 + + + latin1 + + + latin1 + + latin2 - - latin2 - - - latin2 - - + latin2 1 - + latin2 - - latin5 + + latin2 - + + latin2 + + latin5 1 - + + latin5 + + latin7 - - latin7 - - + latin7 1 - + latin7 - - macce + + latin7 - + macce 1 - - macroman + + macce - + macroman 1 - - sjis + + macroman - + sjis 1 - - swe7 + + sjis - + swe7 1 - - tis620 + + swe7 - + tis620 1 - - ucs2 + + tis620 - - ucs2 - - - ucs2 - - - ucs2 - - - ucs2 - - - ucs2 - - + ucs2 1 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + ucs2 - + + ucs2 + + + ucs2 + + + ucs2 + + + ucs2 + + + ucs2 + + + ucs2 + + ucs2 ucs2 - + ucs2 - + ucs2 - - ujis - - + ujis 1 - - utf16 + + ujis - - utf16 - - - utf16 - - - utf16 - - - utf16 - - - utf16 - - + utf16 1 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + utf16 - + + utf16 + + + utf16 + + + utf16 + + + utf16 + + + utf16 + + + utf16 + + utf16 utf16 - - utf16le - - + utf16le 1 - - utf32 + + utf16le - - utf32 - - - utf32 - - - utf32 - - - utf32 - - - utf32 - - + utf32 1 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + utf32 - + + utf32 + + + utf32 + + + utf32 + + + utf32 + + + utf32 + + + utf32 + + utf32 utf32 - - utf8mb3 - - - utf8mb3 - - - utf8mb3 - - - utf8mb3 - - - utf8mb3 - - - utf8mb3 - - + utf8mb3 1 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + utf8mb3 - + + utf8mb3 + + + utf8mb3 + + + utf8mb3 + + + utf8mb3 + + + utf8mb3 + + + utf8mb3 + + utf8mb3 utf8mb3 - + utf8mb3 - + utf8mb3 - - utf8mb4 - 1 - - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + + utf8mb4 + + utf8mb4 utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + + utf8mb4 + 1 + + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - - utf8mb4 - - + utf8mb4 utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - + utf8mb4 - - utf8mb4_general_ci - - - 1 - 2025-09-27.15:34:40 - utf8mb4_0900_ai_ci - - - utf8mb4_0900_ai_ci - - + utf8mb3_general_ci - + + utf8mb4_0900_ai_ci + + + 2025-10-18.02:36:56 + utf8mb4_0900_ai_ci + + + utf8mb4_general_ci + + utf8mb4_0900_ai_ci localhost - +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
- +
InnoDButf8mb4_0900_ai_ci
@@ -1029,7 +1028,7 @@ iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G PRIMARY - 3421 + 1 1 1 int|0s @@ -1115,7 +1114,7 @@ iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G PRIMARY - 7150933 + 1 1 1 int|0s @@ -1165,7 +1164,7 @@ iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G PRIMARY - 34201 + 1 1 1 int|0s @@ -1312,7 +1311,7 @@ iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G PRIMARY - 213115 + 503 1 1 int|0s @@ -1342,82 +1341,86 @@ iiabmdb_20250926|schema||iiabm_user|localhost|UPDATE|G 6 varchar(5)|0s - + + 7 + varchar(50)|0s + + s_id id without_exp_sample - + id btree 1 - + s_id btree - + 1 1 PRIMARY - + 3631 1 1 int|0s - + 1 2 int|0s - + 1 3 int|0s - + 1 4 bigint|0s - + 1 5 int|0s - + 6 varchar(64)|0s - + 7 datetime|0s - + 8 int|0s - + 9 longtext|0s - + e_id id without_exp_experiment - + id btree 1 - + e_id btree - + 1 1 PRIMARY diff --git a/.idea/dataSources/753dde79-ca3d-42e1-8fb4-13124f46b86d/storage_v2/_src_/schema/iiabmdb_20250925.Yu75yQ.meta b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/iiabmdb_20250925.Yu75yQ.meta similarity index 100% rename from .idea/dataSources/753dde79-ca3d-42e1-8fb4-13124f46b86d/storage_v2/_src_/schema/iiabmdb_20250925.Yu75yQ.meta rename to .idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/iiabmdb_20250925.Yu75yQ.meta diff --git a/.idea/misc.xml b/.idea/misc.xml index e557d17..21c5070 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -4,4 +4,7 @@ + + \ No newline at end of file diff --git a/GA_Agent_0925/SQL_analysis_risk_ga.sql b/GA_Agent_0925/SQL_analysis_risk_ga.sql new file mode 100644 index 0000000..5599228 --- /dev/null +++ b/GA_Agent_0925/SQL_analysis_risk_ga.sql @@ -0,0 +1,21 @@ +SELECT * +FROM ( + SELECT s_id, id_firm, id_product, MIN(ts) AS ts + FROM iiabmdb_20250925.without_exp_result + WHERE `status` = 'D' + AND ga_id = :ga_id + GROUP BY s_id, id_firm, id_product +) AS s_disrupt +WHERE s_id IN ( + SELECT s_id + FROM ( + SELECT s_id, id_firm, id_product, MIN(ts) AS ts + FROM iiabmdb_20250925.without_exp_result + WHERE `status` = 'D' + AND ga_id = :ga_id + GROUP BY s_id, id_firm, id_product + ) AS t + GROUP BY s_id + HAVING COUNT(*) > 1 +) +ORDER BY s_id; diff --git a/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc b/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ade30f3a9052e1248aadcd40d9d29c5542613c8 GIT binary patch literal 9554 zcma)CTaX;pdG7o4^v=xAUbVYgNxcGu28*>&85gF6g3tv>V8OZ=r6arPozuHJvvb?i zJ<{%^Cs0VXg<}~xR48IZWj3iwAZ5o?Ol-i(YaV&br7Lei!g@)i?4&A{3Mk)ydUkha zQH*D+PoM5{>2v<;_y1?lX0w)r-~Ri5we*umCF$Sjq4!6vkKzjc8wHb?T#>4>BUcqi zsj7}z)f}y=JGxA53fc_EsHU7$)pShpPOYS?mSc&!RvD;foQ$aJm8_G+7`>8na^gPd zCRIOF~fW-crIKm7@oz9h?1Ysw#I)+O1Q z^wsiId8en2ZEr_jRy;k#!A({(n zY@Jwb_`Fp0Yhi4h_NtAF9~-CqpjinEa-4q#jUILNDknIvbG*|+e>I#d(#Lv#8Xg|S z6?}-ommHZ%j&fG=RbONBVG!QYaT(%S70)T(M4NV4^3%BMxLddzXxq2xoWA~!}PNsmgUx+iu>a~#9 zD;1v~e=Hs-1@6UCxa9h)jk!35r_c|=I0wSlysGauxW7Rk{s!;(LAY$N|T|#n_iSqTA~C`P|q+4bQzdyjY66hhz?yLnqBj%704B&1QZz{ zD%G1Ib%@OklC|z*^EMI6uGK@kRI{n?0lTMf@<5~bFi62iP@Fsyo9LgAI$iZc4^r;4 z(iJoovawcg__bK0&&TP7W~Jg*>K==8M}xrUB*8*{d5)&bQZa}k&az_YHaw2Sgg)na zdS`;4{%R=*`4Bx0Zf(wGUg*WvycZUi+@RF*3r4I3eq|vx5|)bzoHQ-MT_{z2$RFn4 zMk0u7_oHs9Cbsjo#AooS|8h_qd-(iWtT;G-@#4kv$NgYAtT)adI(X>8*@x`pcjM}J zZv6P~KKlDtZ>_)o@f&}AzUl|w{a=|qbnyJ+N8O`~&}8@ER}MXR9-r-stjp$S8*8!Y z;)A8ob$8%9!4wKfw&YPcuUPVwY{=vCux8+HD2Ch`>y`M~jgdu?+l?R96!_o|i*MTrQ$*Z?zz6Uc2dL+eOJVN7W;bKs5 z051YEHNI+9YS%wt(IJ+|Wat!Wjxs5A6b22W`eEo+S!J+D%($x1 zW}q*ZL!G6Vc}aO?xUGanq;yj2upw=AOCv+6Y({FBX6dU+TZ1-Pm=*SmWiCsY8PAwG zc{{KUjjO#R){w;-jL2ZQ%{8EYI9cBy+EP)flVQrb%(X)Q z>t8;vw9{=XvS^<60PaJ$XCkwck1~tWy3)=@15vglw{wx+8H#f2a3m`qiWPLfl29QW z##&Q?3aRo4>};0`q9@wggW+g-EE;4ZY&0D0jIT>1kD!hz|-B)#H4gsx~8l=gn4u^&zCUIRC_1ZcuzDQO+`DS zkp(T<@jXxhRM``aV@GyG`w-HRiL~VsIhWj@21_Jj`3EwkZ728F=*+rl@#DzjYU;JN?zCNLTATbeBqt zOD-=3%P_WKh!L~FN+p&#bXujF3M&)uCWA%}PN*L%1-#jS-=i-#c%AiWA{hf)0WUkJYPl z=oyHS`ja63YMgnt+k@t4K7ciU?ld>9UAcMX+Q&bC_2ZY`r>Pw6?6XfEkCi8n!^Ijw zy~&GUW$5umKa4X)?t7u{HUqG!nZo?P_|8ihXg7DFZh!RN)f+#0{l@jTKKkH$_I$nS z)@Xrv{iD`U|3X$TxSY(naJwyyJ`5(LK^A`%&aAS%f8BF$5nEd`Pu9rU`pn+)nf5}R z+dka5u7?03=)WP*aXxn@fI49Q10W?Nu&1xfva~>;0>1xHb1X^e%nn(|pJZwMh z*P2y=-+=F>pym;L$Hu^bW)(O-?=jZvQ6Gqh7fQhCfF;;O7xWaSg*%Nk66{!Bh*Q|8 zONwg%oMI=ANxO2Pz)rSL_~Z1ZjKqUQ+5|`l;*^TmgzUyDhzOuXI!>)|W<&nz$;nlT zuxxXHN*~D3j71XB8c44%x_EX5>1D;6_bWldAzg65$t()nK^j#K5IZcms~5zr?!}oB zTXi>N3ef@jK;Q(9b5hg>8aWohI=A9q@GEhiUiQjeg28&zAN=C`68tTd? 