From fbbdf6065d70237b92bf8d4c6897d1c30d3c4533 Mon Sep 17 00:00:00 2001
From: Attri <attrighosh@gmail.com>
Date: Wed, 16 Aug 2023 15:07:52 -0700
Subject: [PATCH] FS change

---
 _version.py                                   |   2 +-
 autoqtl/__pycache__/__init__.cpython-310.pyc  | Bin 181 -> 208 bytes
 autoqtl/__pycache__/autoqtl.cpython-310.pyc   | Bin 1055 -> 1053 bytes
 autoqtl/__pycache__/base.cpython-310.pyc      | Bin 70766 -> 70254 bytes
 .../__pycache__/decorators.cpython-310.pyc    | Bin 2517 -> 2515 bytes
 .../__pycache__/export_utils.cpython-310.pyc  | Bin 12257 -> 12255 bytes
 autoqtl/__pycache__/gp_deap.cpython-310.pyc   | Bin 16275 -> 16273 bytes
 autoqtl/__pycache__/gp_types.cpython-310.pyc  | Bin 381 -> 379 bytes
 .../operator_utils.cpython-310.pyc            | Bin 9000 -> 8998 bytes
 autoqtl/base.py                               |  32 +++++++++---------
 .../__pycache__/__init__.cpython-310.pyc      | Bin 392 -> 390 bytes
 ...ncoding_frequency_selector.cpython-310.pyc | Bin 3259 -> 3257 bytes
 .../genetic_encoders.cpython-310.pyc          | Bin 5891 -> 5889 bytes
 .../__pycache__/regressor.cpython-310.pyc     | Bin 1898 -> 1896 bytes
 tests/autoqtl_test.py                         |   7 ++--
 15 files changed, 21 insertions(+), 20 deletions(-)

diff --git a/_version.py b/_version.py
index 5a6012d..30fb820 100644
--- a/_version.py
+++ b/_version.py
@@ -1 +1 @@
-__version__ = '0.1.5-alpha'
\ No newline at end of file
+__version__ = '0.1.6-alpha'
\ No newline at end of file
diff --git a/autoqtl/__pycache__/__init__.cpython-310.pyc b/autoqtl/__pycache__/__init__.cpython-310.pyc
index 9d23c943749b0aabfeb0320c3f7b76eae9f0eb8e..e5e6ece6c29f5e278a016b627fea17706f2380b9 100644
GIT binary patch
delta 109
zcmdnWc!7~OpO=@50SIh4@1&?s<dyXk0CG|oQW$d>av7r-85vTTf*CZKUorxPH5qRS
zIEIG!2Zs0rrKT6978mCi`DrrUVoxkB$uBI)DPjUDTFFqv0;IsiFXxHT`V)K9nFJUB
DG$R?$

delta 83
zcmcb>xRsGNpO=@50SF8n6O;KS^2!?W068fPDU3M`Q9v@7L6hkvNZwDA@fLexX-R%z
cNzO`!B4(fvnE2&7u|l6o*Kp!;btWDL0KpUy_y7O^

diff --git a/autoqtl/__pycache__/autoqtl.cpython-310.pyc b/autoqtl/__pycache__/autoqtl.cpython-310.pyc
index a912c67405db999f0191d65ad5b9ce88aa29bb31..7eb6889157db35b884379adb5dea36bd075bd79c 100644
GIT binary patch
delta 37
scmbQwF_(iUpO=@50SNq--%i=clh4HHG`WUJfAU(U?Toi3r!wyX0Jt>^S^xk5

delta 40
ucmbQsF`t7cpO=@50SF8n6O%Xc<TEk4Os-+lXVNvCyqak{W7Oni=3M}^`3gAz

diff --git a/autoqtl/__pycache__/base.cpython-310.pyc b/autoqtl/__pycache__/base.cpython-310.pyc
index 8136493f74d33db6573c7a8da3465ec49b9932eb..8079b83a575777fe181a75d4875b2e5ad6c3286b 100644
GIT binary patch
delta 9341
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diff --git a/autoqtl/__pycache__/decorators.cpython-310.pyc b/autoqtl/__pycache__/decorators.cpython-310.pyc
index 18d48cd48b3aa303365ba13b1169c13dfe76f92d..33812a5e6a603299770df5b7b9d1f19be479aeb4 100644
GIT binary patch
delta 32
mcmcaAd|8+$pO=@50SNq--%i=cBhSp}I$4KVe{(Ez1sed1^ax1+

