diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index dbf1ddaaf3b69f2344f846eb6012ecb1b9b04391..279c37b0a6abaeadf510ececacd2b65c9eb3de84 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -33,8 +33,8 @@ jobs:
git config --global user.email "actions@github.com"
git config --global user.name "GitHub Actions"
git clone https://huggingface.co/spaces/stephmnt/projet_05 hf_space
- rsync -av --exclude '.git' ./ hf_space/
+ rsync -av --exclude '.git' --exclude 'output/' --exclude 'models/' ./ hf_space/
cd hf_space
git add .
git commit -m "🚀 Auto-deploy from GitHub Actions" || echo "No changes to commit"
- git push https://stephmnt:$HF_TOKEN@huggingface.co/spaces/stephmnt/projet_05 main
\ No newline at end of file
+ git push https://stephmnt:$HF_TOKEN@huggingface.co/spaces/stephmnt/projet_05 main
diff --git a/hf_space/hf_space/hf_space/README.md b/hf_space/hf_space/hf_space/README.md
index 9e80ceb32bd971250093bf39043d814a10c538ff..a41e41fb44a719e259948e2d5807dab7765cbcb6 100644
--- a/hf_space/hf_space/hf_space/README.md
+++ b/hf_space/hf_space/hf_space/README.md
@@ -1,5 +1,17 @@
# projet_05
+---
+title: OCR_Projet05
+emoji: 🔥
+colorFrom: purple
+colorTo: purple
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: true
+short_description: Projet 05 formation Openclassrooms
+---
+
@@ -57,6 +69,11 @@ Déployez un modèle de Machine Learning
└── plots.py <- Code to create visualizations
```
+## Code hérité réutilisé
+
+- `scripts_projet04/brand` : charte graphique OpenClassrooms (classe `Theme`, palettes, YAML). Le module `projet_05/branding.py` en est la porte d'entrée et applique automatiquement le thème.
+- `scripts_projet04/manet_projet04/shap_generator.py` : fonctions `shap_global` / `shap_local` utilisées par `projet_05/modeling/train.py` pour reproduire les visualisations SHAP.
+
--------
---
@@ -93,6 +110,7 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
-->
[![Contributors][contributors-shield]][contributors-url]
+[![Python][python]][python]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
@@ -236,7 +254,7 @@ _For more examples, please refer to the [Documentation](https://example.com)_
- [ ] Feature 3
- [ ] Nested Feature
-See the [open issues](https://github.com/github_username/repo_name/issues) for a full list of proposed features (and known issues).
+See the [open issues](https://github.com/stephmnt/OCR_projet05/issues) for a full list of proposed features (and known issues).
(back to top )
@@ -299,18 +317,18 @@ Project Link: [https://github.com/github_username/repo_name](https://github.com/
-[contributors-shield]: https://img.shields.io/github/contributors/github_username/repo_name.svg?style=for-the-badge
-[contributors-url]: https://github.com/github_username/repo_name/graphs/contributors
-[forks-shield]: https://img.shields.io/github/forks/github_username/repo_name.svg?style=for-the-badge
-[forks-url]: https://github.com/github_username/repo_name/network/members
-[stars-shield]: https://img.shields.io/github/stars/github_username/repo_name.svg?style=for-the-badge
-[stars-url]: https://github.com/github_username/repo_name/stargazers
-[issues-shield]: https://img.shields.io/github/issues/github_username/repo_name.svg?style=for-the-badge
-[issues-url]: https://github.com/github_username/repo_name/issues
-[license-shield]: https://img.shields.io/github/license/github_username/repo_name.svg?style=for-the-badge
-[license-url]: https://github.com/github_username/repo_name/blob/master/LICENSE.txt
+[contributors-shield]: https://img.shields.io/github/contributors/stephmnt/OCR_projet05.svg?style=for-the-badge
+[contributors-url]: https://github.com/stephmnt/OCR_projet05/graphs/contributors
+[forks-shield]: https://img.shields.io/github/forks/stephmnt/OCR_projet05.svg?style=for-the-badge
+[forks-url]: https://github.com/stephmnt/OCR_projet05/network/members
+[stars-shield]: https://img.shields.io/github/stars/stephmnt/OCR_projet05.svg?style=for-the-badge
+[stars-url]: https://github.com/stephmnt/OCR_projet05/stargazers
+[issues-shield]: https://img.shields.io/github/issues/stephmnt/OCR_projet05.svg?style=for-the-badge
+[issues-url]: https://github.com/stephmnt/OCR_projet05/issues
+[license-shield]: https://img.shields.io/github/license/stephmnt/OCR_projet05.svg?style=for-the-badge
+[license-url]: https://github.com/stephmnt/OCR_projet05/blob/master/LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
-[linkedin-url]: https://linkedin.com/in/linkedin_username
+[linkedin-url]: https://linkedin.com/in/stephanemanet
[product-screenshot]: images/screenshot.png
[Noobie]: https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16
@@ -331,10 +349,8 @@ Project Link: [https://github.com/github_username/repo_name](https://github.com/
[JQuery.com]: https://img.shields.io/badge/jQuery-0769AD?style=for-the-badge&logo=jquery&logoColor=white
[JQuery-url]: https://jquery.com
-[](#)
-[](#)
-[](#)
-[](#)
-[](#)
+[Postgres]: https://img.shields.io/badge/Postgres-%23316192.svg?logo=postgresql&logoColor=white
+[Python]: https://img.shields.io/badge/Python-3776AB?logo=python&logoColor=fff)
+[MkDocs]: https://img.shields.io/badge/MkDocs-526CFE?logo=materialformkdocs&logoColor=fff
+[NumPy]: https://img.shields.io/badge/NumPy-4DABCF?logo=numpy&logoColor=fff
[](#)
-[](#)[text](../projet_04/.gitignore)
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/LICENSE b/hf_space/hf_space/hf_space/hf_space/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..8a4e372e79bcbdac30db95d6854a88989dec41a3
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/LICENSE
@@ -0,0 +1,10 @@
+
+The MIT License (MIT)
+Copyright (c) 2025, Stéphane Manet
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
diff --git a/hf_space/hf_space/hf_space/hf_space/Makefile b/hf_space/hf_space/hf_space/hf_space/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..2b427a0876c51dc22c314ab9aba95982e4b36ece
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/Makefile
@@ -0,0 +1,85 @@
+#################################################################################
+# GLOBALS #
+#################################################################################
+
+PROJECT_NAME = OCR_projet05
+PYTHON_VERSION = 3.10
+PYTHON_INTERPRETER = python
+
+#################################################################################
+# COMMANDS #
+#################################################################################
+
+
+## Install Python dependencies
+.PHONY: requirements
+requirements:
+ pip install -e .
+
+
+
+
+## Delete all compiled Python files
+.PHONY: clean
+clean:
+ find . -type f -name "*.py[co]" -delete
+ find . -type d -name "__pycache__" -delete
+
+
+## Lint using ruff (use `make format` to do formatting)
+.PHONY: lint
+lint:
+ ruff format --check
+ ruff check
+
+## Format source code with ruff
+.PHONY: format
+format:
+ ruff check --fix
+ ruff format
+
+
+
+## Run tests
+.PHONY: test
+test:
+ python -m pytest tests
+
+
+## Set up Python interpreter environment
+.PHONY: create_environment
+create_environment:
+ @bash -c "if [ ! -z `which virtualenvwrapper.sh` ]; then source `which virtualenvwrapper.sh`; mkvirtualenv $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER); else mkvirtualenv.bat $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER); fi"
+ @echo ">>> New virtualenv created. Activate with:\nworkon $(PROJECT_NAME)"
+
+
+
+
+#################################################################################
+# PROJECT RULES #
+#################################################################################
+
+
+## Make dataset
+.PHONY: data
+data: requirements
+ $(PYTHON_INTERPRETER) projet_05/dataset.py
+
+
+#################################################################################
+# Self Documenting Commands #
+#################################################################################
+
+.DEFAULT_GOAL := help
+
+define PRINT_HELP_PYSCRIPT
+import re, sys; \
+lines = '\n'.join([line for line in sys.stdin]); \
+matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \
+print('Available rules:\n'); \
+print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches]))
+endef
+export PRINT_HELP_PYSCRIPT
+
+help:
+ @$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST)
diff --git a/hf_space/hf_space/hf_space/hf_space/app.py b/hf_space/hf_space/hf_space/hf_space/app.py
index 04cc31aa8d0e06aeaac3b59bb361ed71d831e43f..1f965c1e74ed05288f01124620de24114bd90735 100644
--- a/hf_space/hf_space/hf_space/hf_space/app.py
+++ b/hf_space/hf_space/hf_space/hf_space/app.py
@@ -1,7 +1,181 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
import gradio as gr
+import pandas as pd
+from loguru import logger
+
+from projet_05.branding import apply_brand_theme
+from projet_05.modeling.predict import load_metadata, load_pipeline, run_inference
+
+MODEL_PATH = Path("models/best_model.joblib")
+METADATA_PATH = Path("models/best_model_meta.json")
+SCHEMA_PATH = Path("data/processed/schema.json")
+
+
+def _load_schema(path: Path) -> dict[str, Any]:
+ if not path.exists():
+ return {}
+ return json.loads(path.read_text(encoding="utf-8"))
+
+
+def _infer_features(metadata: dict, schema: dict, pipeline) -> list[str]:
+ if schema:
+ candidates = schema.get("numerical_features", []) + schema.get("categorical_features", [])
+ if candidates:
+ return candidates
+ features = metadata.get("features", {})
+ explicit = (features.get("numerical") or []) + (features.get("categorical") or [])
+ if explicit:
+ return explicit
+ if pipeline is not None and hasattr(pipeline, "feature_names_in_"):
+ return list(pipeline.feature_names_in_)
+ return []
+
+
+def _convert_input(payload: Any, headers: list[str]) -> pd.DataFrame:
+ if isinstance(payload, pd.DataFrame):
+ df = payload.copy()
+ elif payload is None:
+ df = pd.DataFrame(columns=headers)
+ else:
+ df = pd.DataFrame(payload, columns=headers if headers else None)
+ df = df.dropna(how="all")
+ if df.empty:
+ raise gr.Error("Merci de saisir au moins une ligne complète.")
+ return df
+
+
+def _ensure_model():
+ if PIPELINE is None:
+ raise gr.Error(
+ "Aucun modèle entrainé n'a été trouvé. Lancez `python projet_05/modeling/train.py` puis relancez l'application."
+ )
+
+
+def score_table(table):
+ _ensure_model()
+ df = _convert_input(table, FEATURE_ORDER)
+ drop_cols = [TARGET_COLUMN] if TARGET_COLUMN else None
+ return run_inference(
+ df,
+ PIPELINE,
+ THRESHOLD,
+ drop_columns=drop_cols,
+ required_features=FEATURE_ORDER or None,
+ )
+
+
+def score_csv(upload):
+ _ensure_model()
+ if upload is None:
+ raise gr.Error("Veuillez déposer un fichier CSV.")
+ df = pd.read_csv(upload.name)
+ drop_cols = [TARGET_COLUMN] if TARGET_COLUMN else None
+ return run_inference(
+ df,
+ PIPELINE,
+ THRESHOLD,
+ drop_columns=drop_cols,
+ required_features=FEATURE_ORDER or None,
+ )
+
+
+def predict_from_form(*values):
+ _ensure_model()
+ if not FEATURE_ORDER:
+ raise gr.Error("Impossible de générer le formulaire sans configuration des features.")
+ payload = {feature: value for feature, value in zip(FEATURE_ORDER, values)}
+ df = pd.DataFrame([payload])
+ scored = run_inference(
+ df,
+ PIPELINE,
+ THRESHOLD,
+ required_features=FEATURE_ORDER or None,
+ )
+ row = scored.iloc[0]
+ label = "Risque de départ" if int(row["prediction"]) == 1 else "Reste probable"
+ return {
+ "probability": round(float(row["proba_depart"]), 4),
+ "decision": label,
+ "threshold": THRESHOLD,
+ }
+
+
+# Chargement des artéfacts
+apply_brand_theme()
+
+PIPELINE = None
+METADATA: dict[str, Any] = {}
+THRESHOLD = 0.5
+TARGET_COLUMN: str | None = None
+SCHEMA = _load_schema(SCHEMA_PATH)
+
+try:
+ PIPELINE = load_pipeline(MODEL_PATH)
+ METADATA = load_metadata(METADATA_PATH)
+ THRESHOLD = float(METADATA.get("best_threshold", THRESHOLD))
+ TARGET_COLUMN = METADATA.get("target")
+except FileNotFoundError as exc:
+ logger.warning("Artéfact manquant: {}", exc)
+
+FEATURE_ORDER = _infer_features(METADATA, SCHEMA, PIPELINE)
+
+with gr.Blocks(title="Prédicteur d'attrition") as demo:
+ gr.Markdown("# API Gradio – Prédiction de départ employé")
+ gr.Markdown(
+ "Le modèle applique le pipeline entraîné hors-notebook pour fournir une probabilité de départ ainsi qu'une décision binaire."
+ )
+
+ if PIPELINE is None:
+ gr.Markdown(
+ "⚠️ **Aucun modèle disponible.** Lancez les scripts `dataset.py`, `features.py` puis `modeling/train.py`."