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HE?NH= self.batch_size: + self._commit_batch() + + # 辅助方法：批量提交 + def _commit_batch(self): + db_session.bulk_save_objects(self.experiment_data) + db_session.commit() + self.experiment_data.clear() # 清空队列 + + def finalize_insertion(self): + if self.experiment_data: + self._commit_batch() # 提交剩余的数据 + + def fill_sample_table(self): + rng = random.Random(self.dct_parameter['meta_seed']) + # 根据样本数目 设置 32 位随机整数 + lst_seed = [ + rng.getrandbits(32) + for _ in range(int(self.dct_parameter['n_sample'])) + ] + lst_exp = db_session.query(Experiment).all() + + lst_sample = [] + for experiment in lst_exp: + # idx_sample: 1-50 + for idx_sample in range(int(experiment.n_sample)): + s = Sample(e_id=experiment.id, + idx_sample=idx_sample + 1, + seed=lst_seed[idx_sample], + is_done_flag=-1) + lst_sample.append(s) + # 每当达到批量大小时提交一次 + if len(lst_sample) >= self.batch_size: + db_session.bulk_save_objects(lst_sample) + db_session.commit() + print(f'Inserted {len(lst_sample)} samples!') + lst_sample.clear() # 清空已提交的样本列表 + + # 提交剩余的样本 + if lst_sample: + db_session.bulk_save_objects(lst_sample) + db_session.commit() + print(f'Inserted {len(lst_sample)} samples!') + + def reset_db(self, force_drop=False): + # first, check if tables exist + lst_table_obj = [ + Base.metadata.tables[str_table] + for str_table in ins.get_table_names() + if str_table.startswith(self.db_name_prefix) + ] + self.is_exist = len(lst_table_obj) > 0 + if force_drop: + self.force_drop_db(lst_table_obj) + # while is_exist: + # a_table = random.choice(lst_table_obj) + # try: + # Base.metadata.drop_all(bind=engine, tables=[a_table]) + # except KeyError: + # pass + # except OperationalError: + # pass + # else: + # lst_table_obj.remove(a_table) + # print( + # f"Table {a_table.name} is dropped " + # f"for exp: {self.db_name_prefix}!!!" + # ) + # finally: + # is_exist = len(lst_table_obj) > 0 + + if self.is_exist: + print( + f"All tables exist. No need to reset " + f"for exp: {self.db_name_prefix}." + ) + # change the is_done_flag from 0 to -1 + # rerun the in-finished tasks + self.is_exist_reset_flag_resset_db() + # if self.reset_flag > 0: + # if self.reset_flag == 2: + # sample = db_session.query(Sample).filter( + # Sample.is_done_flag == 0) + # elif self.reset_flag == 1: + # sample = db_session.query(Sample).filter( + # Sample.is_done_flag == 0, + # Sample.computer_name == platform.node()) + # else: + # raise ValueError('Wrong reset flag') + # if sample.count() > 0: + # for s in sample: + # qry_result = db_session.query(Result).filter_by( + # s_id=s.id) + # if qry_result.count() > 0: + # db_session.query(Result).filter(s_id=s.id).delete() + # db_session.commit() + # s.is_done_flag = -1 + # db_session.commit() + # print(f"Reset the sample id {s.id} flag from 0 to -1") + + else: + # 不存在则重新生成所有的表结构 + Base.metadata.create_all(bind=engine) + self.init_tables() + print( + f"All tables are just created and initialized " + f"for exp: {self.db_name_prefix}." + ) + + def force_drop_db(self, lst_table_obj): + self.is_exist = len(lst_table_obj) > 0 + while self.is_exist: + a_table = random.choice(lst_table_obj) + try: + Base.metadata.drop_all(bind=engine, tables=[a_table]) + except KeyError: + pass + except OperationalError: + pass + else: + lst_table_obj.remove(a_table) + print( + f"Table {a_table.name} is dropped " + f"for exp: {self.db_name_prefix}!!!" + ) + finally: + self.is_exist = len(lst_table_obj) > 0 + + def is_exist_reset_flag_resset_db(self): + if self.reset_flag > 0: + if self.reset_flag == 2: + sample = db_session.query(Sample).filter( + Sample.is_done_flag == 0) + elif self.reset_flag == 1: + sample = db_session.query(Sample).filter( + Sample.is_done_flag == 0, + Sample.computer_name == platform.node()) + else: + raise ValueError('Wrong reset flag') + if sample.count() > 0: + for s in sample: + qry_result = db_session.query(Result).filter_by( + s_id=s.id) + if qry_result.count() > 0: + db_session.query(Result).filter(s_id=s.id).delete() + db_session.commit() + s.is_done_flag = -1 + db_session.commit() + print(f"Reset the sample id {s.id} flag from 0 to -1") + + def prepare_list_sample(self): + # 为了符合前面 重置表里面存在 重置本机 或者重置全部 或者不重置的部分 这个部分的 关于样本运行也得重新拿出来 + # 查找一个风险事件中 50 个样本 + res = db_session.execute( + text(f"SELECT count(*) FROM {self.db_name_prefix}_sample s, " + f"{self.db_name_prefix}_experiment e WHERE s.e_id=e.id" + )).scalar() + # 控制 n_sample数量 作为后面的参数 + n_sample = 0 if res is None else res + # print(f'There are a total of {n_sample} samples.') + # 查找 is_done_flag = -1 也就是没有运行的 样本 运行后会改为0 + res = db_session.execute( + text(f"SELECT id FROM {self.db_name_prefix}_sample " + f"WHERE is_done_flag = -1" + )) + for row in res: + s_id = row[0] + self.lst_saved_s_id.append(s_id) + + @staticmethod + def select_random_sample(lst_s_id): + temp_lst = lst_s_id[:] # 复制列表 + while temp_lst: + s_id = random.choice(temp_lst) + temp_lst.remove(s_id) # 从临时列表删除 + res = db_session.query(Sample).filter( + Sample.id == int(s_id), + Sample.is_done_flag == -1 + ) + if res.count() == 1: + return res[0] + # 尝试完所有样本都没找到 + return None + + def fetch_a_sample(self, s_id=None): + # 由Computation 调用 返回 sample对象 同时给出 2中 指定访问模式 抓取特定的 样本 通过s_id + # 默认访问 flag为-1的 lst_saved_s_id + if s_id is not None: + res = db_session.query(Sample).filter(Sample.id == int(s_id)) + if res.count() == 0: + return None + else: + return res[0] + + sample = self.select_random_sample(self.lst_saved_s_id) + if sample is not None: + return sample + + return None + + @staticmethod + def lock_the_sample(sample: Sample): + sample.is_done_flag, sample.computer_name = 0, platform.node() + db_session.commit() + + def reset_sample_db(self): + """ + 将 iiabmdb_20250925.without_exp_sample 表中 + 所有样本的 is_done_flag 更新为 -1 + """ + sql = text("UPDATE iiabmdb_20250925.without_exp_sample SET is_done_flag = -1") + db_session.execute(sql) + db_session.commit() + +if __name__ == '__main__': + print("Testing the database connection...") + try: + controller_db = ControllerDB('test') + Base.metadata.create_all(bind=engine) + except Exception as e: + print("Failed to connect to the database!") + print(e) + exit(1) diff --git a/GA_Agent_0925/count_dcp.csv b/GA_Agent_0925/count_dcp.csv new file mode 100644 index 0000000..d7f8093 --- /dev/null +++ b/GA_Agent_0925/count_dcp.csv @@ -0,0 +1,4571 @@ +up_id_firm,up_id_product,down_id_firm,down_id_product,count +340093034,95,532328014,99,108 +340093034,95,303926772,99,106 +395736790,95,303926772,99,104 +395736790,95,532328014,99,100 +29223617,95,303926772,99,93 +29223617,95,532328014,99,90 +2337727838,90,303926772,99,42 +2326722141,90,303926772,99,41 +331545755,90,303926772,99,41 +2326722141,90,532328014,99,39 +3191869223,91,303926772,99,38 +2337727838,90,532328014,99,38 +3191869223,91,532328014,99,36 +331545755,90,532328014,99,35 +2336923756,91,532328014,99,33 +3111603340,50,303926772,99,33 +214851100,51,303926772,99,32 +2336923756,91,303926772,99,32 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+95,1787 +90,1004 +99,982 +91,727 +92,581 +93,575 +53,559 +55,551 +54,549 +52,547 +51,539 +50,533 +94,433 +9,338 +47,294 +45,275 +46,272 +49,271 +44,264 +48,255 +38,214 +43,212 +39,208 +41,197 +40,194 +42,185 +10,113 +11,45 +15,15 +12,15 +33,15 +7,15 +25,15 +69,10 +74,10 +68,10 +70,10 +71,10 +72,10 +73,10 +19,10 +79,10 +8,10 +66,10 +18,10 +17,10 +16,10 +13,10 +97,10 +67,10 +32,10 +65,10 +64,10 +34,10 +35,10 +37,10 +31,10 +30,10 +29,10 +28,10 +27,10 +26,10 +24,10 +23,10 +20,10 +59,10 +60,10 +62,10 +63,10 +61,10 +36,8 +22,5 diff --git a/GA_Agent_0925/creating.py b/GA_Agent_0925/creating.py new file mode 100644 index 0000000..e47aa65 --- /dev/null +++ b/GA_Agent_0925/creating.py @@ -0,0 +1,56 @@ +import random +from deap import creator, base, tools +from evaluate_func import fitness + +def creating(): + if "FitnessMax" not in creator.__dict__: + creator.create("FitnessMax", base.Fitness, weights=(1.0,)) + if "Individual" not in creator.