delta 35
pcmcaCd{vkypO=@50SF8n6O%Xc$TKs#Pu5}9XVNv?9L-$81^|u@2dV%7

diff --git a/autoqtl/__pycache__/export_utils.cpython-310.pyc b/autoqtl/__pycache__/export_utils.cpython-310.pyc
index 06fa034b0cbdfc195a3e7b0d0c4150ec3bc71e91..46ec0d8e3d93b8dfdb5207c2218ea816fb9be87e 100644
GIT binary patch
delta 32
mcmaDDe?OilpO=@50SNq--%i=cBg(?)K3R!He{(QPpEdxXEeRU{

delta 35
pcmcZ~|1h2>pO=@50SF8n6O%Xch_WzxPF7;kXVNv?9LUnA4FIGk2z&ql

diff --git a/autoqtl/__pycache__/gp_deap.cpython-310.pyc b/autoqtl/__pycache__/gp_deap.cpython-310.pyc
index 6532210bf1e5134532738f9c0dbfeb092dd39e46..a105562a9505f2e858a4d57dab50b767b8822b8e 100644
GIT binary patch
delta 31
mcmbPSKe3)WpO=@50SNq--`>c*h=tK<@;Vm%&G%WhSOEZ+unH~!

delta 34
pcmbPOKe?VepO=@50SLakU){*Ph=tK*@;VlMCSAkLcUiVr0RXoc3ef-n

diff --git a/autoqtl/__pycache__/gp_types.cpython-310.pyc b/autoqtl/__pycache__/gp_types.cpython-310.pyc
index b2877d5ed0ef63429586084b21502a85c3c13bf6..78956ff467ee70de56ab3b0f9295a30387e491ab 100644
GIT binary patch
delta 29
jcmey%^qYw%pO=@50SNq--%gpxbCc0|;xqlp;*4nkg*ypB

delta 32
mcmey(^p}YzpO=@50SF8n6O$+M++=i}_)MQk*Ko2ZV;TUCSO}s3

diff --git a/autoqtl/__pycache__/operator_utils.cpython-310.pyc b/autoqtl/__pycache__/operator_utils.cpython-310.pyc
index c0049cec1aef8aceba1576bf8cf1b018eab21484..a3ca33146c2b20d29fe093017757d3a0cb276590 100644
GIT binary patch
delta 32
mcmZ4Cw#<zupO=@50SNq--%i=cbAXA_bMjdx{muWFv=jiGO9}-5

delta 35
pcmZ4Hw!)1kpO=@50SF8n6O%Xc9AIMfo_v-`pGntn^B*QH1puN|31$EQ

diff --git a/autoqtl/base.py b/autoqtl/base.py
index 3f6e653..a9e6722 100644
--- a/autoqtl/base.py
+++ b/autoqtl/base.py
@@ -1507,22 +1507,22 @@ def _summary_of_best_pipeline(self, features_dataset1, target_dataset1, features
                     
                     print(self.print_pipeline_hyper(pipeline))
 
-                    # print the names of the selected features if FS exists in the pipeline
-                    pipeline_sklearn = self._toolbox.compile(expr=pipeline)
-                    with warnings.catch_warnings():
-                        warnings.simplefilter("ignore")
-                    pipeline_sklearn.fit(selected_features, selected_target)
-                    flag = False
-                    for name, transformer in pipeline_sklearn.steps:
-                        if name=='variancethreshold' or name=='selectpercentile' or name=='featureencodingfrequencyselector':
-                            selected_features_mask = transformer.get_support()
-                            selected_features_names = features_dataset1.columns[selected_features_mask]
-                            print("No of features selected in the pipeline: ", len(selected_features_names))
-                            print("Feature names selected in the pipeline: ", selected_features_names.tolist())
-                            flag = True
-
-                    if flag==False:
-                        print("No feature selectors were selected in the pipeline")
+                    # # print the names of the selected features if FS exists in the pipeline
+                    # pipeline_sklearn = self._toolbox.compile(expr=pipeline)
+                    # with warnings.catch_warnings():
+                    #     warnings.simplefilter("ignore")
+                    # pipeline_sklearn.fit(selected_features, selected_target)
+                    # flag = False
+                    # for name, transformer in pipeline_sklearn.steps:
+                    #     if name=='variancethreshold' or name=='selectpercentile' or name=='featureencodingfrequencyselector':
+                    #         selected_features_mask = transformer.get_support()
+                    #         selected_features_names = features_dataset1.columns[selected_features_mask]
+                    #         print("No of features selected in the pipeline: ", len(selected_features_names))
+                    #         print("Feature names selected in the pipeline: ", selected_features_names.tolist())
+                    #         flag = True
+
+                    # if flag==False:
+                    #     print("No feature selectors were selected in the pipeline")
 