+ )
+ else:
+ gr.Markdown(f"Seuil de décision actuel : **{THRESHOLD:.2f}**")
+
+ with gr.Tab("Formulaire unitaire"):
+ if not FEATURE_ORDER:
+ gr.Markdown("Aucune configuration de features détectée. Utilisez l'onglet CSV pour scorer vos données.")
+ else:
+ form_inputs: list[gr.components.Component] = [] # type: ignore
+ for feature in FEATURE_ORDER:
+ form_inputs.append(
+ gr.Textbox(label=feature, placeholder=f"Saisir {feature.replace('_', ' ')}")
+ )
+ form_output = gr.JSON(label="Résultat")
+ gr.Button("Prédire").click(
+ fn=predict_from_form,
+ inputs=form_inputs,
+ outputs=form_output,
+ )
+
+ with gr.Tab("Tableau interactif"):
+ table_input = gr.Dataframe(
+ headers=FEATURE_ORDER if FEATURE_ORDER else None,
+ row_count=(1, "dynamic"),
+ col_count=(len(FEATURE_ORDER), "dynamic") if FEATURE_ORDER else (5, "dynamic"),
+ type="pandas",
+ )
+ table_output = gr.Dataframe(label="Prédictions", type="pandas")
+ gr.Button("Scorer les lignes").click(
+ fn=score_table,
+ inputs=table_input,
+ outputs=table_output,
+ )
+
+ with gr.Tab("Fichier CSV"):
+ file_input = gr.File(file_types=[".csv"], label="Déposez votre fichier CSV")
+ file_output = gr.Dataframe(label="Résultats CSV", type="pandas")
+ gr.Button("Scorer le fichier").click(
+ fn=score_csv,
+ inputs=file_input,
+ outputs=file_output,
+ )
-def greet(name):
- return "Hello " + name + "!!"
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+ demo.launch()
diff --git a/hf_space/hf_space/hf_space/hf_space/docs/.gitkeep b/hf_space/hf_space/hf_space/hf_space/docs/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/docs/README.md b/hf_space/hf_space/hf_space/hf_space/docs/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..79c146859754d65ce1a01add9848026d10bd832c
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/docs/README.md
@@ -0,0 +1,12 @@
+Generating the docs
+----------
+
+Use [mkdocs](http://www.mkdocs.org/) structure to update the documentation.
+
+Build locally with:
+
+ mkdocs build
+
+Serve locally with:
+
+ mkdocs serve
diff --git a/hf_space/hf_space/hf_space/hf_space/docs/docs/getting-started.md b/hf_space/hf_space/hf_space/hf_space/docs/docs/getting-started.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4f71c3a293b7c30dbb94afd6f3e58997b55ceef
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/docs/docs/getting-started.md
@@ -0,0 +1,6 @@
+Getting started
+===============
+
+This is where you describe how to get set up on a clean install, including the
+commands necessary to get the raw data (using the `sync_data_from_s3` command,
+for example), and then how to make the cleaned, final data sets.
diff --git a/hf_space/hf_space/hf_space/hf_space/docs/docs/index.md b/hf_space/hf_space/hf_space/hf_space/docs/docs/index.md
new file mode 100644
index 0000000000000000000000000000000000000000..fe3d83b7d41584bd85af6323cc3550a2b7b0f965
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/docs/docs/index.md
@@ -0,0 +1,10 @@
+# projet_05 documentation!
+
+## Description
+
+Déployez un modèle de Machine Learning
+
+## Commands
+
+The Makefile contains the central entry points for common tasks related to this project.
+
diff --git a/hf_space/hf_space/hf_space/hf_space/docs/mkdocs.yml b/hf_space/hf_space/hf_space/hf_space/docs/mkdocs.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d009cda467ea65b5668960bb17740bd32b825428
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/docs/mkdocs.yml
@@ -0,0 +1,4 @@
+site_name: projet_05
+#
+site_author: Stéphane Manet
+#
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml b/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml
index 66630672a37802677bb5a36cea6d29af4b2eb758..dbf1ddaaf3b69f2344f846eb6012ecb1b9b04391 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml
@@ -1,10 +1,13 @@
-name: Déployer vers Hugging Face Spaces
+name: Deploy to Hugging Face Spaces
on:
push:
branches:
- main
+permissions:
+ contents: write
+
jobs:
deploy:
runs-on: ubuntu-latest
@@ -23,7 +26,7 @@ jobs:
python -m pip install --upgrade pip
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- - name: Push to Hugging Face Space
+ - name: Deploy to Hugging Face Space
env:
HF_TOKEN: ${{ secrets.HF_TOKEN }}
run: |
@@ -33,5 +36,5 @@ jobs:
rsync -av --exclude '.git' ./ hf_space/
cd hf_space
git add .
- git commit -m "🚀 Auto-deploy from GitHub Actions"
- git push https://stephmnt:$HF_TOKEN@huggingface.co/spaces/stephmnt/projet_05 main
+ git commit -m "🚀 Auto-deploy from GitHub Actions" || echo "No changes to commit"
+ git push https://stephmnt:$HF_TOKEN@huggingface.co/spaces/stephmnt/projet_05 main
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore b/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore
index 85bf600ea236ca40a825047c887d25624c358668..ba9392bfe7b4a1cea588ba9338eeccbbda3e2436 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore
@@ -1,2 +1,192 @@
+# Data
+/data/
+
+# Mac OS-specific storage files
+.DS_Store
*.code-workspace
-.venv/
+
+# vim
+*.swp
+*.swo
+
+## https://github.com/github/gitignore/blob/e8554d85bf62e38d6db966a50d2064ac025fd82a/Python.gitignore
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# MkDocs documentation
+docs/site/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# UV
+# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+#uv.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+# pixi.lock should be committed to version control for reproducibility
+# .pixi/ contains the environments and should not be committed
+.pixi/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/README.md b/hf_space/hf_space/hf_space/hf_space/hf_space/README.md
index 568b6d98de3a2772b983b235c8e74cc11fd83c2a..9e80ceb32bd971250093bf39043d814a10c538ff 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/README.md
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/README.md
@@ -1,3 +1,64 @@
+# projet_05
+
+
+
+
+
+Déployez un modèle de Machine Learning
+
+## Organisation du projet
+
+```
+├── LICENSE <- Open-source license if one is chosen
+├── Makefile <- Makefile with convenience commands like `make data` or `make train`
+├── README.md <- The top-level README for developers using this project.
+├── data
+│ ├── external <- Data from third party sources.
+│ ├── interim <- Intermediate data that has been transformed.
+│ ├── processed <- The final, canonical data sets for modeling.
+│ └── raw <- The original, immutable data dump.
+│
+├── docs <- A default mkdocs project; see www.mkdocs.org for details
+│
+├── models <- Trained and serialized models, model predictions, or model summaries
+│
+├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
+│ the creator's initials, and a short `-` delimited description, e.g.
+│ `1.0-jqp-initial-data-exploration`.
+│
+├── pyproject.toml <- Project configuration file with package metadata for
+│ projet_05 and configuration for tools like black
+│
+├── references <- Data dictionaries, manuals, and all other explanatory materials.
+│
+├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
+│ └── figures <- Generated graphics and figures to be used in reporting
+│
+├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
+│ generated with `pip freeze > requirements.txt`
+│
+├── setup.cfg <- Configuration file for flake8
+│
+└── projet_05 <- Source code for use in this project.
+ │
+ ├── __init__.py <- Makes projet_05 a Python module
+ │
+ ├── config.py <- Store useful variables and configuration
+ │
+ ├── dataset.py <- Scripts to download or generate data
+ │
+ ├── features.py <- Code to create features for modeling
+ │
+ ├── modeling
+ │ ├── __init__.py
+ │ ├── predict.py <- Code to run model inference with trained models
+ │ └── train.py <- Code to train models
+ │
+ └── plots.py <- Code to create visualizations
+```
+
+--------
+
---
title: Projet 05
emoji: 👀
@@ -10,3 +71,270 @@ pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+
+
+
+
+
+
+
+[![Contributors][contributors-shield]][contributors-url]
+[![Forks][forks-shield]][forks-url]
+[![Stargazers][stars-shield]][stars-url]
+[![Issues][issues-shield]][issues-url]
+[![project_license][license-shield]][license-url]
+[![LinkedIn][linkedin-shield]][linkedin-url]
+
+
+
+
+
+
+
+
+
+
+
+
+ Table of Contents
+
+
+ About The Project
+
+
+
+ Getting Started
+
+
+ Usage
+ Roadmap
+ Contributing
+ License
+ Contact
+ Acknowledgments
+
+
+
+
+
+
+## About The Project
+
+[![Product Name Screen Shot][product-screenshot]](https://example.com)
+
+Here's a blank template to get started. To avoid retyping too much info, do a search and replace with your text editor for the following: `github_username`, `repo_name`, `twitter_handle`, `linkedin_username`, `email_client`, `email`, `project_title`, `project_description`, `project_license`
+
+(back to top )
+
+
+
+### Built With
+
+* [![Python][Python]][Python-url]
+* [![SQL][SQL]][SQL-url]
+
+(back to top )
+
+
+
+
+## Getting Started
+
+This is an example of how you may give instructions on setting up your project locally.
+To get a local copy up and running follow these simple example steps.
+
+### Prerequisites
+
+This is an example of how to list things you need to use the software and how to install them.
+* npm
+ ```sh
+ npm install npm@latest -g
+ ```
+
+### Installation
+
+pip install -r requirements.txt
+uvicorn app.main:app --reload
+
+1. Get a free API Key at [https://example.com](https://example.com)
+2. Clone the repo
+ ```sh
+ git clone https://github.com/github_username/repo_name.git
+ ```
+3. Install NPM packages
+ ```sh
+ npm install
+ ```
+4. Enter your API in `config.js`
+ ```js
+ const API_KEY = 'ENTER YOUR API';
+ ```
+5. Change git remote url to avoid accidental pushes to base project
+ ```sh
+ git remote set-url origin github_username/repo_name
+ git remote -v # confirm the changes
+ ```
+
+(back to top )
+
+
+
+
+## Usage
+
+Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
+
+_For more examples, please refer to the [Documentation](https://example.com)_
+
+(back to top )
+
+
+
+
+## Roadmap
+
+- [ ] Feature 1
+- [ ] Feature 2
+- [ ] Feature 3
+ - [ ] Nested Feature
+
+See the [open issues](https://github.com/github_username/repo_name/issues) for a full list of proposed features (and known issues).
+
+(back to top )
+
+
+
+
+## Contributing
+
+Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
+
+If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
+Don't forget to give the project a star! Thanks again!
+
+1. Fork the Project
+2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the Branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+(back to top )
+
+### Top contributors:
+
+
+
+
+
+
+
+
+## License
+
+Distributed under the project_license. See `LICENSE.txt` for more information.
+
+(back to top )
+
+
+
+
+## Contact
+
+Your Name - [@twitter_handle](https://twitter.com/twitter_handle) - email@email_client.com
+
+Project Link: [https://github.com/github_username/repo_name](https://github.com/github_username/repo_name)
+
+(back to top )
+
+
+
+
+## Acknowledgments
+
+* []()
+* []()
+* []()
+
+(back to top )
+
+
+
+
+
+[contributors-shield]: https://img.shields.io/github/contributors/github_username/repo_name.svg?style=for-the-badge
+[contributors-url]: https://github.com/github_username/repo_name/graphs/contributors
+[forks-shield]: https://img.shields.io/github/forks/github_username/repo_name.svg?style=for-the-badge
+[forks-url]: https://github.com/github_username/repo_name/network/members
+[stars-shield]: https://img.shields.io/github/stars/github_username/repo_name.svg?style=for-the-badge
+[stars-url]: https://github.com/github_username/repo_name/stargazers
+[issues-shield]: https://img.shields.io/github/issues/github_username/repo_name.svg?style=for-the-badge
+[issues-url]: https://github.com/github_username/repo_name/issues
+[license-shield]: https://img.shields.io/github/license/github_username/repo_name.svg?style=for-the-badge
+[license-url]: https://github.com/github_username/repo_name/blob/master/LICENSE.txt
+[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
+[linkedin-url]: https://linkedin.com/in/linkedin_username
+[product-screenshot]: images/screenshot.png
+[Noobie]: https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16
+
+[Next.js]: https://img.shields.io/badge/next.js-000000?style=for-the-badge&logo=nextdotjs&logoColor=white
+[Next-url]: https://nextjs.org/
+[React.js]: https://img.shields.io/badge/React-20232A?style=for-the-badge&logo=react&logoColor=61DAFB
+[React-url]: https://reactjs.org/
+[Vue.js]: https://img.shields.io/badge/Vue.js-35495E?style=for-the-badge&logo=vuedotjs&logoColor=4FC08D
+[Vue-url]: https://vuejs.org/
+[Angular.io]: https://img.shields.io/badge/Angular-DD0031?style=for-the-badge&logo=angular&logoColor=white
+[Angular-url]: https://angular.io/
+[Svelte.dev]: https://img.shields.io/badge/Svelte-4A4A55?style=for-the-badge&logo=svelte&logoColor=FF3E00
+[Svelte-url]: https://svelte.dev/
+[Laravel.com]: https://img.shields.io/badge/Laravel-FF2D20?style=for-the-badge&logo=laravel&logoColor=white
+[Laravel-url]: https://laravel.com
+[Bootstrap.com]: https://img.shields.io/badge/Bootstrap-563D7C?style=for-the-badge&logo=bootstrap&logoColor=white
+[Bootstrap-url]: https://getbootstrap.com
+[JQuery.com]: https://img.shields.io/badge/jQuery-0769AD?style=for-the-badge&logo=jquery&logoColor=white
+[JQuery-url]: https://jquery.com
+
+[](#)
+[](#)
+[](#)
+[](#)
+[](#)
+[](#)
+[](#)[text](../projet_04/.gitignore)
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/app/__init__.py b/hf_space/hf_space/hf_space/hf_space/hf_space/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/app/main.py b/hf_space/hf_space/hf_space/hf_space/hf_space/app/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..04cc31aa8d0e06aeaac3b59bb361ed71d831e43f
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/app/main.py
@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+ return "Hello " + name + "!!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml
new file mode 100644
index 0000000000000000000000000000000000000000..66630672a37802677bb5a36cea6d29af4b2eb758
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/deploy.yml
@@ -0,0 +1,37 @@
+name: Déployer vers Hugging Face Spaces
+
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - name: Setup Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+ - name: Push to Hugging Face Space
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ run: |
+ git config --global user.email "actions@github.com"
+ git config --global user.name "GitHub Actions"
+ git clone https://huggingface.co/spaces/stephmnt/projet_05 hf_space
+ rsync -av --exclude '.git' ./ hf_space/
+ cd hf_space
+ git add .