__dict__: + creator.create("Individual", list, fitness=creator.FitnessMax) + + toolbox = base.Toolbox() + + # 基因注册 + toolbox.register("n_max_trial", random.randint, 1, 40) + toolbox.register("prf_size", random.uniform, 0.0, 1.0) + toolbox.register("prf_conn", random.uniform, 0.0, 1.0) + toolbox.register("cap_limit_prob_type", random.randint, 0, 1) + toolbox.register("cap_limit_level", random.randint, 5, 50) + toolbox.register("diff_new_conn", random.uniform, 0.0, 1.0) + toolbox.register("netw_prf_n", random.randint, 1, 20) + toolbox.register("s_r", random.uniform, 0.05, 0.5) + toolbox.register("S_r", random.uniform, 0.5, 1.0) + toolbox.register("x", random.uniform, 0.0, 1) + toolbox.register("k", random.uniform, 0.05, 2.0) + toolbox.register("production_increase_ratio", random.uniform, 0.5, 2.0) + + # 个体与种群注册 + toolbox.register( + "individual", + tools.initCycle, + creator.Individual, + ( + toolbox.n_max_trial, + toolbox.prf_size, + toolbox.prf_conn, + toolbox.cap_limit_prob_type, + toolbox.cap_limit_level, + toolbox.diff_new_conn, + toolbox.netw_prf_n, + toolbox.s_r, + toolbox.S_r, + toolbox.x, + toolbox.k, + toolbox.production_increase_ratio + ), + n=1 + ) + toolbox.register("population", tools.initRepeat, list, toolbox.individual) + + # 遗传算子 + toolbox.register("evaluate", fitness) + toolbox.register("mate", tools.cxTwoPoint) + toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.1) + toolbox.register("select", tools.selTournament, tournsize=3) + + return toolbox diff --git a/GA_Agent_0925/evaluate_func.py b/GA_Agent_0925/evaluate_func.py new file mode 100644 index 0000000..524ef30 --- /dev/null +++ b/GA_Agent_0925/evaluate_func.py @@ -0,0 +1,327 @@ +import os +import time +import traceback +import uuid +from datetime import datetime +from multiprocessing import Process + +import pandas as pd +from sqlalchemy import text +from sqlalchemy.orm import sessionmaker + +from my_model import MyModel +from orm import connection, engine + + +# 🎯 适应度函数（核心目标函数） +def fitness(individual, controller_db_obj): + """ + 遗传算法适应度函数：用于评估个体（模型参数）的优劣。 + + 参数： + individual : list + 个体参数列表： + [n_max_trial, prf_size, prf_conn, cap_limit_prob_type, cap_limit_level, + diff_new_conn, netw_prf_n, s_r, S_r, x, k, production_increase_ratio] + + 目标： + 使 ABM 模型生成的“脆弱产业集合”与目标产业集合尽可能相似。 + - fitness = -error + - error = 模拟结果集合与目标集合的差异度（越小越好） + """ + # 生成唯一 GA ID + ga_id = str(uuid.uuid4())[:8] # 简短随机ID + individual.ga_id = ga_id # 将 ga_id 绑定到个体上 + # ========== 1️⃣ 生成参数字典 ========== + dct_exp = { + 'n_max_trial': individual[0], + 'prf_size': individual[1], + 'prf_conn': individual[2], + 'cap_limit_prob_type': individual[3], + 'cap_limit_level': individual[4], + 'diff_new_conn': individual[5], + 'netw_prf_n': individual[6], + 's_r': individual[7], + 'S_r': individual[8], + 'x': individual[9], + 'k': individual[10], + 'production_increase_ratio': individual[11] + } + # 将 GA 染色体的值映射为实际 ABM 参数 + if dct_exp['cap_limit_prob_type'] == 0: + dct_exp['cap_limit_prob_type'] = "uniform" # 类型A（例如 uniform） + else: + dct_exp['cap_limit_prob_type'] = "normal" # 类型B（例如 normal） + + # 打印 GA ID 和参数 + print(f"\n 正在执行 GA 个体 {ga_id}，参数如下：") + # for key, value in dct_exp.items(): + # print(f" {key}: {value}") + # ========== 2️⃣ 调用 ABM 模型 ========== + # 并行进程数目 + job=6 + + do_process(controller_db_obj,ga_id,dct_exp,job) + # ========== 3️⃣ 获取数据库连接并提取结果 ========== + simulated_vulnerable_industries = get_vulnerable100_code(connection,ga_id) + print(simulated_vulnerable_industries) + # ========== 4️⃣ 获取目标产业集合 ========== + target_vulnerable_industries = get_target_vulnerable_industries() + + # ========== 5️⃣ 计算误差（集合差异度） ========== + set_sim = set(simulated_vulnerable_industries) + set_target = set(target_vulnerable_industries) + error = len(set_sim.symmetric_difference(set_target)) + + simulated_set = set(simulated_vulnerable_industries) + target_set = set(target_vulnerable_industries) + + matching = simulated_set & target_set # 交集 + extra = simulated_set - target_set # 模拟多出的产业 + missing = target_set - simulated_set # 未覆盖产业 + + print(f"符合目标的产业数量: {len(matching)}") + print(matching) + print(f"模拟多出的产业数量: {len(extra)}") + print(f"未覆盖目标产业数量: {len(missing)}") + + # ========== 6️⃣ 返回适应度（越大越好） ========== + return (float(-error),) +# 目标产业集合 +def get_target_vulnerable_industries(): + """ + 获取行业列表中所有产业链编号的集合（整数形式）。 + 说明： + - 输入的 industry_list 是一个字典列表，每个字典包含： + {"product": 产品名称, "category": 产品类别, "chain_id": 产业链编号} + - 某些 chain_id 可能是复合编号，例如 "11 / 513742"，需要拆分成单独整数。 + - 输出是一个 set，包含所有 chain_id（去重、整数形式）。 + + 参数： + industry_list : list of dict + 行业字典列表，每个字典必须包含 "chain_id" 键。 + + 返回： + set + 所有产业链编号的整数集合。 + """ + industry_list = [ + # ① 半导体设备类 + {"product": "离子注入机", "category": "离子注入设备", "chain_id": 34538}, + {"product": "刻蚀设备 / 湿法刻蚀设备", "category": "刻蚀机", "chain_id": 34529}, + {"product": "沉积设备", "category": "薄膜生长设备（CVD/PVD）", "chain_id": 34539}, + {"product": "CVD", "category": "薄膜生长设备", "chain_id": 34539}, + {"product": "PVD", "category": "薄膜生长设备", "chain_id": 34539}, + {"product": "CMP", "category": "化学机械抛光设备", "chain_id": 34530}, + {"product": "光刻机", "category": "光刻机", "chain_id": 34533}, + {"product": "涂胶显影机", "category": "涂胶显影设备", "chain_id": 34535}, + {"product": "晶圆清洗设备", "category": "晶圆清洗机", "chain_id": 34531}, + {"product": "测试设备", "category": "测试机", "chain_id": 34554}, + {"product": "外延生长设备", "category": "薄膜生长设备", "chain_id": 34539}, + + # ② 半导体材料与化学品类 + {"product": "三氯乙烯", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, + {"product": "丙酮", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, + {"product": "异丙醇", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, + {"product": "其他醇类", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, + {"product": "光刻胶", "category": "光刻胶及配套试剂", "chain_id": 32445}, + {"product": "显影液", "category": "显影液", "chain_id": 46504}, + {"product": "蚀刻液", "category": "蚀刻液", "chain_id": 56341}, + {"product": "光阻去除剂", "category": "光阻去除剂", "chain_id": 32442}, + + # ③ 晶圆制造类 + {"product": "晶圆", "category": "单晶硅片 / 多晶硅片", "chain_id": 32338}, + {"product": "硅衬底", "category": "硅衬底", "chain_id": 36914}, + {"product": "外延片", "category": "硅外延片 / GaN外延片 / SiC外延片等", "chain_id": 32338}, + + # ④ 封装与测试类 + {"product": "封装", "category": "IC封装", "chain_id": 10}, + {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 513742}, + {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 11}, + + # ⑤ 芯片与设计EDA类 + {"product": "芯片（通用）", "category": "集成电路制造", "chain_id": 317589}, + {"product": "DRAM", "category": "存储芯片 → 集成电路制造", "chain_id": 317589}, + {"product": "GPU", "category": "图形芯片 → 集成电路制造", "chain_id": 317589}, + {"product": "处理器（CPU/SoC）", "category": "芯片设计", "chain_id": 9}, + {"product": "高频芯片", "category": "芯片设计", "chain_id": 9}, + {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 9}, + {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 2717}, + {"product": "先进节点制造设备", "category": "集成电路制造", "chain_id": 317589}, + {"product": "EDA及IP服务", "category": "设计辅助", "chain_id": 2515}, + {"product": "MPW服务", "category": "多项目晶圆流片", "chain_id": 2514}, + {"product": "芯片设计验证", "category": "设计验证", "chain_id": 513738}, + {"product": "过程工艺检测", "category": "制程检测", "chain_id": 513740} + ] + # 手工转换 + industry_list_index = vulnerable100_index = \ + ['100', '58', '61', '9', '7', '98', '57', '8', '65', '68', '66', '38', + '90', '21', '96', '71', '27', '74', '99', '95', '11', '77', '59', '56', '97'] + # # 提取所有 chain_id，并去重 + # chain_ids = set() + # for item in industry_list: + # # 如果 chain_id 是字符串包含多个编号，用逗号或斜杠拆分 + # if isinstance(item["chain_id"], str): + # for cid in item["chain_id"].replace("/", ",").split(","): + # chain_ids.add(cid.strip()) + # else: + # chain_ids.add(str(item["chain_id"])) + + return industry_list_index +# 从数据库计算脆弱产业集合 +def get_vulnerable100_code(engine, ga_id): + """ + 计算最脆弱前100产品的 Code 列表（去重），只针对指定 ga_id。 + """ + # 生成新的 session + Session = sessionmaker(bind=engine) + session = Session() + # 1️⃣ 读取 SQL 文件 + base_dir = os.path.dirname(os.path.abspath(__file__)) + sql_file = os.path.join(base_dir, "..", "GA_Agent_0925", "SQL_analysis_risk_ga.sql") + sql_file = os.path.abspath(sql_file) + with open(sql_file, "r", encoding="utf-8") as f: + str_sql = f.read() # 注意这里是 str_sql，不是 tr_sql + print(f"[信息] 正在查询 ga_id={ga_id} 的脆弱产品数据...") + + # 2️⃣ 执行 SQL 查询 + # 2️⃣ 新建 connection + # 2️⃣ 新建 session，每次查询都用新的 session/connection + Session = sessionmaker(bind=engine) + with Session() as session: + # 使用 session.connection() 来保证 SQLAlchemy 执行原生 SQL + result = pd.read_sql( + sql=text(str_sql), + con=session.connection(), + params={"ga_id": ga_id} # 绑定参数 + ) + + # =============================== + # 2️⃣ 统计每个企业-产品组合出现次数 + # =============================== + count_firm_prod = result.value_counts(subset=['id_firm', 'id_product']) + count_firm_prod.name = 'count' + count_firm_prod = count_firm_prod.to_frame().reset_index() + count_firm_prod.to_csv('count_firm_prod.csv', index=False, encoding='utf-8-sig') + + # =============================== + # 3️⃣ 统计每个企业出现的总次数 + # =============================== + count_firm = count_firm_prod.groupby('id_firm')['count'].sum().reset_index() + count_firm.sort_values('count', ascending=False, inplace=True) + count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig') + + # =============================== + # 4️⃣ 统计每个产品出现的总次数 + # =============================== + count_prod = count_firm_prod.groupby('id_product')['count'].sum().reset_index() + count_prod.sort_values('count', ascending=False, inplace=True) + count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig') + + # =============================== + # 5️⃣ 选出最脆弱的前100个产品（出现次数最多） + # =============================== + vulnerable100_product = count_prod.nlargest(100, "count")["id_product"].tolist() + print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品") + + # =============================== + # # 6️⃣ 过滤 result，只保留前100脆弱产品 + # # =============================== + # result_vulnerable100 = result[result['id_product'].isin(vulnerable100_product)].copy() + # print(f"[信息] 筛选后剩余记录数: {len(result_vulnerable100)}") + # # =============================== + # # 7️⃣ 构造 DCP（Disruption Causing Probability） + # # =============================== + # result_dcp_list = [] + # for sid, group in result_vulnerable100.groupby('s_id'): + # ts_start = max(group['ts']) + # while ts_start >= 1: + # ts_end = ts_start - 1 + # while ts_end >= 0: + # up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']] + # down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']] + # for _, up_row in up.iterrows(): + # for _, down_row in down.iterrows(): + # result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist()) + # ts_end -= 1 + # ts_start -= 1 + # + # # 转换为 DataFrame + # result_dcp = pd.DataFrame(result_dcp_list, columns=[ + # 's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product' + # ]) + # + # # =============================== + # # 8️⃣ 统计 DCP 出现次数 + # # =============================== + # count_dcp = result_dcp.value_counts( + # subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'] + # ).reset_index(name='count') + # + # # 保存文件 + # count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig') + + # 输出结果 + return vulnerable100_product + +def run_ABM_samples(controller_db_obj,ga_id,dct_exp, str_code="GA",): + """ + 从数据库获取一个随机样本，锁定它，然后运行模型仿真。 + 参数: + controller_db: ControllerDB 对象 + str_code: 可选标识，用于打印 + 返回: + True 如果没有可用样本 + False 如果成功运行 + """ + # 1. 从数据库获取一个随机样本 + sample_random = controller_db_obj.fetch_a_sample(s_id=None) # s_id 可根据需要传入 + if sample_random is None: + #print(ga_id+"无样本") + return True # 没有样本，返回 True 表示结束 + # 2. 锁定该样本 + controller_db_obj.lock_the_sample(sample_random) + # print(f"Pid {pid} ({str_code}) is running sample {sample_random.id} at {datetime.now()}") + # print(f"Pid {pid} ({str_code})") + # print(f"[信息] 当前正在运行的 GA 个体 ID: {ga_id}, 时间: {datetime.now()}") + # 3. 获取 experiment 的所有列及其值 + dct_exp_new = {column: getattr(sample_random.experiment, column) + for column in sample_random.experiment.__table__.c.keys()} + # 删除不需要的主键 id + dct_exp_new.pop('id', None) + dct_exp_new = {'sample': sample_random, + 'seed': sample_random.seed, + **dct_exp_new} + try: + dct_sample_para = { + **dct_exp_new, + **dct_exp} + # 切换工作目录到项目根目录或 ABM 需要的目录 + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) + os.chdir(project_root) + + abm_model = MyModel(dct_sample_para) + abm_model.step() + abm_model.end(ga_id) + except Exception as e: + print(f"[❌ ABM运行错误] 错误：{e} ") + traceback.print_exc() + +def do_computation(controller_db_obj,ga_id,dct_exp,): + """每个进程执行 ABM 样本运行""" + pid = os.getpid() + print(f"[启动] 进程 {pid} 已启动 (PID={pid})") + while 1: + # time.sleep(random.uniform(0, 1)) + is_all_done = run_ABM_samples(controller_db_obj,ga_id,dct_exp,) + if is_all_done: + break + +def do_process(controller_db,ga_id,dct_exp,job): + process_list = [] + for i in range(job): + p = 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0.2 # 设置变异操作的标准差 - self.p_mutation = 1.0 / the_policy.dim # 设置变异概率 - self.tournament_size = 5 # 设置锦标赛选择的大小 - self.p_cross = 0.9 # 设置交叉概率 - - pop_size = the_policy.arr_init_doe_points.shape[0] # 获取种群大小 - self.lst_value = the_policy.lst_y_init_doe_points # 初始化每个个体的适应度值 - - # 如果种群大小是奇数，生成一个随机个体来确保种群大小是偶数 - if pop_size % 2 == 1: - arr_random = np.random.rand(1, self.n_variables) # 生成一个随机的个体 - arr_one_random = self.lower_boundary + arr_random * (self.upper_boundary - self.lower_boundary) # 将随机个体约束在边界内 - self.lst_value.append(the_policy.eval(arr_one_random[0, :], is_init_points=True)) # 评估该个体的适应度 - self.population = np.vstack((the_policy.arr_init_doe_points, arr_one_random)) # 将该个体加入到种群中 - else: - self.population = np.copy(the_policy.arr_init_doe_points) # 直接使用初始种群 - - self.n_individuals = self.population.shape[0] # 获取种群中个体的数量 - assert self.n_individuals == pop_size or self.n_individuals == pop_size + 1, 'Wrong pop size' # 确保种群大小正确 - - # 如果有整数变量，需要进行位置四舍五入 - if len(self.integer_variables) > 0: - self.population[:, self.integer_variables] = np.round(self.population[:, self.integer_variables]) # 对整数变量四舍五入 - for i in self.integer_variables: - ind = np.where(self.population[:, i] < self.lower_boundary[i]) # 如果超出了下界，修正为下界 - self.population[ind, i] += 1 - ind = np.where(self.population[:, i] > self.upper_boundary[i]) # 如果超出了上界，修正为上界 - self.population[ind, i] -= 1 - - self.ind, self.best_individual, self.best_value = None, None, None # 初始化最优个体和最优值 - self.pop_next, self.lst_pop_next_is_evaluated = None, None # 初始化下一代种群和评估标志 - self.update_info() # 更新最优解信息 - - def update_info(self): - # 更新最优个体和适应度值 - self.ind = np.argmin(self.lst_value) # 获取适应度最小的个体（假设目标是最小化） - self.best_individual = np.copy(self.population[self.ind, :]) # 复制最优个体 - self.best_value = self.lst_value[self.ind] # 记录最优值 - - self.pop_next, self.lst_pop_next_is_evaluated = self._generate_next_population() # 生成下一代种群 - self.lst_value = [] # 清空当前种群的适应度值 - - def _generate_next_population(self): - # 生成下一代种群 - competitors = np.random.randint(0, self.n_individuals, (self.n_individuals, self.tournament_size)) # 随机选择竞赛个体 - ind = np.argmin(np.array(self.lst_value)[competitors], axis=1) # 选择每轮锦标赛中的最优个体 - winner_indices = np.zeros(self.n_individuals, dtype=int) # 用于存储胜利个体的索引 - for i in range(self.tournament_size): # 进行锦标赛选择 - winner_indices[np.where(ind == i)] = competitors[np.where(ind == i), i] - - # 按照锦标赛结果将种群分为父母 - parent1 = self.population[winner_indices[0: self.n_individuals // 2], :] - parent2 = self.population[winner_indices[self.n_individuals // 2: self.n_individuals], :] - - # 交叉操作：对父母个体进行交叉 - cross = np.where(np.random.rand(self.n_individuals // 2) < self.p_cross)[0] # 按照概率决定哪些个体进行交叉 - nn = len(cross) # 计算交叉个体的数量 - alpha = np.random.rand(nn, 1) # 生成交叉系数 - - # 创建新的染色体 - parent1_new = np.multiply(alpha, parent1[cross, :]) + np.multiply(1 - alpha, parent2[cross, :]) - parent2_new = np.multiply(alpha, parent2[cross, :]) + np.multiply(1 - alpha, parent1[cross, :]) - parent1[cross, :] = parent1_new - parent2[cross, :] = parent2_new - arr_new_population = np.concatenate((parent1, parent2)) # 合并两个父代得到新的种群 - - # 变异操作 - scale_factors = self.sigma * (self.upper_boundary - self.lower_boundary) # 计算变异的尺度 - perturbation = np.random.randn(self.n_individuals, self.n_variables) # 生成扰动 - perturbation = np.multiply(perturbation, scale_factors) # 根据尺度调整扰动 - perturbation = np.multiply( - perturbation, (np.random.rand(self.n_individuals, self.n_variables) < self.p_mutation) - ) # 根据变异概率决定哪些位置进行扰动 - - arr_new_population += perturbation # 将扰动添加到新种群 - arr_new_population = np.maximum(np.reshape(self.lower_boundary, (1, self.n_variables)), arr_new_population) # 确保不超过下界 - arr_new_population = np.minimum(np.reshape(self.upper_boundary, (1, self.n_variables)), arr_new_population) # 确保不超过上界 - - # 如果有整数变量，进行四舍五入 - if len(self.integer_variables) > 0: - arr_new_population = round_vars(arr_new_population, self.integer_variables, self.lower_boundary, - self.upper_boundary) - - assert arr_new_population.shape[0] == self.n_individuals, 'Wrong arr_new_population shape' # 确保新种群的大小正确 - return arr_new_population, [False] * self.n_individuals # 返回新种群和评估标志（都设为未评估） - - def select_next_point(self): - # 选择下一个要评估的个体 - for idx_ind, is_evaluated in enumerate(self.lst_pop_next_is_evaluated): - if not is_evaluated: # 如果该个体没有被评估 - return self.pop_next[idx_ind, :] # 返回该个体 - - def receive_sim_value(self, the_value): - # 接收评估结果，并更新适应度信息 - self.lst_value.append(the_value) # 将评估值添加到适应度列表 - idx_ind = 0 - for idx_ind, is_evaluated in enumerate(self.lst_pop_next_is_evaluated): - if not is_evaluated: # 找到未评估的个体 - self.lst_pop_next_is_evaluated[idx_ind] = True # 标记该个体为已评估 - break - if idx_ind == len(self.lst_pop_next_is_evaluated) - 1: - assert idx_ind == self.n_individuals - 1, 'Wrong index' # 确保所有个体都已评估 - self.update_info() # 更新最优解信息 diff --git a/GA_Agent_0925/main.py b/GA_Agent_0925/main.py new file mode 100644 index 0000000..70d6d3a --- /dev/null +++ b/GA_Agent_0925/main.py @@ -0,0 +1,109 @@ +import json +import random +from deap import tools +from sqlalchemy.orm import close_all_sessions +from tqdm import tqdm +import matplotlib.pyplot as plt + +from GA_Agent_0925.creating import creating +from GA_Agent_0925.orm import connection +from controller_db import ControllerDB +from evaluate_func import fitness, get_vulnerable100_code, get_target_vulnerable_industries + + +# ============================== +# 遗传算法主函数（单进程） +# ============================== +def main(): + # 1️⃣ 加载配置 + with open("config.json", "r", encoding="utf-8") as f: + cfg = json.load(f) + + random.seed(cfg["seed"]) + + print("\n📘 参数配置:") + for k, v in cfg.items(): + print(f" {k}: {v}") + print("-" * 40) + + # 2️⃣ 初始化 ControllerDB（数据库连接） + controller_db_obj = ControllerDB("without_exp", reset_flag=0) + controller_db_obj.reset_db(force_drop=True) + # 准备样本表 + controller_db_obj.prepare_list_sample() + # 2️⃣ 初始化工具箱 + toolbox = creating() + pop = toolbox.population(n=cfg["pop_size"]) + hof = tools.HallOfFame(1) + stats = tools.Statistics(lambda ind: ind.fitness.values) + stats.register("avg", lambda fits: sum(f[0] for f in fits) / len(fits)) + stats.register("max", lambda fits: max(f[0] for f in fits)) + + best_list = [] + avg_list = [] + + # ============================== + # 主进化循环 + # ============================== + for gen in tqdm(range(cfg["n_gen"]), desc="进化中", ncols=90): + # 计算未评估个体适应度 + invalid_ind = [ind for ind in pop if not ind.fitness.valid] + for ind in invalid_ind: + controller_db_obj.reset_sample_db() + controller_db_obj.prepare_list_sample() + ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj) + + # 选择、交叉、变异 + offspring = toolbox.select(pop, len(pop)) + offspring = list(map(toolbox.clone, offspring)) + + for child1, child2 in zip(offspring[::2], offspring[1::2]): + if random.random() < cfg["cx_prob"]: + toolbox.mate(child1, child2) + del child1.fitness.values, child2.fitness.values + + for mutant in offspring: + if random.random() < cfg["mut_prob"]: + toolbox.mutate(mutant) + del mutant.fitness.values + + # 更新适应度 + invalid_ind = [ind for ind in offspring if not ind.fitness.valid] + for ind in invalid_ind: + controller_db_obj.reset_sample_db() + controller_db_obj.prepare_list_sample() + ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj) + + pop[:] = offspring + hof.update(pop) + + record = stats.compile(pop) + best_list.append(record["max"]) + avg_list.append(record["avg"]) + + # ============================== + # 输出最优结果 + # ============================== + print("\n✅ 进化完成！") + print(f"🏆 最优个体: {hof[0]}") + print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}") + + # 绘制收敛曲线 + plt.figure(figsize=(8, 5)) + plt.plot(best_list, label="Best Fitness", linewidth=2) + plt.plot(avg_list, label="Average Fitness", linestyle="--") + plt.title("Genetic Algorithm Convergence") + plt.xlabel("Generation") + plt.ylabel("Fitness") + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.show() + + # ============================== + # 最优个体产业匹配 + # ============================== + print("\n📊 计算最优个体产业匹配情况...") + +if __name__ == "__main__": + main() diff --git a/GA_Agent_0925/orm.py b/GA_Agent_0925/orm.py new file mode 100644 index 0000000..e197311 --- /dev/null +++ b/GA_Agent_0925/orm.