diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..325c65d --- /dev/null +++ b/.dockerignore @@ -0,0 +1,26 @@ +**/__pycache__ +**/.venv +**/.classpath +**/.dockerignore +**/.env +**/.git +**/.gitignore +**/.project +**/.settings +**/.toolstarget +**/.vs +**/.vscode +**/*.*proj.user +**/*.dbmdl +**/*.jfm +**/bin +**/charts +**/docker-compose* +**/compose* +**/Dockerfile* +**/node_modules +**/npm-debug.log +**/obj +**/secrets.dev.yaml +**/values.dev.yaml +README.md diff --git a/.github/workflows/cml_decission_tree.yml b/.github/workflows/cml_decission_tree.yml index 89cf08b..847785b 100644 --- a/.github/workflows/cml_decission_tree.yml +++ b/.github/workflows/cml_decission_tree.yml @@ -20,15 +20,44 @@ jobs: run: | # Your ML workflow goes here pip install -r requirements.txt + python train/logistic_regression.py python train/decisiontree.py + python train/random_forest.py + # python train/xgboost.py - name: Write CML report env: REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | # Post reports as comments in GitHub PRs + # Logistic Regression echo "### Model Metrics" > report.md - cat train/metrics.txt >> report.md + cat train/logistic_metrics.txt >> report.md + + echo "### Data Visualization" >> report.md + cml-publish train/logistic_accuracy.png --md >> report.md + + # python train/decisiontree.py + # Decission Tree Classifier + echo "### Model Metrics" >> report.md + # cat train/decission_metrics.txt >> report.md echo "### Data Visualization" >> report.md cml-publish train/decision_tree_accuracy.png --md >> report.md + + + # python train/random_forest.py + # Random Forest Classifier + echo "### Model Metrics" >> report.md + # cat train/random_metrics.txt >> report.md + + echo "### Data Visualization" >> report.md + cml-publish train/random_forest_accuracy.png --md >> report.md + + # # XGBoost Classifier + # echo "### Model Metrics" >> report.md + # cat train/xgboost_metrics.txt >> report.md + + # echo "### Data Visualization" >> report.md + # cml-publish train/xgboost_accuracy.png --md >> report.md + cml-send-comment report.md diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..8f49687 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,9 @@ +FROM continuumio/miniconda3:latest + +RUN pip install mlflow boto3 pymysql + +ADD . /app +WORKDIR /app + +COPY time_sc.sh time_sc.sh +RUN chmod +x time_sc.sh \ No newline at end of file diff --git a/data2/.gitignore b/data2/.gitignore new file mode 100644 index 0000000..39e073d --- /dev/null +++ b/data2/.gitignore @@ -0,0 +1 @@ +/ABtwoCampaignEngView.csv diff --git a/data2/ABtwoCampaignEngView.csv.dvc b/data2/ABtwoCampaignEngView.csv.dvc new file mode 100644 index 0000000..de9454a --- /dev/null +++ b/data2/ABtwoCampaignEngView.csv.dvc @@ -0,0 +1,4 @@ +outs: +- md5: 9991aae82c13f36bc8fd180fe30e15bc + size: 125065605 + path: ABtwoCampaignEngView.csv diff --git a/mlruns/0/meta.yaml b/mlruns/0/meta.yaml new file mode 100644 index 0000000..960b5dc --- /dev/null +++ b/mlruns/0/meta.yaml @@ -0,0 +1,4 @@ +artifact_location: file:///home/jedi/Documents/Tenacademy/Week2/abtest-mlops/mlruns/0 +experiment_id: '0' +lifecycle_stage: active +name: Default diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/meta.yaml b/mlruns/1/1092868c57384951a00eb84d658a36f7/meta.yaml new file mode 100644 index 0000000..3ac1256 --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/meta.yaml @@ -0,0 +1,15 @@ +artifact_uri: file:///home/jedi/Documents/Tenacademy/Week2/abtest-mlops/mlruns/1/1092868c57384951a00eb84d658a36f7/artifacts +end_time: 1653254981992 +entry_point_name: '' +experiment_id: '1' +lifecycle_stage: active +name: '' +run_id: 1092868c57384951a00eb84d658a36f7 +run_uuid: 1092868c57384951a00eb84d658a36f7 +source_name: '' +source_type: 4 +source_version: '' +start_time: 1653254980366 +status: 3 +tags: [] +user_id: jedi diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/params/data_url b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/data_url new file mode 100644 index 0000000..1210495 --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/data_url @@ -0,0 +1,14 @@ + auction_id experiment date hour ... platform_os browser yes no +0 0008ef63-77a7-448b-bd1e-075f42c55e39 exposed 2020-07-10 8 ... 6 Chrome Mobile 0 0 +1 000eabc5-17ce-4137-8efe-44734d914446 exposed 2020-07-07 10 ... 6 Chrome Mobile 0 0 +2 0016d14a-ae18-4a02-a204-6ba53b52f2ed exposed 2020-07-05 2 ... 6 Chrome Mobile WebView 0 1 +3 00187412-2932-4542-a8ef-3633901c98d9 control 2020-07-03 15 ... 6 Facebook 0 0 +4 001a7785-d3fe-4e11-a344-c8735acacc2c control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +... ... ... ... ... ... ... ... .. .. +8072 ffea24ec-cec1-43fb-b1d1-8f93828c2be2 exposed 2020-07-05 7 ... 6 Chrome Mobile 0 0 +8073 ffea3210-2c3e-426f-a77d-0aa72e73b20f control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +8074 ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7 control 2020-07-04 9 ... 6 Chrome Mobile 0 0 +8075 ffeeed62-3f7c-4a6e-8ba7-95d303d40969 exposed 2020-07-05 15 ... 6 Samsung Internet 0 0 +8076 fffbb9ff-568a-41a5-a0c3-6866592f80d8 control 2020-07-10 14 ... 6 Facebook 0 0 + +[8077 rows x 9 columns] \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_cols b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_cols new file mode 100644 index 0000000..f11c82a --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_cols @@ -0,0 +1 @@ +9 \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_rows b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_rows new file mode 100644 index 0000000..b30eaab --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/input_rows @@ -0,0 +1 @@ +8077 \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_parameters b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_parameters new file mode 100644 index 0000000..cafe41f --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_parameters @@ -0,0 +1 @@ +n_estimators=100, max_depth=10, max_features=0.5 \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_type b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_type new file mode 100644 index 0000000..5316701 --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/params/model_type @@ -0,0 +1 @@ +Random Forest \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.git.commit b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.git.commit new file mode 100644 index 0000000..3464975 --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.git.commit @@ -0,0 +1 @@ +af78305832f05108e380b8af98e69fcd987872f3 \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.name b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.name new file mode 100644 index 0000000..d58971a --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.name @@ -0,0 +1 @@ +train/random_forest.py \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.type b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.type new file mode 100644 index 0000000..0c2c1fe --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.source.type @@ -0,0 +1 @@ +LOCAL \ No newline at end of file diff --git a/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.user b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.user new file mode 100644 index 0000000..7bb8957 --- /dev/null +++ b/mlruns/1/1092868c57384951a00eb84d658a36f7/tags/mlflow.user @@ -0,0 +1 @@ +jedi \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/meta.yaml b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/meta.yaml new file mode 100644 index 0000000..4304040 --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/meta.yaml @@ -0,0 +1,15 @@ +artifact_uri: file:///home/jedi/Documents/Tenacademy/Week2/abtest-mlops/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/artifacts +end_time: 1653255192472 +entry_point_name: '' +experiment_id: '1' +lifecycle_stage: active +name: '' +run_id: 24b98c419ab0490798ac0ab7a5be592d +run_uuid: 24b98c419ab0490798ac0ab7a5be592d +source_name: '' +source_type: 4 +source_version: '' +start_time: 1653255191795 +status: 3 +tags: [] +user_id: jedi diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/data_url b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/data_url new file mode 100644 index 0000000..1210495 --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/data_url @@ -0,0 +1,14 @@ + auction_id experiment date hour ... platform_os browser yes no +0 0008ef63-77a7-448b-bd1e-075f42c55e39 exposed 2020-07-10 8 ... 6 Chrome Mobile 0 0 +1 000eabc5-17ce-4137-8efe-44734d914446 exposed 2020-07-07 10 ... 6 Chrome Mobile 0 0 +2 0016d14a-ae18-4a02-a204-6ba53b52f2ed exposed 2020-07-05 2 ... 6 Chrome Mobile WebView 0 1 +3 00187412-2932-4542-a8ef-3633901c98d9 control 2020-07-03 15 ... 6 Facebook 0 0 +4 001a7785-d3fe-4e11-a344-c8735acacc2c control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +... ... ... ... ... ... ... ... .. .. +8072 ffea24ec-cec1-43fb-b1d1-8f93828c2be2 exposed 2020-07-05 7 ... 6 Chrome Mobile 0 0 +8073 ffea3210-2c3e-426f-a77d-0aa72e73b20f control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +8074 ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7 control 2020-07-04 9 ... 6 Chrome Mobile 0 0 +8075 ffeeed62-3f7c-4a6e-8ba7-95d303d40969 exposed 2020-07-05 15 ... 6 Samsung Internet 0 0 +8076 fffbb9ff-568a-41a5-a0c3-6866592f80d8 control 2020-07-10 14 ... 6 Facebook 0 0 + +[8077 rows x 9 columns] \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_cols b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_cols new file mode 100644 index 0000000..f11c82a --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_cols @@ -0,0 +1 @@ +9 \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_rows b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_rows new file mode 100644 index 0000000..b30eaab --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/input_rows @@ -0,0 +1 @@ +8077 \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_parameters b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_parameters new file mode 100644 index 0000000..968c90e --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_parameters @@ -0,0 +1 @@ +n_estimators=100 \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_type b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_type new file mode 100644 index 0000000..cf28f6b --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/params/model_type @@ -0,0 +1 @@ +Decision Tree \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.git.commit b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.git.commit new file mode 100644 index 0000000..3464975 --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.git.commit @@ -0,0 +1 @@ +af78305832f05108e380b8af98e69fcd987872f3 \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.name b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.name new file mode 100644 index 0000000..137b2d7 --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.name @@ -0,0 +1 @@ +train/decisiontree.py \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.type b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.type new file mode 100644 index 0000000..0c2c1fe --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.source.type @@ -0,0 +1 @@ +LOCAL \ No newline at end of file diff --git a/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.user b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.user new file mode 100644 index 0000000..7bb8957 --- /dev/null +++ b/mlruns/1/24b98c419ab0490798ac0ab7a5be592d/tags/mlflow.user @@ -0,0 +1 @@ +jedi \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/meta.yaml b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/meta.yaml new file mode 100644 index 0000000..c04f2f9 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/meta.yaml @@ -0,0 +1,15 @@ +artifact_uri: file:///home/jedi/Documents/Tenacademy/Week2/abtest-mlops/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/artifacts +end_time: 1653255103012 +entry_point_name: '' +experiment_id: '1' +lifecycle_stage: active +name: '' +run_id: 7afabc7a1ded48289c61e7d50be128ce +run_uuid: 7afabc7a1ded48289c61e7d50be128ce +source_name: '' +source_type: 4 +source_version: '' +start_time: 1653255102458 +status: 3 +tags: [] +user_id: jedi diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/data_url b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/data_url new file mode 100644 index 0000000..1210495 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/data_url @@ -0,0 +1,14 @@ + auction_id experiment date hour ... platform_os browser yes no +0 0008ef63-77a7-448b-bd1e-075f42c55e39 exposed 2020-07-10 8 ... 6 Chrome Mobile 0 0 +1 000eabc5-17ce-4137-8efe-44734d914446 exposed 2020-07-07 10 ... 6 Chrome Mobile 0 0 +2 0016d14a-ae18-4a02-a204-6ba53b52f2ed exposed 2020-07-05 2 ... 6 Chrome Mobile WebView 0 1 +3 00187412-2932-4542-a8ef-3633901c98d9 control 2020-07-03 15 ... 6 Facebook 0 0 +4 001a7785-d3fe-4e11-a344-c8735acacc2c control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +... ... ... ... ... ... ... ... .. .. +8072 ffea24ec-cec1-43fb-b1d1-8f93828c2be2 exposed 2020-07-05 7 ... 6 Chrome Mobile 0 0 +8073 ffea3210-2c3e-426f-a77d-0aa72e73b20f control 2020-07-03 15 ... 6 Chrome Mobile 0 0 +8074 ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7 control 2020-07-04 9 ... 6 Chrome Mobile 0 0 +8075 ffeeed62-3f7c-4a6e-8ba7-95d303d40969 exposed 2020-07-05 15 ... 6 Samsung Internet 0 0 +8076 fffbb9ff-568a-41a5-a0c3-6866592f80d8 control 2020-07-10 14 ... 6 Facebook 0 0 + +[8077 rows x 9 columns] \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_cols b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_cols new file mode 100644 index 0000000..f11c82a --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_cols @@ -0,0 +1 @@ +9 \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_rows b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_rows new file mode 100644 index 0000000..b30eaab --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/input_rows @@ -0,0 +1 @@ +8077 \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_parameters b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_parameters new file mode 100644 index 0000000..71839a7 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_parameters @@ -0,0 +1 @@ +n_estimators=100, max_depth=10 \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_type b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_type new file mode 100644 index 0000000..da45759 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/params/model_type @@ -0,0 +1 @@ +Logistic Regression \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.git.commit b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.git.commit new file mode 100644 index 0000000..3464975 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.git.commit @@ -0,0 +1 @@ +af78305832f05108e380b8af98e69fcd987872f3 \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.name b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.name new file mode 100644 index 0000000..839d04b --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.name @@ -0,0 +1 @@ +train/logistic_regression.py \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.type b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.type new file mode 100644 index 0000000..0c2c1fe --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.source.type @@ -0,0 +1 @@ +LOCAL \ No newline at end of file diff --git a/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.user b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.user new file mode 100644 index 0000000..7bb8957 --- /dev/null +++ b/mlruns/1/7afabc7a1ded48289c61e7d50be128ce/tags/mlflow.user @@ -0,0 +1 @@ +jedi \ No newline at end of file diff --git a/mlruns/1/meta.yaml b/mlruns/1/meta.yaml new file mode 100644 index 0000000..93ac930 --- /dev/null +++ b/mlruns/1/meta.yaml @@ -0,0 +1,4 @@ +artifact_location: file:///home/jedi/Documents/Tenacademy/Week2/abtest-mlops/mlruns/1 +experiment_id: '1' +lifecycle_stage: active +name: SmartAD data analysis diff --git a/notebooks/Bonus.ipynb b/notebooks/Bonus.ipynb new file mode 100644 index 0000000..32a3c66 --- /dev/null +++ b/notebooks/Bonus.ipynb @@ -0,0 +1,1296 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> Import required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os, sys\n", + "import datetime\n", + "import seaborn as sns\n", + "from scipy.stats import chi2_contingency, beta\n", + "import plotly.graph_objs as go\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> Importing required modules" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "sys.path.append(os.path.abspath(os.path.join('../scripts')))\n", + "from plot import Plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> Initialize plot class" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pl = Plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> Ignore warnings" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> Loading the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/ABtwoCampaignEngView.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> #### Explore the data and identify the features" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Data overview\n", + "df.head()\n", + "df.drop(['Unnamed: 0'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
auction_idexperimentdatehourdevice_makeplatform_osbrowseryesno
08ec30aff-2332-4a1f-9330-b93abb32bc94control<built-in method date of Timestamp object at 0...15LG15660.00.0
14e1bcb56-357c-4186-9d39-3be82e3519f1control<built-in method date of Timestamp object at 0...7Samsung16260.00.0
2dd6ec327-fcfd-4a80-89c0-dccd3dd80f09control<built-in method date of Timestamp object at 0...14Apple178150.00.0
3b5222d0f-39e3-4c02-a12f-1ef09d4f861fcontrol<built-in method date of Timestamp object at 0...22Apple167150.00.0
49428377d-1504-4407-87c2-ce518f67eb87control<built-in method date of Timestamp object at 0...21Samsung155150.00.0
\n", + "
" + ], + "text/plain": [ + " auction_id experiment \\\n", + "0 8ec30aff-2332-4a1f-9330-b93abb32bc94 control \n", + "1 4e1bcb56-357c-4186-9d39-3be82e3519f1 control \n", + "2 dd6ec327-fcfd-4a80-89c0-dccd3dd80f09 control \n", + "3 b5222d0f-39e3-4c02-a12f-1ef09d4f861f control \n", + "4 9428377d-1504-4407-87c2-ce518f67eb87 control \n", + "\n", + " date hour device_make \\\n", + "0 \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hourplatform_osbrowseryesno
count912712.000000912712.000000912712.000000912712.000000912712.000000