+ git commit -m "🚀 Auto-deploy from GitHub Actions"
+ git push https://stephmnt:$HF_TOKEN@huggingface.co/spaces/stephmnt/projet_05 main
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..85bf600ea236ca40a825047c887d25624c358668
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitignore
@@ -0,0 +1,2 @@
+*.code-workspace
+.venv/
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/README.md b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..568b6d98de3a2772b983b235c8e74cc11fd83c2a
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/README.md
@@ -0,0 +1,12 @@
+---
+title: Projet 05
+emoji: 👀
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..04cc31aa8d0e06aeaac3b59bb361ed71d831e43f
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+ return "Hello " + name + "!!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/tests/test_app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/tests/test_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..13b6dde4db31a8562c00dd30d510fc90b3496160
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/tests/test_app.py
@@ -0,0 +1,17 @@
+import pytest
+from app.main import greet
+
+def test_greet_returns_string():
+ """Vérifie que la fonction retourne bien une chaîne de caractères."""
+ result = greet("Alice")
+ assert isinstance(result, str), "Le résultat doit être une chaîne de caractères."
+
+def test_greet_output_content():
+ """Vérifie que la fonction génère la phrase attendue."""
+ result = greet("Bob")
+ assert result == "Hello Bob!!", f"Résultat inattendu : {result}"
+
+def test_greet_with_empty_string():
+ """Vérifie le comportement si l’entrée est vide."""
+ result = greet("")
+ assert result == "Hello !!", "Le résultat doit gérer les entrées vides."
diff --git a/hf_space/hf_space/hf_space/hf_space/notebooks/.gitkeep b/hf_space/hf_space/hf_space/hf_space/notebooks/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb b/hf_space/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e9436e78d88274dbb96302bbbe948e2247093155
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb
@@ -0,0 +1,2319 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d60f947b",
+ "metadata": {},
+ "source": [
+ "# TechNova Partners\n",
+ "\n",
+ "**Sommaire :**\n",
+ "1. Chargement des données \n",
+ "2. Config SQL\n",
+ "3. Diagnostic du déséquilibre\n",
+ "4. Analyse exploratoire\n",
+ "5. Feature engineering\n",
+ "6. 1ᵉ itération : modèle dummy\n",
+ "7. 2ᵉ itération : équilibrage (oversampling simple / `class_weight`), features, CV\n",
+ "8. Interprétation globale & locale (importance des features, SHAP)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7681d4a4",
+ "metadata": {},
+ "source": [
+ "## 0) Imports & configuration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "1cf33291",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/steph/Code/Python/Jupyter/projet_04/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "# === Imports & configuration ===\n",
+ "import warnings\n",
+ "import warnings\n",
+ "import os, sqlite3\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from datetime import datetime\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_predict, GridSearchCV, learning_curve\n",
+ "from sklearn.metrics import (\n",
+ " confusion_matrix, ConfusionMatrixDisplay,\n",
+ " classification_report, f1_score, recall_score, precision_score, \n",
+ " roc_auc_score, precision_recall_curve\n",
+ ")\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from imblearn.pipeline import Pipeline as ImbPipeline\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.dummy import DummyClassifier\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "from scipy.stats import f_oneway, chi2_contingency\n",
+ "from manet_projet04 import load_settings,shap_global, shap_local\n",
+ "from brand.brand import Theme, load_brand, make_diverging_cmap\n",
+ "# Application du thème OpenClassrooms pour uniformiser les prochains graphiques\n",
+ "cfg = load_brand(\"brand/brand.yml\")\n",
+ "Theme.apply()\n",
+ "cmap_heatmap = Theme.colormap(\"diverging\", start=\"primary\", end=\"secondary\")\n",
+ "\n",
+ "\n",
+ "HAS_SHAP = True\n",
+ "HAS_IMBLEARN = True\n",
+ "RANDOM_STATE = 42\n",
+ "np.random.seed(RANDOM_STATE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "016da610",
+ "metadata": {},
+ "source": [
+ "## 1) Paramètres & chemins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ebd95229",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "CFG = load_settings('manet_projet04/settings.yml')\n",
+ "\n",
+ "RANDOM_STATE = CFG[\"RANDOM_STATE\"]\n",
+ "PATH_SIRH = CFG[\"PATH_SIRH\"]\n",
+ "PATH_EVAL = CFG[\"PATH_EVAL\"]\n",
+ "PATH_SONDAGE = CFG[\"PATH_SONDAGE\"]\n",
+ "COL_ID = CFG[\"COL_ID\"]\n",
+ "TARGET = CFG[\"TARGET\"]\n",
+ "NUM_COLS = CFG[\"NUM_COLS\"]\n",
+ "CAT_COLS = CFG[\"CAT_COLS\"]\n",
+ "SAT_COLS = CFG[\"SAT_COLS\"]\n",
+ "FIRST_VARS = CFG[\"FIRST_VARS\"]\n",
+ "SUBSAMPLE_FRAC = CFG[\"SUBSAMPLE_FRAC\"]\n",
+ "SQL_FILE = \"merge_sql.sql\"\n",
+ "DB_FILE = \"merge_temp.db\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d373963",
+ "metadata": {},
+ "source": [
+ "## 2) Chargement SQL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "26b72a3b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Tailles initiales : 1470 1470 1470\n",
+ "\n",
+ "=== Vérification des clés id_employee avant fusion ===\n",
+ " sirh : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
+ "evaluation : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
+ " sond : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
+ "Merge réussi — shape finale : (1470, 32)\n",
+ "Aperçu des identifiants : [1, 2, 4, 5, 7]\n",
+ "Effectifs par genre :\n",
+ "genre\n",
+ "M 882\n",
+ "F 588\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "Répartition en % :\n",
+ "genre\n",
+ "M 60.0\n",
+ "F 40.0\n",
+ "Name: count, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# --- Config ---\n",
+ "DB_FILE = \"merge_temp.db\"\n",
+ "SQL_FILE = \"merge_sql.sql\"\n",
+ "COL_ID = \"id_employee\"\n",
+ "\n",
+ "# --- Fonctions utilitaires ---\n",
+ "def safe_read_csv(path):\n",
+ " \"\"\"Lecture sécurisée d’un CSV.\"\"\"\n",
+ " try:\n",
+ " return pd.read_csv(path)\n",
+ " except Exception as e:\n",
+ " print(f\"[ATTENTION] Lecture impossible {path}: {e}\")\n",
+ " return pd.DataFrame()\n",
+ "\n",
+ "def clean_text_values(df):\n",
+ " \"\"\"Nettoie les valeurs texte incohérentes ou équivalentes à NaN.\"\"\"\n",
+ " df = df.copy()\n",
+ " to_replace = [\n",
+ " '', ' ', ' ', ' ', 'nan', 'NaN', 'NAN', 'None',\n",
+ " 'JE ne sais pas', 'je ne sais pas', 'Je ne sais pas',\n",
+ " 'Unknow', 'Unknown', 'non pertinent', 'Non pertinent', 'NON PERTINENT'\n",
+ " ]\n",
+ " df = df.replace(to_replace, np.nan)\n",
+ " for col in df.select_dtypes(include='object'):\n",
+ " df[col] = df[col].replace(to_replace, np.nan).str.strip()\n",
+ " return df\n",
+ "\n",
+ "# --- Lecture des fichiers ---\n",
+ "sirh = safe_read_csv(PATH_SIRH)\n",
+ "evaluation = safe_read_csv(PATH_EVAL)\n",
+ "sond = safe_read_csv(PATH_SONDAGE)\n",
+ "\n",
+ "print(\"Tailles initiales :\", len(sirh), len(evaluation), len(sond))\n",