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +from datetime import datetime +from sqlalchemy import create_engine, inspect, Inspector, Float +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy import (Column, Integer, DECIMAL, String, ForeignKey, + BigInteger, DateTime, PickleType, Boolean, Text) +from sqlalchemy.sql import func +from sqlalchemy.orm import relationship, Session +from sqlalchemy.pool import NullPool +import yaml + +with open('../conf_db.yaml') as file: + dct_conf_db_all = yaml.full_load(file) + is_local_db = dct_conf_db_all['is_local_db'] + if is_local_db: + dct_conf_db = dct_conf_db_all['local'] + else: + dct_conf_db = dct_conf_db_all['remote'] + +with open('../conf_db_prefix.yaml') as file: + dct_conf_db_prefix = yaml.full_load(file) + db_name_prefix = dct_conf_db_prefix['db_name_prefix'] + +str_login = 'mysql://{}:{}@{}:{}/{}'.format(dct_conf_db['user_name'], + dct_conf_db['password'], + dct_conf_db['address'], + dct_conf_db['port'], + dct_conf_db['db_name']) +# print('DB is {}:{}/{}'.format(dct_conf_db['address'], dct_conf_db['port'], dct_conf_db['db_name'])) + +# must be null pool to avoid connection lost error +engine = create_engine(str_login, poolclass=NullPool) +connection = engine.connect() +ins: Inspector = inspect(engine) + +Base = declarative_base() + +db_session = Session(bind=engine) + + +class Experiment(Base): + __tablename__ = f"{db_name_prefix}_experiment" + id = Column(Integer, primary_key=True, autoincrement=True) + + idx_scenario = Column(Integer, nullable=False) + idx_init_removal = Column(Integer, nullable=False) + + # fixed parameters + n_sample = Column(Integer, nullable=False) + n_iter = Column(Integer, nullable=False) + + # variables + dct_lst_init_disrupt_firm_prod = Column(PickleType, nullable=False) + g_bom = Column(Text(4294000000), nullable=False) + + n_max_trial = Column(Integer, nullable=False) + prf_size = Column(Boolean, nullable=False) + prf_conn = Column(Boolean, nullable=False) + cap_limit_prob_type = Column(String(16), nullable=False) + cap_limit_level = Column(DECIMAL(8, 4), nullable=False) + diff_new_conn = Column(DECIMAL(8, 4), nullable=False) + remove_t = Column(Integer, nullable=False) + netw_prf_n = Column(Integer, nullable=False) + + sample = relationship( + 'Sample', back_populates='experiment', lazy='dynamic') + + def __repr__(self): + return f'' + + +class Sample(Base): + __tablename__ = f"{db_name_prefix}_sample" + id = Column(Integer, primary_key=True, autoincrement=True) + e_id = Column(Integer, ForeignKey('{}.id'.format( + f"{db_name_prefix}_experiment")), nullable=False) + + idx_sample = Column(Integer, nullable=False) + seed = Column(BigInteger, nullable=False) + # -1, waiting; 0, running; 1, done + is_done_flag = Column(Integer, nullable=False) + computer_name = Column(String(64), nullable=True) + ts_done = Column(DateTime(timezone=True), onupdate=func.now()) + stop_t = Column(Integer, nullable=True) + + g_firm = Column(Text(4294000000), nullable=True) + + experiment = relationship( + 'Experiment', back_populates='sample', uselist=False) + result = relationship('Result', back_populates='sample', lazy='dynamic') + + def __repr__(self): + return f'' + + +class Result(Base): + __tablename__ = f"{db_name_prefix}_result" + id = Column(Integer, primary_key=True, autoincrement=True) + s_id = Column(Integer, ForeignKey('{}.id'.format( + f"{db_name_prefix}_sample")), nullable=False) + + id_firm = Column(String(20), nullable=False) + id_product = Column(String(20), nullable=False) + ts = Column(Integer, nullable=False) + status = Column(String(5), nullable=False) + + sample = relationship('Sample', back_populates='result', uselist=False) + + # 💥 新增 GA 调用 ID，用于标记属于哪一次遗传算法运行 + ga_id = Column(String(50), nullable=True) + + def __repr__(self): + return f'' + +if __name__ == '__main__': + Base.metadata.drop_all() + Base.metadata.create_all() diff --git a/GA_Agent_0925/risk_firm_count.py b/GA_Agent_0925/risk_firm_count.py deleted file mode 100644 index 5069c70..0000000 --- a/GA_Agent_0925/risk_firm_count.py +++ /dev/null @@ -1,23 +0,0 @@ -from sqlalchemy import text - -import pandas as pd - -from orm import connection - -# SQL query -with open("../SQL_analysis_risk.sql", "r", encoding="utf-8") as f: - str_sql = text(f.read()) - -result = pd.read_sql(sql=str_sql, con=connection) -# Count firm product -count_firm_prod = result.value_counts(subset=['id_firm', 'id_product']) -count_firm_prod.name = 'count' -count_firm_prod = count_firm_prod.to_frame().reset_index() - -# Count product -count_prod = count_firm_prod.groupby('id_product')['count'].sum() -count_prod = count_prod.to_frame().reset_index() -count_prod.sort_values('count', inplace=True, ascending=False) -print(count_prod) -top100 = count_prod.head(100)['id_product'].tolist() - diff --git a/GA_Agent_0925/多功能.py b/GA_Agent_0925/多功能.py new file mode 100644 index 0000000..64f6e35 --- /dev/null +++ b/GA_Agent_0925/多功能.py @@ -0,0 +1,211 @@ +import json +import os +import pickle +import networkx as nx +from sqlalchemy import text + +import pandas as pd + +from orm import connection + +# """ +# 计算最脆弱前100产品的 Code 列表（去重）。 +# """ +# bom_file = r"../input_data/input_product_data/BomNodes.csv" +# mapping_df = pd.read_csv(bom_file) +# +# with open("../SQL_analysis_risk.sql", "r", encoding="utf-8") as f: +# str_sql = text(f.read()) +# +# result = pd.read_sql(sql=str_sql, con=connection) +# +# count_firm_prod = result.value_counts(subset=['id_firm', 'id_product']) +# count_firm_prod.name = 'count' +# count_firm_prod = count_firm_prod.to_frame().reset_index() +# +# count_prod = ( +# count_firm_prod.groupby("id_product")["count"].sum().reset_index() +# ) +# +# vulnerable100_index = count_prod.nsmallest(100, "count")["id_product"].tolist() +# # 确保 index_to_code 的 key 都是 int +# index_to_code = {int(k): v for k, v in zip(mapping_df["Index"], mapping_df["Code"])} +# +# # vulnerable100_index 也转成 int +# vulnerable100_index_int = [int(i) for i in vulnerable100_index] +# +# # 获取 code +# vulnerable100_code = [index_to_code[i] for i in vulnerable100_index_int if i in index_to_code] +# +# print(vulnerable100_code) + + +# 读取 SQL +ga_id="c943f2c6" +with open("SQL_analysis_risk_ga.sql", "r", encoding="utf-8") as f: + str_sql = text(f.read()) + print(str_sql[:300]) +print(f"[信息] 正在查询 ga_id={ga_id} 的脆弱产品数据...") + +# 执行 SQL 查询，并绑定参数 ga_id +result = pd.read_sql( + sql=str_sql, + con=connection, + params={"ga_id": ga_id} # 绑定参数 +) +# =============================== +# 2️⃣ 统计每个企业-产品组合出现次数 +# =============================== +count_firm_prod = result.value_counts(subset=['id_firm', 'id_product']) +count_firm_prod.name = 'count' +count_firm_prod = count_firm_prod.to_frame().reset_index() +count_firm_prod.to_csv('count_firm_prod.csv', index=False, encoding='utf-8-sig') + +# =============================== +# 3️⃣ 统计每个企业出现的总次数 +# =============================== +count_firm = count_firm_prod.groupby('id_firm')['count'].sum().reset_index() +count_firm.sort_values('count', ascending=False, inplace=True) +count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig') + +# =============================== +# 4️⃣ 统计每个产品出现的总次数 +# =============================== +count_prod = count_firm_prod.groupby('id_product')['count'].sum().reset_index() +count_prod.sort_values('count', ascending=False, inplace=True) +count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig') + +# =============================== +# 5️⃣ 选出最脆弱的前100个产品（出现次数最多） +# =============================== +vulnerable100_product = count_prod.nlargest(100, "count")["id_product"].tolist() +print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品") + +# =============================== +# 6️⃣ 过滤 result，只保留前100脆弱产品 +# =============================== +result_vulnerable100 = result[result['id_product'].isin(vulnerable100_product)].copy() +print(f"[信息] 筛选后剩余记录数: {len(result_vulnerable100)}") + +# =============================== +# 7️⃣ 构造 DCP（Disruption Causing Probability） +# =============================== +result_dcp_list = [] +for sid, group in result_vulnerable100.groupby('s_id'): + ts_start = max(group['ts']) + while ts_start >= 1: + ts_end = ts_start - 1 + while ts_end >= 0: + up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']] + down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']] + for _, up_row in up.iterrows(): + for _, down_row in