mean10.648142127.8133218.2110510.1016450.019499
std7.37263132.2176743.5367800.3021820.138271
min0.000000101.0000001.0000000.0000000.000000
25%4.000000101.0000006.0000000.0000000.000000
50%10.000000101.0000007.0000000.0000000.000000
75%17.000000162.0000007.0000000.0000000.000000
max23.000000181.00000015.0000001.0000001.000000
\n", + "" + ], + "text/plain": [ + " hour platform_os browser yes \\\n", + "count 912712.000000 912712.000000 912712.000000 912712.000000 \n", + "mean 10.648142 127.813321 8.211051 0.101645 \n", + "std 7.372631 32.217674 3.536780 0.302182 \n", + "min 0.000000 101.000000 1.000000 0.000000 \n", + "25% 4.000000 101.000000 6.000000 0.000000 \n", + "50% 10.000000 101.000000 7.000000 0.000000 \n", + "75% 17.000000 162.000000 7.000000 0.000000 \n", + "max 23.000000 181.000000 15.000000 1.000000 \n", + "\n", + " no \n", + "count 912712.000000 \n", + "mean 0.019499 \n", + "std 0.138271 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data distribution\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 912712 entries, 0 to 912711\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 auction_id 912712 non-null object \n", + " 1 experiment 912712 non-null object \n", + " 2 date 912712 non-null object \n", + " 3 hour 912712 non-null int64 \n", + " 4 device_make 912712 non-null object \n", + " 5 platform_os 912712 non-null int64 \n", + " 6 browser 912712 non-null int64 \n", + " 7 yes 912712 non-null float64\n", + " 8 no 912712 non-null float64\n", + "dtypes: float64(2), int64(3), object(4)\n", + "memory usage: 62.7+ MB\n" + ] + } + ], + "source": [ + "#data properties\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['auction_id', 'experiment', 'date', 'hour', 'device_make',\n", + " 'platform_os', 'browser', 'yes', 'no'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data (overall distribution)\n", + "df.hist(bins=10, figsize=(28,12))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82132\n", + "9539\n" + ] + } + ], + "source": [ + "# Number of people who have completed the survey with a yes and no\n", + "print(df[(df['experiment'] == 'exposed') & (df['yes'] == 1)].shape[0])\n", + "print(df[(df['experiment'] == 'exposed') & (df['no'] == 1)].shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_count(df.yes, df.experiment)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_count(df.no, df.experiment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start_date = datetime.datetime.strptime(df['date'].min(), '%Y-%m-%d')\n", + "end_date = datetime.datetime.strptime(df['date'].max(), '%Y-%m-%d')\n", + "date_duration = (end_date - start_date).days\n", + "\n", + "\n", + "print(f\"Number of unique users in the experiement: {df['auction_id'].nunique()}\")\n", + "print(f\"Data collected for {date_duration} days\")\n", + "print(f\"Percentage of users in control group: {round(df[df['experiment'] == 'control']['auction_id'].nunique() / df['auction_id'].nunique() * 100, 2)}%\")\n", + "print(f\"Percentage of users in exposed group: {round(df[df['experiment'] == 'exposed']['auction_id'].nunique() / df['auction_id'].nunique() * 100, 2)}%\")\n", + "# print(f\"Percentage of users who have clicked on the link: {round(df['yes'].sum()/df['yes'].count()*100, 2)}%\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Divide into participated and non-participated\n", + "participated_df = df[~((df['yes']== 0) & (df['no']== 0))]\n", + "nparticipated_df = df[(df['yes']== 0) & (df['no']== 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "110570 participants participated\n", + "802142 participants did not participate\n" + ] + } + ], + "source": [ + "print(f\"{participated_df.shape[0]} participants participated\")\n", + "print(f\"{nparticipated_df.shape[0]} participants did not participate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the ad campaign experiment\n", + "plt.figure(figsize=(6,6))\n", + "sns.set(style=\"darkgrid\")\n", + "ax = sns.countplot(x=\"experiment\", palette=[\"blue\", \"skyblue\"], data=participated_df)\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Treatment groups of participated Users\", \n", + " \"Campaign Experiment\", \"Experiment Frequency\", 15, 10, \"bold\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the ad campaign experiment\n", + "plt.figure(figsize=(6,6))\n", + "sns.set(style=\"darkgrid\")\n", + "ax = sns.countplot(x=\"experiment\", palette=[\"blue\", \"skyblue\"], data=nparticipated_df)\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Treatment group for people who didn't Participate\", \n", + " \"Campaign Experiment\", \"Experiment Frequency\", 15, 10, \"bold\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABcUAAAHBCAYAAACoidMQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABs20lEQVR4nO3de3zO9f/H8ee1C5vZ1g7NzJw2xzmTQ4XGsImhksN3OSRRCkXfWMghhxoqh4R8pRMqSrKcVg7fklOFaOWwTcIYQ8MYu3b9/vBzfa0NF665Np/H/Xb73m67Pu/P4fX5fC6fPt/n9f68Pyar1WoVAAAAAAAAAAAG4OLsAgAAAAAAAAAAuFMIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAVQTEyMqlatqqpVq2rLli03bDt06JBt2owZM25qW19++aVmzJihDz74wFHl3/V++ukndevWTfXr17cd92v5/fffNWPGDM2YMUOHDh3K0fbll19e8zznl9jYWNs2q1atqqSkJLuWu5Xv2C+//KLBgwfroYceUs2aNdW0aVP16NFDixYtksViuZ3dcDpnnLur3ew1AgAAAP9TxNkFAAAAwLmWLl2qrVu3KigoSE8++aSzyynwrFarBg4cqJMnT9o1/++//6533nlHktSoUSOVKVMmP8u7odWrV+f4vHLlSj3//PMO387MmTM1Y8YMWa1W27Tjx4/r+PHj2rp1q9q1aycvLy+HbxcAAAC4EUJxAACAu0CZMmW0Z88eZ5dxyywWi7Kzs1W0aFFnl3JDqamptkC8c+fOeu211+TiUjgewNyxY4cOHz6cY9qqVascHoqvXr1a06dPlyT5+flpzJgxatasmbKysrRt2za99957Dt2eMzz22GN67LHHnF1GgXThwgW5ubk5uwwAAIBrKhx37wAAALiuaw1tsWLFCnXu3FmNGjVS7dq11bx5c/Xv318//fSTJKlq1araunWrJOnw4cO2dcTExNjWsXbtWnXv3l3169dXrVq11L59e82bNy/X8BfLly9X69atVbt2bfXs2VNJSUl5ri88PFxVq1ZVjx49tGzZMkVGRqpWrVrav3+/fvnlF/Xr10/NmzdXnTp1VLt2bbVr105z5sxRVlaWbR0zZsywrXvTpk3q2bOn6tSpo0ceeUQ7d+7UqVOn9MILL6hevXqKiIjQV199Zddx/Pnnn9W3b181bNhQNWvWVGRkpN5++21duHBB0uUhMx566CHb/IsXL1ZoaKh69OiR5/piYmL0yiuv2D737NnzmsOtnD17Vq+++qoaNGigpk2batKkSTn2WZK+//579erVSw0aNLCdiwULFuTojX09K1assP3dvn17SdLevXu1f//+XPO+//77euihh1S3bl31799fx44ds2sb0uVe4le88cYbioiIUPHixeXp6anw8HAtWrRInp6ekqQlS5YoOjpaTZo0Uc2aNVW/fn1169ZNq1atyrHOHj16qGrVqgoPD9f333+vtm3bqk6dOnrmmWeUlpam33//XV27dlXdunXVuXNn7d6927bs1f8+pk2bpnfeeUdNmza1LX/kyBHbvElJSRowYIDCw8NVr1491axZU61bt9akSZOUkZFhm+9aw6f89ttv6tKli2rVqqWoqCitW7cuR+1XXD3EyR9//KEePXqoTp06ioiI0LJly+w+1jfjwoULmjp1qiIjI1WzZk01bNhQffr0sV0Prsjr321e+7tlyxbbtAULFujVV19Vo0aN1K1bN0nSjz/+qB49euj+++9XzZo11axZM/Xp00fffvttvuwfAACAvegpDgAAcJfavn27hgwZkiMwTUlJUUpKiho0aKAGDRrccB0LFy7U2LFjc0zbu3evJk2apF27dmnq1KmSpE2bNunll1+2bWvLli3q1avXddf9xx9/aNiwYTnq27NnjzZs2JBjvv379+utt95Senq6Xn755VzrefHFF3X69GlJl4cq6d+/v8qUKaOdO3dKkv7880/FxMSoZs2aqlSp0jXrWbt2rQYMGJAj7D9w4IBmz56tbdu26cMPP7zu/tyuV199VWlpaZKkM2fOaN68eSpbtqz+9a9/SbocwL/66qs5jtfevXv12muvKTExUaNGjbru+q1Wq23olCpVqqhnz55avny5pMtDqAwcONA275IlSxQbG2v7vHbtWu3atcuu/Th+/LjtqYXg4OAcPyJcYTKZbH9v3LhRP//8s+3zpUuXtH37dm3fvl3vvfeewsLCcix76tQpPffcc7p48aIkaf369RowYICSkpJs34Nff/1VAwYMUHx8fK6nDxYuXGib78ryf/31l5YvXy6z2axDhw4pPj4+xzIHDx7UvHnzdOTIEdt3Pi+nTp1S79699ffff0uS9u3bpwEDBtxwmJju3bvrzJkzki5/X4cNG6YaNWpc9/t6sy5duqSnnnoq17H+4YcftGnTJs2cOVMtWrS45fVPmzbNtt+BgYE6fPiwnn32WWVmZtrmSU1NVWpqqsqXL69WrVrd+s4AAADcJnqKAwAAFHBX9y6uWrWqli5datdy27dvl9VqVYkSJfTtt99q165dio+P1/jx41WlShVJl0PoRo0aSZKCgoK0Z88e7dmzR2+88YbOnj2rKVOmSJICAgK0bNky/fjjj7r//vslXQ5Sr/QYnT59uqxWq1xcXDRr1ixt3brVtt5rSU9PV3R0tLZs2aK1a9eqbNmyuu+++7RgwQJt2rRJv/32mzZu3KjmzZtLkj777DNlZ2fnWk/NmjW1ZcsW9enTR5KUlpam1NRUxcfH24bwsFqtWrNmzTVrsVqtGj9+vCwWi9zd3fXJJ59o69at6tChg6TLPciXL1+uxx57TN99951tuQEDBmjPnj36+OOP81zvG2+8oddff932+aOPPrId438qUaKEVq1apWXLlsnV1VXS/8b/PnfunN544w1ZrVZFRETohx9+0Pbt2/XUU09Juhz0JiYmXnP/pMsvvTx69Kiky731a9WqJX9/f0mXz+UV2dnZtp7eHh4e+uyzz7Rp0yZVrlz5uuu/4upe1yEhITecv0uXLlq2bJl++ukn/fbbb1q5cqUCAwMlSYsWLco1f0ZGhvr27auffvpJ9913n23fateura1bt9p+jElJSbH9MHK1ixcv6qOPPtLWrVttveUTExNtPxCEhIRo3rx52rhxo3bv3q0tW7aoS5cuki4PNXPq1Klr7ssHH3xgC4afffZZ/fTTTxo2bNgNx59v1KiRNm/erHHjxkm68ff1n+y5RixfvtwWiLdv315bt27VJ598Ind3d1ksFo0bN87uJw7ycunSJc2ePVvbt2/XW2+9pd27d9sC8SVLlmjXrl1at26d3nzzTdt5AwAAcBZCcQAAgLtU6dKlJV0OEWfOnKnPP/9cR48eVYcOHdSsWbMbLr99+3adO3dO0uXgslq1avLz88sx/vTGjRtlsVhsvYgbNWqk8PBw3XPPPXrhhReuu/577rlHMTEx8vb2VlBQkDw8PFSqVCmtWLFCjz/+uOrWrasmTZpo/fr1ki73nr7Sk/pqTz/9tLy9vW1hvSQ9+uijKleuXI6er1cC4bwkJyfbxtpu06aNGjZsqHvuuUcvvvhijn3NT71791ZwcLCqVatmG14lJSVF0uVzcfbsWUnSmjVr1LRpU9WrV0/vv/++pMsh6tVDeOTl6uA7PDxcJpPJ9oNDYmKi9u7dK+nycboSbEdGRqpu3bry9fXVM88847idvYq/v7/eeecdRUZGqnbt2nr44Ydt+33gwIFc8xctWlT9+vWTp6en6tevb5v+1FNP6Z577snRMz2vc96qVSs1btw413f0l19+kSSVLFlSW7dutQ0Z1LhxY33++eeSLh/nP//885r7sn37dkmSm5ubnn/+eXl6eqpnz562kP9aXnrpJfn4+Nh+hLlW7bfjhx9+sP09ePBg3XPPPWrYsKHatGkj6fLwSXkdb3s98sgjatGihdzd3VWxYkXb9UeS5syZo4ULFyopKUnNmzdXu3btbnk7AAAAjkAoDgAAUMBd3bt4z549evTRR+1aLiIiQp06dZLZbNbSpUs1btw49ejRQ02bNrUFzddzdY/YUqVK5fn3yZMnderUKV26dEnS5R7lec2XlwoVKqhYsWI5pg0dOlQLFizQ4cOHbeu82tVDMVxxJXy70rv66mlXr//KcBt5sXdf81OFChVsf1/ZlyvHwJ5tX+mhnJfs7Gxbr3MPDw+5ublp7969OYbnuDLe+PHjx23Trj6fJUuWtGMvlCMMTU5Ovu68Z86cUZ8+fRQfH6+0tLRc49Tndc58fX1tL3HM65xfPVxKXstfHVBfvX+pqamSpEmTJmnOnDlKTk7Oc/m8voNXXDl2Pj4+Ob57V28nL+XLl8+1P9f7vv6TPdeIa33Hr67tet+zf56bf7ry9MkVtWrV0rPPPis3NzfFx8fr9ddfV58+fdSkSRMtXrz4hvsEAACQnwjFAQAA7lIuLi6aOHGiNm/erI8//ljjxo1TSEiI0tPTNXHixBsu7+PjY/v76l6rV//t4+MjHx8fWxB5daB6pbfvtVwdAEqXXwJ4ZTzxBx98UD/++KP27NljGyLkWsxms13Trsfefb0VV4+ffT1X1/zPZa7e9vDhw3MEoHv27NEff/yh/v37X3PdP//8sy30PXv2rDp06KD27dvnGNrlSk/yK0OqSMrxcs0ry9+Iv7+/rad7UlJSjh7KV1itVlmtVu3YscN2jPv166cdO3Zoz549qlGjxjXXX6RI3q9FsvecX31Or96/K6H/lRd8VqlSRevXr9eePXv06quv2rXuK+s4ffp0jh91btTr+8o+2ftduRVXf4eu3u+r/74yz5V/z1f/AHDlSYpr+ee/Z+lyj/TNmzdr0aJFeuONN1S3bl1duHBB48ePz/USWQAAgDuJUBwAAOAutXnzZs2fP1/Hjh1TjRo11KZNG1tv5Kt7hN5zzz2SLvckvTrUrlevntzd3SVdfsnjnj17dPLkSc2aNcs2T5MmTWQ2m1W7dm1J0tatW/XDDz8oPT3dNp63vbKysmxjhhcrVkyurq7atWuXli1bdvM7f5OCg4MVFBQk6fI43j/99FOufWjSpMktrfvqlyzu37//lsZtrlevnkqUKCFJmjdvnn7++WddvHhRx44d0+LFi9W2bdvrLn+lF/j1HDhwQH/88YdKlSpl63W9evVq7dy5UydPntR7771nd70DBgyw/T1s2DB9++23On/+vM6ePau1a9eqW7duOnPmTI7guHjx4jKZTPr666+VkJBg97ZuVnx8vLZt26b09HRNmzbNNv3KUCxXaipSpIiKFy+upKQkLViwwK5116tXT5J0/vx5zZ07V2fPntVHH33k8KFQbsXV39+pU6cqPT1dP/30k+0JgqCgIAUHB0v6X0/yHTt26OzZszp69Kjd7zK4Ys+ePZo5c6YOHDigypUrKzIyUqGhoZIu/wB2ZWgmAAAAZ8i7mwUAAAAKvSNHjuiNN97QG2+8kavt6jHFa9Wqpfj4eGVkZKhp06aSpPHjx6tz58566aWXNG7cONtY5FeLjIy0jeM9cOBA9e7dW1lZWbYXXl7d49ieHrAeHh5q0KCBfvrpJ61fv972Mr5y5crlOZa4I5lMJg0fPlyDBg3SuXPn9MQTT+Ror1evXq79t1f16tVVpEgRZWVl6bXXXtNrr72m+vXr5/kSyWvx8PDQ0KFDNXr0aB07dkzR0dF2L5udnW17aaOvr6++//77HL2t4+PjbSH2ihUrVK1aNT3//PMaMWKEzp49a3vJpLe3t93bjIiI0KBBgzR9+nSdOHEixzj0V6tXr568vLxsAfW0adNUrFgxlSxZMkcPZkcqUaKEunfvnmNaxYoVFRUVJUkKCwvTsmXLlJCQoMaNG0u6/B20R69evbRw4UL9/ffftv0xm83y8fG57gs674QOHTpo8eLF2r59u5YtW5bjxyaz2azhw4fb/p1GRkbqP//5j44cOaImTZro0qVLOYalscfp06c1ffr0PH8cq1Onju3HOAAAAGegpzgAAMBdqlatWnrkkUdUoUIFubu7y83NTeXLl9dTTz2lcePG2ebr3r27OnbsmOfwIN27d9fMmTPVoEEDubu7q1ixYqpUqZL+/e9/66233rLN98ADD2jSpEkqW7asihUrpoYNG2rq1Km29qt7S1/PlClT1Lx5c7m7uysgIEBDhw695TD6ZrVq1UoffvihmjZtKk9PTxUtWlTlypVTv379NH/+/JsOBa8IDAzUa6+9pnLlyl1z6A97dOvWTXPnztUDDzwgT09PFStWTGXKlFFERISmTJlyzeW2bt2qEydOSJIefvjhXDWEhYXJ09NT0v+GUHn88cc1bNgwlSxZUm5ubnrooYc0Y8aMm6r3+eef18KFC/Xwww+rZMmSKlq0qO699141atRIo0ePVokSJeTj46NZs2apRo0acnV1VeXKlfXuu+/axtjOD126dNHgwYNVsmRJubq6KiwsTP/5z39sx2XEiBGKioqSl5eXfHx89PTTT6tfv352rdvHx0fvv/++6tSpo6JFiyokJETTp0+3/UDkzCC4aNGimj9/vp599lmVL19eRYsWlaenp5o2baoPP/xQrVq1ss37/PPPq1OnTvL29pabm5u6dOmil19++aa2V758eXXt2lWVK1e2fV+DgoLUtWtXzZw509G7BwAAcFNM1lt5fhMAAAC4yrlz55SQkKD77rtPLi4uunjxoqZMmaIPP/xQkvTuu++qZcuWTq4SRnXo0CHb92/AgAEaOHBgvm1r06ZNqlevnu1loCtWrNCQIUNktVr15JNP6pVXXsm3bQMAAMA+DJ8CAACA23bmzBl1795drq6u8vX11cmTJ20v6WvWrJnCw8OdXCFwZ4waNUpHjhyRn5+fMjIydObMGUlS6dKl1bdvXydXBwAAAInhUwAAAOAAHh4eateunfz8/JSWliYXFxfVqFFDw4YN06xZs+waUxy4G7Rt21blypXTmTNndOHCBZUvX149e/bUF198oXvvvdfZ5QEAAEAMnwIAAAAAAAAAMBB6igMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDKOLsAgqjU6fOKTvbeEOx+/l5KC3trLPLAHAX4HoCwBG4lgBwFK4nAByF6wlQcLi4mOTjUyLPNkLxW5CdbTVkKC7JsPsNwPG4ngBwBK4lAByF6wkAR+F6AhR8DJ+CQuuLLz5Tnz491KLFA5owYUyOtgsXLmjKlDfUrl1LRUaG6fnn++Zo37PnDz3/fF+1bt1M7dtH6PPPF9naUlKOaODAZ9SyZRNFR3fStm1bcix76tQpjRkzQm3aNFebNi00duzIXLWlp/+tqKhW6t+/z3X3Yc2aVerUKUqtWjXVK6+8pPT0v2/yKAAAAAAAAAC4GfQUR6F1773+6tWrj7Zu3aTMzMwcbZMmTZDFkqVPPlkiLy8v7du319Z2+vRpvfTSQA0aNETNm7dUVtYlpaam2trHjBmhmjVracqUadq0aaNefXWYFi1aKh8fH0nSiBEvKzS0upYsiZObm5uSkvbnqm3WrBkqXz5Y2dnZ16w/KSlRkydP1OTJU1WlSjVNmjRBb775hsaOff12Dw0AAAAAAACAa6CnOAqtsLBwPfRQc3l53ZNj+sGDB/TDD//V0KEj5OPjI7PZrGrVQm3tn322QI0b36+IiIdVrFgxubuXUIUKwf+/7J/au/cP9enzjFxd3dS8eUuFhFTShg3fSZK2bt2s1NRjeu65F+Th4aEiRYqoSpVqOba/e/evSkpKVNu27a9bf3z8KjVp0kx169aXu7u7+vbtrw0b1ikj45wjDg8AAAAAAACAPBCK467z22+7VapUKc2bN0ft2rVUz55dtX79d1e175Kn5z169tmnFBXVWkOHDtbRo0clScnJSSpdOkju7v8bhL9SpcpKTk6yLVuuXHlNmDBGbdu21NNP99T27T/b5rVYLHrrrVgNHjxUJpPpunUmJyeqUqXKts9BQWVUtGhRHTx40CHHAQAAAAAAAEBuhOK46xw/nqqkpESVKOGhr75apcGDh2rChDE6cCBZkpSamqpVq+L0wgsv6Ysv4lS6dGmNHTtcknT+fIZKlPDIsb4SJTyUkZFhW3br1s2qX/8+ff31anXr9oReeeUlnT59WpK0ZMmnql69Zo6e6ddy/vx5eXjktS16igMAAAAAAAD5hVAcdx1XV1cVKVJEvXr1UdGiRVWv3n2qV6+Btm7dbGt/6KEWCg2tIVdXV/Xu3Ve7dv2qs2fPqnhx91yhdEbGObm7u9uWDQwsraioR1SkSBG1ahWpkiUDtGvXDp04cVxLlnymfv2es6vO4sWL69y5nNs6d+5cjl7qAAAAAAAAAByLF23irlOxYuXrtleqVCnH5yvDnFitVgUHh+jIkcP/H4RfDqf379+n1q0jbctu3Ph9nutNSPhNaWkn1L17F0lSZuYFZWZmqkOHSC1dukJmsznH/MHBFbV//z7b58OHD+nSpYsqV67cTewtAAAAAAAAgJtBT3EUWllZWcrMzFR2draysy3KzMxUVlaW6tatr4CAUvrkkw+UlZWlX3/doe3bf1bjxg9Iktq27aD//ne99u3bo6ysLH3wwX9Uu3ZdeXp6qly58qpUqYref3+uMjMztWHDOiUm7lNYWEtJ0kMPtdCZM+lauTJOFotF69Z9q+PHj6tWrbq6//4HtXjx15o/f4Hmz1+gPn2eVeXKVTV//oJcgbgktW7dRhs3fq+dO7fr/PnzmjdvjsLCWtBTHAAAAAAAAMhHJqvVanV2EYVNWtpZZWcb77D5+3vq+PEzzi7DZt68OZo/f26Oab1791WfPs8oKSlRsbHjlZi4T6VKBapv3+cUFtbCNt/SpUv04YfzdOHCBdWuXVcvvTRMAQGlJEkpKUc0YcIYJST8poCAAA0ZMkwNGza2Lbtz53a9+eYbSkk5onLlKmjQoCGqU6dervpWrFiu5cu/0qxZ82zTWrdupilTptvmX7NmlWbPnqH09L/VoEEjDR8+Wl5e9zj0OAEFUUG7ngAonLiWAHAUricAHIXrCVBwuLiY5OfnkWcbofgtIBQHgNvD9QSAI3AtAeAoXE8AOArXE6DguF4ozvApAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwjCLOLgCFiOWC/P09nV0FnCD7UobSTlucXQYAAAAAAABw2wjFYT+zm7TQ5Owq4AQu0VZJZ5xdBgAAAAAAAHDbGD4FAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADOOOhOKxsbEKDw9X1apVtXfvXtv0zMxMjR49WhEREWrfvr1effVVW1tycrK6du2qyMhIde3aVQcOHMjXNgAAAAAAAADA3e+OhOItW7bUggULFBQUlGP65MmT5erqqtWrV2v58uV64YUXbG2jR49WdHS0Vq9erejoaI0aNSpf2wAAAAAAAAAAd787Eoo3aNBAgYGBOaadO3dOX331lV544QWZTCZJ0r333itJSktLU0JCgqKioiRJUVFRSkhI0MmTJ/OlDQAAAAAAAABgDEWcteG//vpL3t7eeuedd7RlyxaVKFFCL7zwgho0aKCUlBQFBATIbDZLksxms0qWLKmUlBRZrVaHt/n6+t5U7X5+Hg48EkDh4O/v6ewScJfhOwXAEbiWAHAUricAHIXrCVDwOS0Uz8rK0l9//aXq1atr2LBh2rlzp5599lnFx8c7qyS7paWdVXa21dll3HFc1I3t+PEzzi4BdxF/f0++UwBuG9cSAI7C9QSAo3A9AQoOFxfTNTs3Oy0UL126tIoUKWIbzqROnTry8fFRcnKySpcurWPHjslischsNstisSg1NVWBgYGyWq0ObwMAAAAAAAAAGMMdGVM8L76+vmrcuLE2btwoSUpOTlZaWprKly8vPz8/hYaGKi4uTpIUFxen0NBQ+fr65ksbAAAAAAAAAMAYTFarNd/HARk/frzWrFmjEydOyMfHR97e3vrmm2/0119/afjw4Tp9+rSKFCmiF198UWFhYZKkxMRExcTEKD09XV5eXoqNjVVISEi+td0MQw+fstDk7DLgDNFWHv+CQ/FIIQBH4FoCwFG4ngBwFK4nQMFxveFT7kgofrchFIfhEIrDwbhRBOAIXEsAOArXEwCOwvUEKDiuF4o7bfgUAAAAAAAAAADuNEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYdyRUDw2Nlbh4eGqWrWq9u7dm6v9nXfeydWWnJysrl27KjIyUl27dtWBAwfytQ0AAAAAAAAAcPe7I6F4y5YttWDBAgUFBeVq++2337Rjxw6VLl06x/TRo0crOjpaq1evVnR0tEaNGpWvbQAAAAAAAACAu98dCcUbNGigwMDAXNMvXryo1157TaNHj5bJZLJNT0tLU0JCgqKioiRJUVFRSkhI0MmTJ/OlDQAAAAAAAABgDEWcufFp06apQ4cOKlu2bI7pKSkpCggIkNlsliSZzWaVLFlSKSkpslqtDm/z9fW9g3sNAAAAAAAAAHAWp4Xi27dv165du/Tvf//bWSXcMj8/D2eXANxx/v6ezi4Bdxm+UwAcgWsJAEfhegLAUbieAAWf00Lxbdu2KSkpSS1btpQkHT16VH369NHrr7+u0NBQHTt2TBaLRWazWRaLRampqQoMDJTVanV4281KSzur7Gyrow9JgcdF3diOHz/j7BJwF/H39+Q7BeC2cS0B4ChcTwA4CtcToOBwcTFds3PzHRlTPC/9+vXTDz/8oLVr12rt2rUqVaqU5s2bp6ZNm8rPz0+hoaGKi4uTJMXFxSk0NFS+vr750gYAAAAAAAAAMAaT1WrN9y7P48eP15o1a3TixAn5+PjI29tb33zzTY55wsPDNXv2bFWpUkWSlJiYqJiYGKWnp8vLy0uxsbEKCQnJt7abYeie4gtNN54Rd59oK790w6HoPQHAEbiWAHAUricAHIXrCVBwXK+n+B0Jxe82hOIwHEJxOBg3igAcgWsJAEfhegLAUbieAAVHgRw+BQAAAAAAAACAO41QHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAw7gjoXhsbKzCw8NVtWpV7d27V5J06tQp9e3bV5GRkWrfvr0GDBigkydP2pZJTk5W165dFRkZqa5du+rAgQP52gYAAAAAAAAAuPvdkVC8ZcuWWrBggYKCgmzTTCaTnn76aa1evVrLly9X2bJlNWXKFFv76NGjFR0drdWrVys6OlqjRo3K1zYAAAAAAAAAwN3vjoTiDRo0UGBgYI5p3t7eaty4se1z3bp1deTIEUlSWlqaEhISFBUVJUmKiopSQkKCTp48mS9tAAAAAAAAAABjKOLsAiQpOztbixYtUnh4uCQpJSVFAQEBMpvNkiSz2aySJUsqJSVFVqvV4W2+vr43Va+fn4ejdh0oNPz9PZ1dAu4yfKcAOALXEgCOwvUEgKNwPQEKvgIRio8bN07u7u7q3r27s0uxS1raWWVnW51dxh3HRd3Yjh8/4+wScBfx9/fkOwXgtnEtAeAoXE8AOArXE6DgcHExXbNzs9ND8djYWP3555+aPXu2XFwuj+YSGBioY8eOyWKxyGw2y2KxKDU1VYGBgbJarQ5vAwAAAAAAAAAYwx0ZU/xa3n77be3evVszZ85UsWLFbNP9/PwUGhqquLg4SVJcXJxCQ0Pl6+ubL20ACpcvvvhMffr0UIsWD2jChDG26ZcuXdLIkUP1+OPt1bRpA/3yy085lvvll580cOAziowM0+OPt8/RdvToUbVu3SzH/5o2baBFiz6RJP344w/q37+P2rRprg4dIhUbO14ZGedsy1+8eFETJ45VRESYOnSI1KeffnLdfVizZpU6dYpSq1ZN9corLyk9/e/bPCoAAAAAAACwh8lqteb7OCDjx4/XmjVrdOLECfn4+Mjb21tTp05VVFSUKlSoIDc3N0lSmTJlNHPmTElSYmKiYmJilJ6eLi8vL8XGxiokJCTf2m6GoYdPWWhydhlwhmhrgXr8a8OGtTKZXLR16yZlZmZqxIgxki6H4kuXLlbVqtU1atQwjR49QfXrN7Atl5CwWwcP/qnMzEx9/PF8LVmy/JrbOHLksLp1e1SfffaVAgNLa82aVfLy8lLduvV18eJFjR07UqVKldLLLw+XJM2e/Y5+/XWH3njjLZ08maZBg57V8OGjdf/9D+Zad1JSop55prcmT56qKlWqadKkCbJaszV27OuOPVAFGI8UAnAEriUAHIXrCQBH4XoCFBxOHz5l5MiRGjlyZK7pe/bsueYyFStW1OLFi+9YG4DCIyzs8kt5//gjQcePp9qmFy1aVF26REuSXFzMuZarXr2mqlevqW3bttxwG6tWfaM6deopMLC0JCkioo2tzc3NTR06PKJ58+bkmH/48NHy8vKSl5eX2rd/RCtXLs8zFI+PX6UmTZqpbt36kqS+ffvriSceV0bGObm7l7DnEAAAAAAAAOAWOXX4FAAoqFat+kYPPxx1zfYdO7YrOPjykybp6ek6ceK4KlWqbGuvVKmykpOT8lw2OTkxx7xBQWVUtGhRHTx40EHVAwAAAAAA4Fqc/qJNAChodu7crlOnTqp585Z5tm/btlkrV8bpvfc+kCSdP58hSSpR4n+P5Hh4eCgjIyPP5c+fPy8Pj5yP75Qo4ZFjjHIAAAAAAADkD3qKA8A/rFwZp7CwcLm7u+dq2717l8aOHanx42NVrlx5SVLx4pfnuzrUPnfuXJ7LX56/uM6dyxmAX56foVMAAAAAAADyG6E4AFwlM/OC1q37Ns+hU/bu/UMxMUMUEzNKDRo0sk338vKSn9+92r9/r23a/v37bMOr/FNwcEXt37/P9vnw4UO6dOmiypUr58A9AQAAAAAAQF4IxQEUOllZWcrMzFR2draysy3KzMxUVlaWJOnixYvKzMzMMZ/VapUkZWdn2+a1Wq3KzMzUpUuXcqx7w4b18vDwVP36DXJMT0rar5deGqTBg19W06YP5aqpTZt2+vDD95Wenq4//zyg5cuX6uGH2+dZf+vWbbRx4/fauXO7zp8/r3nz5igsrAU9xQEAAAAAAO4Ak/VKWgS7paWdVXa28Q6bv7+ntNDk7DLgDNFWHT9+xtlV2MybN0fz58/NMa13777q0+cZPf54ex09mpKjbfHirxUYWFq//PKTBg16Nkdb3br19c4779k+DxkyQKGhNdS3b/8c802cOFYrV8bJzc3NNi0gIFCffPK5pMth/JQpr2v9+rVydXXVE0/0VLdu3W3ztm7dTFOmTFedOvUkSWvWrNLs2TOUnv63GjRopOHDR8vL657bOCqFi7+/Z4H6TgEonLiWAHAUricAHIXrCVBwuLiY5OfnkWcbofgtIBSH4RSwUByFHzeKAByBawkAR+F6AsBRuJ4ABcf1QnGGTwEAAAAAAAAAGAahOAAAAAAAAADAMIo4uwAAAAAAACTpiy8+04oVcUpK2q9WrSI1YsQYW9tPP23VW2/F6tixo6pevaZGjBijUqUCbe179vyh6dPf1N69f8jNrbh69OitLl3+JUkaOPAZJScn6uLFSwoMLK2nn35GzZo1lyR99NH7+uSTD2wvZ7dYsnXp0kUtXx4vb29vTZgwRvHxq1S0aFHbtlatWi+z2ZznPqxZs0pz5ryjv/8+rYYNG+uVV0YZ6t0xAAAUBowpfgsYUxyGw5jicDDG2QPgCFxLgLvPhg1rZTK5aOvWTcrMzLSF4qdPn1bXrh01bNiratKkmf7zn9nauXO73nvvA1t79+6dNWjQEDVv3lJZWZeUmpqqChWCJUn79+9ThQrBKlKkiH77bbdefPE5LVr0pe69915JOa8n8+bN0c6d2zV9+mxJ0oQJY+TvX1L9+j13w/qTkhL1zDO9NXnyVFWpUk2TJk2Q1ZqtsWNfd/CRAlBQcX8CFByMKQ4AAAAAKPDCwsL10EPNc/Ws3rBhrYKDKyo8vJVcXV311FP9tH//Pv355wFJ0mefLVDjxvcrIuJhFStWTO7uJWyBuCRVqlRZRYpcflDaZJIsliylph7NtX2r1arVq1fo4Yejbqn++PhVatKkmerWrS93d3f17dtfGzasU0bGuVtaHwAAyB8MnwLgxiwXLj8pAMPJvpShtNMWZ5cBAAAMLjk5SZUqVbZ9Ll68uIKCgpScnKjy5Svot992KSSkkp599ikdOvSXqlevqSFDhqlUqVK2ZYYOfVE//bRVFy9eVKNGD6hateq5trNz53adOnVSYWHhOaYvXbpES5cuUWBgafXs2VvNm7e8Rp2Jqlmztu1zUFAZFS1aVAcPHlS1aqG3exgAAICDEIoDuDGzG0PnGJRLtFUSj/4BAADnOn8+Q97ePjmmeXh4KCMjQ5KUmpqqvXv/0Ntvz1RISCXNmjVdY8cO16xZ79vmnzRpqrKysrRt2xYdPHhALi65H5xeuTJOzZu3lLu7u23a449304ABL6pECQ9t27ZZo0YNl6+vn2rXrptHnefl4ZHzMe0SJTzoKQ4AQAHD8CkAAAAAgAKteHF3nTuXM1g+d+6cLbx2dXXVQw+1UGhoDbm6uqp3777atetXnT17NscyRYoU0QMPNNGWLZv1ww8bcrRlZl7QunXf5Ro6pWrVarrnHu//X7apIiLaaMOGddeos/g16ixxS/sNAADyB6E4AAAAAKBACw4OUWLiXtvn8+fP6/DhQwoOrihJqlSpUo75TabLTzlardY812exWHT48KEc0zZsWCcvLy/Vq3ffdWu5vO681xscXFH79++zfT58+JAuXbqocuXKXXedAADgziIUBwAAAAAUCFlZWcrMzFR2draysy3KzMxUVlaWHnqohZKSErV+/XfKzMzU/PlzVbFiZZUvX0GS1LZtB/33v+u1b98eZWVl6YMP/qPatevK09NTf/55QJs2bVRm5gVlZWVp9eoV2rnzF9WtmzP8XrkyTm3atLMF6lesW/etMjIylJ2dra1bN2v16pVq0uShPOtv3bqNNm78Xjt3btf58+c1b94chYW1oKc4AOThiy8+U58+PdSixQOaMGFMjrafftqq6OhOatmyiQYOfEZHj6bY2j7/fKE6d+6oiIgwdezYRtOnv6msrCxb+9y5s9SzZ1eFhTXWvHlzcm331KlTGjNmhNq0aa42bVpo7NiRueZJT/9bUVGt1L9/n+vuw5o1q9SpU5RatWqqV155Senpf9/kUYCzmKzX+ukc15SWdlbZ2cY7bP7+nowrbVTRVs69UUVbdfy448cU9/f3zJf1AjAWriXA3WfevDmaP39ujmm9e/dVnz7PaNu2LXr77Uk6evSoqlevoREjxigwsLRtvqVLl+jDD+fpwoULql27rl56aZgCAkrpwIFkTZgwRgcOJMtsdlGZMuXUo0dvhYW1sC2bnZ2hFi1aaMGCJSpTpmyO7T/33NNKTNwnq1UKDCytHj2eVKtWkbb21q2bacqU6apTp56kywHJ7NkzlJ7+txo0aKThw0fLy+ue/DhcAAog7k/st2HDWplMLtq6dZMyMzM1YsQYSdLp06fVtWtHDRv2qpo0aab//Ge2du7crvfe+0DS5adwvLzukaenp9LT/9bIkcP04INN1a1bd0mXf+T09vbRsmVfqHLlqurT55kc233uuacVGlpdvXv3k5ubm5KS9qtKlWo55omNHa+DB/9Udna2Zs2al2f9SUmJeuaZ3po8eaqqVKmmSZMmyGrN1tixrzv2QOGWubiY5OfnkWcbL9oEAAAAABQIffo8kyu8uKJhw8ZauPCLay776KOP69FHH881vUKFYM2d++F1txsQEKANG7bk2fbuu/+57rLx8d/n+BwR0UYREW2uuwwAQAoLC5ck/fFHgo4fT7VN37BhrYKDKyo8vJUk6amn+qldu1b6888DKl++goKCytjmtVqtMplcdOjQ/4bEuvJuiPj4Vbm2uXXrZqWmHtOMGXNkNpslKVcgvnv3r0pKSlSHDo8qLm7ZNeuPj1+lJk2aqW7d+pKkvn3764knHldGBu+SKAwYPgUAAAAAAABAgZCcnKRKlSrbPhcvXlxBQUFKTk60TVuzZpUiIsLUrl0rJSbuVceOj9m17t9+26Vy5cprwoQxatu2pZ5+uqe2b//Z1m6xWPTWW7EaPHhoruG0cteZmKPOoKAyKlq0qA4ePGjvrsKJCMUBAAAAAAAAFAjnz2eoRImcQ154eHgoIyPD9jkioo3WrNmgRYu+VMeOneTr62vXulNTU7V162bVr3+fvv56tbp1e0KvvPKSTp8+LUlasuRTVa9eU9WqhdpR53l5eOSss0QJD2VknLOrFjgXoTgAAAAAAACAAqF4cXedO5czWD537pzc3d1zzVu2bDkFB4fozTdj7Vq3q6urAgNLKyrqERUpUkStWkWqZMkA7dq1QydOHNeSJZ+pX7/n7Kyz+DXqZOiUwoAxxQEAAAAAAAAUCMHBIVq1Ks72+fz58zp8+JCCgyvmOb/FYtHhw4fybPunSpUqaePG7/NsS0j4TWlpJ9S9exdJUmbmBWVmZqpDh0gtXbrCNgb5/+qsqP3799k+Hz58SJcuXVS5cuXsqgXORU9xAAAAAAAAAHdUVlaWMjMzlZ2drexsizIzM5WVlaWHHmqhpKRErV//nTIzMzV//lxVrFhZ5ctXkCQtX/6VTp06Keny+OMff/yBGjRomOd6LZbL67VYLJKkhx5qoTNn0rVyZZwsFovWrftWx48fV61adXX//Q9q8eKvNX/+As2fv0B9+jyrypWrav78BbkCcUlq3bqNNm78Xjt3btf58+c1b94chYW1oKd4IWGyWq1WZxdR2KSlnVV2tvEOm7+/p7Tw+i8ZwF0q2sq5N6poq44fP+Pw1fr7e+bLegEYC9cS4Pb5eZvlUjT34+i4+2VfylDaaYuzywDuOtyf2G/evDmaP39ujmm9e/dVnz7PaNu2LXr77Uk6evSoqlevoREjxigwsLQkaeLEsdq0aaPOn8+Qt7ePWrRopaefflaurq6SpAkTxmjlyrgc6x0+fLTatm0vSdq5c7vefPMNpaQcUblyFTRo0BDVqVMvV30rVizX8uVfadasebZprVs305Qp023zr1mzSrNnz1B6+t9q0KCRhg8fLS+vexx3kHBbXFxM8vPzyLONUPwWEIrDcAjFjYtQHEABxrUEuH3c4xtYPt3nAUbH/QlQcFwvFGf4FAAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGEYRZxcAAAAAAAAAIP/4eZvlUtTd2WXACbIvZSjttMXZZRQ4hOIAAAAAAADAXcylqLu00OTsMuAELtFWSWecXUaBw/ApAAAAAAAAAADDsDsUP3bs2C1vJDY2VuHh4apatar27t1rm56cnKyuXbsqMjJSXbt21YEDB5zWBgAAAAAAAAC4+9kdirdo0UK9evXS4sWLlZ6eflMbadmypRYsWKCgoKAc00ePHq3o6GitXr1a0dHRGjVqlNPaAAAAAAAAAAB3P7tD8eLFi2vLli0aNWqUmjZtqgEDBmjlypW6ePHiDZdt0KCBAgMDc0xLS0tTQkKCoqKiJElRUVFKSEjQyZMn73gbAAAAAAAAAMAY7H7R5pYtW7R582atXbtW69ev17fffqvvvvtO7u7uioiIULdu3VSnTh27N5ySkqKAgACZzWZJktlsVsmSJZWSkiKr1XpH23x9fe2uGwAAAAAAAABQeNkdihcpUkRNmzZV06ZN1bVrV02aNEkbN27UuXPntHTpUn311Vd66aWX9PTTT+dnvQWCn5+Hs0sAgDvG39+zUK0XgLFwLQGAW8c1FMgf/NtCQcN3Mje7Q/Fjx44pLi5OX3/9tfbu3Sur1SpXV1dFRESoRYsWmjt3rubOnWt3KB4YGKhjx47JYrHIbDbLYrEoNTVVgYGBslqtd7TtZqWlnVV2tvWmlyvs+AcEGNPx42ccvk5/f898WS8AY+FaAtw+7vGNjWso4HgF9f6E672xFcTv5J3g4mK6Zudmu8cUDw8P15QpU7Rnzx5Vq1ZNr776qn744QdNnjxZbdu2VYcOHfT333/bXZSfn59CQ0MVFxcnSYqLi1NoaKh8fX3veBsAAAAAAAAAwBhMVqvVri7PDRo0UFRUlDp37qwaNWrkaj979qz+/vtvBQUF5WobP3681qxZoxMnTsjHx0fe3t765ptvlJiYqJiYGKWnp8vLy0uxsbEKCQmRpDvedjMM3VN8ocnZZcAZoq2ce6OKttJTHECBxbUEuH3c4xtYPt3nAUZXUO9PuN4bmIGv99frKW53KJ6ZmSlXV1eHFlZYEYrDcAjFjYtQHEABxrUEuH3c4xuYgUMSID8V1PsTrvcGZuDrvUOGT5k4caKefPJJWSwWSZLFYtFTTz2lMWPGOKRIAAAAAAAAAADym92h+KpVq1SyZEmZzWZJktlsVsmSJbVy5cp8Kw4AAAAAAAAAAEeyOxS/dOmSLl68mGPahQsXdOnSJYcXBQAAAAAAAABAfihi74wVKlTQt99+q48++kh16tTRzp079e2336pq1ar5WR8AAAAAAAAAAA5jdyjes2dPxcTE6PXXX7dNs1qt6tWrV74UBgAAAAAAAACAo9kdij/yyCO6ePGiPvroIx0+fFhlypRRz5491aFDh/ysDwAAAAAAAAAAh7E7FJekLl26qEuXLvlVCwAAAAAAAAAA+eqmQvHNmzfr999/17lz53JMHzBggEOLAgAAAAAAAAAgP9gdir/++uv66KOPckyzWq0ymUyE4gAAAAAAAACAQsHuUHzp0qUymUy677775OnpKZPJlJ91AQAAAAAAAADgcHaH4iVKlFBYWJgmT56cn/UAAAAAAAAAAJBvXOydccCAAfr555/122+/KTs7Oz9rAgAAAAAAAAAgX9jdU3zEiBEymUx6/PHHc0w3mUxKSEhweGEAAAAAAAAAADia3aG4dPnFmvZMAwAAAAAAAACgILI7FP/uu+/ysw4AAAAAAAAAAPKd3aF4UFCQJCk5OVkHDx5UWFhYvhUFAAAAAAAAAEB+sPtFm6dPn9aTTz6ptm3bqn///jp06JCqV6+uqVOn5mN5AAAAAAAAAAA4jt2h+KRJk7R582YVLVpUVqtVZcqUUZ06dbR+/fp8LA8AAAAAAAAAAMexOxT//vvv1ahRI3Xr1s02rVKlSvrrr7/ypTAAAAAAAAAAABzN7lA8MzNT/v7+OaadOnVKRYsWdXhRAAAAAAAAAADkB7tD8apVq2r9+vX69ddfJUmxsbFat26dqlWrlm/FAQAAAAAAAADgSHaH4i+88IIuXryoHTt2yGq1av78+TKZTBowYEB+1gcAAAAAAAAAgMMUsXfGBg0a6IsvvtCiRYt05MgRBQUFqUuXLvQUBwAAAAAAAAAUGnaH4pJUpUoVjR49Or9qAQAAAAAAAAAgX9kdivfs2TPP6SaTSR9++KHDCgIAALie1q2b5ficmZmpRx99XIMHD5UkLV/+lT755AOdPJmmWrXqavjwUbr33ssvC3/ppUH69dfttmUvXbqkcuXK66OPPpMkzZ07S99/v15//nlAPXs+pT59nsmzhokTx2rFiuX69NOlKlOmbJ7zpKQc0cSJY5WQsFsBAaU0ePBQNWzY+Db3HgAAAABwu+wOxbdu3ZrndJPJ5LBiAAAAbiQ+/nvb3+fPn1eHDhFq0aKVJGn79p81Z85MTZ8+W2XLltO0aVM0ZswIvfPOe5KkN9+cnmNdAwb00333NbR9LlOmrPr3H6Rly7645vZ37tyhw4cP3bDOMWNGqGbNWpoyZZo2bdqoV18dpkWLlsrHx+em9hcAAAAA4Fh2h+Kvv/667W+LxaK9e/dq0aJF6t69e74UBgAAcCPr138nb29f1alTT5K0ceP3atGilUJCKkqSnnzyaT3yyMM6fPiQgoLK5Fg2JeWIfv11h4YP/9/QcA8/HCVJio9flef2srKyNHXqJI0YMVZPPvmva9Z18OCf2rv3D7399jtydXVT8+Yt9fnni7Rhw3d65JHHb2ufAQC4293oqbDvvovX++/PUWpqqgICAtSv3/N66KHmkm78VJgkff75Ii1evEinTp1UQEApvf76mypXrrx++eUnvfBCf7m5udnmHTJkmO3+4J94KgwACi+7Q/FHH3001zSr1apdu3Y5tCAAAAB7rVwZpzZt2tqeXLNarbJarbb2K38nJe3PFYqvWvWNateuq9Klg+ze3uefL1SdOvVVqVLl686XnJyk0qWD5O5ewjatUqXKSk5OsntbAAAY1fWeCjt+PFXjxr2q119/U/ff/6DtaawlS5bLx8f3hk+FLV/+lb75ZpkmTZqqChWCdeTIYXl6etra773XX0uXrrCrTp4KA4DCy8XeGbdt25bjf+vWrdOWLVuUkJCQn/UBAADk6ejRo9qx45ccvbceeKCJ1q2L1/79+5SZeUHz58+VyWTShQsXci2/atU3atu2vd3bO3bsqJYt+1JPP/3sDec9fz5DJUp45JhWooSHMjIy7N4eAADI/VRYamqqPDw89cADTWQymfTgg01VvHjxPIc2u/JUWGRkW0lSdna25s+fq4EDhyg4OEQmk0lBQWXk5XXPTdd15amwPn2esT0VFhJSSRs2fHd7OwwAuCPs7ineo0ePXOOHW61W1atXz+FFAQAA3MiqVXG5eno3aNBITz31jEaOHKqzZ8+qa9doubu7q2TJgBzL7ty5QydPpql585Z2b2/69Df15JNPy8PD44bzFi/uroyMczmmZWSck7u7u93bAwAAuZ8Kq1YtVBUqBOuHHzbogQeaauPG71W0aDFVrJj7Ka5/PhWWmpqq1NRjSkpK1MSJY2U2m9WmTTv17t1XLi6X+wyeOnVS7dtHyM3NTc2ahalv3+dUvHjxXOvmqTAAKNzsDsVLly6d47O7u7tCQ0P14osvOromAACAG1q1aoW6d++Va3qnTl3UqVMXSZd7cX344TwFB1f8x7JxeuihFjcVUv/88zb9+utOzZo1wzbt2Wd7a9Cgfysiok2OeYODQ3TkyOH/D8Iv/5/l/fv3qXXrSLu3BwCA0V15Kiwm5lXbtMtBdluNHTtSFy9eVJEiRTRuXGyewfWqVd+oV68+ts/Hjx+TJG3btlkffvipzp49o8GDB8jfv6Q6dHhU5ctX0Pz5C1W+fAUdPZqiCRPGaMaMtzR06Ihc677WU2EnThx31O4b0vXGk1+zZqUmT55oa8vOzlZmZqb+85+PVa1aqKxWq2bNmqG4uGWSpKioDurff5DtB5V9+/bo7bcnKzFxn9zdS6hDh0fVu3dfSWI8ecCA7A7F165dm591AAAA2G3Xrp06cSJV4eGtckzPzMzU4cN/KTi4oo4dO6ZJkyaoc+d/ycvL66p5Lmjdum81YcLkXOvNysqSxWJRdna2LBaLMjMzVaRIEZnNZi1a9KWys7Nt83bs2EZvvPG2KlfO3TOtXLnyqlSpit5/f6769u2vzZt/VGLiPo0fP8mBRwEAgLtbXk+Fbdu2Re++O0MzZsxRlSrVtGfP74qJGaIpU6arcuWqtvnyeirM1dVVkhQd3VOenp7y9PRUx46PadOmjerQ4VH5+d0rP797JUmlSwepf/9BGjr0xTxDcZ4Kyx/XG08+IuJhRUQ8bGtfsWK5PvjgP6patZokadmyL/X99+v1wQcLZTKZNHjw8ypdOsj2kvOxY0eqWbPmmjFjjo4eTdFzz/VR5cpV1LRpmCTGkweMxu5Q/I8//rBrvmrVqt1yMQAAAPZYuTJOYWEtcjyyLEkXL17U2LEjdfjwIbm7l1Dbtu1zjQH+3/+uV4kSHqpfv0Gu9cbGjtfKlXG2zx999L6GDx+ttm3by8fHN9f83t7ecnW93KPoSs+ll18eLkkaO3aiJkwYo4cfDldAQIDGjYvl/ygBAHAT8noqbN++vapTp56qVasuSQoNraHq1Wtq27atOULxvJ4KK1eugooWLZpraNhrMZlMOV7gfTWeCst//xxP/p8uD63TznY+V636Rt26dbcNm9et2xP6+uuvbKF4SsoRRUQ8LLPZrKCgMqpVq66Sk5Nsobi9rown//bb79jGk//880XasOE727YAFHx2h+KPPPLIDf/DYTKZePEmAADId3n12JIkT09Pffjhp9ddtnXrNmrduk2ebSNGjNGIEWPsquGHH37K8flKGH5FYGBpvfPOe3atCwAA5HStp8JCQ6trwYIPtG/fHlWuXFV79/6hnTt36NFHO9vmudZTYW5ubgoPb62FCz9SlSpVdfbsWS1fvlTR0T0kXR5Co3TpMgoICFBq6jHNnj1DzZrlHZjyVFj+++d48lc7ejRFO3du1yuvjLJNS05OVKVKVWyfK1WqkmOM9y5dorVq1Tfq27e/jhw5pN9+26Unnuhpa2c8ecBY7A7FJV3zF1J72wEAAAAAAG7kWk+F1at3n556qp9GjhymkydPytvbRz169FajRvfb5rneU2FDhgzVpEkT1LHjw/L09FT79o+oXbuOkqS9e//Qa6+9qjNn0nXPPd5q1ixMzzzzvG1Zngq7c/IaT/5q/3yJqnR5uJWrX4heooSHzp/PkNVqlclk0oMPNtX48aP16aefyGKxqHfvvgoNrSFJjCcPGJDdoXhsbKzeffddzZgxQxUqVFBycrIGDRqk5557Th07drytItatW6dp06bJarUqOztbAwcOVEREhJKTkxUTE6PTp0/L29tbsbGxqlChgiTlSxsAAAAAAHC+az0VJkmdOnVVp05dr9l+vafCSpTw0Nixr+fZ1q1bd3Xr1v2a6+WpsDsnr/Hkc7Z/ox49eueYVrx4cZ07d9b2+dy5cype3F0mk0np6X/rpZcGafDgl9W6dRudPJmmkSOHycfHV4891pnx5AEDcrF3xrfeekv33XefqlSpomLFiqlq1aqqX7++3nzzzdsqwGq1aujQoZo0aZKWLVumyZMna9iwYcrOztbo0aMVHR2t1atXKzo6WqNG/e+xmPxoAwAAAAAAgHOtWrVCbdq0y7Pt11936MSJ42rRomWO6cHBFbV//z7b5/379yo4OESSdOTIYbm4uOjhh6NUpEgRlSwZoFatIrR588Y8t2HvePL/29Y+27YAFA52h+KXLl3SihUrNHv2bK1atUqzZs3SihUrZLFYbr8IFxedOXNGknTmzBmVLFlSp06dUkJCgqKioiRJUVFRSkhI0MmTJ5WWlubwNgAAAAAAADjXtcaTv2Llym8UFhaea2idNm3a6rPPFuj48VSdOHFcn366QG3bXs5/ypYtJ8mqNWtWKTs7W2lpJ/Tdd/GqWLGypMvjyR89elRWq1XHjh21ezz5zMxMbdiwTomJ+xQW1jLP+QEUTHYPn9K5c2fNmTNH06ZNs02zWq166qmnbqsAk8mkqVOn6rnnnpO7u7vOnTunOXPmKCUlRQEBATKbzZIks9mskiVLKiUlRVar1eFtvr6+t7UfAAAAAAAAuD3XGk9ekjIzM7VuXXyeLzTt2LGTjhw5rJ49u0mS2rfvqI4dO0m6PGzOhAmTNWvWDL355utydXVTkybN1KtXH0mMJw8Ykd2h+IsvvqiyZctq+fLlOn78uAICAhQVFaXHHnvstgrIysrSnDlz9O677+q+++7Tzz//rMGDB2vSpIL7xmY/P48bzwQAdwl/f89CtV44iOWCZHZzdhW40wrheedaAgC3jmsoCqLJk9+4Tqunfv7552u2jh49UqNHj8yzrU2bcLVpE55n28CB/TVwYP9rrnfSpJzj0Pv7V9Vnny265vz820JBw3cyN7tDcZPJpMcff1yPP/64zp8/r+LFizukgN9//12pqam67777JEn33XefihcvLldXVx07dkwWi0Vms1kWi0WpqakKDAz8/8dZHNt2M9LSzio7O++xpe5m/AMCjOn48TMOX6e/v2e+rBeO4+/vKS00ObsM3GnR1kL1b5NrCXD7uMc3Nq6hgOMV1PsTrvfGVhC/k3eCi4vpmp2b7Q7FLRaLZs+erUWLFunkyZOKj4/XK6+8os6dO6t9+/a3XFypUqV09OhRJSUlKSQkRImJiTpx4oTKly+v0NBQxcXFqWPHjoqLi1NoaKhtmJP8aAMAAAAAwBAsFwjJDCr7UobSTt/+++EAoDAzWa/1Ot1/mDNnjt5+++3LC5lM+v3339WuXTv5+vrq448/vq0ivv76a82dO1cm0+UeaYMGDVKrVq2UmJiomJgYpaeny8vLS7GxsQoJufw23/xos5ehe4rTa9CYoq2ce6PKp16jjug90bp1sxyfMzMz9eijj2vw4KG6dOmSxo4doT/++F1Hj6Zo+vTZql+/Qa51XLp0Sb16ddP58+e1dOkK2/Rdu3Zq+vQ3deDAAZUuXVpDhsSoTp26kqQff/xBH388X8nJiSpWzFVNmjTTwIGD8xzzUJJSUo5o4sSxSkjYrYCAUho8eKgaNmx8W/t+J3DNNyh6igOGw/XewLjHN65C9t/7wqag3p9wvTcwA/+bd0hP8S+//FIhISGqUaOG4uLiJEk1atTQ999/f9sFdujQQR06dMg1vWLFilq8eHGey+RHGwCg8IiP/99/f86fP68OHSLUosX/3lBfu3Zdde4crVGjhl1zHQsXfiQfH1+dP3/YNi09/W/FxAzRSy+9orCwFvr229UaNmywPv98mby8vHT27Fn16tVHdevW18WLFzV27EjNnDnN9tKdfxozZoRq1qylKVOmadOmjXr11WFatGgpL+IBAAAAAMBJXOydMTU1VXXq1Mnxf+LNZrMyMzPzpTAAAOy1fv138vb2VZ069SRJRYsWVZcu0apTp65cXMx5LnPkyGGtWbNS3bs/mWP6rl2/ysfHT+HhrWQ2mxUZ2Vbe3j7673/XSpIiItro/vsflJubm7y8vNShwyPatWtnnts4ePBP7d37h/r0eUaurm5q3rylQkIqacOG7xy38wAAAAAA4KbY3VO8XLly2rJli2rVqiVJWr9+veLj41W+fPl8Kw4AAHusXBmnNm3a2obhssfUqZPVr9/zcnV1zTH98qhi/xwiy6qkpMQ817Njx3YFB+c9DFdycpJKlw7KMbRKpUqVlZycZHedAAAAgEMxnny+4/gCBZ/doXiPHj00cuRIpaSkSJL69+8vq9Wq7t2751txAADcyNGjR7Vjxy+KiXnV7mU2bFinrCyLwsJa6JdffsrRVqtWbZ04cVzx8avUokUrxcev0uHDh3ThwoVc69m2bbNWrozTe+99kOd2zp/PUIkSOccvK1HCQydOHLe7VgAAAMChzG6MLW1E0cZ7Nx5wPXaH4o8//riysrL0ySef6PDhwwoKCtITTzyhTp065Wd9AABc16pVcapdu65Klw6ya/7z589r1qzpmjx5Wp7t99zjrddff1MzZ07TW29NUqNG96tBg0YqWTIgx3y7d+/S2LEjNX58rMqVy/upqeLF3ZWRcS7HtIyMc3J3d7erVgAAAAAA4Hh2heJZWVmKi4tTmTJlbC/ZBACgIFi1aoW6d+9l9/yHDh1USsoRPf98X0nSpUuXdO7cWXXoEKk5c+YrMLC06tW7T//5z0eSLv83sGvXR9St2/+ejNq79w/FxAxRTMwoNWjQ6JrbCg4O0ZEjh/8/CL88hMr+/fvUunXkrewqAAAAAABwALtetFmkSBGNHj1a8fHx+V0PAAB227Vrp06cSFV4eKtcbRcvXrS9DDorK0uZmZmyWq0KDq6oL7/8RvPnL9D8+QsUEzNSPj6+mj9/ga03+N69fygrK0vnzp3VzJnTVLJkSTVu/IAkKSlpv156aZAGD35ZTZs+dN36ypUrr0qVquj99+cqMzNTGzasU2LiPoWFtXTwkQAAAAAAAPaye/iUFi1aaPfu3crOzpaLi11ZOgAA+WrlyjiFhbXI8SLLK6KjO+no0cvvwRgyZIAkafHirxUYWFp+fvfa5vP09JKLi0uOaQsWfKTNmzdKkho3flATJ06xtX366QKdPn1Kb7wxTm+8MU6SFBAQqE8++VySNHnyREnSyy8PlySNHTtREyaM0cMPhysgIEDjxsXKx8fHYccAAAAAAADcHJPVarVrpP1BgwYpPj5eZcuWVa1atVSsWLHLKzCZNHHixHwtsqBJSzur7GzjvaDA39+Tl3EYVbSVc29U0VYdP37G4av19/fMl/XCcbjmG1Q+/ZvPL1xLgNvH9d7AuMc3Ls69MXHejauQ3eM7kouLSX5+Hnm22d1TfM2aNZKkgwcP6uDBg7bpRgzFAQAAAAAAAACF0w1D8T/++EO+vr4aMGDAnagHAAAAAAAAAIB8c8NQ/NFHH1WvXr0UExOjiRMn6uOPP9bvv/9+J2oDAAAAAAAAAMChbvjGTKvVKjuHHQcAAAAAAAAAoEC7YSgOAAAAAAAAAMDdwq4XbR48eFDfffed7QWba9euzdF7vGXLlvlTHQDAuSwX5O/vmS+rzq/1AgAAAAAAXI9dofj69eu1fv162+fnn3/e9rfJZFJCQoLDCwMAFABmN2mhydlVwBmiGToNAAAAAHB3umEoXrp06TtRBwAAAAAAAAAA+e6GofjatWvvRB0AAAAAAAAAAOQ7XrQJAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAgEJjwIB+Cg9/UK1bN1O9evX0r389Zmtbvvwrde36iFq3bqYhQwbqxInjtrbPP1+ozp07KiIiTB07ttH06W8qKysrx7o//3yROnfuoFatmuqJJx7XwYN/5tr+xIlj1bRpAx069Nc1a0xJOaKBA59Ry5ZNFB3dSdu2bXHAngMAAABwlCLOLgAAAAC4GYMHD1X79o/I399Tx4+fkSRt3/6z5syZqenTZ6ts2XKaNm2KxowZoXfeeU+S1KTJQ3r44fby9PRUevrfGjlymJYs+VTdunWXdDlQ/+abZZo0aaoqVAjWkSOH5enpmWO7O3fu0OHDh25Y35gxI1SzZi1NmTJNmzZt1KuvDtOiRUvl4+Pj4CNhLAMG9FNCwm6ZzWZJ0r33+mvRoi8lSd99F6/335+j1NRUBQQEqF+/5/XQQ80lSWfOnNG0aVO0efOPkqRHH31cffo8k2Pdn3++SIsXL9KpUycVEFBKr7/+psqVKy+r1aqPPnpfy5Z9qbNnz+qBBx7U0KEjVKKER541pqQc0cSJY5WQsFsBAaU0ePBQNWzYOJ+OCAAAAG4VPcUBAABQ6G3c+L1atGilkJCKKlq0qJ588mnt2PGLLcQOCipjC7mtVqtMJhcdOnS5LTs7W/Pnz9XAgUMUHBwik8mkoKAy8vK6x7b+rKwsTZ06SYMHD71uHQcP/qm9e/9Qnz7PyNXVTc2bt1RISCVt2PBdPu25sQwePFTx8d8rPv57WyB+/Hiqxo17VQMGDNaaNRv03HMvaOzYETp16qQkacaMt3ThwgUtWbJcc+d+qNWrV+ibb762rfPqH0Ti47/XpElT5e3tLUlateobrV69QrNmzdNXX61UZmam3n578jXrGzNmhKpUqaoVK75Tv37P6dVXh+nUqVP5d0AAAABwSwjFAQAAUKjMmfOO2rVrqW7duumXX36SdDnotlqttnmu/J2UtN82bc2aVYqICFO7dq2UmLhXHTteHnolNTVVqanHlJSUqMcea6fOnTto3rw5ys7Oti37+ecLVadOfVWqVPm6tSUnJ6l06SC5u5ewTatUqbKSk5Nuf8eRp9TUVHl4eOqBB5rIZDLpwQebqnjx4rYfRDZu/K+eeKKn3NzcFBhYWlFRHW2h+I1+ENm48b+KiuqogIBScnd31xNP9NLatfG6cOFCrjr4QQQAAKDwIBQHAABAodG//yB9/vkyLV26Ul27dtWwYUN0+PAhPfBAE61bF6/9+/cpM/OC5s+fK5PJlCO8jIhoozVrNmjRoi/VsWMn+fr6SpKOHz8mSdq2bbM+/PBTTZ8+W/HxqxUXt0ySdOzYUS1b9qWefvrZG9Z3/nxGrqE1SpTwUEZGhqMOgaFd+UGkf/+nbD+IVKsWqgoVgvXDDxtksVj03/+uV9GixVSx4v9+wPjnDyZJSYmSbvyDSF4/tly8eFF//XUwV238IAIAAFB4EIoDAACg0KhRo6bc3UuoWLFievTRR1WrVh1t2vSDGjRopKeeekYjRw5Vp07tFRhYWu7u7ipZMiDXOsqWLafg4BC9+WasJMnV1VWSFB3dU56engoMLK2OHR/Tpk0bJUnTp7+pJ598Wh4eeY8jfbXixd2VkXEux7SMjHNyd3e/3V03vKt/EOnQ4THbDyJms1lt2rTV2LEjFR7+oMaOHaGXXx6u4sWLS5IaN35Qn3zyoTIyzunQob/0zTdfKzPz8o8lN/pB5P77m2j58mVKSTmis2fPasGCDyXJtvzV+EEEAACg8CAUBwAAQKFlMpl0pSNvp05d9OmnSxUXF6+wsHBZLBYFB1fMczmLxWIbXqNcuQoqWrSoTCZTnvP+/PM2vfvudHXoEKkOHSIlSc8+21tr1qzKNW9wcIiOHDmcIxjfv3+fgoNDbmc3oZw/iDz8cJTtB5Ft27bo3XdnaMaMOVq3bpPeeec9xcaO0759eyRJL774b7m6uqpbt8cUE/OSWrWKlL9/SUk3/kGkXbsOat06UgMHPqMePbqofv0GkmRb/mr8IAIAAFB4EIoDAACgUDhz5oy2bNmkzMxMZWVl6euvv9bOnb+oceP7lZmZqaSk/bJarTp69KgmTZqgzp3/JS8vL0mXX6Z45cWLyclJ+vjjD9SgQUNJkpubm8LDW2vhwo+UkXFOqanHtHz5UjVp0lSStGjRl/rgg4WaP3+B5s9fIEl64423FRbWPFeN5cqVV6VKVfT++3OVmZmpDRvWKTFxn8LCWt6BI2QsV34Q2bdvr+rUqadq1arLxcVFoaE1VL16TW3btlWS5OV1j0aPHq+vv16tTz75XFarVaGhNSTd+AcRFxcX9enzjJYsWa6lS1coOLii/P1L5hmK84MIAABA4VHE2QUAAAAA9sjKytLcubP0558HZDa7qGLFinr99SkqV66Czpw5o7FjR+rw4UNydy+htm3b5xgDfNeunXrvvXd1/nyGvL191KJFqxztQ4YM1aRJE9Sx48Py9PRU+/aPqF27jpIkHx/fXLV4e3vL1dVNkjR58kRJ0ssvD5ckjR07URMmjNHDD4crICBA48bFysfHJ9+OixGcOXNGCQm7VbdufZnNZq1dG6+dO3/RCy8MUVpamhYs+ED79u1R5cpVtXfvH9q5c4cefbSzJOnw4UPy8PCQh4entm7drK+//lIzZrwnKecPIlWqVNXZs2e1fPlSRUf3kCSlp/+tM2fOqHTpIB04kKwZM97Sk08+LReX3H2Lrv5BpG/f/tq8+UclJu7T+PGT7tyBAgAAgF1M1qvfHAO7pKWdVXa28Q6bv7+ntDDvXjS4y0VbOfdGxbk3Ls69MUVbdfz4GWdXYTd/f89CVS9u3alTp/Tyyy/YfhApV66C+vZ9Vg0b3i9J+uKLz/T554t08uRJeXv76LHHOutf/+ouSfruu3hNn/6mzp49o7Jly6t//4Fq3PgB27rPnTurSZMm6McfN9p+EHnyyadlMpl08OCfGjZssFJTj8nb20edO3dTt27dbcv+8weRlJQjmjBhjBISflNAQICGDBmmhg0b36nDdEu4xzcw/ltvXJx7Y+K8G1chu8d3JBcXk/z88n4vUIEIxTMzMzVx4kRt2rRJrq6uqlu3rsaNG6fk5GTFxMTo9OnT8vb2VmxsrCpUqCBJ+dJmL0JxGA7/8TQuzr1xce6NqZDdMBOKA7ePe3wD47/1xsW5NybOu3EVsnt8R7peKF4gxhSfPHmyXF1dtXr1ai1fvlwvvPCCJGn06NGKjo7W6tWrFR0drVGjRtmWyY82AAAAAAAAAMDdzemh+Llz5/TVV1/phRdesL3g5t5771VaWpoSEhIUFRUlSYqKilJCQoJOnjyZL20AAAAAAAAAgLuf01+0+ddff8nb21vvvPOOtmzZohIlSuiFF16Qm5ubAgICZDabJUlms1klS5ZUSkqKrFarw9t8fXO/QOlartXtHgAAoLDz9/d0dgk3pbDVCwAAANxp3DPn5vRQPCsrS3/99ZeqV6+uYcOGaefOnXr22Wc1bdo0Z5d2TYYeUxwAANzVCtN4g4wpDtw+7vEBALj7GfWe+Xpjijs9FC9durSKFCliG9KkTp068vHxkZubm44dOyaLxSKz2SyLxaLU1FQFBgbKarU6vA0AAMDwLBcKXUBW2OotqLIvZSjttMXZZQAAAAB3hNNDcV9fXzVu3FgbN25U06ZNlZycrLS0NFWoUEGhoaGKi4tTx44dFRcXp9DQUNswJ/nRBgAAYGhmN2mhydlVwAlcup6Xv7+7s8sAAAAA7giT1Wp1+jggf/31l4YPH67Tp0+rSJEievHFFxUWFqbExETFxMQoPT1dXl5eio2NVUhIiCTlS5u9DD18Cv9H2ZiirZx7o+LcGxfn3pg478bFuTcuzr1xce6Ni3NvTJx344q2MnxKHpzeU1ySypYtq48//jjX9IoVK2rx4sV5LpMfbQAAAAAAAACAu5uLswsAAAAAAAAAAOBOIRQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYRoEKxd955x1VrVpVe/fulSQlJyera9euioyMVNeuXXXgwAHbvPnRBgAAAAAAAAC4uxWYUPy3337Tjh07VLp0adu00aNHKzo6WqtXr1Z0dLRGjRqVr20AAAAAAAAAgLtbgQjFL168qNdee02jR4+WyWSSJKWlpSkhIUFRUVGSpKioKCUkJOjkyZP50gYAAAAAAAAAuPsVcXYBkjRt2jR16NBBZcuWtU1LSUlRQECAzGazJMlsNqtkyZJKSUmR1Wp1eJuvr+8d3msAAAAAAAAAwJ3m9FB8+/bt2rVrl/797387uxS7+fl5OLsEAAAAAAAAALghf39PZ5dQ4Dg9FN+2bZuSkpLUsmVLSdLRo0fVp08fvfLKKzp27JgsFovMZrMsFotSU1MVGBgoq9Xq8LabkZZ2VtnZ1vw4HAUa/4AAAAAAAACAwuX48TPOLsEpXFxM1+zc7PQxxfv166cffvhBa9eu1dq1a1WqVCnNmzdPbdu2VWhoqOLi4iRJcXFxCg0Nla+vr/z8/BzeBgAAAAAAAAC4+zm9p/j1jBkzRjExMXr33Xfl5eWl2NjYfG0DAAAAAAAAANzdClwovnbtWtvfFStW1OLFi/OcLz/aAAAAAAAAAAB3N6cPnwIAAAAAAAAAwJ1CKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGE4PRQ/deqU+vbtq8jISLVv314DBgzQyZMnJUnJycnq2rWrIiMj1bVrVx04cMC2XH60AQAAAAAAAADubk4PxU0mk55++mmtXr1ay5cvV9myZTVlyhRJ0ujRoxUdHa3Vq1crOjpao0aNsi2XH20AAAAAAAAAgLub00Nxb29vNW7c2Pa5bt26OnLkiNLS0pSQkKCoqChJUlRUlBISEnTy5Ml8aQMAAAAAAAAA3P2KOLuAq2VnZ2vRokUKDw9XSkqKAgICZDabJUlms1klS5ZUSkqKrFarw9t8fX2ds9MAAAAAAAAAgDumQIXi48aNk7u7u7p3766EhARnl3NNfn4ezi4BAAAAAAAAAG7I39/T2SUUOAUmFI+NjdWff/6p2bNny8XFRYGBgTp27JgsFovMZrMsFotSU1MVGBgoq9Xq8LabkZZ2VtnZ1nw6EgUX/4AAAAAAAACAwuX48TPOLsEpXFxM1+zc7PQxxSXp7bff1u7duzVz5kwVK1ZMkuTn56fQ0FDFxcVJkuLi4hQaGipfX998aQMAAAAAAAAA3P1MVqvVqV2e9+3bp6ioKFWoUEFubm6SpDJlymjmzJlKTExUTEyM0tPT5eXlpdjYWIWEhEhSvrTZy9A9xReanF0GnCHayrk3Ks69cXHujYnzblyce+Pi3BsX5964OPfGxHk3rmgrPcXz4PThUypXrqw9e/bk2VaxYkUtXrz4jrUBAAAAAAAAAO5uBWL4FAAAAAAAAAAA7gRCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADINQHAAAAAAAAABgGITiAAAAAAAAAADDIBQHAAAAAAAAABgGoTgAAAAAAAAAwDAIxQEAAAAAAAAAhkEoDgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGAahOAAAAAAAAADAMAjFAQAAAAAAAACGQSgOAAAAAAAAADAMQnEAAAAAAAAAgGEQigMAAAAAAAAADMOQoXhycrK6du2qyMhIde3aVQcOHHB2SQAAAAAAAACAO8CQofjo0aMVHR2t1atXKzo6WqNGjXJ2SQAAAAAAAACAO6CIswu409LS0pSQkKD58+dLkqKiojRu3DidPHlSvr6+dq3DxcWUnyUWbCXKO7sCOAvn3rg498bFuTcmzrtxce6Ni3NvXJx74+LcGxPn3bCMmmVeb79NVqvVegdrcbrdu3dr2LBh+uabb2zT2rZtq8mTJ6tGjRpOrAwAAAAAAAAAkN8MOXwKAAAAAAAAAMCYDBeKBwYG6tixY7JYLJIki8Wi1NRUBQYGOrkyAAAAAAAAAEB+M1wo7ufnp9DQUMXFxUmS4uLiFBoaavd44gAAAAAAAACAwstwY4pLUmJiomJiYpSeni4vLy/FxsYqJCTE2WUBAAAAAAAAAPKZIUNxAAAAAAAAAIAxGW74FAAAAAAAAACAcRGKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMo4uwCUPAlJycrJiZGp0+flre3t2JjY1WhQgVnlwWgEAoPD1exYsXk6uoqSfr3v/+tZs2aObkqAIVBbGysVq9ercOHD2v58uWqUqWKJO5TANyca11LuEcBcLNOnTqloUOH6uDBgypWrJjKly+v1157Tb6+vtyfAIUAPcVxQ6NHj1Z0dLRWr16t6OhojRo1ytklASjEpk+frmXLlmnZsmX8n00AdmvZsqUWLFigoKCgHNO5TwFwM651LZG4RwFwc0wmk55++mmtXr1ay5cvV9myZTVlyhRJ3J8AhQGhOK4rLS1NCQkJioqKkiRFRUUpISFBJ0+edHJlAADASBo0aKDAwMAc07hPAXCz8rqWAMCt8Pb2VuPGjW2f69atqyNHjnB/AhQSDJ+C60pJSVFAQIDMZrMkyWw2q2TJkkpJSZGvr6+TqwNQGP373/+W1WrVfffdpyFDhsjLy8vZJQEopLhPAeBI3KMAuFXZ2dlatGiRwsPDuT8BCgl6igMA7pgFCxbo66+/1hdffCGr1arXXnvN2SUBAABwjwLgtowbN07u7u7q3r27s0sBYCdCcVxXYGCgjh07JovFIkmyWCxKTU3lkUMAt+TKtaNYsWKKjo7WL7/84uSKABRm3KcAcBTuUQDcqtjYWP3555+aOnWqXFxcuD8BCglCcVyXn5+fQkNDFRcXJ0mKi4tTaGgoj/wAuGkZGRk6c+aMJMlqtWrFihUKDQ11clUACjPuUwA4AvcoAG7V22+/rd27d2vmzJkqVqyYJO5PgMLCZLVarc4uAgVbYmKiYmJilJ6eLi8vL8XGxiokJMTZZQEoZP766y8NHDhQFotF2dnZqlixokaOHKmSJUs6uzQAhcD48eO1Zs0anThxQj4+PvL29tY333zDfQqAm5LXtWT27NncowC4afv27VNUVJQqVKggNzc3SVKZMmU0c+ZM7k+AQoBQHAAAAAAAAABgGAyfAgAAAAAAAAAwDEJxAAAAAAAAAIBhEIoDAAAAAAAAAAyDUBwAAAAAAAAAYBiE4gAAAAAAAAAAwyAUBwAAAAAAAAAYBqE4AAAAUAiFh4eratWq+vbbb23TtmzZoqpVq6pBgwZOrAwAAAAo2AjFAQAAANjt0qVLzi4BAAAAuC2E4gAAAMBd6tChQxo0aJCaNm2qhg0bqkePHtq5c6et/Upv8y1btkiSvvzyS1WtWlU9evSQ9L+e5+Hh4ZoxY4YaN26sUaNG6e+//9agQYPUuHFj1apVS+Hh4Ro1apRT9hEAAAC4WUWcXQAAAACAW7dkyRJbqH3s2DHb9IyMDPXq1UuHDh1Sw4YN5ePjozVr1qhXr176+uuvVa5cObu3cfjwYS1evFgREREKCQnR+++/r9WrV6tu3bqqVq2ajhw5ou3btzt83wAAAID8QCgOAAAAFGLr1q3Lc/r69et16NAhlS1bVh9//LFMJpOef/55ffvtt1qyZImGDBli9zZMJpM+/vhjlS9fXpI0efJkSVLt2rXVvn17VaxYUW5ubre/MwAAAMAdwPApAAAAQCE2c+ZM7dmzR3v27NFHH31km3748GFJUnBwsEwmkyQpJCQkR9s/WSyWPKffe++9tkBcknr16qWmTZtq0aJF6ty5sxo2bKihQ4cqOzvbIfsEAAAA5CdCcQAAAOAuFBQUJEk6cOCArFarJCk5OTlHW/HixSVJZ8+elSTt27cvz3UVK1Ysx2dvb2/NmzdPv/zyi5YtW6ZKlSopLi5Ov/zyi+N3BAAAAHAwhk8BAAAA7kLNmzdXUFCQDh48qJ49e8rHx0fx8fFyc3NTp06dJEmhoaHav3+/pk6dqk2bNunTTz+1a93vvfee1q5dqypVqqho0aK2nuceHh75tj8AAACAo9BTHAAAALgLubu768MPP1RkZKSSkpL0448/qlGjRvrggw9sQ6G8+OKLqlevng4dOqSEhAR1797drnVXr15dZrNZ3377rb766ivde++9GjlypKpVq5afuwQAAAA4hMl65VlKAAAAAAAAAADucvQUBwAAAAAAAAAYBqE4AAAAAAAAAMAwCMUBAAAAAAAAAIZBKA4AAAAAAAAAMAxCcQAAAAAAAACAYRCKAwAAAAAAAAAMg1AcAAAAAAAAAGAYhOIAAAAAAAAAAMMgFAcAAAAAAAAAGMb/AfelQtnVcX/7AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# time distribution for the AD campaign\n", + "hour_plot = df.hour.plot(kind='hist', color='orange', figsize=(25,7))\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(hour_plot)\n", + "\n", + "#set the figure parameters\n", + "pl.fig_att(hour_plot, 'Histogram of the Ad Campaign Hours', \n", + " 'Hours', 'Frequency', 15, 12, 'bold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> ##### Time distribution for the AD campaign for those who did not participate\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hour_plot = nparticipated_df.hour.plot(kind='hist', color='orange', figsize=(25,7))\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(hour_plot)\n", + "\n", + "#set the figure parameters\n", + "pl.fig_att(hour_plot, 'Histogram of the Ad Campaign Hours for people who didn\\'t Participate', \n", + " 'Hours', 'Frequency', 15, 12, 'bold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> #### Time distribution for the AD campaign for those who participated" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hour_plot = participated_df.hour.plot(kind='hist', color='orange', figsize=(25,7))\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(hour_plot)\n", + "\n", + "#set the figure parameters\n", + "pl.fig_att(hour_plot, 'Histogram of the Ad Campaign Hours for people who participated', \n", + " 'Hours', 'Frequency', 15, 12, 'bold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time distribution for the AD campaign is similar for those who did not participate and those who participated." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#pie chart showing the prcentage of the experiment carried out\n", + "pl.plot_pie(participated_df[['experiment']].value_counts(), ['control','exposed'], \"Pie chart showing the percentage of Experiments for people who participated\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_pie(nparticipated_df[['experiment']].value_counts().value_counts(), ['control','exposed'], \"Pie chart showing the percentage of Experiments for people who didn't Participate\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Date distribution for the AD\n", + "# plt.figure(figsize=(20,10))\n", + "# sns.set(style=\"darkgrid\")\n", + "\n", + "# #countplot\n", + "# ax = sns.countplot(x=\"date\", palette='deep', data=participated_df, hue='yes', )\n", + "\n", + "# #getting the values of the data\n", + "# pl.get_value(ax)\n", + "\n", + "# #set the figure paremeters\n", + "# pl.fig_att(ax, \"Date distribution for participated Users\", \n", + "# \"Date\", \"Frequency\", 25, 20, \"bold\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Date distribution of the AD campaign for thos who did not participate" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# # Date distribution for the AD\n", + "# plt.figure(figsize=(20,10))\n", + "# sns.set(style=\"darkgrid\")\n", + "\n", + "# #countplot\n", + "# ax = sns.countplot(x=\"date\", palette='deep', data=nparticipated_df )\n", + "\n", + "# #getting the values of the data\n", + "# pl.get_value(ax)\n", + "\n", + "# #set the figure paremeters\n", + "# pl.fig_att(ax, \"Date distribution for people who didn't Participate\", \n", + "# \"Date\", \"Frequency\", 25, 20, \"bold\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Date distribution of the AD campaign for thos who participated based on treatment grouo" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# #seaborn countplot to show the Ad campaign dates\n", + "# plt.figure(figsize=(20,10))\n", + "# sns.set(style=\"darkgrid\")\n", + "\n", + "# #countplot\n", + "# ax = sns.countplot(x=\"date\", palette='deep', data=participated_df, hue='experiment', )\n", + "\n", + "# #getting the values of the data\n", + "# pl.get_value(ax)\n", + "\n", + "# #set the figure paremeters\n", + "# pl.fig_att(ax, \"Date distribution for participated users by experiment\", \n", + "# \"Date\", \"Frequency\", 25, 20, \"bold\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be seen that most of the reponse is on 03/07/2020 and most of them said no on each day." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "101 529469\n", + "162 118039\n", + "178 77651\n", + "156 48979\n", + "155 39381\n", + " ... \n", + "130 1\n", + "118 1\n", + "106 1\n", + "134 1\n", + "181 1\n", + "Name: platform_os, Length: 68, dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['platform_os'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 1226\n", + "5 17\n", + "Name: platform_os, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "participated_df['platform_os'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the Ad campaign dates\n", + "plt.figure(figsize=(20,10))\n", + "sns.set(style=\"darkgrid\")\n", + "\n", + "#countplot\n", + "ax = sns.countplot(x=\"platform_os\", palette='deep', data=participated_df, hue='experiment', )\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Countplot Showing the Users OS\", \n", + " \"OS\", \"Frequency\", 25, 20, \"bold\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the OS is not represented among the participant,\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the Ad campaign dates\n", + "plt.figure(figsize=(20,10))\n", + "sns.set(style=\"darkgrid\")\n", + "\n", + "#countplot\n", + "ax = sns.countplot(x=\"platform_os\", palette='deep', data=nparticipated_df, hue='experiment', )\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Countplot Showing the Users OS\", \n", + " \"OS\", \"Frequency\", 25, 20, \"bold\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot showing participant and non participant Operating System distribution¶\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 427413\n", + "7 289305\n", + "15 191935\n", + "5 2715\n", + "12 870\n", + "1 310\n", + "8 153\n", + "14 7\n", + "10 4\n", + "Name: browser, dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Browser list for all users\n", + "df['browser'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 72954\n", + "15 22489\n", + "7 14979\n", + "5 86\n", + "12 38\n", + "1 14\n", + "8 10\n", + "Name: browser, dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Browser list for participated users\n", + "participated_df['browser'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 72954\n", + "15 22489\n", + "7 14979\n", + "5 86\n", + "12 38\n", + "1 14\n", + "8 10\n", + "Name: browser, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Browser list for users\n", + "participated_df['browser'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the Ad campaign dates\n", + "plt.figure(figsize=(20,10))\n", + "sns.set(style=\"darkgrid\")\n", + "\n", + "#countplot\n", + "ax = sns.countplot(x=\"browser\", palette='deep', data=participated_df, hue='experiment', )\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Countplot Showing the Users Browsers for people who participated\", \n", + " \"Browser\", \"Frequency\", 25, 20, \"bold\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the Ad campaign dates\n", + "plt.figure(figsize=(20,10))\n", + "sns.set(style=\"darkgrid\")\n", + "\n", + "#countplot\n", + "ax = sns.countplot(x=\"browser\", palette='deep', data=nparticipated_df, hue='experiment', )\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Countplot Showing the Users Browsers for people who didn't Participate\", \n", + " \"Browser\", \"Frequency\", 25, 20, \"bold\")\n", + "pl.rotate(ax, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seaborn countplot to show the Ad campaign dates\n", + "plt.figure(figsize=(20,10))\n", + "sns.set(style=\"darkgrid\")\n", + "\n", + "#countplot\n", + "ax = sns.countplot(x=\"browser\", palette='deep', data=participated_df, hue='yes', )\n", + "\n", + "#getting the values of the data\n", + "pl.get_value(ax)\n", + "\n", + "#set the figure paremeters\n", + "pl.fig_att(ax, \"Countplot Showing the Users Browsers for people who participated with Yes\", \n", + " \"Browser\", \"Frequency\", 25, 20, \"bold\")\n", + "pl.rotate(ax, 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAApIAAAJUCAYAAACmOcbPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA+SklEQVR4nO3de3yMd/738fckRJ2CpES0DpWSpq1jtY2ytA4NJeIYREkPtEvpr7aU1iIOt5bYRWXVj6pS7bLUoc1SqroOLdrSBpXQOtvGMYhISDKZ+w+3uUUSuTKumczI69nHPJq5cs11fWZC8+n7+n6/l8Vms9kEAAAAFJFXcRcAAAAAz0QjCQAAAIfQSAIAAMAhNJIAAABwCI0kAAAAHEIjCQAAAIfQSAI3GT16tIKDgxUcHKydO3ca/p6ZNm7cqNmzZ2v27NlOO4c7mTt3rtq2batHH31UwcHBGj16dIH7rly5UrNnz9bHH3+c53v9+/dXcHCw2rRp48Rq83fy5En7nw1Hfm63e1/F4eTJk/Y/g4mJicVdDgA3Vqq4CwCQ28aNG7Vq1SpJ0rBhw4q5Guf6z3/+oxkzZhjef9WqVfrhhx9033336YUXXnBeYS7mbu/rv//9r+Li4iRJ9913n0JCQoq5IgDuikYSQLG5Oe1asmSJHn/88WKsBgBQVFzaBu5QZmam4uLi1LFjRzVo0EDNmjXTK6+8on379uXab968eerVq5dCQ0P1yCOP6PHHH9eLL76oHTt22Pdp06aNPY2UZL9c2r9/f0m5L6/v27dPPXr0UMOGDRUVFaVjx47p5MmTeumll9S4cWOFh4dr27ZtuWp47733FBERoSeffFKPPPKIQkNDNXjw4DyXL2+cY/To0fr000/Vtm1bNWjQQFFRUTpw4EChn4nVatVHH32k8PBwNWzYUE2aNFG/fv30zTff2Pfp37+/Zs6caX/+/PPPKzg4WCtXrsz3mMHBwfrhhx8kXU/Mbq7xVgkJCerTp48aNWqU7+dg9GdWkI8++kitWrVS48aNNXjwYJ0+fTrf/Yx83oW9rxUrVigqKkotWrTQo48+qqZNm6pPnz766quvcp1r3759evXVV+37tWjRQv3799fy5ctz7bd161ZFR0erWbNmatCggcLDw/Xpp5/qxk3OZs+erQEDBtj3f/vtt+01nTx50tDnA6DkIJEE7kB2drYGDhyYa8xkZmamNm/erO3bt+vjjz/WY489Jun6Jes9e/bY90tNTdX333+vH3/8UStWrNBDDz1UpHO//PLLunjxoiRp165dGjJkiLKysnTs2DFJ0sGDBzVs2DBt2rRJVapUkSR9+eWXOnfunP0YFy5c0KZNm/Tjjz9q7dq1qlatWq5zfPvtt7ka2127dumFF17Qhg0bVLFixQJre/PNN7Vu3bpc23766Sf99NNPGj9+vKKioor0XoviwoULio6OVkZGhqS8n0NRfmb5WbFihaZOnWp/vmnTJu3duzfffYv6eefnu+++065du+zPs7Ky9PPPP+vnn3/WvHnz1Lp1a6Wnp+f68yBJ586d07lz51SuXDn16tVLkrR8+XKNHTtWN98Z9+DBg5o4caIOHTqkcePGFVoPANyMRBIowIABA+xJTHBwcK6G6ob4+Hh7QzJ16lTt2bNH33zzjYKCgpSZmZmr4RgyZIjWrl2rXbt2ad++ffr8889VtmxZZWVlacWKFZKuNyXdunWzv+bAgQM6cOCAPvnkkzznbtu2rX788Ud16tRJkvT777+rfPny2rZtm95++21JUnp6urZs2WJ/zdixY/X111/rl19+0Z49e7RgwQJJ0uXLl/Xvf/87zzkuXbqkWbNmadeuXRo4cKAkKSUlRYsXLy7wc9u5c6e9iQwNDdV3332nNWvW2Jum6dOnKy0tTZ988omGDh1qf90333yjAwcOqHv37vke98CBA3riiSckXR+3d+Ozee+993Ltl56erq5du+qHH37QkCFD8nwORfmZ3SonJ0f/+Mc/JEkVKlTQsmXLtH37dtWrVy/f/Y183oW9r8jISK1Zs0Y//fSTfv31V61bt06BgYGSpH/+85+SpMOHD9ubyNmzZ2vfvn3asmWL5syZo2eeeUaSdOXKFb333nuy2Wx69tlntW3bNv3888966aWXJEmfffaZDh06pGHDhuX6+b777rv2mu6///4CPxsAJROJJHAHtm7dav961KhRGjVqVK7v79mzRxkZGSpbtqx8fX01ZcoU7du3T6mpqcrJybHvd+TIkSKfe/DgwfL19dXjjz9ub0r69eunqlWrqnXr1nr33XclSadOnbK/xsfHR++8846SkpKUlpaWK5nKr4amTZuqQ4cOkq5P/Fm0aJGysrK0e/fuAuu6+TLya6+9pnvvvVf33nuvevfurdmzZ+vKlSv65Zdf1LJlyyK/ZyNKlSqlESNGqEKFCurcubPmzJkj6f9/DkX5md3q1KlT+uOPPyRJYWFhaty4sSTp1Vdf1ffff59n/6J+3vmpWrWqZs6cqd27d+vixYuyWq327x09elSSFBAQIG9vb1mtVn366ac6duyY6tWrp6ZNm9rT6J9//llpaWmSpA0bNmjDhg25zmOz2bRz504FBQUZqgsAJBpJoECLFy/Wk08+aX8+evToPKlkSkrKbY9hs9mUmpqq8+fPa+DAgbpy5Uq++2VmZha5vhupVJkyZezbatSoIUkqXbp0nmMnJCRo2LBhys7ONlzDjXNI0j333KPKlSvr7NmzOnPmTIF1XbhwId/XBwQE2L8u7HO7E/7+/qpQoYKk643cDTfen9GfWX6N5NmzZ+1f3/x+8rtE7cjnfavLly/r5ZdfzvU/A/kdo2rVqhozZoxmzJihHTt22MfdlipVSoMHD9bQoUMNfeaXLl0qdB8AuBmNJHAHbqQ9Xl5e2rFjhypVqpTr+zabTRaLRUuXLrU3kePGjVOvXr3k4+OjJ598Mte4NkmyWCyGzl2qVN6/vt7e3gXuv3HjRntTM3fuXLVs2VKZmZlq2rRpga+5uYG5du2avdbbje278ZnceH3NmjUlKdeElJv3MdvNn0t+n6XRn1l+qlatav/65veTX2PtyOd9q19++cX+M3jllVc0ZMgQlS1bVt27d9evv/6aa99+/fopMjJSiYmJOnbsmL744gtt2bJFcXFx6tmzZ67P/J133lF0dHSB79von0EAYIwkcAf+9Kc/Sbo+dm78+PE6ffq0MjMzlZSUpClTpmjy5MmSrk+QuKF8+fLKzs7W/Pnz8zSRkuTr62v/2sgMaaNureHatWuFruG4a9cubdiwQWlpaXr//fftx7hdM9SiRQv713PmzNH58+d18OBB++zhcuXKqUmTJg69hxtN34ULF3Klg0Vh9GeWn+rVq9tT3/Xr1yshIUEpKSmaN29enn2L8nkX9L5uPkbZsmVlsVj0xRdfaP/+/blef+7cOU2fPl379+9XzZo1FRYWZp8wZLPZdOHCBTVp0kTly5eXJC1YsEC7du1SZmamTp8+reXLl+u5556zH+/mP4O///57rsvpAHAzEkngDnTu3Fmff/65fvzxR61bty7PTOUbE2datGih0qVLKysryz4ur3LlyvL19VVqamqu1zRo0MD+dZcuXSRJf/7znzV8+PA7qrV169ZauHChJNmXE6pVq9ZtX3PvvffmWRTdz88v1/IwtwoNDVVYWJjWr1+v77//Xk899VSu77/55pv2S89F1aBBA3399ddKT0+3j7GcPHmyfVayEUZ/Zvnx8vLSa6+9pjFjxigtLU2RkZGSpMqVK+fZtyifd0Hvq127dvY/I7NmzdKsWbPk4+OjatWq5UpEr169qvnz52v+/Pl5jh0YGKigoCD5+PjorbfesjfPt5s5X7t2bVWqVEmXLl3SggULtGDBAgUEBOSauAUAEokkcEdKly6tBQsWaNiwYfZf1hUrVlT9+vUVHR1tv0tJ3bp1NWPGDNWtW1dlypRRgwYN9OGHH+a7hE7Hjh01YMCAXJdRzdC8eXPFxMTovvvu0z333KPQ0FD7LOKCtGzZUpMnT1bNmjXl4+Ojxx57TAsXLrzt0j+S9Pe//10jR45UvXr15OPjo3Llyqlp06aKi4vT888/7/B7eP755xUREXFHl8aN/swK0rNnT40aNUrVqlXTPffco1atWuV7W8SifN4Fva8qVarogw8+0COPPKIyZcqoXr16mjNnjmrXrp1rv8qVK6t///56+OGHValSJZUuXVoBAQHq3LmzFi5caB8r2qdPH82fP1/NmzdXxYoV5ePjo/vvv1/PPvuspk+fbj9e2bJlNW3aNNWrVy/XeFsAuJXFdvM0QgDQ9UWypevp3K3L6wAAcAOJJAAAABxCIwkAAHAXmzp1qtq0aaPg4GAdPHgw332sVqsmTJigdu3aqX379nlur1oQJtsAyMPM2eIAgOLVtm1bDRgwQP369Stwny+//FLHjx/Xhg0bdPHiRXXt2lXNmzcv9I5WJJIAAAB3sWbNmuW6QUR+1q5dq169esnLy0t+fn5q166dvvrqq0KPTSIJAADgYVJTU/MsHyddXwf25rVgjUpOTravkytdXzqsoLtq3czljWTWucOuPiUc9EhIZHGXAIPu8WaJFk9xOSu9uEuAQeVL5b1NJtzTvtM7irsESa7tcRb989+Ki4vLs33o0KF51v91JhJJAAAADxMdHZ3vDRQcSSOl6wnkH3/8oYYNG0rKm1AWhEYSAADADDmuu52oo5ewC9KhQwctX75czz77rC5evKiNGzfq008/LfR1TLYBAAC4i02ePFmtWrXSqVOn9OKLL6pTp06SpEGDBmnv3r2SpIiICPudriIjI/Xaa6+pZs2ahR7b5Xe2YYyk52CMpOdgjKTnYIyk52CMpOdwmzGSp123dFrpgGCXnet2SCQBAADgEMZIAgAAmCEnp7grcDkSSQAAADiERBIAAMAENhuJJAAAAGAIiSQAAIAZGCMJAAAAGEMjCQAAAIdwaRsAAMAMTLYBAAAAjCGRBAAAMEOOtbgrcDkSSQAAADiERBIAAMAMjJEEAAAAjCGRBAAAMAMLkgMAAADGkEgCAACYwMYYSQAAAMAYEkkAAAAzMEYSAAAAMIZEEgAAwAyMkQQAAACMIZEEAAAwA/faBgAAAIyhkQQAAIBDuLQNAABgBibbAAAAAMaQSAIAAJiBBckBAAAAY0gkAQAAzMAYSQAAAMAYEkkAAAAzMEYSAAAAMIZEEgAAwAQ2G7dIBAAAAAwhkQQAADADs7YBAAAAY0gkAQAAzMCs7fzl5OQoKSnJ2bUAAADAgxhqJL28vDRmzBhn1wIAAOC5bDmue7gJw2Mkg4KCdPLkSWfWAgAAAA9ieIxkSkqKunTposcee0zlypWzb581a5ZTCgMAAIB7M9xIdurUSZ06dXJmLQAAAJ4rp+QtSG64kezWrZsz6wAAAICHMdxIvv7667JYLHm2c2kbAABAbjUJxlUMN5LPPPOM/etr165p/fr1CgoKckpRAAAAcH8OX9ru3r27Bg8ebHpBAAAAHokFyY2zWCwsBwQAAFCCOTRG0maz6cCBA2revLnTCgMAAPAojJEs2M1jJL29vfXSSy+pcePGzqgJAAAAHqDIYyTT09MlKdei5AAAACUeYyQLduLECUVGRio0NFShoaHq06ePTpw44czaAAAA4MYMN5Ljxo1TZGSkEhISlJCQoF69emncuHHOrA0AAMBz5OS47uEmDDeSKSkp6tmzpywWiywWi3r06KGUlBRn1gYAAAA3ZniMpJeXlw4fPqy6detKko4cOSJvb2+nFQYAAOBJbDbutV2g4cOHq1+/fgoJCZEkJSUladq0aU4rDAAAAO7NcCPZqlUrxcfHa8+ePbLZbGrcuLH8/PycWRsAAIDncKOxi65iuJGUJH9/f7Vo0UJW6/XoNiMjQ2XLlnVKYQAAAHBvhhvJDRs2aPLkyTp79qyk63e3sVgsSkxMdFpxAAAAHoM72xQsNjZWM2fOVOPGjeXl5fAtugEAAHCXMNxIVqpUSU2bNnVmLQAAAPAghUaLGRkZysjIUPv27fXZZ5/p4sWL9m0ZGRmuqBEAAMD9lcAFyQtNJJs0aSKLxSKbzSZJmjhxov05YyQBAABKrkIbyaSkJFfUAQAA4NlK4GQbZs0AAADAIUVaRxIAAAAFcKOxi65CIgkAAACHkEgCAACYgTGSAAAAgDEkkgAAAGZgjCQAAABgDIkkAACAGUgkAQAAAGNIJAEAAMzArG0AAADAGBJJAAAAMzBGEgAAADCGRhIAAAAO4dI2AACAGZhsAwAAABhDIgkAAGAGJtsAAAAAxpBIAgAAmIExkgAAAIAxJJIAAABmYIwkAAAAYAyJJAAAgBlIJAEAAABjSCQBAADMYLMVdwX5OnLkiEaPHq2LFy+qcuXKmjp1qurUqZNrn/Pnz+vtt99WcnKysrKyFBoaqr/+9a8qVer2rSKJJAAAwF1s/PjxioqK0vr16xUVFaVx48bl2Wfu3LkKCgrSl19+qS+//FK//vqrNmzYUOixSSQBAADM4MIxkqmpqUpNTc2z3dfXV76+vvbn58+f1/79+7Vw4UJJUufOnTVp0iSlpKTIz8/Pvp/FYtGVK1eUk5OjzMxMZWVlKSAgoNA6aCQBAAA8zKJFixQXF5dn+9ChQzVs2DD78+TkZAUEBMjb21uS5O3trWrVqik5OTlXIzlkyBANGzZMLVu2VEZGhvr166fHHnus0DpoJAEAAMzgwkQyOjpa3bp1y7P95jSyKL766isFBwdr0aJFunLligYNGqSvvvpKHTp0uO3raCQBAAA8zK2XsAsSGBio06dPy2q1ytvbW1arVWfOnFFgYGCu/ZYsWaIpU6bIy8tLFStWVJs2bbRz585CG0km2wAAANyl/P39FRISovj4eElSfHy8QkJCcl3WlqT7779fW7ZskSRlZmZq+/btqlevXqHHp5EEAAAwgy3HdY8iiImJ0ZIlSxQWFqYlS5ZowoQJkqRBgwZp7969kqR33nlHu3btUnh4uLp27ao6deooMjKy0GNbbDbXLnqUde6wK0+HO/BISOF/gOAe7vEuXdwlwKDLWenFXQIMKl+qbHGXAIP2nd5R3CVIkjKWjHHZuco+/39cdq7bYYwkAACAGbhFIgAAAGAMiSQAAIAZ3PQWic5EIgkAAACHkEgCAACYgTGSAAAAgDEkkgAAAGYogYmkyxtJ1ib0HL8m/qu4S4BB9YPz3m8V7unE5XPFXQIMqlspsPCdgBKORBIAAMAMRbzjzN2AMZIAAABwCIkkAACACWw5rCMJAAAAGEIiCQAAYIYSOGubRBIAAAAOoZEEAACAQ7i0DQAAYAaW/wEAAACMIZEEAAAwA8v/AAAAAMaQSAIAAJiB5X8AAAAAY0gkAQAAzEAiCQAAABhDIgkAAGAGG7O2AQAAAENIJAEAAMzAGEkAAADAGBJJAAAAM3BnGwAAAMAYEkkAAAAz2BgjCQAAABhiuJE8cuSIrl27JknaunWr5s2bp0uXLjmtMAAAALg3w43kG2+8IS8vL504cULjx4/XiRMnNGrUKGfWBgAA4DlybK57uAnDjaSXl5dKly6tzZs3q2/fvpo0aZKSk5OdWRsAAADcmOFG8tq1azp9+rQ2bdqk0NBQSZKtBN4KCAAAID+2nByXPdyF4UYyOjpanTp1Uvny5dWgQQOdOHFCFStWdGZtAAAAcGOGl//p3bu3evfubX9+3333aeHChU4pCgAAwOO40dhFVzHcSNpsNi1btkzff/+9LBaLnnrqKUVGRjqzNgAAALgxw43ktGnTlJiYqO7du0uSVq9erWPHjumtt95yWnEAAAAeowQuSG64kdy2bZtWrVqlUqWuv6Rjx47q3r07jSQAAEAJVaRbJFoslny/BgAAKPEYI1mwli1batCgQerWrZssFotWrVqlli1bOrM2AAAAuDHDjeTIkSO1bNkyff3117LZbGrXrl2uWdwAAAAlmhut7+gqhhtJLy8v9e3bV3379s33+zExMYqJiTGrLgAAALi5Io2RvJ2EhASzDgUAAOB5SuAYScN3tgEAAABuZloiCQAAUKKVwHUkSSQBAADgENMaSS8velIAAICSpEiXtjMyMnTq1ClZrVb7tgcffFCS9Pnnn5tbGQAAgCcpgZNtDDeSixcv1owZM1S5cmX7XW0sFou++eYbpxUHAAAA92W4kVy0aJG++uorBQQEOLMeAAAAj2QrgQuSGx7YWL16dZpIAAAA2BlOJIcNG6YxY8aodevWKlOmjH1769atnVIYAACAR2GMZMG+/fZbffvttzp69Kh9hrbFYqGRBAAAKKEMN5Jff/21Nm3apHvuuceZ9QAAAHimEphIGh4jWbNmTZUqxY1wAAAAcJ3hzrB27dqKjo5Wu3bt5OPjY9/er18/pxQGAADgUUrgLRINN5JZWVmqVauWDh486Mx6AAAA4CEMNZJWq1WBgYF6/fXXnV0PAACAZ2KMZP68vb31448/OrsWAAAAeBDDk22efvppLViwQOfPn1dGRob9AQAAAMmWY3PZw10YHiMZGxub69/S9XUkExMTza8KAAAAbs9wI5mUlOTMOgAAADybGyWFrlKkhSEvXLighIQEWSwWNWrUSJUrV3ZSWQAAAHB3hhvJrVu3auTIkQoJCZEkHThwQLGxsWrRooXTigMAAID7MtxIzpgxQ59++qmCgoIkSYcOHdLIkSNpJAEAACQpp+QtSG541nZ2dra9iZSkoKAgZWdnO6UoAAAAuD/DjaSfn59Wrlxpf75q1Sr5+fk5pSgAAACPk2Nz3cNNGG4kJ06cqKVLl6phw4Zq2LChli5dqkmTJjmzNgAAALixQsdIxsfHq3PnzrJYLPrXv/6lK1euyGazqUKFCq6oDwAAwDO4UVLoKoUmkh999JEk2e+zXb58eZpIAAAAFJ5I2mw2TZo0SWfOnNG0adPyfP+tt95ySmEAAACexGYjkcxj5syZql69uiwWi8qVK5fnAQAAgJKp0ESydu3aGjRokKpXr67w8HBX1AQAAOB5GCNZsM8++yzPtr59+5paDAAAADyH4TvbXL16Nddzq9WqS5cumV4QAACARyqBiWShjeSHH36oDz/8UGlpaWrevLl9+9WrV7nUDQAAUIIV2kj27t1bHTp00KRJkzRu3Dj79goVKqhSpUpOLQ4AAMBT2Egk86pYsaIqVqyo//3f/3VFPQAAAPAQhsdIJicnKzY2VklJSbp27Zp9+zfffOOUwgAAADxKCUwkDc/afuedd9S8eXPZbDZNnz5djz32mLp16+bM2gAAAODGDDeSFy5cUK9evVSqVCk1adJE7733nn744Qdn1gYAAAA3ZvjSdunSpSVJ5cqV0x9//KF7771Xf/zxh9MKAwAA8Cg5xV2A6xluJJs1a6aLFy+qb9++6t69u3x8fNShQwdn1gYAAAA3ZriRHDVqlCSpa9eueuKJJ5SWlqb69es7rTAAAABPwvI/+cjIyMizrUqVKqpSpYoyMjJUtmxZpxQGAAAA91ZoI9mkSRNZLBbZbHm7bIvFosTERKcUBgAA4FFIJPNKSkpyRR0AAABwgiNHjmj06NG6ePGiKleurKlTp6pOnTp59lu7dq0++OAD2Ww2WSwWLVy4UPfee+9tj214jKQkpaSkKCEhQRaLRY0aNVKVKlWK9EYAAADuWm46a3v8+PGKiopSRESE1qxZo3Hjxmnx4sW59tm7d6/i4uK0aNEiVa1aVZcvX5aPj0+hxza8juSGDRvUsWNHffLJJ1q0aJGee+45bdy4sejvBgAAAHckNTVVJ0+ezPNITU3Ntd/58+e1f/9+de7cWZLUuXNn7d+/XykpKbn2+/jjj/XSSy+patWqkq7fIrtMmTKF1mE4kZwxY4aWLl2qBx54QJJ09OhRDR48WO3atTN6CAAAgLuWK2dtL1q0SHFxcXm2Dx06VMOGDbM/T05OVkBAgLy9vSVJ3t7eqlatmpKTk+Xn52ff79ChQ7r//vvVr18/paenq3379ho8eLAsFstt6zDcSFaqVMneREpSnTp1VLlyZaMvBwAAgEmio6PzvVW1r6+vQ8ezWq06cOCAFi5cqMzMTA0cOFA1atRQ165db/s6w41ky5Yt9cEHH6hnz56y2WxauXKl2rdvb18eiGWAAABAiebCMZK+vr6GmsbAwECdPn1aVqtV3t7eslqtOnPmjAIDA3PtV6NGDXXo0EE+Pj7y8fFR27ZttWfPHvMayRvx6axZs3JtnzZtGssAAQAAuCF/f3+FhIQoPj5eERERio+PV0hISK7L2tL1sZObN29WRESEsrOztWPHDoWFhRV6fMONJMsAAQAAFMxd72wTExOj0aNHa86cOfL19dXUqVMlSYMGDdLrr7+uBg0aqFOnTtq3b5+ee+45eXl5qWXLlurZs2ehx7bY8ltp3InqV23mytPhDvya+K/iLgEG1Q/OO04G7unE5XPFXQIMqlspsPCd4BYOnv2puEuQJKV0a+2yc/mt2uyyc91OkdaRBAAAQAHcdB1JZzK8jiQAAABwMxpJAAAAOIRL2wAAACawcWkbAAAAMIZEEgAAwAwkkgAAAIAxJJIAAAAmYIwkAAAAYBCJJAAAgBlIJAEAAABjSCQBAABMwBhJAAAAwCASSQAAABOQSAIAAAAGkUgCAACYgEQSAAAAMIhEEgAAwAw2S3FX4HIubyTv8S7t6lPCQfWDuxV3CTDo4IFVxV0CDAqqH1HcJcAg31Jli7sEwO1xaRsAAAAO4dI2AACACZhsAwAAABhEIgkAAGACW07Jm2xDIgkAAACHkEgCAACYgDGSAAAAgEEkkgAAACawlcAFyUkkAQAA4BASSQAAABMwRhIAAAAwiEQSAADABKwjCQAAABhEIgkAAGACm624K3A9EkkAAAA4hEQSAADABIyRBAAAAAwikQQAADABiSQAAABgEI0kAAAAHMKlbQAAABOw/A8AAABgEIkkAACACZhsAwAAABhEIgkAAGACm41EEgAAADCERBIAAMAEtpzirsD1SCQBAADgEBJJAAAAE+QwRhIAAAAwhkQSAADABMzaBgAAAAwikQQAADABd7YpQE5OjjZv3uzsWgAAAOBBDDWSXl5e+uCDD5xdCwAAgMey2Vz3cBeGx0g++uij2rNnjzNrAQAAgAcxPEbyp59+0j//+U/Vrl1b5cqVs29fsWKFUwoDAACAezPcSL7zzjvOrAMAAMCjlcTJNoYbySeeeEKSlJKSIj8/P6cVBAAAAM9geIxkQkKCnnnmGXXr1k2StHfvXo0dO9ZphQEAAHiSHJvFZQ93YbiRfPfddzV//nxVqVJFktSgQQPt3r3baYUBAADAvRm+tJ2VlaUHH3ww17bSpUubXhAAAIAn4haJt+Hj46MrV67IYrn+If3+++8qU6aM0woDAACAezOcSP75z3/Wyy+/rDNnzmj06NHaunWrYmNjnVkbAACAx3CnhcJdxXAj2bp1a9WtW1dbt26VzWbT4MGDVbt2bWfWBgAAADdmuJGUpJo1ayoqKkrnz5/XiRMnaCQBAAD+H3eaTe0qhsdIRkVF6fLly0pNTVXXrl01ZswYTZ061Zm1AQAAwI0ZbiTT09NVsWJFffvttwoPD9eXX36pbdu2ObM2AAAAj2GzWVz2cBeGG8nMzExJ0s6dO/XUU0/Jy8tL3t7eTisMAAAA7s1wI/nEE08oLCxMP/30k5544gmlpqbKy8vwywEAAO5qNpvrHu7C8GSb8ePHKykpSTVr1pSPj4/S0tI0efJkZ9YGAAAAN2Y4Upw5c6YuXbokHx8fSZKfn58efvhhpxUGAADgSUrivbYNJ5IVK1bU/PnztW/fPoWEhKh58+YKDQ1Vo0aNnFkfAAAA3JThRHLgwIFasGCBtm3bpoiICC1btkx9+vRxZm0AAABwY4YTyfXr12v79u3avXu3/P391adPH4WGhjqzNgAAAI/hTsvyuIrhRvJ//ud/1KRJE7311lsKDQ1VqVJFuikOAAAA7jKGu8HvvvtOO3bs0Lp16zR16lRVr15dTz31lF588UVn1gcAAOAR3GkSjKsYbiT9/f3VoUMHVa9eXYGBgVq5cqV27dpFIwkAAFBCGW4kX331VSUkJKhevXp68sknFRsby4xtAACA/8eN1gl3GcONZP/+/TVr1izdc889zqwHAAAAHsJwI9myZUv9/vvv2rlzpyQpNDRUQUFBTisMAADAk5TEMZKG15FcvXq1XnjhBSUmJioxMVEvvviivvjiC2fWBgAAADdmOJH86KOPtGrVKlWtWlWSdPbsWb388svq0qWL04oDAADwFCVxHUnDiaQkexN569cAAAAoeQw3krVq1dL777+v06dP68yZM4qLi1PNmjWdWRsAAIDHyHHhw10YbiQnTJigI0eOqEuXLurSpYsOHz6siRMnOrM2AAAAuDFDYyStVqs+/fRTzZgxw9n1AAAAeCSbGCOZL29vb/3444/OrgUAAAAexPCl7aeffloLFizQ+fPnlZGRYX8AAABAyrG57lEUR44cUe/evRUWFqbevXvr6NGjBe57+PBhNWrUSFOnTjV0bMPL/8TGxub6tyRZLBYlJiYaPQQAAABcbPz48YqKilJERITWrFmjcePGafHixXn2s1qtGj9+vNq1a2f42IYbyaSkJMMHBQAAQPE7f/689u/fr4ULF0qSOnfurEmTJiklJUV+fn659p03b56efvpppaenKz093dDxDTeSkpSSkqKEhARZLBY1atRIVapUKcrLAQAA7lo5Lpxsk5qaqtTU1DzbfX195evra3+enJysgIAAeXt7S7o+76VatWpKTk7O1UgmJSVp27ZtWrx4sebMmWO4DsON5IYNGzR27Fg98sgjstlsSkpK0qRJk4oUfwIAAODOLVq0SHFxcXm2Dx06VMOGDSvSsbKysjR27Fi9++679obTKMON5IwZM7R06VI98MADkqSjR49q8ODBNJIAAABy7fI/0dHR6tatW57tN6eRkhQYGKjTp0/LarXK29tbVqtVZ86cUWBgoH2fs2fP6vjx43rllVckXU87bTab0tLSNGnSpNvWYbiRrFSpkr2JlKQ6deqocuXKRl8OAAAAk9x6Cbsg/v7+CgkJUXx8vCIiIhQfH6+QkJBcl7Vr1KihnTt32p/Pnj1b6enpGjVqVKHHL3T5nxvL/LRs2VIffPCBzp49qzNnzmju3Llq3759oScAAAAoCdz1FokxMTFasmSJwsLCtGTJEk2YMEGSNGjQIO3du9fRtytJsthsttuuRvTQQw/JYrEov90cWf6nYfXmRasQxeZylrEZWyh+Bw+sKu4SYFBQ/YjiLgEGVStTubhLgEE/JW8t7hIkSV8H9HbZudqfXuayc91OoZe2WfYHAACgcNwiEQAAADCoSOtIAgAAIH9FHbt4NyCRBAAAgENIJAEAAExAIgkAAAAYRCIJAABgAmZtAwAAAAaRSAIAAJggp+QFkiSSAAAAcAyNJAAAABzCpW0AAAAT5DDZBgAAADCGRBIAAMAEtuIuoBiQSAIAAMAhJJIAAAAm4BaJAAAAgEEkkgAAACbIsTBrGwAAADCERBIAAMAEzNoGAAAADCKRBAAAMAGztgEAAACDSCQBAABMkFPyJm2TSAIAAMAxJJIAAAAmyFHJiyRJJAEAAOAQGkkAAAA4hEvbAAAAJmBBcgAAAMAgEkkAAAATlMTlf1zeSF7OSnf1KeGgE5fPFXcJMCiofkRxlwCDDh1cU9wlwKA69cKLuwTA7ZFIAgAAmIBbJAIAAAAGkUgCAACYgFnbAAAAgEEkkgAAACYoibO2SSQBAADgEBJJAAAAEzBrGwAAADCIRBIAAMAEJJIAAACAQSSSAAAAJrAxaxsAAAAwhkYSAAAADuHSNgAAgAmYbAMAAAAYRCIJAABgAhJJAAAAwCASSQAAABPYiruAYkAiCQAAAIeQSAIAAJgghwXJAQAAAGNIJAEAAEzArG0AAADAIBJJAAAAE5BIAgAAAAaRSAIAAJiAdSQBAAAAg0gkAQAATMA6kgAAAIBBNJIAAABwCJe2AQAATMDyPwAAAIBBJJIAAAAmYPkfAAAAwCASSQAAABPklMBMkkQSAAAADiGRBAAAMAGztgEAAACDSCQBAABMUPJGSJJIAgAAwEEkkgAAACZgjCQAAABgEIkkAACACXIsxV2B65FIAgAAwCEkkgAAACbgzjYAAACAQYYaySNHjujatWuSpK1bt2revHm6dOmSUwsDAACAezPUSL7xxhvy8vLSiRMnNH78eJ04cUKjRo1ydm0AAAAew+bCh7sw1Eh6eXmpdOnS2rx5s/r27atJkyYpOTnZ2bUBAADAjRlqJK9du6bTp09r06ZNCg0NlSTZbO7UDwMAABSvHBc+3IWhRjI6OlqdOnVS+fLl1aBBA504cUIVK1Z0dm0AAABwY4aW/+ndu7d69+5tf16jRg0tXLjQaUUBAAB4Gpb/KUBGRoZmzpypN998U5J09OhRbdmyxamFAQAAwL0ZaiRjYmKUnZ2tpKQkSVL16tUVFxfn1MIAAAA8CbO2C3Dw4EGNGDFCpUuXliSVL19eOTnuNNQTAAAArmZojOSNBvKGa9euMWsbAADgJiUxYjPUSDZr1kxz585VZmamdu7cqYULF6pNmzbOrg0AAAB36MiRIxo9erQuXryoypUra+rUqapTp06uff7xj39o7dq18vb2VqlSpTR8+HD96U9/KvTYhi5tv/baa7LZbCpfvrxiY2PVsGFDDRs2zKE3AwAAcDfKkc1lj6IYP368oqKitH79ekVFRWncuHF59mnYsKFWrFihL774QlOmTNHw4cN19erVQo9tKJF89tlnFR4erunTp6t27dpFKh4AAADmSk1NVWpqap7tvr6+8vX1tT8/f/689u/fb1+2sXPnzpo0aZJSUlLk5+dn3+/m9DE4OFg2m00XL15U9erVb1uHoUTyiy++kK+vr1544QUNHDhQ//nPfxgjCQAAcBNXztpetGiR2rZtm+exaNGiXDUlJycrICBA3t7ekiRvb29Vq1bttre6Xr16tWrVqlVoEykZTCT9/f01ZMgQvfrqq/rmm28UExMjLy8vPf/88+rXr5/KlClj5DAAAAAwQXR0tLp165Zn+81ppCN++OEHzZo1Sx999JGh/Q01ktL1RcnXrFmjzz77TLVq1VKvXr20c+dODRo0SIsXL3a4YAAAgLuBK2dt33oJuyCBgYE6ffq0rFarvL29ZbVadebMGQUGBubZ9+eff9bIkSM1Z84c1a1b11AdhhrJSZMmaf369WrTpo3+9re/qV69epKk8PBwdejQwdCJAAAA4Fr+/v4KCQlRfHy8IiIiFB8fr5CQkFzjIyVpz549Gj58uN5//3098sgjho9vqJEMDAzU2rVr8+18SSMBAADcV0xMjEaPHq05c+bI19dXU6dOlSQNGjRIr7/+uho0aKAJEybo6tWruWZ0T5s2TcHBwbc9tsXm4lkzD/g3cuXpcAdOXD5X3CXAoBoV/ArfCW7h0ME1xV0CDKpTL7y4S4BB/73wa3GXIEl6vU5vl53r/aPLXHau2zE0axsAAAC4leHJNgAAAChYSbxFIokkAAAAHEIiCQAAYIKi3rrwbkAiCQAAAIeQSAIAAJig5OWRJJIAAABwEIkkAACACRgjCQAAABhEIgkAAGAC1pEEAAAADCKRBAAAMIGNMZIAAACAMSSSAAAAJmCMJAAAAGAQiSQAAIAJGCMJAAAAGEQjCQAAAIdwaRsAAMAETLYBAAAADCKRBAAAMEGOjck2AAAAgCEkkgAAACYoeXkkiSQAAAAcRCIJAABggpwSmEmSSAIAAMAhJJIAAAAm4BaJAAAAgEEkkgAAACbgzjYAAACAQSSSAAAAJmDWNgAAAGAQiSQAAIAJmLUNAAAAGEQjCQAAAIdwaRsAAMAELP8DAAAAGEQiCQAAYAKbjck2AAAAgCEkkgAAACZgQXIAAADAIBJJAAAAEzBrGwAAADDI5Ylk+VJlXX1KOKhupcDiLgEG+fL3ymPUqRde3CXAoKO/fVncJcDDcItEAAAAwCDGSAIAAJiAWdsAAACAQSSSAAAAJuDONgAAAIBBJJIAAAAmYB1JAAAAwCAaSQAAADiES9sAAAAmYEFyAAAAwCASSQAAABOwIDkAAABgEIkkAACACViQHAAAADCIRBIAAMAEjJEEAAAADCKRBAAAMAHrSAIAAAAGkUgCAACYIIdZ2wAAAIAxJJIAAAAmKHl5JIkkAAAAHEQiCQAAYALWkQQAAAAMopEEAACAQ7i0DQAAYAIubQMAAAAGkUgCAACYwMaC5AAAAIAxJJIAAAAmYIwkAAAAYBCJJAAAgAlsJJIAAACAMSSSAAAAJmDWNgAAAGAQiSQAAIAJmLUNAAAAGEQiCQAAYALGSAIAAAAGkUgCAACYgDGSAAAAgEE0kgAAAHAIl7YBAABMwC0SAQAAAINoJAEAAEyQY7O57FEUR44cUe/evRUWFqbevXvr6NGjefaxWq2aMGGC2rVrp/bt22v58uWGjk0jCQAAcBcbP368oqKitH79ekVFRWncuHF59vnyyy91/PhxbdiwQcuWLdPs2bN18uTJQo9NIwkAAGACmwv/SU1N1cmTJ/M8UlNTc9V0/vx57d+/X507d5Ykde7cWfv371dKSkqu/dauXatevXrJy8tLfn5+ateunb766qtC3zOTbQAAADzMokWLFBcXl2f70KFDNWzYMPvz5ORkBQQEyNvbW5Lk7e2tatWqKTk5WX5+frn2q1Gjhv15YGCgTp06VWgdRW4k09PTJUnlypUr6ksBAADuWkUdu3gnoqOj1a1btzzbfX19XVaDVIRG8vjx4xoxYoQSExNlsVj08MMPKzY2VjVr1nRmfQAAALiFr6+voaYxMDBQp0+fltVqlbe3t6xWq86cOaPAwMA8+/3xxx9q2LChpLwJZUEMj5EcP368IiMjtWfPHiUkJKhXr175DtYEAAAoiVw5RtIof39/hYSEKD4+XpIUHx+vkJCQXJe1JalDhw5avny5cnJylJKSoo0bNyosLKzQ4xtuJFNSUtSzZ09ZLBZZLBb16NEjz0BNAAAAuJeYmBgtWbJEYWFhWrJkiSZMmCBJGjRokPbu3StJioiI0P33369nn31WkZGReu211wxddTZ8advLy0uHDx9W3bp1JV1fk+jGwE0AAICSzpVjJIsiKCgo33Uh58+fb//a29vb3mAWheFGcvjw4erXr59CQkIkSUlJSZo2bVqRTwgAAIC7g+FGslWrVoqPj9eePXtks9nUuHHjPNfXAQAASqqSeK/tIi3/4+/vrxYtWshqtUqSMjIyVLZsWacUBgAAAPdmuJHcsGGDJk+erLNnz0qSbDabLBaLEhMTnVYcAACAp3DXMZLOZLiRjI2N1cyZM9W4cWN5eXFnRQAAgJLOcCNZqVIlNW3a1Jm1AAAAwIMYjhbbt2+vzz77TBcvXlRGRob9AQAAAPdckNzZDCeSM2bMkCRNnDhRFouFMZIAAAAlnOFGMikpyZl1AAAAeDSbLae4S3A5Zs0AAADAIUVaRxIAAAD5y3GjsYuuQiIJAAAAh5BIAgAAmMBWAhckJ5EEAACAQ0gkAQAATMAYSQAAAMAgEkkAAAATMEYSAAAAMIhEEgAAwAQ5JJIAAACAMSSSAAAAJrAxaxsAAAAwhkYSAAAADuHSNgAAgAlY/gcAAAAwiEQSAADABNwiEQAAADCIRBIAAMAEjJEEAAAADCKRBAAAMAG3SAQAAAAMIpEEAAAwAWMkAQAAAINIJAEAAEzAOpIAAACAQSSSAAAAJmCMJAAAAGAQiSQAAIAJWEcSAAAAMIhGEgAAAA7h0jYAAIAJbCz/AwAAABhDIgkAAGACJtsAAAAABpFIAgAAmIAFyQEAAACDSCQBAABMwKxtAAAAwCASSQAAABMwRhIAAAAwiEQSAADABCSSAAAAgEEkkgAAACYoeXkkiSQAAAAcZLGVxAv6AAAAuGMkkgAAAHAIjSQAAAAcQiMJAAAAh9BIAgAAwCE0kgAAAHAIjSQAAAAcQiMJAAAAh9BIAgAAwCE0kgAAAHAIjWQBgoODdeXKleIuA/AI/H0BgJKJRrKYWK3W4i7BLRlpSFJTUzV//vxc244ePaquXbuqa9eu+uKLL5xZIu5AdnZ2cZfA3z0AMBGN5G188skn6tGjh9q2bav169fbt2/ZskVdu3ZVeHi4oqOjdezYMUnSypUr9frrr9v3u/n5ypUr9fLLL2vkyJHq3r27Dh486No3cxdJTU3Vhx9+mGvbhg0b1KRJE61evVpdunQxfCx3aGzuFh999JH69OmjsLCwXH9fgoOD9eGHH6p///6Ki4vTuXPn9Nprryk8PFzh4eFavXq1JGnr1q165ZVXJEnnz59XcHCw1q1bJ0maP3++/v73vysnJ0cxMTHq0KGDunTpoj59+tjPs3nzZvXp00fdu3dX79699csvv0iSdu7cqYiICE2aNEmRkZHasmWLaz6Qu8T8+fM1ceJE+/Nz587pqaeeUlpamqZOnaqePXsqIiJCI0eOtP9P4LJly9SxY0dFREQoPDxchw4dKq7yS7Tg4GDNnTu3SL/HgKIqVdwFuLMKFSro888/165du/TGG28oLCxM58+f11tvvaUlS5bowQcf1PLlyzVixAgtX7680OPt3r1ba9asUa1atVxQvfsKDg7W0KFD9d133+nChQv6y1/+orCwsDz7TZ06VT/88IOysrJUpUoVTZkyRffdd58mTpyoy5cvKyIiQmXLllVUVJQWLVqknJwc7d69W7Nnz5bNZtO4ceOUkpKiUqVKafjw4WrVqpX9/CNHjtTmzZv12GOP6dSpU/Lx8dHRo0d14sQJtW/fXs8884xmz56tU6dOKTo6WtHR0QW+H6vVqunTp2vr1q2SpD/96U8aMWKEvL29tWzZMn388cfy8fFRTk6OZs6cqaCgIOd8sMXMYrFo6dKlOnz4sPr27atmzZrJ399fkpSTk6NPPvlEkvTGG2+oXr16+sc//qEzZ86oe/fuevjhh9WsWTONGDFCWVlZ2r59u5o0aaLt27erY8eO2rFjhwYOHKikpCRt375d69atk5eXly5duiRJOn78uObMmaMFCxaoQoUK+u233zRo0CD95z//kSQdPHhQMTExGjt2bLF8Np4sMjJSzz33nN58802VL19ey5YtU+fOnbV48WJVrFhRK1askCTFxsZq3rx5Gj58uKZNm6b4+HgFBgYqMzOTFLgYmf17DLgVjeRtPPfcc5Kkxo0b68yZM7p27ZoSEhL00EMP6cEHH5Qk9ejRQxMmTFBaWlqhx2vatGmJbyJvuF3TccOgQYM0atQoSdLy5cs1ffp0zZgxQ+PGjVOPHj20Zs0a+77Hjh1Tenq6ff9evXopMjJSvXr10u+//65+/fpp3bp18vPzk5S7sRk9erR+++03LVq0SFarVW3atNHly5e1ZMkSnT17Vh06dFDPnj1Vvnz5fN/LsmXLlJiYqJUrV9rrXrZsmaKiokrUL9RevXpJkurWrauHH35Yv/zyi9q2bStJ6tatm32/7du3a/To0ZKkatWqqXXr1tq5c6fq16+vBx98UAkJCfr+++81ZMgQxcbGKjMzU/v27VPTpk3tn+GYMWP05JNP6plnnpF0Pc08fvy4+vXrZz9Pdna2zp07J0mqXbu2mjRp4pLP4W5TqVIltWnTRmvWrFFkZKSWL1+uhQsXauTIkUpLS7OnXJmZmXrooYckSaGhoXr77bfVtm1bPf3006pZs2ZxvoUSrai/xypUqFCc5cID0UjeRpkyZSRJ3t7ekq7/YrLZbLJYLPnu7+3trZycHPvza9eu5fp+QY1ISXS7puOGLVu26LPPPlN6enqRLkGnpaUpMTFRPXr0kCQ9+OCDCgkJ0S+//KI2bdpIyt3YSFK7du3k4+MjSXrggQfUunVreXl5KSAgQL6+vjp16lSBSeL27dvVrVs3++u7d++ujRs3KioqqsT+Qr3170m5cuVyff/Wv0M3njdv3lw7duxQQkKCYmJi5O/vr/j4eAUHB6tMmTIqU6aM/v3vf2vnzp3avn27pk+frlWrVkm6ngRPmzYtTy2HDh3Kc34UTf/+/fXmm2/K399fQUFBeuCBB2Sz2TR+/Hg1b948z/5xcXHau3evduzYoQEDBigmJkatW7cuhspR1N9jQFExRrKImjRposTERPuYn1WrVunhhx9WhQoVVKtWLR04cECZmZnKzMzMNR4FBcvvP2r//e9/9e677+pvf/ub4uPjNWXKFGVmZt7ReW7X2Nz4j610/T+4tz6/XZKYX/03nsfFxekvf/mLMjIyNGDAAG3evPmO3oM7+/zzzyVdn/iUmJioRo0a5btf8+bNtWzZMknS2bNntXnzZj355JOSridZK1euVPXq1eXj46PmzZsrLi7O3qykpKTo6tWratWqlUaMGKGKFSvqxIkTatGihbZu3arffvvNfp49e/Y48+2WKPXr11flypU1ZcoURUVFSZLatGmjjz/+WFevXpV0/X/gDh06pOzsbJ04cUINGzbUK6+8ohYtWigxMbE4y8ctbvd7DCgqEski8vPz07Rp0zRixAhlZ2fLz89PsbGxkq7/5WzevLk6d+6s+++/X0FBQTp79mwxV+yePv/8cw0ZMqTApiMtLU2lS5dW1apVlZOTo6VLl9q/V6FCBV29elXZ2dkqVSrvH+EKFSooJCREq1atUo8ePXTo0CElJSUV2NjcqaeeekqrVq1Sx44dJUmrV6/Ws88+q+zsbP3xxx9q2LChGjZsqOPHjysxMfGuTWZ8fHzUp08fXbhwQRMnTswzVOGGv/71rxo3bpzCw8MlSSNGjFC9evUkSY0aNdKFCxfszUrz5s3197//XaGhoZKk5ORkjR07VtnZ2bJarWrVqpUaN24sLy8vxcbGasyYMbp69aqysrLUtGlTNWzY0AXvvGTo1auXZsyYoaefflqS9MorryguLk49e/aUxWKRxWLR0KFDVbNmTY0ePVqXL1+WxWJRYGCg3nzzzeItHrnc7vcYUFQWm81mK+4iULLcmOyycePGPJNtgoODtXv3bpUvX16TJ0/Wt99+qxo1aujxxx/X6tWrtWnTJknXm5Fdu3apUqVKWrp0qWbPnp1rjOSxY8duO9nmxjmk62MkH330UT3//POSrl/Ge+mll+zj79q0aaO5c+eqfv36+b4fq9Wq2NhYbdu2TZLUsmVLjRw5UlarVS+88EKuX6jvvfeeqlSp4qRPFnCeMWPG6IEHHtDAgQOLuxQAboRGEi53ayMHwH2dPn1aAwYMUNWqVTV//nyVLVu2uEsC4Ea4tA0AKFBAQADjvQEUiEQSMKh79+55Jt00atQo12LNAACUJDSSAAAAcAjL/wAAAMAhNJIAAABwCI0kAAAAHEIjCQAAAIfQSAIAAMAh/xemZ+r2bsF+9gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 10))\n", + "corr = df.corr()\n", + "sns.heatmap(corr)\n", + "plt.title('Heatmap of the dataset', fontsize=15, fontweight='bold')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "264" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['experiment'] == 'control') & (df['yes'] == 1)].shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "322" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['experiment'] == 'control') & (df['no'] == 1)].shape[0]" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "75f376effe886d8b51843b632a4b92665fc33b9300797299f553ce70034dd324" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Bonus_decisiontree.ipynb b/notebooks/Bonus_decisiontree.ipynb new file mode 100644 index 0000000..233f61f --- /dev/null +++ b/notebooks/Bonus_decisiontree.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Autoreload notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "import sys\n", + "\n", + "import dvc.api\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import ElasticNet, LogisticRegression\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from fast_ml.model_development import train_valid_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from urllib.parse import urlparse\n", + "import mlflow\n", + "import mlflow.sklearn\n", + "import logging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> #### Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "sys.path.append(os.path.abspath(os.path.join('../scripts')))\n", + "from ml import Ml\n", + "from preprocess import Preprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate preprocessing & ml class" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ml = Ml()\n", + "preprocess = Preprocess()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets or creates a logger\n", + "logger = logging.getLogger(__name__)\n", + "# set log level\n", + "logger.setLevel(logging.INFO)\n", + "file_handler = logging.FileHandler(f'../logs/decission_trees.log')\n", + "formatter = logging.Formatter(\n", + " '%(asctime)s : %(levelname)s : %(name)s : %(message)s', '%m-%d-%Y %H:%M:%S')\n", + "file_handler.setFormatter(formatter)\n", + "logger.addHandler(file_handler)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Get URL from DVC\n", + "path = 'data/AdSmartABdata.csv'\n", + "repo = 'https://github.com/jedisam/abtest-mlops'\n", + "version = '6db449393c9626c4fbca44946dfa103660685a27'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data from dvc using the dvc.api.Dataset class\n", + "data_url = dvc.api.get_url(\n", + " path=path,\n", + " repo=repo,\n", + " rev=version\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Read CSV file from remote repository\n", + "data = pd.read_csv('../data/ABtwoCampaignEngView.csv', sep=',')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert date column to datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# change the date column to datetime\n", + "# data = preprocess.convert_to_datetime(data, 'date')\n", + "data.drop(['date'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get numerical & categorical features" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "numerical_column = preprocess.get_numerical_columns(data)\n", + "categorical_column = preprocess.get_categorical_columns(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove the id column" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# drop auction_id from categorical_column\n", + "categorical_column.remove('auction_id')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Get column names have less than 10 more than 2 unique values\n", + "to_one_hot_encoding = [col for col in categorical_column if data[col].nunique() <= 10 and data[col].nunique() > 2]\n", + "\n", + "# Get Categorical Column names thoose are not in \"to_one_hot_encoding\"\n", + "to_label_encoding = [col for col in categorical_column if not col in to_one_hot_encoding]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Encode categorical features" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Label encoding\n", + "label_encoded_columns = preprocess.label_encode(data, to_label_encoding)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Select relevant rows" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Copy our DataFrame to X variable\n", + "X = data.copy()\n", + "\n", + "# Droping Categorical Columns,\n", + "# \"inplace\" means replace our data with new one\n", + "# Don't forget to \"axis=1\"\n", + "X.drop(categorical_column, axis=1, inplace=True)\n", + "\n", + "# Merge DataFrames\n", + "X = pd.concat([X, label_encoded_columns], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Select only rows with responses\n", + "X = X.query('yes == 1 | no == 1')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop auction_id column\n", + "X.drop([\"auction_id\"], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Split data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X['target'] = [1] * X.shape[0]\n", + "X.loc[X['no'] == 1, 'target'] = 0\n", + "y = X['target']\n", + "X.drop([\"target\"], axis=1, inplace=True)\n", + "X.drop(['yes', 'no'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# # Get the day of the week from the date column as a new column\n", + "# X['day'] = X['date'].dt.dayofweek\n", + "# X.drop([\"date\"], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">> ### Decision Tree Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "decision_tree_model = DecisionTreeClassifier(criterion=\"entropy\",\n", + " random_state=0)\n", + "decision_tree_result = ml.cross_validation(decision_tree_model, X, y, 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot accuacy results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the accuracy result of the new data, it seems that exposed from this new data has a better performance over control" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Accuracy Result\n", + "model_name = \"Decision Tree\"\n", + "ml.plot_result(model_name, \"Accuracy\", \"Accuracy scores in 5 Folds\", decision_tree_result[\"Training Accuracy scores\"], decision_tree_result[\"Validation Accuracy scores\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Precision Results" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAGUCAYAAAAVnbUBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA6P0lEQVR4nO3deZwdVZnw8d9jAgQIIIJEILwEBMMimpAQHNZEcQBhUBGEiGhwQRTE0UER5x2J4+CrQ9xBGBeMCxJUBFmCKBhARIWEJRA2I0YNKAgOm6zB5/2jqkNxc7v73q7u293p3/fzqU/qVp176rl1Kt1Pn3vqVGQmkiRJkvrmBYMdgCRJkjScmVBLkiRJNZhQS5IkSTWYUEuSJEk1mFBLkiRJNZhQS5IkSTWYUEsjTETMjYgslwkDUP/sSv3T+7t+qTcRMaFyDc4d7HgElfa4sh/qurKrvn4ITeoXJtQacSo/2Jstj0bE0oj4fkQcHhFrDHa8klYP1USwhWVZPxxvVhvH61om1f+k0shjQi0931jgpcChwDnAooh42eCGJEmShrLRgx2ANMje2PB6Q2A34AhgbWAn4GcRMTkz/9bp4AZCZs4CZg1g/bOB2QNVv9SbzFwGxGDH0YvGnz2NHu/n4y0AvtRCud/383GlEcGEWiNaZl7QZPM3I+ILwJXAxsD/AU4sF0mqrZufPQPpj4NwTGnEcMiH1ERmLgE+Vtl0yGDFIkmShjYTaql7l1TWt46IdbpeNJspIyIOjogLI+KPEfF0d3egR8Q/RcQZEXFbRDwUEU+W7zk3Ig5oNbiIWCci3hcRF0fEnyLiiXK5OyJ+FBFHR8T6Td7X6ywfETEqIo6MiIvKup8s6/5TRNwQEV8tP+86Td7b8iwfEbFFRHy6rPNvEfFURNxTHndWRIzq5f2r3O0fEYdFxM8i4i9lfX+IiG9GxHY91dWqOuemoZ6XRcR/R8T1EfHXiHgmIh4u6zg9Il4TEd0OW+jvc1d+rlnlubsnIlZ0d2NcROxXXke/jeJG3scj4nfltj1aOIdjymv3ZxHx5zL2xyJiWURcFxFfjIj9o483BUcLs3wMxrUznEXEP0fEd8qfL4+X7X5HRJwZEVP68TjrRsTHyuv6kfL/xOKI+EREbNxGPS+MiBMj4qqIuD+Kn8mPlPFfW/7fmd7T/zGpLZnp4jKiFiC7ll7KrVEtC2xW2Te3sn0i8KOGsqvUD6xLcaPjKuUalouB9XqJbT/gLy3U9c0m763GPqHJ/o2B61qoO4E3NHn/7Mr+6T18hvdQjBPtqf7FzWKs1HFlpewY4Pwe6noS2L/mtVPr3JR1jAY+D6xooY69O3TuXgT8okkdyxre82Lgihbi/jqwRjfHfSnw2xbP4aQ+ttOESh1zh8K103jMunW1eLxZvZ2HFuoYC1zYSzv9A/gi8IIe6ukqe2UPZbYF7u7hOH8CJvV2HoFdgPtavMZe2Im2cFn9F8dQS917ccPrR7op93lgf+B3wHeAO4F1gL27CkTEWsDlwKvKTX+kSK6XAE8B2wBvo0jODwAuiIjXZuY/Gg8WEW8Gvgd09UAuBs4rj/8PYAuKGyv3pW83Zn2N4hcSwNIyzruAJ4D1yxj3AnbtQ90ARMR7gDMrmy6i+EbgIeBlwFHAVhQ3hV4TxU2hf+2l2rOANwCLgHkU53hjihtMdwPWAr4bERMz84E+hl7r3JS9YecBB5WbngUuoLhh7H6K62Z7irabRJP2G6Bz911gD+Dm8jMtKz/PKyrHfRHwK4qEGOA24Afl5/8HsCNFAjceeCfFHw6zmnz+H1Bc7wA3AT+kSKKeobgpeHtgRvn5O6UT187zRMQlwM7ARsCjFMniL4BvZOZN/XGMuspvOS6luDaguMbOAm6gaN89KH5urQkcT3Ej99F9PNaGwM8prh8obo48i+KPrxcBBwP7UPzh81AP9axTltmk3HQ1RSfFHymu042BlwOvofj/KvWPwc7oXVw6vVDpneil3LsrZX/fsG8uz+/l+D6wZg91fb5S9oxmZSl6xL9VKXdMkzJbAY+V+58FPgBEN8fckCY9xPTQQ03xS+jZct/1wLo9fKYtgS2bbJ9dqb/Z8SfwXO/qCuDNTcqsTfFLsKueH3QTw5UN7fBfjeeDYmhbtffxI328bvrj3HykEscfgJ16qGNKYx0DfO4+T889jNVz+H+blaXozbysUm6/hv1TK/suAkb1cLwdgI362FYTKseZO9jXTg/H7G45C1i7zrHK483q7Tz08v4TK++/g8q3dJUyk4EHK+UO7Kaurv1XdrP/65UyPwHWaVLmA43nqkmZQyr7v9LL59sVWKvueXZxyUwTapeRt/T0w7hSZjuKHsOusqc27J9b2fcnek6uNqXohU7g8l5iW4OipzmBu5rs/2rluJ/q4+evxj6hYd+rKvs+2Mf6Z1fqmN5k/+cq+z/TQz3rA/fy3FfKL2tSppqgXNFDXdu0Uq6Xz1Xr3FAkm12Jx1P0kEz3UMdAnbuF9JxM71wp+41eYtyQogcxgZ827Du8Us8b+9IOLZ6nCZXjzO2mTMeunYZjPgCcDXyoPB8zgZOAX1aOkxTfaI2uebxZDXX2tkyvvHdNnhtW9kxP1yvPT2Kv6aZMtwk1xbeBXT8jHwBe1MOxflCNucn+j1b2Tx6oa8zFpXHxpkSNaBHxhoZlVkR8leKr364hH/cC/91DNWdl5t972P9mil9OAJ/tKZ7MfAY4t3y5bVRuGiy/fj2sfPko8P96qquPqnPf7jgA9UPx1S0UPazdno/MfAT4SvkyKL6S78kXe6hrKcUfPlD0fPZF3XOzP8VX1wDfy8xb+lDHQJ2707PJ8KKKIyvrc3qqKDP/F5hfvtyrHO7UpRPXV18M9LXT5SRg08w8IjM/l5nzMvOczPx/mbk7Rft2naPXMLhTde4GjCvXL+3pes3MH1IMgQLYPSI26a5sNw7kuZ+Rc7PnOf9P7aWuoXqNaTXnGGqNdOf3sv924NDseQzqL3qpY8/K+iYR8YZeym9YWd+eYjwrFONZu2btWJCZj/ZST18sofgDYjPgneWY168B1/WScLWk/EW7Zfny5sy8v5e3/BT4ZLne25jtX/ey/x6K8eUb9lKuO3XPTXX2iwvbPfgAn7tWr+GngYkR0dvY07Uq/25N8f8I4BqK8eZrAyeX42a/lZmLe6lvoA30tQNAZv6ql/3nR8S7KXqwAT4cEXMy86k6xy0toPcHu9xaWZ9WWf9pC/X/jOfGxu9KMaSnVbtU1q/opez1FPezrDKDUelyit7pAM6MiJdS/AH72zbikdpmQi09398phnrcSJFs/6CFX2b39LJ/QmV9bpvxVH+Bj6+s395YsD9k5rPlTW/nUfQYvaNcHoqIX1EkRJdl5qI+HmLTyvpdLZSvltm021KF3m4W62rHtXos1Y1+ODd1228gz12r1/Ca9P5HaKOV13Bm/i0iPkhxH8FoimEPH4qI+4FrKRL7SzNzQK7vHgzotdOOzPxeRHyc4oa5DYDdKW7Wq6vdB7sM5PXWaLPK+tJuS1GM8YiIu+nmptXMvC0iPk3xbcC6FEPQZkfEnyiusauBSzLzD23GKPXIIR8a0TIzGpaxmbl1Zr4pM7/bYs/QE73s36BGiGtW1qs9Mo/VqLNHmXkxRe/UBRRjJwFeSDFk4RRgYUTcEhH79aH69SrrPQ2T6VL9nOt1Wwrojx703tQ8N3XbbyDPXaeuYTLzfyhm8biCYnw3FDd8voFiGMttEfHLiKj2kA6oTlw7bbqysj5YM1EM2PXWxNjKeiuPXO8xnsz8GMXwmd9UNm9BMWTudOD3ETE/Il7WZpxSt0yopYHX9YtmBcXcvI1JfE/L3Eo91Wn7qr+A+l1m3pyZb6SY0mt/iqEDV/FcEvlyYH5EHNFm1dVhKuu2UL76OQdiiEvbapybuu03mOeu6xpe1ub1G5l5ZWNlmXlVZu5DkUi/keIehd/wXIK9G8WUf9Nrxj1cPVhZrzXMpIZOXm/VZLzHByKVeo0nM8/PzFcBm1Pc/PkliilGoRgOsj9wXURs32asUlMm1NLA6/o6fTTFPMF9tbyy3pFfApn5aGb+JDM/npnTKb7K/Xy5O4DPRS9P5Gvw58r6ti2Ur5a5t43jDLg+nJu67TeY567rGt4imjx9s68y88HMvCAzTyyTn/9DMcc6FDPe9HgD5Gpso8r6Q4MUQyevt2r5bbotxcq5zLduteLMvDczz83MD2TmKyl+Bl9e7t6A5+4zkGoxoZYG3lWV9TfWqGcxz/VyzoiIdr9Wra1MgD5EMc0aFD2Mrfyy7Xr//RTzLwNMiojGh+c0+ufK+nUtBzoIWjg31Rv/DqJNg3zuuq7hUcC/1KyrW5l5D/B2iunaAKZExNoDdbwhbO/KeivjlwdC9Zp5bQvlq2Xavd6q5V/dS9ld6P6GxF6VNyceQjGnPDz/ZmGpz0yopYE3j2J2BIAPRsRL+lJJZj5L8RQ7KMYontQPsfXVssp6uzc3n1d53792V6j8g+F95cuk/ZvhBsuyynr13FwKdE0H9paI2KkPdQ/Wuft2Zf3jEdHKEIA+ycwVPL83f0TdPB8RMynmwYdi6MQ1gxTKtTz3h80BEdHtlIERcTDP/fF4TQsz0DS6hOd+Rs4qZ3/pzr+1WfcqMvNh4H/LlyPq+tLAMaGWBlhm/gn4cvlyI+CyiOj2a80ovCYi/r3J7s/w3HjDEyPiA+VXoM3qeWFE7N1sXw/H3ress9ub0MrYu3qjHqN4EE07vsxzN3J+JCLe1OQYYygeh9119/95gz3tVd1zU85V/uny5ZrART0l1RExKSK2bNg8KOcuM3/Dc8n8y8rYx3VXPiJGl/O6v69h+xERcVRPvc4R8SqKp+8B3D1A00N2XEQcHxE9Tl9YTqn59cqmz2bmkwMaWDcy82meG8I0GvhBRKwye0dEvAL4n8qmTzeWaeFYf+W5P9o2Bs5udo1ExHEU8/p3qzzPb4qINXooc2h5HICb241Xasa/zKTOOIlimqfXUMwnfVtE/JhiCqe/UIwXHQe8kiIh24xiFoRTqpVk5u8j4p0UPdUvAL4AvCMiuh6skBQ34fwTxU03P+D5Q056s2lZ539HxAKKG8XuprjzfmOKr1vfzHM3BX2hhRkiniczl5VTp51J8TPoh+W5mE8xXnRbiunousZJ3gMc284xBkh/nJs5FF8xH0Qxp/SNEXE+xawO91PMzzyRYrjGVIrZMFZO7zXI5+4dFMn0TmVcd5fX3a8opp0bQ3GOdi7jfxHwjYY6tgVOBr4cET+jmFP4TxTT0m1CMd/1GyiGlgB8qp9iHwpeDXwxIu6k+L+9hOLmw6CYlvBfKG7G7LKAgXl4Uzs+SxHXHhQPtVkSEWcBN1Bcf7tTDNHpmk7wa5l5SR+PdSKwH8X0kvsDt5bHWkpxY+bBFD8bfw88TDfT5lFcf18E/jcifkrxkK57KG54fQnFtblvWTYZ/HOs1UUOgcc1urh0cqGFR4+3UMfcSj0TWnzPmhQ9jCuqMfSwfKuHug4E/tpCHWe1EzvwthZj+wdFcrnK46rp5dHjlXLHUPS29nScW3o6v1QeH93C+W+5bDfvr31uynrWoJi669kW6tprKJy7ynvWoxjC1Mp5SOA/G97/8Rbf9zRwYo3/nxMqdc0d7GunrOOCNq6f/wHW6euxKsec1dt5aKGOsRQPaekt5i93d82X9XSVvbKHMi+jSJi7O86fKDodum0P4KwWz/NjwJF1z7GLS9diD7XUIVl8hfr+iPgi8E6KXr6tKXpfngbuo3jgxzXAxdnzo34vjoitgXcBB1BM1fYiimT9XooH01xC0UPdju8AN1H0pO9N8ejeTSl6Hx+j+GV3DUWifmObdTd+hjMj4hKKHtR9KZKgdSh67W4sY/9OFmPHh4J+OTdZPF7+2Ig4g6L9Xk0xR+56FGNmf0cxfvUHmdn0CYaDde6yGH5xeER8huIPjL0pZubYAHiSYmaIJRTfvPw4M+9uqOIU4DKKz7wXxVjhcRR/ZDwK/JaiZ/brWTzye3Xyb8DFFN8evYKiR35jip7ehyhuPrwG+GZmDtaNiKvIzMeAf4mIfSnafHeKNnuWouf3SuCr2feHPVWPdVdEvBz4AHAoxYwfSfEtzfnAlzLzgW5GuXU5BvgWxTW2J0WSvjHFtx4PAXdQPNXx65k5pGYO0vAWmTnYMUiSJEnDljclSpIkSTV0LKGOiLMi4v6IuLWb/RERX4qIpRGxOCJ27lRskiRJUl91sod6LsUdvN3Zn+IO8G2Bo4EzOhCTJEmSVEvHEurMvJrnHmrQzOuBb2fh18ALm815KUmSJA0lQ2kM9eYUU+J0WV5ukyRJkoasoTRtXrN5cJpOQRIRR1MMC2HttdeessUWWwxkXN16/PFBOWy/G7PGM7zgmWcGO4z+sc46A36I1aXdAcaM+QcveMFQ+rt6aLPtRy7bfuSy7VV11113PZCZL27cPpQS6uUUc7F2GU8xn+4qMvOrwFcBpk6dmgsXLhz46JroeSrM4WPBnM8y/YQTBjuM/tGBaSBXl3YHWLDgSqZPnz7YYQwbtv3IZduPXLa9qiLiD822D6U/Uy4E3lbO9vEq4OHM/PNgByVJkiT1pGM91BFxDjAd2DgilgMnUzwdi8w8E5gPvA5YCjwOHNWp2CRJkqS+6lhCnZkze9mfFI/RlSRJkoaNoTSGWpIkSW3acMNnmD17Odts8yQ93XO4wQYbcPvtt3cusGFszJgxjB8/njXWWKOl8ibUkiRJw9js2cuZNm09Ro+eQPNJ0wrbbPMo6623XsfiGq4ykwcffJDly5ez1VZbtfSeoXRToiRJktq0zTZPMnr0RvSUTKt1EcFGG23Ek08+2fJ7TKglSZKGsWKYh8l0f4o250s0oZYkSVKfPfjgg0yaNIlJkybxkpe8hM0333zl66effrrH9y5cuJDjjz++12Pstttu/RXugHAMtSRJ0mpkl12629O38dO9PTNto4024qabbgJg9uzZjB07lhMqD4xbsWIFo0c3TzmnTp3K1KlTe43h2muvbTnewWAPtSRJkvrVrFmz+NCHPsSMGTM48cQTue6669htt92YPHkyu+22G3feeScAV155JQceeCBQJOPveMc7mD59OltvvTVf+tKXVtY3duzYleWnT5/OIYccwnbbbccRRxxBlhn//Pnz2W677dhjjz04/vjjV9bbCfZQS5Ikqd/dddddXH755YwaNYpHHnmEq6++mtGjR3P55ZfzsY99jPPOO2+V99xxxx0sWLCARx99lIkTJ/Le9753lanrbrzxRpYsWcJmm23G7rvvzi9/+UumTp3Ke97zHq6++mq22morZs7s8fEn/c6EWpIkSf3u0EMPZdSoUQA8/PDDvP3tb+e3v/0tEcEzzzzT9D0HHHAAa621FmuttRabbLIJ9913H+PHj39emWnTpq3cNmnSJJYtW8bYsWPZeuutV05zN3PmTL761a8O4Kd7Pod8SJIkqd+tu+66K9f/4z/+gxkzZnDrrbdy0UUXdTsl3VprrbVyfdSoUaxYsaKlMtnbQO8BZkItSZKkAfXwww+z+eabAzB37tx+r3+77bbj7rvvZtmyZQCce+65/X6MnphQS5IkaUB95CMf4aSTTmL33Xfn2Wef7ff61157bb7yla+w3377scceezBu3Dg22GCDfj9Od2Kwu8jrmjp1ai5cuHBQjt3mnN9D1oI5n2V6ZXqbYa0D1/Pq0u4ACxYUd0urNbb9yGXbj1zDoe0vvfR2Nt54+17LTZy4ej96/LHHHmPs2LFkJsceeyzbbrstH/zgB/tc3+2338722z//vEbEosxcZZ4/e6glSZI07H3ta19j0qRJ7Ljjjjz88MO85z3v6dixneVDkiRJw94HP/jBWj3SddhDLUmSJNVgQi1JkiTVYEItSZIk1WBCLUmSJNVgQi1JkqQ+mz59Opdddtnztn3hC1/gfe97X7flu6Y8ft3rXsdDDz20SpnZs2czZ86cHo97wQUXcNttt618/fGPf5zLL7+8zej7h7N8SJIkrUam7tLPk2f38oyHmTNnMm/ePPbdd9+V2+bNm8epp57aa9Xz58/vc1gXXHABBx54IDvssAMA//mf/9nnuuqyh1qSJEl9dsghh3DxxRfz1FNPAbBs2TLuvfdevve97zF16lR23HFHTj755KbvnTBhAg888AAAp5xyChMnTmSfffbhzjvvXFnma1/7GrvssguvfOUredOb3sTjjz/Otddey4UXXsiHP/xhJk2axO9+9ztmzZrFD3/4QwCuuOIKJk+ezE477cQ73vGOlbFNmDCBk08+mZ133pmddtqJO+64o1/OgQm1JEmS+myjjTZi2rRp/OQnPwGK3unDDjuMU045hYULF7J48WKuuuoqFi9e3G0dixYtYt68edx444386Ec/4vrrr1+57+CDD+b666/n5ptvZvvtt+cb3/gGu+22GwcddBCnnnoqN910Ey996UtXln/yySeZNWsW5557LrfccgsrVqzgjDPOWLl/44035oYbbuC9731vr8NKWmVCLUmSpFq6hn1AkVDPnDmT73//++y8885MnjyZJUuWPG+8c6Nf/OIXvPGNb2SdddZh/fXX56CDDlq579Zbb2XPPfdkp5124uyzz2bJkiU9xnLnnXey1VZb8bKXvQyAt7/97Vx99dUr9x988MEATJkyhWXLlvX1Iz+PCbUkSZJqecMb3sAVV1zBDTfcwBNPPMGGG27InDlzuOKKK1i8eDEHHHAATz75ZI91RDQf+z1r1ixOO+00brnlFk4++eRe68lexnyvtdZaAIwaNYoVK1b0WLZVJtSSJEmqZezYsUyfPp13vOMdzJw5k0ceeYR1112XDTbYgPvuu49LL720x/fvtddenH/++TzxxBM8+uijXHTRRSv3Pfroo2y66aY888wznH322Su3r7feejz66KOr1LXddtuxbNkyli5dCsB3vvMd9t577376pM05y4ckSZJqmzlzJgcffDDz5s1ju+22Y/Lkyey4445svfXW7L777j2+d+edd+awww5j0qRJbLnlluy5554r933yk59k1113Zcstt2SnnXZamUQffvjhvPvd7+ZLX/rSypsRAcaMGcM3v/lNDj30UFasWMEuu+zCMcccMzAfuhS9dYsPdVOnTs2uuQw7rZtvJoadBXM+y/QTThjsMPpHB67n1aXdARYsuJLp06cPdhjDhm0/ctn2I9dwaPtLL72djTfevtdyEyc+ynrrrdeBiFYPt99+O9tv//zzGhGLMnNqY1mHfEiSJEk1mFBLkiRJNZhQS5IkSTWYUEuSJA1j//gHwPC+J26oafceQxNqSZKkYWzp0jGsWPEgJtX9IzN58MEHGTNmTMvvcdo8SZKkYWz27PHMnr2cbbb5Ky/ooat01Kgn20oSR7IxY8Ywfvz4lsubUEuSJA1j//u/a/CBD2zVa7kFC65k8uTJHYho5HHIhyRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUQ0cT6ojYLyLujIilEfHRJvs3jIjzI2JxRFwXES/vZHySJElSuzqWUEfEKOB0YH9gB2BmROzQUOxjwE2Z+QrgbcAXOxWfJEmS1Bed7KGeBizNzLsz82lgHvD6hjI7AFcAZOYdwISIGNfBGCVJkqS2RGZ25kARhwD7Zea7ytdHArtm5nGVMp8CxmTmhyJiGnBtWWZRQ11HA0cDjBs3bsq8efM68hkaLVrUe5nhYOL4+xi7fPlgh9E/pkwZ8EOsLu0OMHHiY4wdO3awwxg2bPuRy7YfuWx7Vc2YMWNRZk5t3N7JhPpQYN+GhHpaZr6/UmZ9imEek4FbgO2Ad2Xmzd3VO3Xq1Fy4cOGAxt6diEE5bL9bMOezTD/hhMEOo3904HpeXdodYMGCK5k+ffpghzFs2PYjl20/ctn2qoqIpgn16A7GsBzYovJ6PHBvtUBmPgIcBRARAfy+XCRJkqQhqZNjqK8Hto2IrSJiTeBw4MJqgYh4YbkP4F3A1WWSLUmSJA1JHeuhzswVEXEccBkwCjgrM5dExDHl/jOB7YFvR8SzwG3AOzsVnyRJktQXnRzyQWbOB+Y3bDuzsv4rYNtOxiRJkiTV4ZMSJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKilkWzRouIxYKvDIknSIDGhliRJkmowoZYkSZJqMKGWpJHI4T6S1G9MqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUkaSZzhZeRaXdp+CDKhliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSauhoQh0R+0XEnRGxNCI+2mT/BhFxUUTcHBFLIuKoTsYnSZIktatjCXVEjAJOB/YHdgBmRsQODcWOBW7LzFcC04HPRsSanYpRkiRJalcne6inAUsz8+7MfBqYB7y+oUwC60VEAGOBvwErOhijJEmS1JbIzM4cKOIQYL/MfFf5+khg18w8rlJmPeBCYDtgPeCwzLykSV1HA0cDjBs3bsq8efM68AlWtWjRoBy2300cfx9jly8f7DD6x5QpA36I1aXdwbZvl20/RNn2bbHt22PbD0EdaPfuzJgxY1FmTl1lR2Z2ZAEOBb5eeX0k8OWGMocAnwcC2Ab4PbB+T/VOmTIlBwusHsuCOXMGP4j+Wmx32962t+1te9vetl+9234QAQszV81HOznkYzmwReX1eODehjJHAT8qY15KkVBv16H4JEmSpLZ1MqG+Htg2IrYqbzQ8nGJ4R9UfgdcARMQ4YCJwdwdjlCRJktoyulMHyswVEXEccBkwCjgrM5dExDHl/jOBTwJzI+IWimEfJ2bmA52KUZIkSWpXxxJqgMycD8xv2HZmZf1e4J87GZMkSZJUh09KlCRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmoY3e4bImJX4DXAJjQk5Jl5fD/FJUmSJA0LbSXUEXEC8N/AUuBeICu7s+mbJEmSpNVYuz3UHwCOz8zTBiIYSZIkabhpdwz1+sD8gQhEkiRJGo7aTajPAfYbiEAkSZKk4ajdIR9/Aj4REbsDi4Fnqjsz83P9FZgkSZI0HLSbUL8LeAzYrVyqEjChliRJ0ojSVkKdmVsNVCCSJEnScNTnB7tExNiIWLc/g5EkSZKGm7YT6og4NiL+CDwMPBIRf4iI9/V/aJIkSdLQ1+6DXT4GnATMAa4pN+8JfDoi1s/MT/dzfJIkSdKQ1u5NiccAR2fmOZVtV0TEb4FPASbUkiRJGlHaHfKxCXB9k+3XAePqhyNJkiQNL+0m1HcBb2my/S3AnfXDkSRJkoaXdod8zAa+HxF7Ab+kmHt6D2Bv4ND+DU2SJEka+trqoc7MHwG7An8BDgQOKtenZeYF/R6dJEmSNMS120NNZi4C3joAsUiSJEnDTq8JdUS8KDP/1rXeU9mucpIkSdJI0UoP9V8jYtPMvB94gGLcdKMot4/qz+AkSZKkoa6VhPrVQFfP84wBjEWSJEkadnpNqDPzqmbrkiRJktqc5SMidoiIiZXXr42I70bESRHhcA9JkiSNOO0+2OUbwGSAiBgP/Bh4EXAs8F/9G5okSZI09LWbUG8P3FCuHwr8JjNfBxwJzOztzRGxX0TcGRFLI+KjTfZ/OCJuKpdbI+LZ3mYWkSRJkgZTuwn1KODpcv01wPxy/XfAuJ7eWA4JOR3YH9gBmBkRO1TLZOapmTkpMycBJwFXORWfJEmShrJ2E+pbgfdGxJ4UCfVPyu2bU0yp15NpwNLMvDsznwbmAa/vofxM4Jw245MkSZI6qt2E+kTg3cCVwDmZeUu5/SDgul7euznwp8rr5eW2VUTEOsB+wHltxidJkiR1VGQ2e05LD28ohm6sn5n/W9k2AXi8fPhLd+87FNg3M99Vvj4SmJaZ729S9jDgrZn5L93UdTRwNMC4ceOmzJs3r63P0F8WLRqUw/a7iePvY+zy5YMdRv+YMmXAD7G6tDvY9u2y7Yco274ttn17bPshqAPt3p0ZM2Ysysypq+zIzI4swD8Bl1VenwSc1E3Z84G3tFLvlClTcrDA6rEsmDNn8IPor8V2t+1te9vetrftbfvVu+0HEbAwc9V8tNcHu0TEhRS9xY+U693KzIN62H09sG1EbAXcAxwOvKXJ8TYA9gbe2ltskiRJ0mBr5dHjDwJZWe+TzFwREccBl1HMFnJWZi6JiGPK/WeWRd8I/DQz/97XY0mSJEmd0sqjx49qtt4XmTmf56ba69p2ZsPrucDcOseRJEmSOqXdR4+/pHxCYuP28RHR4zzUkiRJ0uqo3WnzvkPxYJZG+5b7JEmSpBGl3YR6F+DqJtt/Aaw6hYgkSZK0mms3oR4NrNVk+5hutkuSJEmrtXYT6t8A722y/ViKafEkSZKkEaWVafOq/h34eUS8Erii3PZqYDKwT38GJkmSJA0HbfVQZ+avKZ54+HvgYOBN5fo/Zea1/R+eJEmSNLS120NNZt4MHDEAsUiSJEnDTrtjqImIcRFxQkR8JSI2LrftXj5SXJIkSRpR2n2wyxTgTooe6ncB65e7Xguc0r+hSZIkSUNfuz3Uc4AvZuZk4KnK9suA3fstKkmSJGmYaDehngJ8q8n2PwM+elySJEkjTrsJ9RPAhk22bwfcXz8cSZIkaXhpN6H+MXByRHQ9FTEjYgLwGeC8/gxMkiRJGg7aTahPAF4E/BVYB7gGWAo8BPzffo1MkiRJGgbanYd6BTAd2AvYmSIhvyEzL+/nuCRJkqRhoeWEOiJGAQ8Dr8zMnwM/H7CoJEmSpGGi5SEfmfks8AdgzYELR5IkSRpe2h1D/Ung011PSJQkSZJGunbHUJ8AbAXcExHLgb9Xd2bmK/orMEmSJGk4aDeh/iGQQAxALJIkSdKw01JCHRHrAKcCbwDWAK4A3p+ZDwxcaJIkSdLQ1+oY6k8As4BLgHOAfYAzBigmSZIkadhodcjHwcA7M3MeQEScDfwyIkaVs39IkiRJI1KrPdRbAL/oepGZ11E85GWzgQhKkiRJGi5aTahHAU83bFtB+zc1SpIkSauVVhPiAL4bEU9Vto0BvhYRj3dtyMyD+jM4SZIkaahrNaH+VpNt3+3PQCRJkqThqKWEOjOPGuhAJEmSpOGo3UePS5IkSaowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJqMKGWJEmSajChliRJkmowoZYkSZJq6GhCHRH7RcSdEbE0Ij7aTZnpEXFTRCyJiKs6GZ8kSZLUrtGdOlBEjAJOB14LLAeuj4gLM/O2SpkXAl8B9svMP0bEJp2KT5IkSeqLTvZQTwOWZubdmfk0MA94fUOZtwA/ysw/AmTm/R2MT5IkSWpbZGZnDhRxCEXP87vK10cCu2bmcZUyXwDWAHYE1gO+mJnfblLX0cDRAOPGjZsyb968gf8ATSxaNCiH7XcTx9/H2OXLBzuM/jFlyoAfYnVpd7Dt22XbD1G2fVts+/bY9kNQB9q9OzNmzFiUmVNX2ZGZHVmAQ4GvV14fCXy5ocxpwK+BdYGNgd8CL+up3ilTpuRggdVjWTBnzuAH0V+L7W7b2/a2vW1v29v2q3fbDyJgYeaq+WjHxlBTjJveovJ6PHBvkzIPZObfgb9HxNXAK4G7OhOiJEmS1J5OjqG+Htg2IraKiDWBw4ELG8r8GNgzIkZHxDrArsDtHYxRkiRJakvHeqgzc0VEHAdcBowCzsrMJRFxTLn/zMy8PSJ+AiwG/kExROTWTsUoSZIktauTQz7IzPnA/IZtZza8PhU4tZNxSZIkSX3lkxIlSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaTKglSZKkGkyoJUmSpBpMqCVJkqQaOppQR8R+EXFnRCyNiI822T89Ih6OiJvK5eOdjE+SJElq1+hOHSgiRgGnA68FlgPXR8SFmXlbQ9FfZOaBnYpLkiRJqqOTPdTTgKWZeXdmPg3MA17fweNLkiRJ/S4yszMHijgE2C8z31W+PhLYNTOPq5SZDpxH0YN9L3BCZi5pUtfRwNEA48aNmzJv3rwBj7+ZRYsG5bD9buL4+xi7fPlgh9E/pkwZ8EOsLu0Otn27bPshyrZvi23fHtt+COpAu3dnxowZizJz6io7MrMjC3Ao8PXK6yOBLzeUWR8YW66/Dvhtb/VOmTIlBwusHsuCOXMGP4j+Wmx32962t+1te9vetl+9234QAQszV81HOznkYzmwReX1eIpe6JUy85HMfKxcnw+sEREbdy5ESZIkqT2dTKivB7aNiK0iYk3gcODCaoGIeElERLk+rYzvwQ7GKEmSJLWlY7N8ZOaKiDgOuAwYBZyVmUsi4phy/5nAIcB7I2IF8ARweNm9LkmSJA1JHUuoYeUwjvkN286srJ8GnNbJmCRJkqQ6fFKiJEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUgwm1JEmSVIMJtSRJklSDCbUkSZJUQ0cT6ojYLyLujIilEfHRHsrtEhHPRsQhnYxPkiRJalfHEuqIGAWcDuwP7ADMjIgduin3GeCyTsUmSZIk9VUne6inAUsz8+7MfBqYB7y+Sbn3A+cB93cwNkmSJKlPOplQbw78qfJ6ebltpYjYHHgjcGYH45IkSZL6LDKzMweKOBTYNzPfVb4+EpiWme+vlPkB8NnM/HVEzAUuzswfNqnraODo8uVE4M6Bjn81tzHwwGAHoUFh249ctv3IZduPXLZ9fVtm5osbN47uYADLgS0qr8cD9zaUmQrMiwgoGv11EbEiMy+oFsrMrwJfHbhQR5aIWJiZUwc7DnWebT9y2fYjl20/ctn2A6eTCfX1wLYRsRVwD3A48JZqgczcqmu90kN9QQdjlCRJktrSsYQ6M1dExHEUs3eMAs7KzCURcUy533HTkiRJGnY62UNNZs4H5jdsa5pIZ+asTsQkwOEzI5ltP3LZ9iOXbT9y2fYDpGM3JUqSJEmrIx89LkmSJNVgQj2MRcRZEXF/RNzaQtnpEbFbN/tmRcRfI+Kmcvl2L3U91s32uT4ufuBExBYRsSAibo+IJRHxgT7UcWVErHKHd7n9zso10G07ltfLad3sa3ptqL6IGBMR10XEzWX7f6LF901o9jOi3P5Epc1viog1e6inu2un2+tB/SsiRkXEjRFxcWXbrIjYrPJ6WURs3Es90yPi4Uq7X95L+aZ1RsTsiDihL59FrSnP/S1lOy2sbLfdh5iOjqFWv5sLnAb0mACXpgOPAdd2s//czDyuf8LSAFkB/Ftm3hAR6wGLIuJnmXlbP9V/RGYu7L2YBslTwKsz87GIWAO4JiIuzcxfVwtFxOjMXNFinb/LzEn9HagGzAeA24H1K9tmAbey6jS0vflFZh7YT3FpYM3IzMa5o2dhuw8p9lAPY5l5NfC3xu0RcXxE3BYRiyNiXkRMAI4BPlj+VbpnK/VHxIci4tZy+dcm+yMiTiuPdQmwSb1PpJ5k5p8z84Zy/VGKX6ybw8rew8+UPZh3dbVxRKxdXgOLI+JcYO1WjxcRL4qIC8r3/joiXtGkzFYR8auIuD4iPtkvH1RNZaHrG4A1yiVhZft/KiKuAj4QEVPKnuxfAce2c5yIeE3ZC3pLFN+CrdWkzFHldXYVsHu9T6ZWRMR44ADg65Vth1A8v+Hs8md71//v90fEDWUbbtfGMWaW77k1Ij7TTZl/j+LbrMspHqymDrPdhyYT6tXTR4HJmfkK4JjMXEbxOPfPZ+akzPxFk/ccVvka6KiImAIcBewKvAp4d0RMbnjPGyn+Y+0EvBtoOqRE/a/8I2ky8JvK5tGZOQ34V+Dkctt7gcfLa+EUYEoP1Z5duQY2Aj4B3Fi+92M0/ybki8AZmbkL8JcaH0ktiOIr/5uA+4GfZWa1/V+YmXtn5meBbwLHZ+Y/9VLlSyttfnpEjKH45uuwzNyJ4lvM9zbEsCnFtbE78Fpgh/74bOrVF4CPAP/o2lA+SXghxbdLkzLziXLXA5m5M3AG0N1X83tW2v7fy+EDnwFeDUwCdomIN1TfUP5eOJziZ8/BwC799NnUvQR+GhGLonhKtO0+RJlQr54WUyRHb6UYJtCKc8v/mJMy85vAHsD5mfn3slfsR0Bjz/ZewDmZ+Wxm3gv8vL8+gLoXEWOB84B/zcxHKrt+VP67CJhQru8FfBcgMxdTXBvdOaJyDTxIcQ18p3zvz4GNImKDhvfsDpxTrn+nb59IrSr/r02ieNLstIh4eWX3uQBlG70wM68qt/fULr+rtPmxFH8g/z4z7yr3f4viGqraFbgyM/+amU93HVcDJyIOBO7PzEUtvqXZz4JGv6i0/SkUSVJXu64AzmbVtt+T4vfC4+XPngvb+iDqi93LJHl/4NiIaGyTKtt9EJlQr54OAE6n6I1cFBF9GSsfLZZz3sUOKsfOngecnZk/atj9VPnvszz//oi+tlGza6BZXV4DHZaZDwFXAvtVNv+9/Dfo3zZvGkIf61ff7A4cFBHLgHnAqyPiuz2U7+5nQU9s+yGo7KwiM+8Hzgem9VDcdh9EJtSrmYh4AbBFZi6g+HrwhcBY4FFgvTaquhp4Q0SsExHrUgzvaBwqcjVwePk19KbAjLrxq3sREcA3gNsz83Mtvu1q4Ijy/S8HVhkH3eJ7p1N8nfhIQ5lfUnwVSFdZDYyIeHFEvLBcXxvYB7ijsVyZbD8cEXuUm9pplzuACRGxTfn6SOCqhjK/AaZHxEblH3iHtlG/+iAzT8rM8Zk5geL/288z863l7nZ/tnfnN8DeEbFxRIwCZrJq218NvLG8N2M94F/64bjqRkSsW55nyt/D/0xxIyLY7kOOs3wMYxFxDsXsHRtHxHKKcbPfBr5bfu0bFOOmH4qIi4AfRsTrgfd3M456pXImibnAdeWmr2fmjQ3FzqcYd3ULcBer/idU/9qdIsG5pRxHC/Cx8gmk3TkD+GZELAZu4rn2bMXsynsfB97epMwHgO9FMYXfeW3UrfZtCnyr/KX3AuD7mXlxN2WPAs6KiMeBy1o9QGY+GRFHAT8ov9m6nuL+i2qZP0fEbOBXwJ+BG4BR7X4Y9Zu5wJkR8QTQ25j5bpXtehKwgOJ3x/zM/HFDmRuiuLn5JuAPrNrJov41Dji/6EthNPC9zPxJuW8utvuQ4pMSJUmSpBoc8iFJkiTVYEItSZIk1WBCLUmSJNVgQi1JkiTVYEItSZIk1WBCLUnDRETMjohbey9ZPJ4+IjIipg50XJI00plQS1INETG3TFwzIp6JiPsjYkFEHFs++KQ/zQH2brHsnyjmrr6pn2NYqeGzN10G6tiSNJQ4D7Uk1VA+AGlziofujAJeTPHAo48BS4HXZObfu61gGCsfILV2ZdPvKD73uV0bMvMvlfJrZubTnYtQkjrDHmpJqu+pzPxLZt6TmTeVj4afDuwMfKSrUESsGRGfiYjlEfH3iLg+IvatVhQR20XEhRHxcEQ8FhG/ioidyn3PG/IRETtFxBUR8UhEPBoRN0fEjHLfKkM+ImKviPhNRDwZEfdFxOcjYs3K/isj4isR8amIeKDsbZ8TEU1/V2Tmw+Xn/kuZOCfwcOX1vIg4o6zjrxSPqicidoiIS8qY74+IcyLiJQ3n4aiIuK2M9a6I+GB3cUjSYPOHkyQNgMy8FfgJ8KbK5m9SDNl4C7AT8C3gooh4JUBEbAZcQ5GYvpYiIT+d7h/t/T2Kx39PAyZTPC7+yWYFI2Jz4FLgxrLsO4GZwP9rKHoEsALYDTgO+FfgsFY+czfeSvFI4z2Bt0XEpsDVwK1l3PsAY4ELuxLmiHg38Cng48D2wL8BJwLvqxGHJA2Y0YMdgCStxm6jSBiJiJdSJLATMvOP5f7TImIf4D0UyeKxwN+BQytDI+7qof4tgTmZeUf5emkPZd9HkXy/LzP/AdweER8F/ici/iMzH++KOTM/3nXsMrl9DXBOax95Fb/PzH/rehER/wncnJknVra9DfgbMBW4DvgP4COZ+cOuOiLi0+VnOK2PcUjSgDGhlqSBExS9zVD0NgdwW0RUy6wF/Lxcnwxc08Y4488BX4+ItwNXAOdVkutG2wO/KpPpLtcAawLbAIvLbYsb3ncvsEmL8TSzqOH1FGCviHisSdmXRsTvgS0oEv0zKvtGU5w/SRpyTKglaeDsANxdrr+AIrneBXimodwT5b9tJYyZOTsizgb2B/YFTo6IYzLzrCbFq8n9KlVV1htjS+oND2y8IfMFwCXACU3K3gesU64fA1xb47iS1DEm1JI0ACLi5cB+wH+Vm26kSGpfkpkLunnbDcBb25kNIzN/C/wW+FLZo/suoFlCfRvw5oh4QaWXeg/gaYrZOTrlBuDNwB8yszF5B3g0Iu4BXpqZ3+5gXJLUZ96UKEn1rRURL4mIzSLilRHxIeBKiuEOcwAy8y7gbGBuRBwSEVtHxNSIOCEiDi7r+QrFDXrfj4hdImKbiJgZEZMaDxgRa0fE6RExvZzRY1eKBPm2bmL8CrAZ8JWI2D4iDgA+DZxWGT/dCacDGwDnRsSu5XnYJyK+GhHrlWVmAx8pZ/aYGBEvj4i3RcRJHYxTklpmD7Uk1bcPxQ1/zwIPUcxg8Qngfxp6mo8C/h34b2A8xY141wELADLznojYCzi13JbALcDRTY75LLAhxUwhLwEeBC6m+VCKrrr3L+u+qYzzexTzRndMZt4bEbtTzC7yE2AM8Efgp8BTZZmvR8TfgQ+X5Z4AluANiZKGKB/sIkmSJNXgkA9JkiSpBhNqSZIkqQYTakmSJKkGE2pJkiSpBhNqSZIkqQYTakmSJKkGE2pJkiSpBhNqSZIkqQYTakmSJKmG/w9z2GcO5y6OxQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Precision