                     self.score1_final_list.append(pipeline_scores.wvalues[0])
                     self.score2_final_list.append(pipeline_scores.wvalues[1])
diff --git a/autoqtl/builtins/__pycache__/__init__.cpython-310.pyc b/autoqtl/builtins/__pycache__/__init__.cpython-310.pyc
index f44c2f61c8c22cb22ebe5b74960414142c642b22..66dcb7bb4b01998d41cdaec603f7e3e9a29abb21 100644
GIT binary patch
delta 31
lcmeBRZe!-j=jG*M00O_|w^KIq2rx4GOqONTpX|qI1psUi2LAv6

delta 34
ocmZo;?qKH0=jG*M00INY#N>@U0*s7)lVut8nRE>&`!HGo0BlMIU;qFB

diff --git a/autoqtl/builtins/__pycache__/feature_encoding_frequency_selector.cpython-310.pyc b/autoqtl/builtins/__pycache__/feature_encoding_frequency_selector.cpython-310.pyc
index be973e8762dcfc316b5f0bae6d5a147cb70175c5..304d903a66600c055440be2a185a204710956679 100644
GIT binary patch
delta 32
mcmdljxl@uSpO=@50SNq--%i=cbB>8Iee!K4{mneg>p20H*$IdM

delta 35
pcmdlfxm%JapO=@50SF8n6O%XcoMU3loP3)}pGntnGZ*uEP5_(r2<ZR-

diff --git a/autoqtl/builtins/__pycache__/genetic_encoders.cpython-310.pyc b/autoqtl/builtins/__pycache__/genetic_encoders.cpython-310.pyc
index c76e9671e34b16f1bddc4f1924e8e4d3a3d46220..4faebd3e7eda9f87417ba600d6657339b5f40410 100644
GIT binary patch
delta 32
mcmZqHYt-Y(=jG*M00O_|w^KIq>|tgMnS7F2fAcS9PGJCzh6!H)

delta 35
pcmZqFYu4k*=jG*M00INY#N>@Udzcx+CZA;1XVNv?{DYZO7yyzn2*3aU

diff --git a/autoqtl/config/__pycache__/regressor.cpython-310.pyc b/autoqtl/config/__pycache__/regressor.cpython-310.pyc
index 01d66cbfcbbb47f5f61393b753198518a4412ce1..b4e475c874294f618fe66f27cfce246286c8b639 100644
GIT binary patch
delta 31
lcmaFG_kxcnpO=@50SNq--%i=cbDWLQd-4@F{mHECsQ{A02{-@%

delta 34
ocmaFC_ll1vpO=@50SF8n6O%Xc9A{(noqUB&pGntnGBbNB0GaRznE(I)

diff --git a/tests/autoqtl_test.py b/tests/autoqtl_test.py
index 81e4616..3fb9470 100644
--- a/tests/autoqtl_test.py
+++ b/tests/autoqtl_test.py
@@ -10,7 +10,8 @@
 
 # Insert the path of the currently cloned github repo folder
 #sys.path.append("Insert path")
-sys.path.append("/Users/ghosha/Documents/VSCode Projects/autoqtl-1")
+#sys.path.append("/Users/ghosha/Documents/VSCode Projects/autoqtl-1")
+sys.path.append("/Users/ghosha/Documents/VSCode Projects/autoqtl")
 
 import autoqtl
 
@@ -156,7 +157,7 @@ def test_summary_of_best_pipeline():
         verbosity=3
     )
     #autoqtl_obj.fit(features_dataset1, target_dataset1, features_dataset2, target_dataset2)
-    autoqtl_obj.fit(features_80, target_80,32, 0.5)
+    autoqtl_obj.fit(features_80, target_80,42, 0.5)
     #autoqtl_obj._summary_of_best_pipeline(features_dataset1, target_dataset2, features_dataset2, target_dataset2)
     assert isinstance(autoqtl_obj._optimized_pipeline, creator.Individual)
     #autoqtl_obj.get_feature_importance(features_dataset1, target_dataset1, random_state=0)
@@ -168,7 +169,7 @@ def test_summary_of_best_pipeline():
     # this method outputs a text file named EvaluationOnHoldout.txt with the performance on holdout data for each pareto front pipeline
     #autoqtl_obj.get_test_r2(features_dataset1, target_dataset1, features_dataset2, target_dataset2 ,features_20, target_20, features_80, target_80, test_X, test_y)
     #autoqtl_obj.shap_feature_importance(test_X, test_y, random_state=0)
-    autoqtl_obj.get_final_output(test_X, test_y, features_20, target_20, file_path = '/Users/ghosha/Library/CloudStorage/Box-Box/AutoQTL/Mouse Data/Analysis/AutoQTL Outputs/')
+    autoqtl_obj.get_final_output(test_X, test_y, features_20, target_20, file_path = '/Users/ghosha/Library/CloudStorage/Box-Box/AutoQTL/Mouse Data/Analysis/AutoQTL Outputs/Package Tests/')
     #autoqtl_obj.average_feature_importance(test_X, test_y)
 
     # # individual SHAP plot