+ "\n",
+ "# --- Harmonisation des clés ---\n",
+ "if 'id_employee' in sirh.columns:\n",
+ " sirh['id_employee'] = sirh['id_employee'].astype(str).str.extract(r'(\\d+)').astype(int)\n",
+ "\n",
+ "if 'eval_number' in evaluation.columns:\n",
+ " evaluation['eval_number'] = evaluation['eval_number'].astype(str).str.replace(r'[^0-9]', '', regex=True)\n",
+ " evaluation['eval_number'] = evaluation['eval_number'].astype(int)\n",
+ " evaluation.rename(columns={'eval_number': 'id_employee'}, inplace=True)\n",
+ "\n",
+ "if 'code_sondage' in sond.columns:\n",
+ " sond['code_sondage'] = sond['code_sondage'].astype(str).str.replace(r'[^0-9]', '', regex=True)\n",
+ " sond['code_sondage'] = sond['code_sondage'].astype(int)\n",
+ " sond.rename(columns={'code_sondage': 'id_employee'}, inplace=True)\n",
+ "\n",
+ "# --- Nettoyage des valeurs texte ---\n",
+ "sirh = clean_text_values(sirh)\n",
+ "evaluation = clean_text_values(evaluation)\n",
+ "sond = clean_text_values(sond)\n",
+ "\n",
+ "# --- Vérifications ---\n",
+ "print(\"\\n=== Vérification des clés id_employee avant fusion ===\")\n",
+ "\n",
+ "for name, df_ in [('sirh', sirh), ('evaluation', evaluation), ('sond', sond)]:\n",
+ " total = len(df_)\n",
+ " uniques = df_['id_employee'].nunique(dropna=True)\n",
+ " doublons = total - uniques\n",
+ "\n",
+ " print(f\"{name:>6} : {total:5d} lignes | {uniques:5d} identifiants uniques | {doublons:4d} doublons\")\n",
+ "\n",
+ " # Liste les ID en doublon si nécessaire\n",
+ " if doublons > 0:\n",
+ " dup_ids = (\n",
+ " df_['id_employee']\n",
+ " .value_counts()\n",
+ " .loc[lambda x: x > 1]\n",
+ " .index.tolist()\n",
+ " )\n",
+ " print(f\" ⚠️ Attention : {len(dup_ids)} doublons détectés dans {name} → ex: {dup_ids[:5]}\")\n",
+ "\n",
+ "if 'id_employee' not in sirh.columns:\n",
+ " raise ValueError(\"La clé 'id_employee' doit exister dans SIRH après harmonisation.\")\n",
+ "\n",
+ "# --- Merge via SQL (traçable et sécurisé) ---\n",
+ "if os.path.exists(DB_FILE):\n",
+ " os.remove(DB_FILE)\n",
+ "conn = sqlite3.connect(DB_FILE)\n",
+ "\n",
+ "sirh.to_sql(\"sirh\", conn, index=False)\n",
+ "evaluation.to_sql(\"evaluation\", conn, index=False)\n",
+ "sond.to_sql(\"sond\", conn, index=False)\n",
+ "\n",
+ "sql_query = f\"\"\"\n",
+ "SELECT *\n",
+ "FROM sirh\n",
+ "INNER JOIN evaluation USING ({COL_ID})\n",
+ "INNER JOIN sond USING ({COL_ID});\n",
+ "\"\"\"\n",
+ "\n",
+ "with open(SQL_FILE, \"w\", encoding=\"utf-8\") as f:\n",
+ " f.write(sql_query)\n",
+ "\n",
+ "df = pd.read_sql_query(sql_query, conn)\n",
+ "conn.close()\n",
+ "\n",
+ "print(f\"Merge réussi — shape finale : {df.shape}\")\n",
+ "print(\"Aperçu des identifiants :\", df['id_employee'].head().tolist())\n",
+ "\n",
+ "df['genre'].value_counts()\n",
+ "\n",
+ "# Comptage\n",
+ "genre_counts = df['genre'].value_counts()\n",
+ "\n",
+ "# Pourcentage\n",
+ "genre_pct = (genre_counts / genre_counts.sum() * 100).round(2)\n",
+ "\n",
+ "print(\"Effectifs par genre :\")\n",
+ "print(genre_counts)\n",
+ "\n",
+ "print(\"\\nRépartition en % :\")\n",
+ "print(genre_pct)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be1facc5",
+ "metadata": {},
+ "source": [
+ "## 3) Déséquilibre de la cible"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9db19117",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Effectifs par classe:\n",
+ " a_quitte_l_entreprise\n",
+ "0 1233\n",
+ "1 237\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "Répartition (%):\n",
+ " a_quitte_l_entreprise\n",
+ "0 83.88\n",
+ "1 16.12\n",
+ "Name: count, dtype: float64\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# === Diagnostic du déséquilibre de la cible (version robuste) ===\n",
+ "if TARGET not in df.columns:\n",
+ " raise ValueError(f\"Cible {TARGET} absente. Assurez-vous qu'elle est dans le fichier sondage.\")\n",
+ "\n",
+ "# Normalisation des valeurs de la cible (convertit oui/non, True/False, etc.)\n",
+ "df[TARGET] = df[TARGET].astype(str).str.strip().str.lower().map({\n",
+ " '1': 1, '0': 0,\n",
+ " 'oui': 1, 'non': 0,\n",
+ " 'True': 1, 'False': 0,\n",
+ " 'quitte': 1, 'reste': 0\n",
+ "})\n",
+ "\n",
+ "# Suppression des valeurs non mappées\n",
+ "df = df[df[TARGET].isin([0, 1])].copy()\n",
+ "\n",
+ "# Vérification du contenu\n",
+ "if df.empty or df[TARGET].nunique() < 2:\n",
+ " raise ValueError(f\"La variable cible {TARGET} ne contient pas de valeurs valides (0/1) après nettoyage.\")\n",
+ "\n",
+ "# Comptage et affichage\n",
+ "counts = df[TARGET].value_counts().sort_index()\n",
+ "ratio = (counts / counts.sum() * 100).round(2)\n",
+ "print('Effectifs par classe:\\n', counts)\n",
+ "print('\\nRépartition (%):\\n', ratio)\n",
+ "\n",
+ "# Bar plot sécurisé\n",
+ "if counts.empty:\n",
+ " print(\"[INFO] Aucun effectif valide à afficher pour la cible.\")\n",
+ "else:\n",
+ " plt.figure()\n",
+ " counts.plot(kind='bar', color=Theme.PALETTE[:len(counts)])\n",
+ " plt.title('Répartition de la cible (0=reste, 1=quitte)')\n",
+ " plt.xlabel('Classe')\n",
+ " plt.ylabel('Effectif')\n",
+ " plt.savefig(f\"output/repartition_cible.png\", dpi=300)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "510220d9",
+ "metadata": {},
+ "source": [
+ "## 4) Analyse exploratoire"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "be2df4ea",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Âge moyen : 36.9\n",
+ "Ancienneté moyenne (années) : 7.0\n",
+ "Taux d’attrition global : 16.1%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "INSIGHTS CLÉS :\n",
+ "• Les employés du département commercial présentent le taux d’attrition le plus élevé.\n",
+ "• Les employés à faible satisfaction environnementale quittent plus souvent l’entreprise.\n",
+ "• La corrélation entre revenu mensuel et ancienneté semble faible et non linéaire.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# --- Dossier de sortie ---\n",
+ "os.makedirs(\"output\", exist_ok=True)\n",
+ "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+ "\n",
+ "# --- Statistiques descriptives ---\n",
+ "print(\"Âge moyen :\", round(df['age'].mean(), 1))\n",
+ "print(\"Ancienneté moyenne (années) :\", round(df['annees_dans_l_entreprise'].mean(), 1))\n",
+ "taux_attrition = df['a_quitte_l_entreprise'].mean() * 100\n",
+ "print(f\"Taux d’attrition global : {taux_attrition:.1f}%\")\n",
+ "\n",
+ "# === GRAPHIQUE 1 : Taux d’attrition par département ===\n",
+ "plt.figure(figsize=(8,5))\n",
+ "dept_attrition = df.groupby('departement')['a_quitte_l_entreprise'].mean().sort_values(ascending=False) * 100\n",
+ "sns.barplot(x=dept_attrition.index, y=dept_attrition.values, hue=dept_attrition.index, palette=Theme.PALETTE)\n",
+ "plt.title(\"Taux d’attrition par département\")\n",
+ "plt.ylabel(\"Attrition (%)\")\n",
+ "plt.xlabel(\"Département\")\n",
+ "plt.xticks(rotation=45)\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(f\"output/attrition_par_departement.png\", dpi=300)\n",
+ "plt.show()\n",
+ "\n",
+ "# === GRAPHIQUE 2 : Distribution de l’ancienneté selon l’attrition ===\n",
+ "plt.figure(figsize=(8,5))\n",
+ "sns.kdeplot(\n",
+ " data=df,\n",
+ " x='annees_dans_l_entreprise',\n",
+ " hue='a_quitte_l_entreprise',\n",
+ " fill=True,\n",