Result\n", + "ml.plot_result(model_name, \"Precision\", \"Precision scores in 5 Folds\", decision_tree_result[\"Training Precision scores\"], decision_tree_result[\"Validation Precision scores\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall Results plot" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Recall Result\n", + "ml.plot_result(model_name, \"Recall\", \"Recall scores in 5 Folds\", decision_tree_result[\"Training Recall scores\"], decision_tree_result[\"Validation Recall scores\"])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "F1 Score Results" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot F1-Score Result\n", + "ml.plot_result(model_name, \"F1\", \"F1 Scores in 5 Folds\", decision_tree_result[\"Training F1 scores\"], decision_tree_result[\"Validation F1 scores\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is overfitting as it is working well on the training data but not on the validation set.\n", + "We will adjust the min_samples_split hyperparameter to fix this." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Fine tunin the min_samples_split parameter\n", + "decision_tree_model_2 = DecisionTreeClassifier(criterion=\"entropy\",\n", + " min_samples_split=4,\n", + " random_state=0)\n", + "decision_tree_result_2 = ml.cross_validation(decision_tree_model_2, X, y, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Accuracy Result\n", + "ml.plot_result(model_name, \"Accuracy\", \"Accuracy scores in 5 Folds\", decision_tree_result_2[\"Training Accuracy scores\"], decision_tree_result_2[\"Validation Accuracy scores\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Precision Result\n", + "ml.plot_result(model_name, \"precision\", \"precision scores in 5 Folds\", decision_tree_result_2[\"Training Precision scores\"], decision_tree_result_2[\"Validation Precision scores\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Recall Result\n", + "ml.plot_result(model_name, \"Recall\", \"Recall scores in 5 Folds\", decision_tree_result_2[\"Training Recall scores\"], decision_tree_result_2[\"Validation Recall scores\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot F1-Score Result\n", + "ml.plot_result(model_name, \"F1\", \"F1 Scores in 5 Folds\", decision_tree_result_2[\"Training F1 scores\"], decision_tree_result_2[\"Validation F1 scores\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "75f376effe886d8b51843b632a4b92665fc33b9300797299f553ce70034dd324" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/time_sc.sh b/time_sc.sh new file mode 100644 index 0000000..3974640 --- /dev/null +++ b/time_sc.sh @@ -0,0 +1,182 @@ +#!/usr/bin/env bash +# Use this script to test if a given TCP host/port are available + +WAITFORIT_cmdname=${0##*/} + +echoerr() { if [[ $WAITFORIT_QUIET -ne 1 ]]; then echo "$@" 1>&2; fi } + +usage() +{ + cat << USAGE >&2 +Usage: + $WAITFORIT_cmdname host:port [-s] [-t timeout] [-- command args] + -h HOST | --host=HOST Host or IP under test + -p PORT | --port=PORT TCP port under test + Alternatively, you specify the host and port as host:port + -s | --strict Only execute subcommand if the test succeeds + -q | --quiet Don't output any status messages + -t TIMEOUT | --timeout=TIMEOUT + Timeout in seconds, zero for no timeout + -- COMMAND ARGS Execute command with args after the test finishes +USAGE + exit 1 +} + +wait_for() +{ + if [[ $WAITFORIT_TIMEOUT -gt 0 ]]; then + echoerr "$WAITFORIT_cmdname: waiting $WAITFORIT_TIMEOUT seconds for $WAITFORIT_HOST:$WAITFORIT_PORT" + else + echoerr "$WAITFORIT_cmdname: waiting for $WAITFORIT_HOST:$WAITFORIT_PORT without a timeout" + fi + WAITFORIT_start_ts=$(date +%s) + while : + do + if [[ $WAITFORIT_ISBUSY -eq 1 ]]; then + nc -z $WAITFORIT_HOST $WAITFORIT_PORT + WAITFORIT_result=$? + else + (echo -n > /dev/tcp/$WAITFORIT_HOST/$WAITFORIT_PORT) >/dev/null 2>&1 + WAITFORIT_result=$? + fi + if [[ $WAITFORIT_result -eq 0 ]]; then + WAITFORIT_end_ts=$(date +%s) + echoerr "$WAITFORIT_cmdname: $WAITFORIT_HOST:$WAITFORIT_PORT is available after $((WAITFORIT_end_ts - WAITFORIT_start_ts)) seconds" + break + fi + sleep 1 + done + return $WAITFORIT_result +} + +wait_for_wrapper() +{ + # In order to support SIGINT during timeout: http://unix.stackexchange.com/a/57692 + if [[ $WAITFORIT_QUIET -eq 1 ]]; then + timeout $WAITFORIT_BUSYTIMEFLAG $WAITFORIT_TIMEOUT $0 --quiet --child --host=$WAITFORIT_HOST --port=$WAITFORIT_PORT --timeout=$WAITFORIT_TIMEOUT & + else + timeout $WAITFORIT_BUSYTIMEFLAG $WAITFORIT_TIMEOUT $0 --child --host=$WAITFORIT_HOST --port=$WAITFORIT_PORT --timeout=$WAITFORIT_TIMEOUT & + fi + WAITFORIT_PID=$! + trap "kill -INT -$WAITFORIT_PID" INT + wait $WAITFORIT_PID + WAITFORIT_RESULT=$? + if [[ $WAITFORIT_RESULT -ne 0 ]]; then + echoerr "$WAITFORIT_cmdname: timeout occurred after waiting $WAITFORIT_TIMEOUT seconds for $WAITFORIT_HOST:$WAITFORIT_PORT" + fi + return $WAITFORIT_RESULT +} + +# process arguments +while [[ $# -gt 0 ]] +do + case "$1" in + *:* ) + WAITFORIT_hostport=(${1//:/ }) + WAITFORIT_HOST=${WAITFORIT_hostport[0]} + WAITFORIT_PORT=${WAITFORIT_hostport[1]} + shift 1 + ;; + --child) + WAITFORIT_CHILD=1 + shift 1 + ;; + -q | --quiet) + WAITFORIT_QUIET=1 + shift 1 + ;; + -s | --strict) + WAITFORIT_STRICT=1 + shift 1 + ;; + -h) + WAITFORIT_HOST="$2" + if [[ $WAITFORIT_HOST == "" ]]; then break; fi + shift 2 + ;; + --host=*) + WAITFORIT_HOST="${1#*=}" + shift 1 + ;; + -p) + WAITFORIT_PORT="$2" + if [[ $WAITFORIT_PORT == "" ]]; then break; fi + shift 2 + ;; + --port=*) + WAITFORIT_PORT="${1#*=}" + shift 1 + ;; + -t) + WAITFORIT_TIMEOUT="$2" + if [[ $WAITFORIT_TIMEOUT == "" ]]; then break; fi + shift 2 + ;; + --timeout=*) + WAITFORIT_TIMEOUT="${1#*=}" + shift 1 + ;; + --) + shift + WAITFORIT_CLI=("$@") + break + ;; + --help) + usage + ;; + *) + echoerr "Unknown argument: $1" + usage + ;; + esac +done + +if [[ "$WAITFORIT_HOST" == "" || "$WAITFORIT_PORT" == "" ]]; then + echoerr "Error: you need to provide a host and port to test." + usage +fi + +WAITFORIT_TIMEOUT=${WAITFORIT_TIMEOUT:-15} +WAITFORIT_STRICT=${WAITFORIT_STRICT:-0} +WAITFORIT_CHILD=${WAITFORIT_CHILD:-0} +WAITFORIT_QUIET=${WAITFORIT_QUIET:-0} + +# Check to see if timeout is from busybox? +WAITFORIT_TIMEOUT_PATH=$(type -p timeout) +WAITFORIT_TIMEOUT_PATH=$(realpath $WAITFORIT_TIMEOUT_PATH 2>/dev/null || readlink -f $WAITFORIT_TIMEOUT_PATH) + +WAITFORIT_BUSYTIMEFLAG="" +if [[ $WAITFORIT_TIMEOUT_PATH =~ "busybox" ]]; then + WAITFORIT_ISBUSY=1 + # Check if busybox timeout uses -t flag + # (recent Alpine versions don't support -t anymore) + if timeout &>/dev/stdout | grep -q -e '-t '; then + WAITFORIT_BUSYTIMEFLAG="-t" + fi +else + WAITFORIT_ISBUSY=0 +fi + +if [[ $WAITFORIT_CHILD -gt 0 ]]; then + wait_for + WAITFORIT_RESULT=$? + exit $WAITFORIT_RESULT +else + if [[ $WAITFORIT_TIMEOUT -gt 0 ]]; then + wait_for_wrapper + WAITFORIT_RESULT=$? + else + wait_for + WAITFORIT_RESULT=$? + fi +fi + +if [[ $WAITFORIT_CLI != "" ]]; then + if [[ $WAITFORIT_RESULT -ne 0 && $WAITFORIT_STRICT -eq 1 ]]; then + echoerr "$WAITFORIT_cmdname: strict mode, refusing to execute subprocess" + exit $WAITFORIT_RESULT + fi + exec "${WAITFORIT_CLI[@]}" +else + exit $WAITFORIT_RESULT +fi \ No newline at end of file diff --git a/train/AdSmartABdata.csv b/train/AdSmartABdata.csv deleted file mode 100644 index 3c69d45..0000000 --- a/train/AdSmartABdata.csv +++ /dev/null @@ -1,18 +0,0 @@ -,experiment,hour,platform_os,browser,day_of_week,brand,response -0,exposed,8,5,Mobile Safari,Friday,known brand,0 -1,control,15,5,Mobile Safari,Friday,known brand,0 -2,control,15,5,Mobile Safari UI/WKWebView,Friday,known brand,0 -3,control,3,5,Mobile Safari,Saturday,known brand,1 -4,exposed,3,5,Mobile Safari,Sunday,known brand,1 -5,exposed,5,5,Mobile Safari,Wednesday,known brand,0 -6,control,14,5,Mobile Safari UI/WKWebView,Friday,known brand,0 -7,control,12,5,Mobile Safari,Saturday,known brand,0 -8,control,1,5,Mobile Safari,Monday,known brand,0 -9,control,9,5,Mobile Safari,Thursday,known brand,1 -10,control,15,5,Mobile Safari,Friday,known brand,0 -11,control,15,5,Mobile Safari,Friday,known brand,0 -12,control,11,5,Mobile Safari,Saturday,known brand,0 -13,exposed,2,5,Mobile Safari,Thursday,known brand,0 -14,control,5,5,Mobile Safari,Thursday,known brand,1 -15,control,11,5,Chrome Mobile iOS,Saturday,known brand,1 -16,control,17,5,Mobile Safari UI/WKWebView,Tuesday,known brand,0 diff --git a/train/decision_tree_accuracy.png b/train/decision_tree_accuracy.png deleted file mode 100644 index eb00173..0000000 Binary files a/train/decision_tree_accuracy.png and /dev/null differ diff --git a/train/decision_tree_f1_score.png b/train/decision_tree_f1_score.png deleted file mode 100644 index 8338763..0000000 Binary files a/train/decision_tree_f1_score.png and /dev/null differ diff --git a/train/decision_tree_preicision.png b/train/decision_tree_preicision.png deleted file mode 100644 index 41d53c8..0000000 Binary files a/train/decision_tree_preicision.png and /dev/null differ diff --git a/train/decision_tree_recall.png b/train/decision_tree_recall.png deleted file mode 100644 index eb5c742..0000000 Binary files a/train/decision_tree_recall.png and /dev/null differ diff --git a/train/decisiontree.py b/train/decisiontree.py index 8110494..84d7815 100644 --- a/train/decisiontree.py +++ b/train/decisiontree.py @@ -27,18 +27,10 @@ from preprocess import Preprocess from ml import Ml -# from ml import Ml -# from ml import Ml -# from preprocess import Preprocess -# from preprocess import Preprocess -# from ml import Ml ml = Ml() preprocess = Preprocess() -# gauth = GoogleAuth() -# gauth.LocalWebserverAuth() - # path = 'data/AdSmartABdata.csv' # repo = 'https://github.com/abtesting10academy/abtest-mlops' # version = 'ea64953afc441740caf168dbfaf6ce3ad1bd1d16' @@ -55,104 +47,113 @@ # import AdSmartABdata.csv data = pd.read_csv('data/AdSmartABdata.csv', sep=',') +mlflow.set_experiment('SmartAD data analysis') + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + mlflow.log_param('data_url', data) + mlflow.log_param('input_rows', data.shape[0]) + mlflow.log_param('input_cols', data.shape[1]) + mlflow.log_param('model_type', 'Decision Tree') + mlflow.log_param('model_parameters', 'n_estimators=100') -# change the date column to datetime -data = preprocess.convert_to_datetime(data, 'date') -numerical_column = preprocess.get_numerical_columns(data) -categorical_column = preprocess.get_categorical_columns(data) + # change the date column to datetime + data = preprocess.convert_to_datetime(data, 'date') + numerical_column = preprocess.get_numerical_columns(data) + categorical_column = preprocess.get_categorical_columns(data) -# drop auction_id from categorical_column -categorical_column.remove('auction_id') + # drop auction_id from categorical_column + categorical_column.remove('auction_id') -# Get column names have less than 10 more than 2 unique values -to_one_hot_encoding = [col for col in categorical_column if data[col].nunique( -) <= 10 and data[col].nunique() > 2] + # Get column names have less than 10 more than 2 unique values + to_one_hot_encoding = [col for col in categorical_column if data[col].nunique( + ) <= 10 and data[col].nunique() > 2] -# Get Categorical Column names thoose are not in "to_one_hot_encoding" -to_label_encoding = [ - col for col in categorical_column if not col in to_one_hot_encoding] + # Get Categorical Column names thoose are not in "to_one_hot_encoding" + to_label_encoding = [ + col for col in categorical_column if not col in to_one_hot_encoding] -# Label encoding -label_encoded_columns = preprocess.label_encode(data, to_label_encoding) + # Label encoding + label_encoded_columns = preprocess.label_encode(data, to_label_encoding) -# Select relevant rows + # Select relevant rows -# Copy our DataFrame to X variable -X = data.copy() + # Copy our DataFrame to X variable + X = data.copy() -# Droping Categorical Columns, -# "inplace" means replace our data with new one -# Don't forget to "axis=1" -X.drop(categorical_column, axis=1, inplace=True) + # Droping Categorical Columns, + # "inplace" means replace our data with new one + # Don't forget to "axis=1" + X.drop(categorical_column, axis=1, inplace=True) -# Merge DataFrames -X = pd.concat([X, label_encoded_columns], axis=1) + # Merge DataFrames + X = pd.concat([X, label_encoded_columns], axis=1) -# Select only rows with responses -X = X.query('yes == 1 | no == 1') + # Select only rows with responses + X = X.query('yes == 1 | no == 1') -# Drop auction_id column -X.drop(["auction_id"], axis=1, inplace=True) + # Drop auction_id column + X.drop(["auction_id"], axis=1, inplace=True) -# Split data + # Split data -X['target'] = [1] * X.shape[0] -X.loc[X['no'] == 1, 'target'] = 0 -y = X['target'] -X.drop(["target"], axis=1, inplace=True) -X.drop(['yes', 'no'], axis=1, inplace=True) + X['target'] = [1] * X.shape[0] + X.loc[X['no'] == 1, 'target'] = 0 + y = X['target'] + X.drop(["target"], axis=1, inplace=True) + X.drop(['yes', 'no'], axis=1, inplace=True) -# Get the day of the week from the date column as a new column -X['day'] = X['date'].dt.dayofweek -X.drop(["date"], axis=1, inplace=True) + # Get the day of the week from the date column as a new column + X['day'] = X['date'].dt.dayofweek + X.drop(["date"], axis=1, inplace=True) -# >> ### Decision Tree Classifier + # >> ### Decision Tree Classifier -decision_tree_model = DecisionTreeClassifier(criterion="entropy", - random_state=0) -decision_tree_result = ml.cross_validation(decision_tree_model, X, y, 5) + decision_tree_model = DecisionTreeClassifier(criterion="entropy", + random_state=0) + decision_tree_result = ml.cross_validation(decision_tree_model, X, y, 5) -# Write scores to file -with open("train/metrics.txt", 'w') as outfile: - outfile.write( - f"Training data accuracy: {decision_tree_result['Training Accuracy scores'][0]}") - outfile.write( - f"Validation data accuracy: {decision_tree_result['Validation Accuracy scores'][0]}") + # Write scores to file + with open("train/decission_metrics.txt", 'w') as outfile: + outfile.write( + f"Training data accuracy: {decision_tree_result['Training Accuracy scores'][0]}") + outfile.write( + f"Validation data accuracy: {decision_tree_result['Validation Accuracy scores'][0]}") -# Plot accuacy results to cml + # Plot accuacy results to cml -# Plot Accuracy Result -model_name = "Decision Tree" -ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", - decision_tree_result["Training Accuracy scores"], - decision_tree_result["Validation Accuracy scores"], - 'train/decision_tree_accuracy.png') + # Plot Accuracy Result + model_name = "Decision Tree" + ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", + decision_tree_result["Training Accuracy scores"], + decision_tree_result["Validation Accuracy scores"], + 'train/decision_tree_accuracy.png') -# Precision Results + # Precision Results -# Plot Precision Result -ml.plot_result(model_name, "Precision", "Precision scores in 5 Folds", - decision_tree_result["Training Precision scores"], - decision_tree_result["Validation Precision scores"], - 'train/decision_tree_preicision.png') + # Plot Precision Result + ml.plot_result(model_name, "Precision", "Precision scores in 5 Folds", + decision_tree_result["Training Precision scores"], + decision_tree_result["Validation Precision scores"], + 'train/decision_tree_preicision.png') -# Recall Results plot + # Recall Results plot -# Plot Recall Result -ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", - decision_tree_result["Training Recall scores"], - decision_tree_result["Validation Recall scores"], - 'train/decision_tree_recall.png') + # Plot Recall Result + ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", + decision_tree_result["Training Recall scores"], + decision_tree_result["Validation Recall scores"], + 'train/decision_tree_recall.png') -# f1 Score Results + # f1 Score Results -# Plot F1-Score Result -ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", - decision_tree_result["Training F1 scores"], - decision_tree_result["Validation F1 scores"], - 'train/decision_tree_f1_score.png') + # Plot F1-Score Result + ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", + decision_tree_result["Training F1 scores"], + decision_tree_result["Validation F1 scores"], + 