down.iterrows(): + result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist()) + ts_end -= 1 + ts_start -= 1 + +# 转换为 DataFrame +result_dcp = pd.DataFrame(result_dcp_list, columns=[ + 's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product' +]) + +# =============================== +# 8️⃣ 统计 DCP 出现次数 +# =============================== +count_dcp = result_dcp.value_counts( + subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'] +).reset_index(name='count') + +# 保存文件 +count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig') + +# 输出结果 +print(count_dcp) +print(type(vulnerable100_product[0])) + +# industry_list = [ +# # ① 半导体设备类 +# {"product": "离子注入机", "category": "离子注入设备", "chain_id": 34538}, +# {"product": "刻蚀设备 / 湿法刻蚀设备", "category": "刻蚀机", "chain_id": 34529}, +# {"product": "沉积设备", "category": "薄膜生长设备（CVD/PVD）", "chain_id": 34539}, +# {"product": "CVD", "category": "薄膜生长设备", "chain_id": 34539}, +# {"product": "PVD", "category": "薄膜生长设备", "chain_id": 34539}, +# {"product": "CMP", "category": "化学机械抛光设备", "chain_id": 34530}, +# {"product": "光刻机", "category": "光刻机", "chain_id": 34533}, +# {"product": "涂胶显影机", "category": "涂胶显影设备", "chain_id": 34535}, +# {"product": "晶圆清洗设备", "category": "晶圆清洗机", "chain_id": 34531}, +# {"product": "测试设备", "category": "测试机", "chain_id": 34554}, +# {"product": "外延生长设备", "category": "薄膜生长设备", "chain_id": 34539}, +# +# # ② 半导体材料与化学品类 +# {"product": "三氯乙烯", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, +# {"product": "丙酮", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, +# {"product": "异丙醇", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, +# {"product": "其他醇类", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438}, +# {"product": "光刻胶", "category": "光刻胶及配套试剂", "chain_id": 32445}, +# {"product": "显影液", "category": "显影液", "chain_id": 46504}, +# {"product": "蚀刻液", "category": "蚀刻液", "chain_id": 56341}, +# {"product": "光阻去除剂", "category": "光阻去除剂", "chain_id": 32442}, +# +# # ③ 晶圆制造类 +# {"product": "晶圆", "category": "单晶硅片 / 多晶硅片", "chain_id": 32338}, +# {"product": "硅衬底", "category": "硅衬底", "chain_id": 36914}, +# {"product": "外延片", "category": "硅外延片 / GaN外延片 / SiC外延片等", "chain_id": 32338}, +# +# # ④ 封装与测试类 +# {"product": "封装", "category": "IC封装", "chain_id": 10}, +# {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 513742}, +# {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 11}, +# +# # ⑤ 芯片与设计EDA类 +# {"product": "芯片（通用）", "category": "集成电路制造", "chain_id": 317589}, +# {"product": "DRAM", "category": "存储芯片 → 集成电路制造", "chain_id": 317589}, +# {"product": "GPU", "category": "图形芯片 → 集成电路制造", "chain_id": 317589}, +# {"product": "处理器（CPU/SoC）", "category": "芯片设计", "chain_id": 9}, +# {"product": "高频芯片", "category": "芯片设计", "chain_id": 9}, +# {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 9}, +# {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 2717}, +# {"product": "先进节点制造设备", "category": "集成电路制造", "chain_id": 317589}, +# {"product": "EDA及IP服务", "category": "设计辅助", "chain_id": 2515}, +# {"product": "MPW服务", "category": "多项目晶圆流片", "chain_id": 2514}, +# {"product": "芯片设计验证", "category": "设计验证", "chain_id": 513738}, +# {"product": "过程工艺检测", "category": "制程检测", "chain_id": 513740} +# ] +# # 提取所有 chain_id，并去重 +# chain_ids = set() +# for item in industry_list: +# # 如果 chain_id 是字符串包含多个编号，用逗号或斜杠拆分 +# if isinstance(item["chain_id"], str): +# for cid in item["chain_id"].replace("/", ",").split(","): +# chain_ids.add(cid.strip()) +# else: +# chain_ids.add(str(item["chain_id"])) +# print(list(chain_ids)) +# fill g_bom +# 结点属性值 相当于 图上点的 原始 产品名称 +# bom_nodes = pd.read_csv('../input_data/input_product_data/BomNodes.csv') +# bom_nodes['Code'] = bom_nodes['Code'].astype(str) +# bom_nodes.set_index('Index', inplace=True) +# +# bom_cate_net = pd.read_csv('../input_data/input_product_data/合成结点.csv') +# g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph()) +# # 填充每一个结点 的具体内容 通过 相同的 code 并且通过BomNodes.loc[code].to_dict()字典化 格式类似 格式 { code（0） : {level: 0 ,name: 工业互联网 }} +# bom_labels_dict = {} +# for index in g_bom.nodes: +# try: +# bom_labels_dict[index] = bom_nodes.loc[index].to_dict() +# # print(bom_labels_dict[index]) +# except KeyError: +# print(f"节点 {index} 不存在于 bom_nodes 中") +# # 分配属性 给每一个结点 获得类似 格式：{1: {'label': 'A', 'value': 10}, +# nx.set_node_attributes(g_bom, bom_labels_dict) +# # 改为json 格式 +# g_product_js = json.dumps(nx.adjacency_data(g_bom)) +# # 假设 g_bom 是你的 NetworkX 图 +# g_product_data = nx.adjacency_data(g_bom) +# +# # 保存为 pkl 文件 +# with open("g_bom.pkl", "wb") as f: +# pickle.dump(g_product_data, f) +# +# print("✅ 图数据已保存为 g_bom.pkl") diff --git a/GA_Agent_0925/GA_random.py b/GA_Agent_0925/废案/GA_random.py similarity index 86% rename from GA_Agent_0925/GA_random.py rename to GA_Agent_0925/废案/GA_random.py index 8c4a1bc..5c75385 100644 --- a/GA_Agent_0925/GA_random.py +++ b/GA_Agent_0925/废案/GA_random.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- # 文件的编码格式设置为 UTF-8 from __future__ import division # 为了兼容 Python 2 和 3，保证除法始终返回浮点数 +import multiprocessing import random # 导入 random 库，用于生成随机数 from deap import base # 从 DEAP 库导入 base 模块，提供一些遗传算法相关的功能 @@ -11,6 +12,77 @@ from my_model import MyModel from sqlalchemy import text import pandas as pd from orm import connection + +def main(): + random.seed(42) # 可复现结果 + print("Start of evolution") + + ga = creating() + pop = ga.population(n=50) + CXPB, MUTPB, NGEN = 0.5, 0.2, 200 + + # # 并行计算 + # pool = multiprocessing.Pool() + # ga.register("map", pool.map) + + # 改为： + ga.register("map", map) # 单进程 + + # 评估初始种群 + fitnesses = list(ga.map(ga.evaluate, pop)) + for ind, fit in zip(pop, fitnesses): + ind.fitness.values = fit + print(f"Evaluated {len(pop)} individuals") + + best_log = [] + + for g in range(NGEN): + print(f"-- Generation {g} --") + + # 选择并克隆 + offspring = list(map(ga.clone, ga.select(pop, len(pop)))) + + # 交叉与变异 + for child1, child2 in zip(offspring[::2], offspring[1::2]): + if random.random() < CXPB: + ga.mate(child1, child2) + del child1.fitness.values + del child2.fitness.values + + for mutant in offspring: + if random.random() < MUTPB: + ga.mutate(mutant) + del mutant.fitness.values + + # 重新计算失效适应度 + invalid_ind = [ind for ind in offspring if not ind.fitness.valid] + fitnesses = list(ga.map(ga.evaluate, invalid_ind)) + for ind, fit in zip(invalid_ind, fitnesses): + ind.fitness.values = fit + + pop[:] = offspring + + # 最优个体 + best_ind = tools.selBest(pop, 1)[0] + best_log.append((g, best_ind.fitness.values[0])) + + print(f"Best individual {g}: {best_ind}, Fitness: {best_ind.fitness.values[0]:.3f}") + + # 写入数据库 + result_sql = text(f""" + INSERT INTO ga (generation, stu_beta, stu_nmb, gtu_mgf, gtu_discount, fitness, remark) + VALUES ({g}, {best_ind[0]}, {best_ind[1]}, {best_ind[2]}, {best_ind[3]}, {best_ind.fitness.values[0]}, 'Random2') + """) + with connection.connect() as conn: + conn.execute(result_sql) + conn.commit() + + # pool.close() + # pool.join() + + pd.DataFrame(best_log, columns=["generation", "fitness"]).to_csv("ga_log.csv", index=False) + print("-- End of (successful) evolution --") + # 目标函数（适应度函数），用于评估个体的适应度 def fitness(individual): """ @@ -77,6 +149,10 @@ def creating(): 创建遗传算法工具箱，用于优化 ABM 模型参数，使生成的脆弱产业集合 与目标产业集合误差最小化（fitness 最大化）。 """ + if "FitnessMax" not in creator.__dict__: + creator.create("FitnessMax", base.Fitness, weights=(1.0,)) + if "Individual" not in creator.