'train/decision_tree_f1_score.png') # The model is overfitting as it is working well on the training data but not on the validation set. diff --git a/train/logistic_regression.py b/train/logistic_regression.py new file mode 100644 index 0000000..9e55b6e --- /dev/null +++ b/train/logistic_regression.py @@ -0,0 +1,182 @@ +import sys +import logging +import mlflow.sklearn +import mlflow +from urllib.parse import urlparse +from sklearn.linear_model import LogisticRegression + +import numpy as np +import pandas as pd +import dvc.api +import sys +import warnings +import os + + +# >> #### Import modules +# sys.path.append(os.path.abspath(os.path.join('../scripts'))) +# from abtest-mlops2 +# sys.path.insert( +# 0, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/scripts') +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../scripts'))) + +from preprocess import Preprocess +from ml import Ml + +ml = Ml() +preprocess = Preprocess() + +# path = 'data/AdSmartABdata.csv' +# repo = 'https://github.com/abtesting10academy/abtest-mlops' +# version = 'ea64953afc441740caf168dbfaf6ce3ad1bd1d16' + +# data_url = dvc.api.get_url( +# path=path, +# repo=repo, +# rev=version +# ) + + +# data = pd.read_csv(data_url, sep=',') +# sys.path.insert(1, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/data/') +# import AdSmartABdata.csv +data = pd.read_csv('data/AdSmartABdata.csv', sep=',') + + +mlflow.set_experiment('SmartAD data analysis') + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + mlflow.log_param('data_url', data) + mlflow.log_param('input_rows', data.shape[0]) + mlflow.log_param('input_cols', data.shape[1]) + mlflow.log_param('model_type','Logistic Regression') + mlflow.log_param('model_parameters', 'n_estimators=100, max_depth=10') + + # change the date column to datetime + data = preprocess.convert_to_datetime(data, 'date') + numerical_column = preprocess.get_numerical_columns(data) + categorical_column = preprocess.get_categorical_columns(data) + + # drop auction_id from categorical_column + categorical_column.remove('auction_id') + + # Get column names have less than 10 more than 2 unique values + to_one_hot_encoding = [col for col in categorical_column if data[col].nunique( + ) <= 10 and data[col].nunique() > 2] + + # Get Categorical Column names thoose are not in "to_one_hot_encoding" + to_label_encoding = [ + col for col in categorical_column if not col in to_one_hot_encoding] + + # Label encoding + label_encoded_columns = preprocess.label_encode(data, to_label_encoding) + + # Select relevant rows + + # Copy our DataFrame to X variable + X = data.copy() + + # Droping Categorical Columns, + # "inplace" means replace our data with new one + # Don't forget to "axis=1" + X.drop(categorical_column, axis=1, inplace=True) + + # Merge DataFrames + X = pd.concat([X, label_encoded_columns], axis=1) + + # Select only rows with responses + X = X.query('yes == 1 | no == 1') + + # Drop auction_id column + X.drop(["auction_id"], axis=1, inplace=True) + + # Split data + + X['target'] = [1] * X.shape[0] + X.loc[X['no'] == 1, 'target'] = 0 + y = X['target'] + X.drop(["target"], axis=1, inplace=True) + X.drop(['yes', 'no'], axis=1, inplace=True) + + # Get the day of the week from the date column as a new column + X['day'] = X['date'].dt.dayofweek + X.drop(["date"], axis=1, inplace=True) + + # use logistic regression + logistic_regression_model = LogisticRegression(random_state=0) + + logistic_regression_result = ml.cross_validation(logistic_regression_model, X, y, 5) + + # Write scores to file + with open("train/logistic_metrics.txt", 'w') as outfile: + outfile.write( + f"Training data accuracy: {logistic_regression_result['Training Accuracy scores'][0]}") + outfile.write( + f"Validation data accuracy: {logistic_regression_result['Validation Accuracy scores'][0]}") + + + # Plot accuacy results to cml + + # Plot Accuracy Result + model_name = "Logistic Regression" + ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", + logistic_regression_result["Training Accuracy scores"], + logistic_regression_result["Validation Accuracy scores"], + 'train/logistic_accuracy.png') + + # Precision Results + + # Plot Precision Result + ml.plot_result(model_name, "Precision", "Precision scores in 5 Folds", + logistic_regression_result["Training Precision scores"], + logistic_regression_result["Validation Precision scores"], + 'train/logistic_preicision.png') + + # Recall Results plot + + # Plot Recall Result + ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", + logistic_regression_result["Training Recall scores"], + logistic_regression_result["Validation Recall scores"], + 'train/logistic_recall.png') + + + # f1 Score Results + + # Plot F1-Score Result + ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", + logistic_regression_result["Training F1 scores"], + logistic_regression_result["Validation F1 scores"], + 'train/logistic_f1_score.png') + + +# The model is overfitting as it is working well on the training data but not on the validation set. +# We will adjust the min_samples_split hyperparameter to fix this. + +# Fine tunin the min_samples_split parameter +# logistic_model_2 = Logistic(criterion="entropy", +# min_samples_split=4, +# random_state=0) +# logistic_regression_result_2 = ml.cross_validation(logistic_model_2, X, y, 5) + +# # Plot Accuracy Result +# ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", +# logistic_regression_result_2["Training Accuracy scores"], +# logistic_regression_result_2["Validation Accuracy scores"]) + + +# # Plot Precision Result +# ml.plot_result(model_name, "precision", "precision scores in 5 Folds", +# logistic_regression_result_2["Training Precision scores"], +# logistic_regression_result_2["Validation Precision scores"]) +# # Plot Recall Result +# ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", +# logistic_regression_result_2["Training Recall scores"], +# logistic_regression_result_2["Validation Recall scores"]) + + +# # Plot F1-Score Result +# ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", +# logistic_regression_result_2["Training F1 scores"], +# logistic_regression_result_2["Validation F1 scores"]) diff --git a/train/metrics.txt b/train/metrics.txt deleted file mode 100644 index ffee6dc..0000000 --- a/train/metrics.txt +++ /dev/null @@ -1 +0,0 @@ -Training data accuracy: 0.8511066398390342Validation data accuracy: 0.5060240963855421 \ No newline at end of file diff --git a/train/random_forest.py b/train/random_forest.py new file mode 100644 index 0000000..3379b00 --- /dev/null +++ b/train/random_forest.py @@ -0,0 +1,189 @@ +from random import random +import sys +import logging +import mlflow.sklearn +import mlflow +from urllib.parse import urlparse +from sklearn.ensemble import RandomForestClassifier +from sklearn.tree import DecisionTreeClassifier +from sklearn.preprocessing import LabelEncoder +from sklearn.linear_model import ElasticNet, LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +# from pydrive.drive import GoogleAuth + +import numpy as np +import pandas as pd +import dvc.api +import sys +import warnings +import os + + +# >> #### Import modules +# sys.path.append(os.path.abspath(os.path.join('../scripts'))) +# from abtest-mlops2 +# sys.path.insert( +# 0, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/scripts') +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../scripts'))) + +from preprocess import Preprocess +from ml import Ml + + +ml = Ml() +preprocess = Preprocess() + +# path = 'data/AdSmartABdata.csv' +# repo = 'https://github.com/abtesting10academy/abtest-mlops' +# version = 'ea64953afc441740caf168dbfaf6ce3ad1bd1d16' + +# data_url = dvc.api.get_url( +# path=path, +# repo=repo, +# rev=version +# ) + + +# data = pd.read_csv(data_url, sep=',') +# sys.path.insert(1, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/data/') +# import AdSmartABdata.csv +data = pd.read_csv('data/AdSmartABdata.csv', sep=',') + + +mlflow.set_experiment('SmartAD data analysis') + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + mlflow.log_param('data_url', data) + mlflow.log_param('input_rows', data.shape[0]) + mlflow.log_param('input_cols', data.shape[1]) + mlflow.log_param('model_type','Random Forest') + mlflow.log_param('model_parameters', 'n_estimators=100, max_depth=10, max_features=0.5') + # change the date column to datetime + data = preprocess.convert_to_datetime(data, 'date') + numerical_column = preprocess.get_numerical_columns(data) + categorical_column = preprocess.get_categorical_columns(data) + + # drop auction_id from categorical_column + categorical_column.remove('auction_id') + + # Get column names have less than 10 more than 2 unique values + to_one_hot_encoding = [col for col in categorical_column if data[col].nunique( + ) <= 10 and data[col].nunique() > 2] + + # Get Categorical Column names thoose are not in "to_one_hot_encoding" + to_label_encoding = [ + col for col in categorical_column if not col in to_one_hot_encoding] + + # Label encoding + label_encoded_columns = preprocess.label_encode(data, to_label_encoding) + + # Select relevant rows + + # Copy our DataFrame to X variable + X = data.copy() + + # Droping Categorical Columns, + # "inplace" means replace our data with new one + # Don't forget to "axis=1" + X.drop(categorical_column, axis=1, inplace=True) + + # Merge DataFrames + X = pd.concat([X, label_encoded_columns], axis=1) + + # Select only rows with responses + X = X.query('yes == 1 | no == 1') + + # Drop auction_id column + X.drop(["auction_id"], axis=1, inplace=True) + + # Split data + + X['target'] = [1] * X.shape[0] + X.loc[X['no'] == 1, 'target'] = 0 + y = X['target'] + X.drop(["target"], axis=1, inplace=True) + X.drop(['yes', 'no'], axis=1, inplace=True) + + # Get the day of the week from the date column as a new column + X['day'] = X['date'].dt.dayofweek + X.drop(["date"], axis=1, inplace=True) + + # IMport random forest model + random_forest_model = RandomForestClassifier(n_estimators=100, random_state=0) + + random_forest_result = ml.cross_validation(random_forest_model, X, y, 5) + + # Write scores to file + with open("train/random_metrics.txt", 'w') as outfile: + outfile.write( + f"Training data accuracy: {random_forest_result['Training Accuracy scores'][0]}") + outfile.write( + f"Validation data accuracy: {random_forest_result['Validation Accuracy scores'][0]}") + + + # Plot accuacy results to cml + + # Plot Accuracy Result + model_name = "Random" + ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", + random_forest_result["Training Accuracy scores"], + random_forest_result["Validation Accuracy scores"], + 'train/random_forest_accuracy.png') + + # Precision Results + + # Plot Precision Result + ml.plot_result(model_name, "Precision", "Precision scores in 5 Folds", + random_forest_result["Training Precision scores"], + random_forest_result["Validation Precision scores"], + 'train/random_forest_preicision.png') + + # Recall Results plot + + # Plot Recall Result + ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", + random_forest_result["Training Recall scores"], + random_forest_result["Validation Recall scores"], + 'train/random_forest_recall.png') + + + # f1 Score Results + + # Plot F1-Score Result + ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", + random_forest_result["Training F1 scores"], + random_forest_result["Validation F1 scores"], + 'train/random_forest_f1_score.png') + + +# The model is overfitting as it is working well on the training data but not on the validation set. +# We will adjust the min_samples_split hyperparameter to fix this. + +# Fine tunin the min_samples_split parameter +# random_forest_model_2 = DecisionTreeClassifier(criterion="entropy", +# min_samples_split=4, +# random_state=0) +# random_forest_result_2 = ml.cross_validation(random_forest_model_2, X, y, 5) + +# # Plot Accuracy Result +# ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", +# random_forest_result_2["Training Accuracy scores"], +# random_forest_result_2["Validation Accuracy scores"]) + + +# # Plot Precision Result +# ml.plot_result(model_name, "precision", "precision scores in 5 Folds", +# random_forest_result_2["Training Precision scores"], +# random_forest_result_2["Validation Precision scores"]) +# # Plot Recall Result +# ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", +# random_forest_result_2["Training Recall scores"], +# random_forest_result_2["Validation Recall scores"]) + + +# # Plot F1-Score Result +# ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", +# random_forest_result_2["Training F1 scores"], +# random_forest_result_2["Validation F1 scores"]) diff --git a/train/xgboost.py b/train/xgboost.py new file mode 100644 index 0000000..fb4257e --- /dev/null +++ b/train/xgboost.py @@ -0,0 +1,184 @@ +import sys +import logging +import mlflow.sklearn +import mlflow +from urllib.parse import urlparse + +# import xgboost model +from xgboost import XGBClassifier + +import numpy as np +import pandas as pd +import dvc.api +import sys +import warnings +import os + + +# >> #### Import modules +# sys.path.append(os.path.abspath(os.path.join('../scripts'))) +# from abtest-mlops2 +# sys.path.insert( +# 0, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/scripts') +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../scripts'))) + +from preprocess import Preprocess +from ml import Ml + +ml = Ml() +preprocess = Preprocess() + +# path = 'data/AdSmartABdata.csv' +# repo = 'https://github.com/abtesting10academy/abtest-mlops' +# version = 'ea64953afc441740caf168dbfaf6ce3ad1bd1d16' + +# data_url = dvc.api.get_url( +# path=path, +# repo=repo, +# rev=version +# ) + + +# data = pd.read_csv(data_url, sep=',') +# sys.path.insert(1, '/home/jedi/Documents/Tenacademy/Week2/abtest_mlops2/data/') +# import AdSmartABdata.csv +data = pd.read_csv('data/AdSmartABdata.csv', sep=',') + +mlflow.set_experiment('SmartAD data analysis') + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + mlflow.log_param('data_url', data) + mlflow.log_param('input_rows', data.shape[0]) + mlflow.log_param('input_cols', data.shape[1]) + mlflow.log_param('model_type','XGboost') + mlflow.log_param('model_parameters', 'n_estimators=100, max_depth=10, max_features=0.5') + + # change the date column to datetime + data = preprocess.convert_to_datetime(data, 'date') + numerical_column = preprocess.get_numerical_columns(data) + categorical_column = preprocess.get_categorical_columns(data) + + # drop auction_id from categorical_column + categorical_column.remove('auction_id') + + # Get column names have less than 10 more than 2 unique values + to_one_hot_encoding = [col for col in categorical_column if data[col].nunique( + ) <= 10 and data[col].nunique() > 2] + + # Get Categorical Column names thoose are not in "to_one_hot_encoding" + to_label_encoding = [ + col for col in categorical_column if not col in to_one_hot_encoding] + + # Label encoding + label_encoded_columns = preprocess.label_encode(data, to_label_encoding) + + # Select relevant rows + + # Copy our DataFrame to X variable + X = data.copy() + + # Droping Categorical Columns, + # "inplace" means replace our data with new one + # Don't forget to "axis=1" + X.drop(categorical_column, axis=1, inplace=True) + + # Merge DataFrames + X = pd.concat([X, label_encoded_columns], axis=1) + + # Select only rows with responses + X = X.query('yes == 1 | no == 1') + + # Drop auction_id column + X.drop(["auction_id"], axis=1, inplace=True) + + # Split data + + X['target'] = [1] * X.shape[0] + X.loc[X['no'] == 1, 'target'] = 0 + y = X['target'] + X.drop(["target"], axis=1, inplace=True) + X.drop(['yes', 'no'], axis=1, inplace=True) + + # Get the day of the week from the date column as a new column + X['day'] = X['date'].dt.dayofweek + X.drop(["date"], axis=1, inplace=True) + + # Import XGboost classifier + xgboost_model = XGBClassifier(random_state=0) + + # xgboost_model = xgbClassifier(random_state=0) + xgboost_result = ml.cross_validation(xgboost_model, X, y, 5) + + # Write scores to file + with open("train/xgboost_metrics.txt", 'w') as outfile: + outfile.write( + f"Training data accuracy: {xgboost_result['Training Accuracy scores'][0]}") + outfile.write( + f"Validation data accuracy: {xgboost_result['Validation Accuracy scores'][0]}") + + + # Plot accuacy results to cml + + # Plot Accuracy Result + model_name = "XGBoost" + ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", + xgboost_result["Training Accuracy scores"], + xgboost_result["Validation Accuracy scores"], + 'train/xgboost_accuracy.png') + + # Precision Results + + # Plot Precision Result + ml.plot_result(model_name, "Precision", "Precision scores in 5 Folds", + xgboost_result["Training Precision scores"], + xgboost_result["Validation Precision scores"], + 'train/xgboost_preicision.png') + + # Recall Results plot + + # Plot Recall Result + ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", + xgboost_result["Training Recall scores"], + xgboost_result["Validation Recall scores"], + 'train/xgboost_recall.png') + + + # f1 Score Results + + # Plot F1-Score Result + ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", + xgboost_result["Training F1 scores"], + xgboost_result["Validation F1 scores"], + 'train/xgboost_f1_score.png') + + +# The model is overfitting as it is working well on the training data but not on the validation set. +# We will adjust the min_samples_split hyperparameter to fix this. + +# Fine tunin the min_samples_split parameter +# xgboost_model_2 = XGBoostClassifer(criterion="entropy", +# min_samples_split=4, +# random_state=0) +# xgboost_result_2 = ml.cross_validation(xgboost_model_2, X, y, 5) + +# # Plot Accuracy Result +# ml.plot_result(model_name, "Accuracy", "Accuracy scores in 5 Folds", +# xgboost_result_2["Training Accuracy scores"], +# xgboost_result_2["Validation Accuracy scores"]) + + +# # Plot Precision Result +# ml.plot_result(model_name, "precision", "precision scores in 5 Folds", +# xgboost_result_2["Training Precision scores"], +# xgboost_result_2["Validation Precision scores"]) +# # Plot Recall Result +# ml.plot_result(model_name, "Recall", "Recall scores in 5 Folds", +# xgboost_result_2["Training Recall scores"], +# xgboost_result_2["Validation Recall scores"]) + + +# # Plot F1-Score Result +# ml.plot_result(model_name, "F1", "F1 Scores in 5 Folds", +# xgboost_result_2["Training F1 scores"], +# xgboost_result_2["Validation F1 scores"])