__dict__: + creator.create("Individual", list, fitness=creator.FitnessMax) # 定义最大化适应度 creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # 定义个体类 @@ -124,7 +200,7 @@ def creating(): toolbox.register("population", tools.initRepeat, list, toolbox.individual) # 注册 fitness 函数（需要在调用时传入目标产业集合） - # toolbox.register("evaluate", fitness) # 可以在 main 中使用 lambda 包装 target_chain_set + toolbox.register("evaluate", fitness) # 可以在 main 中使用 lambda 包装 target_chain_set # 交叉、变异和选择操作 toolbox.register("mate", tools.cxTwoPoint) @@ -133,76 +209,6 @@ def creating(): return toolbox -def main(): - # 创建遗传算法的工具箱 - ga = creating() - - # 初始化种群大小为 50 - pop = ga.population(n=50) - - # 交叉概率、变异概率和代数 - CXPB, MUTPB, NGEN = 0.5, 0.2, 500 - - print("Start of evolution") - - # 评估整个种群的适应度 - fitnesses = list(map(ga.evaluate, pop)) - for ind, fit in zip(pop, fitnesses): - ind.fitness.values = fit - - print(" Evaluated %i individuals" % len(pop)) - # my_sql = Sql() # 创建 Sql 类的实例，用于与数据库交互 - - # 开始演化 - for g in range(NGEN): - print("-- Generation %i --" % g) - - # 选择下一代的个体 - offspring = ga.select(pop, len(pop)) - # 克隆选择的个体 - offspring = list(map(ga.clone, offspring)) - - # 对后代进行交叉和变异 - for child1, child2 in zip(offspring[::2], offspring[1::2]): - # 以 CXPB 的概率交叉两个个体 - if random.random() < CXPB: - ga.mate(child1, child2) - - # 交叉后的适应度值需要重新计算 - del child1.fitness.values - del child2.fitness.values - - for mutant in offspring: - # 以 MUTPB 的概率变异个体 - if random.random() < MUTPB: - ga.mutate(mutant) - del mutant.fitness.values - - # 评估适应度无效的个体 - invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - fitnesses = map(ga.evaluate, invalid_ind) - for ind, fit in zip(invalid_ind, fitnesses): - ind.fitness.values = fit - - print(" Evaluated %i individuals" % len(invalid_ind)) - - # 将种群完全替换为后代 - pop[:] = offspring - - # 收集所有个体的适应度并打印统计信息 - fits = [ind.fitness.values[0] for ind in pop] - - # 获取当前最好的个体并打印 - best_ind = tools.selBest(pop, 1)[0] - print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values)) - - # 将最优个体的信息插入数据库 - result_string = '''INSERT INTO ga (generation, stu_beta, stu_nmb, gtu_mgf, gtu_discount, fitness, remark) - VALUES ({}, {}, {}, {}, {}, {}, 'Random2')'''.format(g, best_ind[0], best_ind[1], best_ind[2], best_ind[3], best_ind.fitness.values[0]) - # my_sql.insert_one_row_and_return_new_id(result_string) - - print("-- End of (successful) evolution --") - def get_target_vulnerable_industries(): """ 获取行业列表中所有产业链编号的集合（整数形式）。 @@ -280,11 +286,6 @@ def get_target_vulnerable_industries(): return chain_ids - -import pandas as pd -from sqlalchemy import text # 用于 SQL 查询 - - def get_vulnerable100_code(connection): """ 计算最脆弱前100产品的 Code 列表（去重）。 @@ -294,11 +295,11 @@ def get_vulnerable100_code(connection): List[int]: 最脆弱前100产品对应的 Code 列表 """ # 读取映射表 - bom_file = r"../input_data/input_product_data/BomNodes.csv" # 直接给出路径 + bom_file = r"../../input_data/input_product_data/BomNodes.csv" # 直接给出路径 mapping_df = pd.read_csv(bom_file) # 执行 SQL 获取结果 - with open("../SQL_analysis_risk.sql", "r", encoding="utf-8") as f: + with open("../../SQL_analysis_risk.sql", "r", encoding="utf-8") as f: str_sql = text(f.read()) result = pd.read_sql(sql=str_sql, con=connection) diff --git a/GA_Agent_0925/进度.py b/GA_Agent_0925/进度.py new file mode 100644 index 0000000..7c31837 --- /dev/null +++ b/GA_Agent_0925/进度.py @@ -0,0 +1,43 @@ +from matplotlib import rcParams, pyplot as plt +from sqlalchemy import func +from orm import db_session, Sample + +# 🔹 全局创建绘图对象 +plt.ion() # 启用交互模式 +fig, ax = plt.subplots(figsize=(8, 5)) +rcParams['font.family'] = 'Microsoft YaHei' +rcParams['font.size'] = 12 + +# 初始化柱状图 +labels = ['未完成 (-1)', '计算中(0)', '完成 (1)'] +initial_values = [0, 0, 0] +bars = ax.bar(labels, initial_values, color=['red', 'orange', 'green']) +value_texts = [ax.text(bar.get_x() + bar.get_width()/2, 0, '0', + ha='center', va='bottom', fontsize=12) + for bar in bars] + +ax.set_title('任务进度分布', fontsize=16) +ax.set_xlabel('任务状态', fontsize=14) +ax.set_ylabel('数量', fontsize=14) +ax.tick_params(axis='both', labelsize=12) + +def visualize_progress(): + """ + 实时更新 Sample 表中 is_done_flag 的分布。 + """ + # 查询数据库 + result = db_session.query(Sample.is_done_flag, func.count(Sample.id))\ + .group_by(Sample.is_done_flag).all() + data = {flag: count for flag, count in result} + for flag in [-1, 0, 1]: + data.setdefault(flag, 0) + values = [data[-1], data[0], data[1]] + + # 更新柱子高度和文本 + for bar, new_val, txt in zip(bars, values, value_texts): + bar.set_height(new_val) + txt.set_y(new_val + 0.5) + txt.set_text(str(new_val)) + + plt.draw() + plt.pause(0.1) # 刷新图表 diff --git a/__pycache__/controller_db.cpython-38.pyc b/__pycache__/controller_db.cpython-38.pyc index 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if not firm.R: # 跳过没有资源的企业 continue - # 遍历资源列表，检查哪些资源需要补货 - for resource_id, resource_quantity in firm.R: - if resource_quantity <= firm.s_r: # 如果资源低于阈值，记录需求 - required_quantity = firm.S_r - resource_quantity + # purchase_material_firms 用于记录需要补充材料的企业 + purchase_material_firms = {} + + for i, (resource_id, resource_quantity) in enumerate(firm.R): + # 对应位置的初始值 + init_quantity = firm.R1[i][1] # R1[i] = [resource_id, 初始数量] + + if resource_quantity <= init_quantity * firm.s_r: # 判断阈值 + required_quantity = init_quantity * firm.S_r - resource_quantity + if firm not in purchase_material_firms: purchase_material_firms[firm] = [] + purchase_material_firms[firm].append((resource_id, required_quantity)) # 寻找供应商并处理补货 @@ -754,7 +758,7 @@ class MyModel(Model): # 检查设备残值，记录需要补充的设备 for equipment in firm.C: equipment_id, equipment_quantity, equipment_salvage = equipment - equipment_salvage -= firm.x # 减少设备残值 + equipment_salvage -= equipment_salvage*firm.x # 减少设备残值 if equipment_salvage <= 0: # 如果残值小于等于 0 equipment_quantity -= 1 required_quantity = 1 # 需要补充的设备数量 @@ -869,22 +873,29 @@ class MyModel(Model): else: self.int_stop_ts = self.t - def end(self): + def end(self, ga_id=None): """ 结束模型运行并保存结果。 - - 如果当前样本的结果未保存，则保存所有生产状态为非正常状态的结果。 - - 更新样本状态为完成，并记录相关信息。 + - 按照 (sample.id, ga_id) 双键标识每次运行。 + - 若该组合结果已存在，则不重复保存。 + - 更新样本状态。 """ - # 检查当前样本结果是否已存在 - if not db_session.query(Result).filter_by(s_id=self.sample.id).first(): - # 生成需要保存的结果列表 + # 1️⃣ 检查该 sample + ga_id 是否已有结果 + exists = db_session.query(Result).filter_by( + s_id=self.sample.id, + ga_id=ga_id + ).first() + + if not exists: + # 2️⃣ 构造要保存的结果列表 lst_result_info = [ Result( s_id=self.sample.id, id_firm=firm.unique_id, id_product=prod.unique_id, ts=ts, - status=status + status=status, + ga_id=ga_id, # 标记当前代次 ) for firm in self.company_agents for prod, dct_status_supply in firm.dct_prod_up_prod_stat.items() @@ -892,13 +903,14 @@ class MyModel(Model): for status, ts in dct_status_supply['p_stat'] ] - # 批量保存结果到数据库 + # 3️⃣ 插入结果 if lst_result_info: db_session.bulk_save_objects(lst_result_info) db_session.commit() - # 更新样本状态为已完成 + # 4️⃣ 更新样本状态 self.sample.is_done_flag = 1 self.sample.computer_name = platform.node() self.sample.stop_t = self.int_stop_ts db_session.commit() + diff --git a/orm.py b/orm.py index ffe995f..a02ccfa 100644 --- a/orm.py +++ b/orm.py @@ -105,6 +105,9 @@ class Result(Base): sample = relationship('Sample', back_populates='result', uselist=False) + # 💥 新增 GA 调用 ID，用于标记属于哪一次遗传算法运行 + ga_id = Column(String(50), nullable=True) + def __repr__(self): return f''