+ " common_norm=False,\n",
+ " palette=Theme.PALETTE\n",
+ ")\n",
+ "plt.title(\"Ancienneté et probabilité de départ\")\n",
+ "plt.xlabel(\"Années dans l’entreprise\")\n",
+ "plt.ylabel(\"Densité\")\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(f\"output/anciennete_attrition.png\", dpi=300)\n",
+ "plt.show()\n",
+ "\n",
+ "# === GRAPHIQUE 3 : Satisfaction environnement vs attrition ===\n",
+ "plt.figure(figsize=(8,5))\n",
+ "sns.boxplot(\n",
+ " data=df,\n",
+ " x='a_quitte_l_entreprise',\n",
+ " hue='a_quitte_l_entreprise',\n",
+ " y='satisfaction_employee_environnement',\n",
+ " palette=Theme.PALETTE\n",
+ ")\n",
+ "plt.title(\"Satisfaction environnement de travail selon l’attrition\")\n",
+ "plt.ylabel(\"Satisfaction environnement\")\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(f\"output/satisfaction_env_attrition.png\", dpi=300)\n",
+ "plt.show()\n",
+ "\n",
+ "# === GRAPHIQUE 4 : Lien entre revenu et ancienneté ===\n",
+ "plt.figure(figsize=(6,5))\n",
+ "sns.scatterplot(\n",
+ " data=df,\n",
+ " x='annees_dans_l_entreprise',\n",
+ " y='revenu_mensuel',\n",
+ " hue='a_quitte_l_entreprise',\n",
+ " alpha=0.7,\n",
+ " palette=Theme.PALETTE\n",
+ ")\n",
+ "plt.title(\"Lien entre revenu mensuel et ancienneté\")\n",
+ "plt.xlabel(\"Années dans l’entreprise\")\n",
+ "plt.ylabel(\"Revenu mensuel (€)\")\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(f\"output/revenu_anciennete.png\", dpi=300)\n",
+ "plt.show()\n",
+ "\n",
+ "# === Insights automatiques à commenter ===\n",
+ "print(\"\\nINSIGHTS CLÉS :\")\n",
+ "print(\"• Les employés du département commercial présentent le taux d’attrition le plus élevé.\")\n",
+ "print(\"• Les employés à faible satisfaction environnementale quittent plus souvent l’entreprise.\")\n",
+ "print(\"• La corrélation entre revenu mensuel et ancienneté semble faible et non linéaire.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "79804aff",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# === Visualisation des variables numériques selon le statut de départ ===\n",
+ "\n",
+ "# Choix de variables numériques pertinentes à comparer\n",
+ "num_vars = [\n",
+ " 'revenu_mensuel',\n",
+ " 'annees_dans_l_entreprise',\n",
+ " 'nombre_participation_pee',\n",
+ " 'distance_domicile_travail',\n",
+ "\n",
+ "] \n",
+ "\n",
+ "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
+ "axes = axes.flatten()\n",
+ "\n",
+ "for i, var in enumerate(num_vars):\n",
+ " sns.violinplot(\n",
+ " data=df,\n",
+ " x=TARGET, y=var,\n",
+ " ax=axes[i],\n",
+ " hue=TARGET, \n",
+ " palette=[Theme.PRIMARY, Theme.SECONDARY]\n",
+ " )\n",
+ " axes[i].set_title(f'{var.replace(\"_\", \" \").capitalize()} selon le statut de départ')\n",
+ " axes[i].set_xlabel('')\n",
+ " axes[i].set_ylabel(var.replace('_', ' ').capitalize())\n",
+ "\n",
+ "plt.suptitle(\"Comparaison des variables numériques selon le statut de départ\", fontsize=14, weight='bold')\n",
+ "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
+ "plt.savefig(f\"output/variables_numeriques_statut_depart.png\", dpi=300)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "6125c5aa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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XESJDGLgSEUURuZEoVbKYujmVIXCCt2+Vm7A9HvtCLZM4cWLUqFEV5cu74NSps6qE9eSfp3EkWPs8ucmKjDJGdsR0MVj7vKpVKur+luD76qVTevNeuHhJvcuNlrRxM0R6xQyLtNXVBlJFChfU60hEggq5iQleuiBBvrQRExIcWcZ1CHObYQWuFy8G+36Vg76f0N6Aa0kbVW27tyaN6uuCViElrpI+chMm+yj7JUFocGG1XxbyMCLk/P81PbWlNPL655+7qm3i6TPncfDQEV2Q8OWLP76HlCJKPtRuQxu0avN4uzYt0LvvYPW/lFCHvOGsWMFVb36nDOnVzbp23WEFrtpOzMSgISPUKyR5UCHHKWTP2HKzr5UlS1B6+vl9isQ3l5L2nCpoNbRfUrIu7S0NOXPmfJiBq5BSYwlc5VzesnWnKp2SBwwifvz4aNigju6Gv0SJYuol+X7xkuXqWBw4eCTC60GZwBofEcmePat6SeAmgZGUGsr1Rh5GhJdvarvVCPF/dRW4ius3boYZuF64EHRcGzWRhwuaBwyG8nqF8i66Tux69h6IYb+MhXOp4qq0sEb1Kqo0MjJcyjqroFUrS+ZMKnD9nrxhiJRySyCpJdcxbeCqrbmifXihpd3u914D5KFDREGr+N+1G7q/69WppXfeNahf+z8HrnId1D6Mld82Q2NwS3Ar1+FKlcqjcaN6qiRaAnhpi50/f171W1WpQjm4uJRWDzSJIsKqwkREUUhKVoWUHG0KrNIngWChQgXCXEYGm0+ZOgsqVHbDLyPHqWqPwQOd8KrhGaIttY2IVCk2dhm5aY/IkyeGS2rEx49BnZJoS3GDC1ll89270B1DGd6m5ibREC9vL6O/3+s3b3R/p07tGOrz1KmDquMZGgc1vPUb+uy/pqeQwKZEqfLIkr2AKoGWm8LkyZKparbfk2+Cfz/tsqkNVEMMnj6G0kJKc4LTllBGtE/GpIk8CDJUfTL4Nq2t433/uZP0O4/V07DzoZDaEtp8INVUpZMhKaUV9eq66T0okQde0qmadLDWuWtvbNy0DdmyZgn3O0lJsfa4R0RKvqUzJ+mkrGnzdpg7T1N6pz0Hw0qzJEkSh3nOBj+/Q3prRIdK8mBDtisP8o4c3IPKlcqr0kypDSHpJNX48xUsqTpB0j6YMYaU5EUmL4acJoFXeOzt9YO14AGllNAbmq7dxvdeA4y9vgfvQC144GzoWBoSPC0MpYMx+x/83JAS6AH9eqkHQ1LDRaqEz56zEG51GquOnbTVxYnCwxJXIqIopC15kx99bVuxkiWKhlnKJNUQx46frP6WNlID+/eGk1MG1e7LmDachhg7TquDQ9BNl7R5cwh2k3fjxi04OqbUTdPeWEvpxcb1mqqTISVJnNiobRm68QwZjCRMaKf7W0r9pOqoIcFv6ENt095B7/sFJz1eShs3KfmVUq4UyZOHGww/fhw0LXny0DeOtrY2kToe/zU95UayRq2GePDgoQokpXqpVH2VAEYCkpC9/kZG8uTJVJpIHn5ioO2xts2wdt6QjCkNMiT4Me/Xp0eYJYiGHnx87zaNPVYSaEo6z5vzm8Hl0gYrdTNEjnOLZo0xc/Z8HDt+Ul0btNX0W7fUVBMWUo1Yqjl7e3urdtbz50xHyZLF1PGQXljD3u+w819ITZq1VbU65HstXTxHVfOUh2tlXKqE265aW2pu6JwKfn6HlNAu6LhOmjhGlfYaoq02KyX4ez22q3N03/6DOH7ilCqlk7bMUqOgd99BWLsmdFtnQ4zJF8Gr/YcszX5voHmHsesP65r/X68BtjY2kQ7aQ17/Hj0KOoeNSYv37z+Ee77Kg4aw+lMokD+v7qHB1CnjMWH8SPVwRqr+Hzv+J06cPKXyVuNmbXH/n79UtWKisLDElYgoChUvXkT3VD+omrCmFDas9l9aw4YMUEGruHxF0zY2OG3nKRGVJAWfLzyFCubX/R28GrN0lFGydEU1NIN2aBftzYfczDimSqna18pLqoFKFUjpQCq8p/hyk5Ypk6bzmfMXLuHffx/pDfUQ8oZZAmZtWkigJFUItdvUtiGWZQwFMVoFC+bT/e2xd79emo0cPUG1I5NhGa5du4FChfLrqqqt37hFL/CTEiptW1mpFhi8KqoxaW7os/+anlJlVoJWIYGHtG2UoFVu9kMGGCH3IaJSSLnhlurc4vLlq7rqoNo8vWLVWr2qmFGlQP6g4+Xj66tLE3lJ+ksJjey7BFnfw5g0CO9YSfXVwoUK6PYpvq2t2i/Jn+HlQy1pA6l96CD5T1sNXZoJaEmVXQlatVXWJXiX4yHHwdjvFh4JjCVoFcWLFVFtDCU95SHOzVuaqvlhWbdBU7VZa8fO3bq/8+fTpJEhBQrof6ZNP231e6mdYpcggfoOMrSLDDFTrkINdR1q2aKpajt5+8YlXaD356moHToleEl1yAc1wZsbRLXvvQYYe6ylzwWtXbs99PK8PBg1RNrLGyrtDd6sREseQMg1Rzx+8lR12KX9DvKQ4cSJU6rmjJwbcq36ZcRY1K3fTHWwVqGCK8aNHYGjhz3QuZOmbwC5rv/1l6aNO1FYWOJKRBSFJGgtVrSwrnfaiAJXe/ugKoJSwtqyRRPVc+b8BUt106VdoJCbBLlpkWpWf/39P9WuUaqqSedB30N6O5XSXin5GTDoFxXw5MyZHctXuOvGS9S2V5RSIRkrUNSp3wyDBvRW1fomTZmua4sqHS6Fp1WLpqrDDrlxr1ytjqo2Jjfp2p41Q2rTqpmaX0poZUxL6bxK5h8xaoK6yZHvHl7vqWnTplE9xEpwIe3aZLzAJo3rq/3V3rhJqbK2sxfpAXf5ijWqLa4E7t27dlRp89uMubr2r0MHa3ok/q/+a3oGzzfSVlI6rZHSQinR01br0+YbIb0ea61cvRY3bt5SHceEVe2wR/dOaN22i7rZlWMlvc9K6arsszaIalC/DrJlM1x69j2krbN0IiOd/UjbTklmqcHg4blfdSQmWrVs+t35Paw0iEib1s1U6ZDkBanOL6XBkgekl2zJK1LqJvksItKmT4IJCcC1VaxbNm+iF4jYB6syPGfeIpXmEsBNmx40Bm3w4xpZ0p5WAmHJI9JhkIyRLMHzwkXLdDUhwlq/9KwseUweVmzdtgtnz11Q06UZhFw3wiKBTKJEQ9U1RXqOlSqm+fPnUevTPjAb+ctgVKxYDi9evsK8+UvUNCl5lo7mJK8fOnxMNwaudFoVlSQ4lOqrsn/ykEY6JpPvuNp9vSoNjC5RcU0Nj9QQKOdaVh1neTgg1z9pZyptmhcsXGZwGTn/tKQd+8TxI3Hr9h0MHPxLqHnl90iup/IdpNlC9Zr11TX0wYN/1e+K5DF5WCm/M4kTB2DWnIXqui3ns+Q1qVEg827eskOtT86pDBmC+j4gMohjBnH8TuYB5gHmge/PA8HHOdROGzZkgN44j34fX4YaE1A7hqaMAWloXDsZH1H799LFc3XrLlSogN58lSqWC7Uff10+HWo/wxqfb8G8GeGO46rdd3n16N4pzHH4OndqF2FayTiphsZotLe31xs/VTv/R+9nAcWKFg5znEP31Usj3Kakc+rUjgbXIWOgasfplJf328dhbk9ebdu0MDiOa8g0DTnWY8jxUSObnobGUJT9CGvMWW3ekfEetevYuW1DqPm0Y9iGtZ8yHm1Y+yfH8c3LBxGO32lMOgV/HT+y1+C4mPKSMYAfP7z53WNOhpUG4Y3jKa+vn98F1HKrHmZa/DphtNHXi9kzp+gte/PahVDnSNasmcM9rqWdS4Yax1XGZTW0PUPHNqwxabXrl7GgfT+80LuuyBisNapXiXD8zbDywYZ1K8Icm7ZokULq3NOO3exWs1qYaS3bk/GLw0vj4GOe9u/bM8zrdfDrZJ9e3Qxur07tmuGO4xrynJH/Q17HwtuusdeA8L5TeOn+95UzBsdxDT72avA8f/7MUb3xgLUvV5cyAYkSJQo1//Mn/wRkzpwxzOvrof1/6OZdu2ZZmHkgrO/FF9MggOO4EhH9mPFchZS+Bu8QJCQpZZChQ6Qdp5SGSOmHjFl68dxxXdU4qTamtXjBLBQtUki1i5LSsmzZwm7jaQwpxdznsR0VK5RT1XOl2pw8CZcxNPd5btfb99kzp+rGVJR903Y6JWMPypinEZHSmkP7d6m2vFKSJNuSaq6nTx40OMSFpMfhg7vVGJNSoiNP+OU7Sw+yB/buRLOmQWOEhkWqG58/cxRdu7RXaSvVgaVdq1SRPHvqsBqnU0u+04lj+9TYmXLctN9RhmuQcSB/Xzo/SkpboyI9ZT+2bFqjqp/K8D2yvOQhGRdXhubRVnHWlo7WqFFFlXBLtT1JR2lHGLwTI0NkH3Zu26CGcpF0l7wgx0nGRD1xdK8qHYpqUuoqx0tKcqRDGTleMqSJlACfPLbfYMdZxvqeNBBSIippLUPuSImp5EspoZN8IefukMFBYwFHRMbf1FZJL1G8aKgSazlHZGgUuQZImst2pEqvjNnZvl0rNY+UmP2XMTjluPbt3V2dB/JdJB1GDB+EZUvmqs+lpEzbcVTI/CZjgEp+k/2sUN4Vx4946vU6HRY5344f2auqk2rTX65dw4cOwP69O3TXOikNljSVMZ7le0sayDSpoi9pJ+dssWJFENWmTB6nvptsR/ZNjo0MU9a+bUtEp/96TY2IHNtjhz1QvpyLOmbSoZbUGNi4znCbWukZetf2Depd0kHyyC/DBsJj9xZYWYWuoizrk2Mi+UlKVyVvp0qVUtWKOHlsH8qVK6ubV4Y/kmkyFI5cl2Ve+c2RWhUyvva0qZrq80ThsQh8ckRERERERGZO2qBmzKJpYyuBpHbcZSJTx86ZiIiIiIiIyKQxcCUiIiIiIiKTxsCViIiIiIiIjCZDgSVJnh4HDhyOcN7Dh4+hpHMFJLBPpcYaHzlqvK4H/MjgcDhERERERD8J6RwpwF8zXjDR95Cx2KtUr6uGt4rImTPnUK1mfdRyq45RI4bg8pW/1FjW3u/fY+b0yZHaLgNXIiIiIiIiCpeMI79q9ToMGDRcjfNtDBm/WUYG2LBuheqhvGrVSqpH94GDR2DQgD6R6i2eVYWJiIiIiIgoXFev/o0u3fqgVYumWL1icfgzA/j06RMOHzmOenXc9IaTkyGyvn79GmrorYiwxJWIiIiIiIjClT59Wty5eRlp06bBkSPHw58ZwN279/H582dkz64/ZrWMmSxjC1+/fhORwcCViIiIiIjIjNk5RFwl94PX03A/T5IkCZIkMX6bXl5e6t0+YcJQnyVMaKfauUYGA1ci+rlYhb54EhERkZn5GrmgKCZNK1IY5ujbt/DbwQavPmwMBq5E9NOZVjBbTO+CWRlw6RZejekX07thVpKNmo73Xn4xvRtmI6GDDdMzGtLUe+3SqF7tT82+WQf4+frG9G6YDZt4Mb0HpuVDBKWp0SFRIgf1/v7Dh1CfvX//AQ729pFaHztnIiIiIiIioiiVOXNGWFlZ4c6du6HGgPX19VW9DUcGA1ciIiIiIiKKUtbW1nB1KYOt23eqoXS0Nm7aijhx4qB8ubKRWh+rChMREREREcUQy0i29TRVMvzNpUtXVK/D8hIjhg9C+Uo1Ub9hC3Rs3xpX//ofRo6egK5d2iN9+nSRWj9LXImIiIiIiOg/efr0GUqWroily1bqprm4lMb2Letw/8FD1G3QHPMXLsXQwf0w47dJkV4/S1yJiIiIiIjIaK6uZRDg7603zckpQ6hpws2tmnr9VwxciYiIiIiIYoiFpXlUFY5urCpMREREREREJo2BKxEREREREZk0Bq5ERERERERk0tjGlYiIiIiIKIZYWrAs0RhMJSIiIiIiIjJpDFyJiIiIiIjIpDFwJSIiIiIiIpPGNq5EREREREQxxMKC47gagyWuREREREREZNIYuBIREREREZFJY+BKREREREREJo1tXImIiIiIiGKIJdu4GoUlrkRERERERGTSGLgSERERERGRSWPgSkRERERERCaNbVyJiIiIiIhiiIUlx3E1BktciYiIiIiIyKQxcCUiIiIiIiKTxsCViIiIiIiITBrbuBIREREREcUQSwuWJRqDqUREREREREQmjYErERERERERmTQGrkRERERERGTS2MaViIiIiIgohlhYcBxXY7DElYiIiIiIiEwaA1ciIiIiIiIyaawqTET0AxRr0xGFm7fBgkrORs1vYWmJIi3bIV+9RrBLkQpvH97HmWULcXPfnlDz5qldH0VatIVDmnR4/+IZLq5dhcsb3WGOLO0TIUElN8TNmBWwssKXe3fwcd8OfHv3Jtzl4pevjvhlKhr87PWkYQj45Bd6W4mSIHG3QfDesAJf/rkBc+fn54clSxdi7z5PvH37FtmyZkPXLj1QrFhxo5bfsXMb3NeuxuPHj5EyRQo0adIcjRo20ZsnICAAGzetx5atm/Dvvw9hY2ODwoWKoEf33nByyghzwvSMOs+8vDF7/1Gcu/cAX75+RdGM6dGncjmkSZwo3OXe+fhg/sHjOHnnHrx9/ZDDMQXaly2JEpnDz2vTPA5iw9mL2NG7E1IncoA5evr0KWbMnImzZ8/iy5cvKFq0KAYMGIC0adJEmK8XLloET8/A60S2bOjRvTuKFw/7OvHo8WPUr18f03/7Dc7Oxv0GEhnCElciMnn37z+ARRx7LF22ErFRRueycO7WO1LLuPQdDJc+g/D48kUcnjYBvm/fwG3yTOSoWkNvvkJNW6Hq6F/x7tFDHJn+K17evI6KQ0ehWLvOMDcWNvHh0LqbClp9zxyD77H9iJsuAxza9oCFbYJwl42TwhFf37zC+63uoV4BXz4b2JYt7Ju0g0XcePhZ/DJiCNa4r4KrSzn07d0f/v7+6Nm7Gy5duhjhsuvWu2Pc+NFImzYd+vbpj2zZcmDK1F+xfMUyvfkWL1mIqdMmIVXKVOjbZwCaNG6u1t+uQys8evQvzAnTM2p4+fqiy8r1KmhtWrywCjyv/vsEnZavU4FpWD75+6Pbqo3w+Os6qufLhZ4Vy+LL12/otWYzjt/6J8zlLtx/iI1nI87zsZmXlxc6dOyogtbmzZqhU8eOuHr1Ktq2bauC0fAMGToUq1atQjlXV/Tv109dJ7p1746LFw2nmbe3N/r06aMCXgqbpaVFtL/MAUtciYiiUcHGLeDafwisIhEAJUqfQQWkF9auxOGpE9S0v7ZtQpNl7nDtNwS3DuzDN/8vsLZLiNLd++D24QPY0a+bmu/K5vWqVKtkh274a9tG+EZwExKb2JYsC8vESfBuyUx8ffpITft85wYSdekP29Ll4bN/V5jLWqVIBf/HD/HprwsRbscqhSPsG7WGVdIU+FmcPXsGR44eVsFk82Yt1bQaNWqiSbOGmD5jKlavWhfmsu/fe2PBwrlwKVsO06bOUJ2MNKjfCEOHW2DpssWoU7suEidOglevXuL35UtRrlwFTJ08Xbd8hfIV0bxlEyxZughjRo+HOWB6Rp21py7gyVsvrOzYEjlTp1LTSmbJiBYLV2LFiTOq5NWQnZf+wu3nLzGhvhsq58mhptUqmBcN5i7DgkPHUSZb5lDL+Hz+jLE7PBFHanN8/QpztXrNGlUzYq27O3LlyqWmlXJ2RuPGjfH78uUqIDXkzJkzOHz4MAb074+WLTXXiZpubmjYsCGmTpuGdWvX6s1/+/Zt9OvfHw8fPvwB34p+BixxJSKKJs1WbkSFISPx8OxpPLv2t9HL5ahSA5ZWVri8Iai6b8C3b7i0wR12yVMiXeGialpml/KIl8AuVLXgi+tWIa6tLbK4VoI5sc5TCP6PHuiCVvH15TNVXVg+C4tFPGtYJkoM/1fPI9yGTdHSSNSpHyysbeF38TR+Fp579yBOnDioV7e+bpqtbXzUrlUX129cw8OHD8Jc9tjxo/Dx8UHDBo30esZs0rgZPn3yUwGxuHjpAr5+9Udttzp6y2fJkhWZM2fG5SuXYC6YnlFn79/XkCdtal3QKrKkSI4iGdNj399hV+H3+/xFLVMhVzbdNJu4cZE7jSP+efFKPeALada+I/D/9g21CuSBOfPw8EC+vHl1QavImiWLqi4sn4Vlj4eHuk7Ub9BANy2+rS3q1qmDa9eu4cGDoOvEuvXr0aRpU3z48AH16taNxm9DPxMGrmQ2nDLnQb/+Q1GpSm3Y2qVA0+aaKi9du/dFqjRZYJMgOYoUd8GePXt1y3Tu2hvJU2VUVV2CGzV6AuwcHNUFV1y7dgNutRvBPnEaNb1mrYa4efN2qKqsW7buQJNmbeCQJK2aV/5+/vyFbj7X8tVRumxlvW0dOHBYLXvkyPFIfV9ZZt78xWjXoZvaXuJk6dGrz0D4+vpi8JCR6nslSZ4ebdt3VdO0vn37hqnTZiFrjgKwjp8MmbPlw7TfZuv9iLdp1wUVK9fC6jXrkDNPETVfjtyFscZ9vd4+zJ6zQH0u6S1p3LptZzx79lzvmLRo1UFvGanuK/suaaYVUfrGVvaOjtg3bgS29OiALz4fjV4uVa48+PT+vWrXGtzz6/9T7ylz59W859LcXD0PERRr55P1mAupumuVJBn8nwQFrVr+Tx/Byt4BFnYJDS5rlTwVLCws8fVlYN6ME1fWaHDeOKlS49OVc3i7YCq+PLyHn8X169dUG1MJVoPLmSOner92/Vq4y6p5c+bWm54ju3ZZTX4sW8YF69dtRqFCRUKtw+vdO1hZWcFcMD2jhrRLffzWC7mCBa1aORxT4eX7D3j1XvM7HVJL52JY1bElrCyDbnUlKJWgNYV9wlDDj5z+5x62XbiC4W5VkMDGGuZKqu4+evQIuXLrn69CAtmXL1+qlyESnGbMmFEFq8HlDAyA5XOtWzdvws3NDZs3b0aBAgWi/HvQz4mBK5mVOfMWoUD+vNi5bT26dGqPCpVrqWByzKhh2LbZHdmyZoZbncbYtUvzRLFl8yZ49eq1Ch6DW7t+E+rWqQk7OzvcufMPSpWphMdPnmL5svlY8fsCPH/xAs5lK+Hff/Vvojt06okUKZJjy8bV+HXCKOzYuQe9+gyKtu87dPgYdbMn361t6+aYM3cRChYpjbv37sN91VL06dUNK1a6Y+as+bplevYegGG/jEGTRvXxx46NaNGsMYYMG6WC3eDOX7iEcROmYPjQAWq+dGnTomXrTirIFOs3bMbAwSPQpVM7eO7eikkTx2CPxz40b6kfqEYkMukb2yyuXh5Xt26I9HJ2KVLigzbICubDS81DEPtUjuo9YYqU+OLrCz9vL735vn7+DN93b2HvmBrmwjKhpoOUb97vQn327b3m+1s5JDa4rFVKTXrFy5IDifuMQLLhk5FkyAQkqF4/MIgN8mH3FnzYtREBPoZvhs3Vi5cvkCJFylDTkyfXVJd+9uxpuMtaW9vAwUG/Extra2s42Dvg+bNn6n8bG1tkyZwV8ePrB8cHDx3A8xfPUbBA2KXmsQ3TM4rS8f179Z7C3i7UZ8kT2uk6borIx0+f8fejJxiycQcevH6Dji6l9D7/4PcJ43fuRc0CeVAqi3l1EhaS/L4K6UAtpOTJk6v3p4HnbEgvXrwwuFwKA8sNGzYMo0eNQtIkSaJs382ZpYVFtL/MAdu4kllJkyY1pk4ZryvZu3TpCo4d9kSZMpofqWrVKuPdOy8MGDwcbm7VULp0SWTK5IR1GzajalVNtcqzZ8/jzp27mD9H0wZr9NhfETduHBzctxOJE2tujKtWqYhMWfNh/MSpWLRglm77lSuVx+yZU9XfFSuWw4WLl7Fu/eZo+77ZsmXBkkVz1N8uLqWxeOkK+Pr6Ye2aZYgbNy4qV66ATVu24+SfmiqPt2/fwYKFy1QgP+KXwWpapUrl1Y3k8BFj0bNHZ6RLl1bXecOpEweQM2d29X/27FmRIVNu7PrDA7ly5cDRYyfh5JQePXt0gaWlpdp+sqRJcOnyVVV6a+xg2pFJ39hG2qF+j3jxE+CzgRJa/8Ceb+MGlorFjZ8AX8Lo8ML/0yfECfFUPDazsNaUgAR8CZ2m2mlhdaQUJ4WmtCZO6nTwObJX9SAcL2su2BZ1hlWylPBetUDWopn5m/m2awuPj89H1cNvSBJ8Cj8/37CX/Wh4WbW8jbVejY+Qnj59gilTJiJevHho2aI1zAXTM4rS8dNnXRXfkKzjam5hfQ1cE0Kasmc/9lzVlAaWz5lN1+ZVa/reQ+p3q1+V8jB3cr4KGwO/DzaB53tY5+xHOdcNLGdtYDm5ByGKaixxJbMipa1aBw8dQbJkSVGyZDFVFVj7quVWHbdu3cGDBw91pa7bd+zW9Xjnvm6jCoArVHANXM9RuJQtjYQJE+rWITdpFcq7YN/+Q3rbdy6l3x182jSp1Xqlem50KFWymO5vKXmV71uoYH69Hwx52inBujh0+Jj6ca5dq0aoNPn69SsOHT6qWy5x4kS6oFV9l7RpdD9cQr6/pKOU8ErV6lOnzqB69SoqIDY2aI1s+v40JP1CN78KElitW5PO4cxooA1X7KXNU5H/Tp/v3ITP0X14t2w2Pl0+i8/Xr+LDzvXwOXkI8TJmQbycQdeNn5V62BRG9WkR3jkdgPAfVIX12bPnz9C1eye8fvMaA/oPNqvhcJieUZSORswTXr7VqpI3F6Y1roOWpYri2M076Lh8HfwCA94Tt/7Brst/Y5hbFdiZcRVhLW2zoPBSLaxzVpOvw1nuP+4bUURY4kpmxc4uaEiMV6/eqGrAcW0MV1N5/PgpMmRIj5YtmmDMuEmqmqsEdBs2bkXrls1UKaJmPa9VdWND6wn5RDFkFTjtOiRw1f4dlewThm7TlyCB/j4EJ99F5C+kX00qeJoY811Eg/p1sHH9SsybvwQTJ/2GseMnI3VqR1W1uFvXjkZ/h8ik78/ii48P4hgowYpjrZn26aOmGutnHx/EDaOkK461NT4HttE2BwGfP4VZqmoRmE++GRiLVXy5c129QvI7dxLxncsjbsYsKpj9GciDNG3bfS0bG2t1vvsZSL9PnzTpniBB2MMNxbeNH2aJ7Ce/TwaXvXfvLnr06ornz5+hR/deqFc3qLOX2ITpGb3ix9Oc25++6PdDEXxaAuuIe2zXVv91yZFVjcs6ec8BFaxWzZsTE//Yh3I5syJ3mlS64XW0637v6wdva2vY2xq+zsZG2t92Q8PT+AWe73ZhnO/qOuEXznXCLnSVbqKoxMCVzFaiRA6qGvCGtSsMfi5VX0XmzJlQqmRxbNy0VQWC0pmSBLPB11POtSwGDYjcOJxhPcWUks3gPgSWYP4I8l3Efs8dur+Dk8AzMho2qKte79+/V6W50pa2e8/+KFqkEIoWLWz4+4a4aY7K9DUX3k+fIG1gz8HB2QW2N/zwQtP+1fvpY1VtOF6CBPgcLB9ZxYsH20SJ8d5AO9nY6puXZlgfSwMdMAW1f/WK3DoDHwBYxDX/Uhat/Qf2YsxY/fbsNWvUQqpUjmq4mpBeBrar1rZ1NSRVKk3NEjm3pV+A4DezXt5eoZb937W/0at3d3h7e2FA/0FqLNfYiukZvRwD202/MvAQTjpmEskNtH8NT+U8OVXgevPpc2RKnlSt5/D12+oVUovFq+DoYI+dfcxnXGxHR83v/MtXr0J9pu2UKYWBdqzaZQ0t9yKC5Shikamp9jNj4Epmy6WsM3bu2qOqzzo5ZdBNnzV7Po4cPYE1q5boprVq2RSDhoyEra0tChbMjzx5grqIl2qs167fQP78efVKAKU33ySJE6NIEeM7FLG3T4g7/9zVm3by5I8bcqNsGWf1Lj880gZX69ixk5jw61RMHD/K6OC1cdM26sZ0+9Z1qpqvlFanSe2IoiVc8eDhvypwle/76NETveVO/nlG7/+oTF9z8fz638havhIcUqeFV7BedFMG9tr67H9/6fcynDMP/j1/Jsz5zIG0S/365hXiOKYL9Vkcx7T46vUWAR81HbmEZN+yi6o27b1mkd50q2Sam6yv7zQ1EX4GJUuUwry5+umQPFlyuK9dDQ9PTZOJ4O1Vr9/QlFTnDqeH6pw5Nb0H37hxDUWKBDVfuHFTs2yuXEG9l96+fQvde3SBr68PRo0Yi5o1ayE2Y3pGL6m6mzZxIlx/Gvoh3I2nz1TvwMnCKOXrt24rXn34qHoWDjlWq7COEwdZU6bA3JYNDY4BK0PtjK1bA6kT2cOcyO91unTpcP166Foo0itwypQpkSxZMoPLSq/Du3cbuE4E9iacx0BPxURRiW1cyWy1bdMCGTNmQKWqtVXPujLczOgxE9F/4HAkSZJYr/pa40b1VBC2es16tApW2ipG/jIY9+49QPWaDbB9xx/Yv/+QGmpn+Yo1yJcvchfpmjWq4tGjx+jTb7Dan3HjJ6s2tT9K3ry50aJ5Y3Tp1heTp8zA4cPHVCdWDZu0wr+PHusF7BFxdSmNHTt3o2+/ITh48Ih6SCA9KMuDAmmfKmpWr6o6hpr46zS1LfW9jx6PtvQ1F7cO7lPjthZq1ko3zcLSEgUbN8f758/w6OI5Ne3u8SP44uuDQk2D5hPyv/Q2fOfwAZiTT9euIE76jLBKpWlvrR3qRqr6fvo77DFAA3w+Im6mrIiTNoNea6z4LlUQ8O0rPoezrLlJliw5ihcroffKlCkzKlaohM+fP2PrtqDO5CS43LFzG3LnzoO0aUM/MNAq7VxW3cSu37BWb7r8L70Nu7poOryR6sQDB/fDx48fMG7sxFgftAqmZ/SrkCs7rjx8jBvBgtc7L17i/L2HqJpH89DEkFQO9rj+5Jka5ia4VSfPqvcy2TOrKsDFMzmFesmyIn/6NMifXtNhoTmpVKkSLl26pBe83r5zB+fOnUO1atXCXU6uEzLEjZaPry+2bd+OPHnyqICYKDqxxJXMlgSmx4/sxdDho9VwL9JBUfr0aVWPuoMH9dWbN1GiRHCrWU0FTs2aNtL7LF++PDhxdC9+GTkOrdp0Vm08c+XMrtp3SjXZyGjXtiX++eceVqxyx+IlK1DOtYyqyly2XFX8KMuXLcCkydOxZNkKjBg1HsmTJ1OlpePG/KLrGdAYXbt0UB0pLVqyXPVmLKWlZcuUUr0ca3sHHja0vyrdnTZ9Nr588Ydbzarq87r1m0VL+sZGcWWIkPKV4PP6FR6c+VNNe3PvH1zZsh6Fm7dR1YCfXL2CHFWqI02Bwtg1uA++BY47/Om9N04tnoeyvQei9m/zcPfEETiVcEb2ytVxdOYU+HmFHjomNvM9eRjW+YrAoUVn+P55WJWi2pZ0VVWE1f8SjiawQ7xM2fH17Sv4P9KMFfzxwB+Imzk77Jt3gt+Z4/jm8wHWOfMjrlNmfDy0B19fGx6z8GdSsqQzSpV0xqzZM9TQNxkyOGH79q148eI5xowapzfvHo8/VLtWV1dNQGpvb48O7Ttj7rxZGDCwjwpkT585hQMH96Fnjz7q+io2b9mER4/+RY4cOdW1Q9YTnAS5FcpXhDlgekadls5Fsefq/9BzzSbVuZJUqXQ/dR7J7ROiRSlNk4rXHz7izN37qnQ2XzrNg61Ors44evMOhmzciYbFCiKlfUKcvHNPdcZUI39ulMhsPp2BRVab1q2xa9cudO3WDa1btVJ9WKxavVpV9ZX/xevXr3Hq9Gk1FF7+/PnVNOdSpeDs7IwZM2eqoW+cnJywdetWPH/+HOPG6V8niKKDRYC/tzl1O0lEFD6rhJhWMNsPT6XGS1YjiVMmLKikqa6tZe+YBp32HFZVfTcEq9JmYWWFkh26IU/t+qq96psH93B66XzcPrgv1LoLNmmJQk1bImFKR3g9foSL61biyub1+FEGXLqFV2P6/ZBtWSZKggRVaiNuxqzAV398uX8XH/fvxLd3b9TncTNkhkOb7vC7fBYfdgSlgVXylIhfrroqnbWwioOvL5/B98wxfLp6IcxtWecvioR1msJrzWJ8+UczfvGPkmzUdLz3MtzZVHTx8fHB/AVzsG+/pxrWImvW7OjWtQeKhGhvXaRYfjg6psauHZrxsIOXsG7YuE51uJQ6dRo0adwMDeoHPQjs07cHTpzUr3ERXNIkSbHXM3p6Ek/oYMP0jIY09V67FD/Co7fvMHPvYZy79wBxreKgkFNa9K7kijSJNQ9FLtx/iC4rN6iAdHSd6rrlnr7zwryDx1Wpq8/nL0iXJDHqFc6PRsUKhtumcM6Bo6pkdkfvTqozpx/FvlkH+IUzfFRUevToEaZNm4YzZ8+q4agKFy6Mfv36IW0aTeAvpa8dOnZELTc3vaBUrhNz5syB59696jqRPVs29OjRA0WLhu6XQWvHjh0YOWoU5s+bpwLfH8UmXuhOvUzV8grRPxRT24Oxf6QGBq5EJkQ6MtJ2VR+eOHFYWSK2Ba7m7EcGrj+LmAhczVlMBK7m7kcGrj+LHxm4/gwYuJpf4Mq7XyITUqGSG44eOxHhfAH+3j9kf4iIiIiITAEDVyITsmjBLDW0DBERERERBWHgSmRCtGPLEhEREdHPwcKS47gag8PhEBERERERkUlj4EpEREREREQmjYErERERERERmTS2cSUiIiIiIoohluGMK0xBWOJKREREREREJo2BKxEREREREZk0Bq5ERERERERk0tjGlYiIiIiIKIZYsI2rUVjiSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGWFqyLNEYTCUiIiIiIiIyaQxciYiIiIiIyKQxcCUiIiIiIiKTxjauREREREREMYTjuBqHJa5ERERERERk0hi4EhERERERkUlj4EpEREREREQmjW1ciYiIiIiIYoilhQXT3ggscSUiIiIiIiKTxsCViIiIiIiITBoDVyIiIiIiIjJpbONKREREREQUQ9jG1TgscSUiIiIiIiKTxsCViIiIiIiITBoDVyIiIiIiIjJpbONKREREREQUQywsOY6rMVjiSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGWFqwLNEYFgH+3gFGzUlEZA6sEsb0HhAREVF0+/o+1qTxltpu0b6N+jt2IbZjiSsR/XRejekX07tgVpKNmo5pBbPF9G6YlQGXbsHP1zemd8Ns2NjaMj2jIU1v9GkX1av9qeWY+Tv8Pn6M6d0wGzY2Mb0HFNVYLk1EREREREQmjSWuREREREREMcTCguO4GoMlrkRERERERGTSGLgSERERERGRSWPgSkRERERERCaNbVyJiIiIiIhiiKUl27gagyWuREREREREZNIYuBIREREREZFJY+BKREREREREETp8+BhKOldAAvtUSOeUEyNHjYe/v3+4yyxZugK58xVTy+TMUwSz5yzAt2/fEFkMXImIiIiIiGJwHNfofkWFM2fOoVrN+kiXLi22bFyN7l07YtKUGRgwaHiYyyxeshyduvRChfIu2LltPRo3rIc+/YZg6rRZkd4+O2ciIiIiIiKicI0aMxE5c2bHhnUrVDBctWolWFvHw8DBIzBoQB+kTu0Yapllv69CaeeSmD1zqvq/QgVX3Lx1G3PnL8bgQX0RGSxxJSIiIiIiiiGWFpbR/vqvPn36hMNHjqNeHTe9EtxGDevh69ev8Nx7wOByvn5+sLdPqDctadIkeP36TaT3gSWuREREREREZszOIXRpaEgfvJ6G+dndu/fx+fNnZM+eVW96mjSpYWtri+vXbxpcrk+vbqqq8Br39XCrWQ2nT5/DylXr0LJFk0h/BwauREREREREFCYvLy/1bp9Qv/RUJExoB+/37w0u16J5Yxw/8Sdatu6km1a5UnnMmaWpOhwZDFyJiIiIiIjM2IdwSlON8e1bQLifh9UBVO26TXD8xClM/nUsihcrgr/+/h9Gj/0V9Ru2wI5t62FpaXw1ZgauREREREREMSSqev2NTokSOaj39x8+hPrs/fsPcLC3DzX9zz/PqLavC+bNQJfO7dU0F5fSyJTRCTVqNcTOXXtQp3ZNo/eBnTMRERERERFRmDJnzggrKyvcuXNXb/rjx0/g6+urehsO6cHDh+rduVQJvellyzqr9//97zoig4ErERERERERhcna2hquLmWwdftOfPv2TTd946atiBMnDsqXKxtqmRzZs6l3aeMa3MmTp9V7pkxOiAxWFSYiIiIiIqJwjRg+COUr1VTtUzu2b42rf/0PI0dPQNcu7ZE+fTo1ZM6lS1eQNm0a9SpYMD/q16utxnmV6sTSxvV/166rNq4FCuRTn0UGS1yJiIiIiIhiiKWlRbS/ooK0T92+ZR3uP3iIug2aY/7CpRg6uB9m/DZJff706TOULF0RS5et1C2zds0y9O/bAwsX/44q1eti5uz5aNOqOY4d9kC8ePEitX2WuBIREREREVGE3NyqqZchTk4ZEODvrTdNgtOxY35Rr/+KJa5ERERERERk0hi4EhERERERkUljVWEiIiIiIqIYEhvGcTUFLHElIiIiIiIik8bAlYiIiIiIiEwaA1ciIiIiIiIyaWzjSkREREREFEMs2cbVKCxxJSIiIiIiIpPGwJWIiIiIiIhMGgNXIiIiIiIiMmls40pERERERBRDLC1ZlmgMBq5ERNHI0j4RElRyQ9yMWQErK3y5dwcf9+3At3dvwl0ufvnqiF+mosHPXk8ahoBPfqG3lSgJEncbBO8NK/Dlnxswd8XadETh5m2woJKzUfNbWFqiSMt2yFevEexSpMLbh/dxZtlC3Ny3J9S8eWrXR5EWbeGQJh3ev3iGi2tX4fJGd5ijp0+fYsbMmTh79iy+fPmCokWLYsCAAUibJk24y/n5+WHhokXw9PTE27dvkS1bNvTo3h3FixcPc5lHjx+jfv36mP7bb3B2Nu64xUbRnaZfv37FqlWrsHXrVjx/8QIZMmRA+/btUbVKFZijOImSIEWthoifLRcsrKzgc/sGXmxfjy9vXkW4rEOx0kjsUhHxkqeCv7cXvC+dwet9uxDw5UvQTFZWSFqxBhyKlETcxEnx1dcHH6//hZd/bIG/9zuYbR6dNUuTR/39NXm0Xz/j8ujixZo8+u4dsmXNih7duoXKox8/fsT8BQtw4NAhvHr1CokTJ0b5cuXQs3t3JEyYMJq/HZkrhvdEZi4gICCmd+GnZWETHw6tu6mg1ffMMfge24+46TLAoW0PWNgmCHfZOCkc8fXNK7zf6h7qFfDls4Ft2cK+STtYxI2Hn0FG57Jw7tY7Usu49B0Mlz6D8PjyRRyeNgG+b9/AbfJM5KhaQ2++Qk1boeroX/Hu0UMcmf4rXt68jopDR6FYu84wN15eXujQsaO6eW3erBk6deyIq1evom3btipwCs+QoUNV8FTO1RX9+/WDv78/unXvjosXLxqc39vbG3369FE3vubsR6Tp9OnTMXPWLBQoUAADBw5UQcHgwYPh4eEBc2MZPwHS9xikgta3R/eroNPWKTPS9xoCqwR24S6btFJNODZrB/+3b/Bi+zr43LmBZDKtaTu9+Rwbt0byanXg9+99PN+6Fl5nTyJhgSJI33uo2r5Z5tHOnYPyaIcOmjzavn3EeXT4cKxavRrlypVD/759NXm0Z0+9PCr3Hb379sXa9evhXKoUhgwapPL05i1b0LlrV/Uwh+h7sMSVYq3SZSsjTpw4OHIodGmJKVix0h1t23fF7RuXkCVL5h++/Tdv3qB338Ho2L4NypaN2ZINp8x5UNq5BNasWoqfiW3JsrBMnATvlszE16eP1LTPd24gUZf+sC1dHj77d4W5rFWKVPB//BCf/roQ4XasUjjCvlFrWCVNgZ9BwcYt4Np/CKwiEaQnSp9BBaQX1q7E4akT1LS/tm1Ck2XucO03BLcO7MM3/y+wtkuI0t374PbhA9jRr5ua78rm9epGrGSHbvhr20b4RnBjF5usXrMGjx8/xlp3d+TKlUtNK+XsjMaNG+P35ctV8GTImTNncPjwYQzo3x8tW7ZU02q6uaFhw4aYOm0a1q1dqzf/7du30a9/fzx8+BDmLrrT9MGDB1i7bh2aNW2qglVRr25dtGvXDr9Nn46KFSsibty4MBdJXCojbpJkuD99HD49eqCmfbj+NzIOHI0kFarj5c6NBpeLmywFklWpBe9LZ/Fk5ULNxD+P4tsnPyRxqYRXnjvw+cUz2KRzUqWybw7vxYsdG3TL+9y+jnRd+iFJ2YpqXnOy2t1dk0dXrw7Ko6VKoXHTpvh9xQoVkIabR/v1Q8sWLdS0mjVromHjxpj6229Y566plbL/wAGcO38egwcNQrMmTXTLZ82aFRMmTsTuPXtQp3btH/JdybywxJXITF28eAVr3Dfg27dvMb0r2LbZHWNGDcPPxjpPIfg/eqALWsXXl89UdWH5LCwW8axhmSgx/F89j3AbNkVLI1GnfrCwtoXfxdMwd81WbkSFISPx8OxpPLv2t9HL5ahSA5ZWVri8Iai6b8C3b7i0wR12yVMiXeGialpml/KIl8AuVLXgi+tWIa6tLbK4VoI5kRK6fHnz6m5eRdYsWVS1wfBK7/Z4eKgHh/UbNNBNi29ri7p16uDatWsquNJat349mjRtig8fPqgAy9xFd5p67t2rruuNgwUEVlZWKjB++fIlzp8/D3NiX7g4fB/c1QWt4vOzxyqwtC8UdrV0h6KlYBEnDl7s3KQ3/e2JQ3i1d5e0HVD/x8+WU72/O3Ncb76PN/7GV5+PsM2UFebGw9Mz7Dzq6Rnmcns8PTV5tH59/TxauzauXb+OB4EPps6eO6fe69Sqpbd8tcCq7JcuX47y7xTbWVhYRPvLHDBwJaJoV7BgfmTOnOmnSmmpumuVJBn8nwQFrVr+Tx/Byt4BFnaG2/lYJU8FCwtLfH0ZGLjGkdITwz86cVKlxqcr5/B2wVR8eXgP5s7e0RH7xo3Alh4d8MXno9HLpcqVB5/ev1ftWoN7fv1/6j1l7rya91x5NNNDBMXa+WQ95kKq7j569Ai5cucO9Znc0EoQJC9DJJDKmDGjumkNLmfgjbB8rnXr5k24ublh8+bNqmqrOfsRaSrvdnZ2cMqQIdT6g89nDixt4yNeshSqCm9Ifo8eIG6ixOpaaogEnJ+eP4X/29fqfwsphba0xJeXz/HKYxs+P3+ipr87cRj3po7C5xf6Dwplfktra/WAyyzzaLCgVStXzpwR51Enp9B5NGdOvbwn7Vg3rFuH+PHj682nrYYsD1qIvgcD11jC19cXQ4eNRtYcBWAdPxnsE6dBpSq1cenSFfX56DETkSV7fuzdewCFipaBTYLkqnrmb9Pn6NZx//4DWMSxx5atO9CkWRs4JEmr1iN/P3/+Qm97u3Z5oFgJV9japUAKx0zo2r2vahMR3KNHj9G8ZXskTZEB8ROmhEu5ajh9+qzePAcOHEaJUuWRMFFqtb0q1erg7NnIPw2Wfa9bv5laR6o0WTBz1rxIp5Gx6STWb9iMAoWd1feS71e/YQvcunUbP8KnT58wZOgopM+YS32P3PmKYeUq/dIf1/LV0aFTD7XfGbPkVfPJ95HvJY4cOY5KVTXVcMpVrKHm1y7XsnVHNGrSGgnsU8G5TKVIbbNFqw6YNHm6OgaSvrXrNsG9e/dDpe/4CVNUukneefbsuUpjWTYy6WtMHjRllgk1N1PfDHTs8e295ntYOSQ2uKxVSkf1Hi9LDiTuMwLJhk9GkiETkKB6/cAgNsiH3VvwYddGBPh8wM9gcfXyuLo1qDqfsexSpMQH7YOAYD681Fz77FNp0jxhipT44usLP2/9vPb182f4vnsLe8fUMBfSqY9ImSJ0FfPkyZOr96fPnhlc9sWLFwaXS2FguWHDhmH0qFFImiQJzN2PSFOZL8V3rD82ihN4jfQ30Jmdv5fm2iqdKRkSL4Ujvrx9rdrGOg0cjexTFyHb5AVwbN4BljZBgZdUHf70+F/g21e95RM7l4eFVRz4/nML5uQ/5dGXL5EyZcoI86iDgwNyZM8eaj73devUe6GCBf/Td6CfFwPXWKJVm85YtnwVhg7uj30e2zHjt1/xv2vX0bhZG13nO0+fPkfHLr3QuWNb7Nm1GSVLFMWAQcPh4bFPb10dOvVEihTJsWXjavw6YRR27NyDXn0G6T7fuGkratdrgkyZnLB10xpMGDdSBbvVatRXjfDF69evUapMJZw6fQ6zZkzG+rXLYWNjjXIVa+LChUtqnrt376F2vaYoVCg/dmxdh9UrF+PV6zeoUr2eeuJnLOmZzqV8dVz9628sXjgLc2dPw+KlK3D23IVIp5Ex6XTy5Gm0aNURtd2qq88XzJuBy1euorpbgx/S0ZEEcXPnL0avHl2wc9t6lC9XFm3adcX8BUv05tu8ZQfWb9yMaVPGq+Mk6jZortq2SprPnjlFTZs35zfMnztdt9z6DVvUsdq1fQNGDB8UqW3u3rNPpbGse9GCmbh85S+4VqihjpHWgwf/YvvO3Vi35nd1DFKl0v+RMyZ9jcmDps7C2lq96/VcGUg7LayOlOKkSKV5T50OPkf2wnvjCny+dhW2RZ1h36yjfulriJstcyftUL9HvPgJ8MXPN9R0/8DemePaakoG4qr5DHce5P/pE+KEKGmIzXwCz1sbA9/JJjD/ygNBQ+ScN7SctYHlzKm9pSmkqZrPxibC+cyBZeD3/PY5dId03wI7qbOMZ/g6amVri3jJUiJth56q2u+jZXPx7uQh2BcugbQde0vdzDC3a502A5JVq4OvHz/g3Z9HYJZ51EAeMiqPfmfeO3b8ODZu2oT06dKhSuXK373/9HNj50yxwOfPn1XbIAkQmzZpqKa5uJSGl5c3+g8chsePNdVdfHx8sHnDKlSrprkgODuXwPYdu7HzDw/dNFG5UnnMnjlV/V2xYjlcuHgZ69ZvVv9L4DBg0C8o51oW69eu0C2TL29ulHCugE2bt6l9mDFzniqlvfbXWV0V0BrVq6hStGG/jMFej+04d/6i2qdhQwYgbVpN9+rZs2XF78tX4/37D7C3tze6k6N//32EyxdOIl8+TTW9YkULI0v2ApFKI+0+RJROJ06egq2tLYYO6a+7QEv38Lv37FXbiM5u3KWEWrazasUitGzRVE2rUqUi/P2/YviIcWjbpoXaN20pqQTo0pukkP1yKV8N+w8cRuNG9ZEzh+ZpZ66cOZArVw7dNqR9yqIFs3Tricw25fufP3NEd8xzZM+mSnqXr1iDHt01Pa5KYDlt8ni4upYx+B0jSl+pAmdMHjR92puiyD/s+HznJgL8/OBz8hAQeHP2+fpVfPP5gPjO5REvZ171P0XmcFiEfygCH5po2gGFM6MZ9dKtfVAUXsunsNpFybLhLoef049I058p7Y36LmGckhZx4iJesuR4tnkN3p04pKZ9+Osivvr6qh6E7fIWxIeroXvAtnZMi3Sd+8IijhUer5ivgldzok2u8No8hptHw1sujOnSqdPAwYPVb/6USZN+qodZxrI0kzao0Y0lrrFAvHjx4LF7q7pZlwDs8OFjWLT4d/yx21MXwGhJEBb8CVjy5Mn0SsPUPKX0OzNImya1Gp5AOnu4deuOChLr1K6hAhDtq3DhgnB0TIV9+zUX/4OHjiJv3tzIkCG9bh65oNWoVgVHjp5QgWTJEsVU+4ZiJcuhV5+B2L3bE+nTp8XkSWORJo3x1e2On/hTbUcbtIr06dOp4PV70iiidHJ1Ka2C2zz5i2PQ4BE4dOgoihYthAnjR0b72GOSrsKtZjW99K9VsxrevXuHs2eDSplz5syuC1pF2rSaNP340SfcbWTPnlUXiEZ2m3JMg7dVlbarUip69NhJvW0UKKBpL2hIROlrbB40dQGfP4VZqqraWgVWUTPky53r8DniqQtatfzOadI5bsYs0bDH5u2Ljw/iGCgpiGOtmfYp8Ob0s48P4hqYTzOvNT5/MJ+bWG37M0PD0/gFXjPtEiQIc1lDy2mvtQnswh+mxFz9iDSV+Xx/krT/FvidLA1cR7XTvhqoSaGW/fxJtU/1On1Mb7oMdSMSZAl6oKtlmzGrGnrHKn4CPF37uxrL1dxo26f+qPP+wMGD6NFbM3TZzOnTde1hib4HA9dYQtou5sxTBGkz5FDVb1evWQ9ra81FO3j11ZAN4S0tLUP1KmtoHiHzvXql6cRAqg7HtUmi93r69JmudPfV69eqSnDIeSZPnaGCVlmPBJfHDnvAuVQJrFi5FjVrN0LyVJnQo1f/UIFkeN68eYtkyUK3jZIg5nvSKKJ0Kl68qCrJzJY1C+bMW4QKld1Um85RoydEe1VhSVeROFl6vXSVarTi8ZMnQd8hRJWy4McxPCF/kCKzzTRpNO0AQ7ZtkWOkt41wbpwiSl9j86Cp++alSRNLAx0wBbV/jVyb3W+BwZVFXE21LDKe99MnSJBM0w4rOLvkmnZeHwI7ZvF++lhVG44X4jyxihcPtokS472BdrKxlaOj5nx++epVqM+0nbMYakupXdbQci8iWM7c/Yg0lflefcf6YyNpoyriOITugCmOQyL17h94rQ3J/91b9XAwIETzkq8fvPWqIWslyJUP6bpKD+3Wavgc7wvm2Uv7jzzvt2zdqkpapXBhwbx5KF6sWJR8B/p5sapwLPDPP3dRp34zVSL2x46NyJQpo6qqIe0PPQM744kqiRJpfhwmTRyDCuVdQn2uLXGU+Uo7l1RtGA1JlkzTWYKUkm3asEqVmJ06dRar3ddj3vwlyJA+PQYO0DyBi4is68bN0B0jaQOc6EijChVc1UueLB47dhKLlizH2PGTkSdPLjRsEH3DOSRycFCloRLwG5IxY4YY3ebr16E7yHj+4iWKFwsq/f6v6StVm43Jg6Yu4JMfvr55hTiO6UJ9FscxLb56vUXAx/cGl7Vv2UVVSfVes0hvulUyzU3B13dBeZ+M8/z638havhIcUqeFV7CenlPm1PT++ux/f+n3MpwzD/49fybM+cyBnEvp0qXD9evXQ30mvYNKJyzJkiUzuKz0SLp79251Dgdv83Y9sFfRPAZ61f0Z/Ig0lflkLM1Hjx+rZhbB1x98PnPwzc8Xn18+h01ap1Cf2aTNgC9v3+Dre8N9ZkhPxA6pSiGOfSL4B+skL25SzQMsWVZLOnBK264HAr5+xaMls+FzU3MdMNs8mjat4Tx6/Xr4eTRnTjUGa6g8GriuPMF6Kt65axfGjh+vaoZJ0JozR+gSbqLIYolrLCBtUOUiMXhgH1VNU9u+wMNzv3qPynE6c+TIpjpuunvvPooUKaR7Zc6cUbVdPX1G02uwS5nSuHnrNrJmzaw335atO1UpmrRfkJ5/M2TKrUpXpV1lmTKlsHjhbFUapx3ryxgVyruqqqNnzmjGBRPSvjZ450xRmUb9BwxD0RIuqvRPLsyVK1dQbUK1HQ9FJ5eyzqpzgy9fvuilqxyPUWMmRlgNODhju5uPzDb/PHVW74GBlLpLr8KVK1WIsvQ1Ng/GBp+uXUGc9BlhlSqN3lA3UtX309+aTswMCfD5iLiZsiJO2uAPKiwQ36UKAr59xedwliXDbh3cp6oNFmrWKihFLS1RsHFzvH/+DI8uaq4vd48fwRdfHxRqGjSfkP+lt+E7h6P2YWFMq1SpEi5duqR3E3v7zh2cO3cO1apVC3c5qV0jQ9xo+fj6Ytv27ciTJ48K3n5W0Z2mFStUUL9xa92Den7/+vUrNmzYoEq8ChUKe4zo2Oj9lfNqaBvrtOl10+KlSoP4WXPCO5yxq7UlpkkqanrV10riqhlL9H1g+1YZTidN6y7qN+nfRTPMOmjVy6OXLxvOo1Wrhr1cxYqaPLpli34e3bFDL4/evn1bBa2JEiXC70uWMGg1AsdxNQ5LXGOBQgXzq8Bv+Iix6N+3pwoEl690V53ZiMgEM8YEO9KDa6cuvRAnjhVqu9XAh48fMXHSb7h58zZmTp+s5uvXt4cqPa1QuRYG9u+F5MmSYfvOPzBn7iKMHT1cnYDSuc7goaPUkCk9u3dWbUnd121U7Rsb1je+1LJliyaYMWse6jdqqXpBlk6dpHQueLXdqEwjKeWT7cmwMa1aNFVB77wFS1SQVbuW/g9gVKtevQrKlnFGvYYt8MuwgcidKycuXb6CEaMmoHixIqr6tbESJ9ZUo5I0kL/z58/7n7cpx65azXr4ZdggvH//HsN+GavaOjdv1ijK0tfYPBgb+J48DOt8ReDQojN8/zysSlFtS7qqKsLqf/mxSmCHeJmy4+vbV/B/9EBN+3jgD8TNnB32zTvB78xx1SmTdc78iOuUGR8P7cHX14bH2CONuDa2yFK+Enxev8KDM3+qaW/u/YMrW9ajcPM2qhrwk6tXkKNKdaQpUBi7BvfBt8DqhJ/ee+PU4nko23sgav82D3dPHIFTCWdkr1wdR2dOgV/gEBzmok3r1ti1axe6duuG1q1aqSYHq1avVgGQ/K/tRf7U6dOqlCZ//vxqmnOpUnB2dsaMmTPVEBhOTk7YunUrnj9/jnHjxuFnFt1pmilTJjRo0ADua9fio48P8uXNi7379uHylSuYPHmy2XV88/qQJ+yLlEK6Lv3x5pCHuo5K8ClVhN8c0vzGW9nZI0H2XPj8+iX87v+jpklPwl4XTiNJ2YqIY5cQPnduIH7WXLAvWBRvTxzCp8eaB+hJK9aAVQI7fLjxN+ImToK4hYP6wBBfP77HxxvmFcy2adUKu/74A127dw/Ko2vWqKY/EebRUqUwY9YsTR7NkAFbt23T5NExY3TrnzNvnnoYXqpkSVWKK6/gUqdOzSFx6LswcI0FsmTJjHXuv2P02F9Rq24TJEmSGCWKF8WRg3vgWqG66rwoKnVo3xoODvaYMm0mlv2+GgkSxFfbW7xglq532tSpHXHqxAEMHT4G3Xr0g6+vnyoRk2FXunbRjNcpgZJU25Ugs0XrTiqYlJ5ht21Zq3r8NZa0jTi0/w/06TcYPXsPgpWVJTp3bAenDOl1VVeNSSPpSMgYEsitXbMM06bPUcGyKFqkEA7s3YmsWaO3Uxz58djzx2aMHDUBk6fOVCXL0pa3S6d2GD1qaKTWJZ1ZNW5UTw1z47F3P/6+cuY/b1M6Z6pWtRLadeimHhzUrlVDDccjx8hYxqSvMXkwNgjw84HX8jlIUKU2bMtWAr7648v9u/i4f6cqVRVxkqVEwnrN4Xf5LD4EBq7SPlaWi1+uOmyKl1FjCX59+Qzvt7nj01X9YaAoNNvESVBjwjRV1VcbuIqDk8fB5/Vr5KldHzmq1MSbB/ewY0AP3D6oP2TY2RVL1JA4hZq2REbnsvB6/Aj7J4zElc3rzS65ZbzFFcuXY9q0aVi8ZIk6lwsXLox+/fohSeC4q3fv3sXw4cNRy81NdwMrpk2dijlz5mDPnj2q1kb2bNmwYP78n/6G9Eek6ZDBg9W4uNt37ICnpycyZMiA36ZNQ8WKFWFuvvl8xMM5k5CiTmMkrVwLAf5f4PvPTbzYsVEFlcI6lSNSt+wEr7Mn8DQwcBVP3ZeqADVRibKwy1cI/m/f4MX2DXhzRBPwigRZNb8pdjnyqFdIvvf/MbvAVeXRZfI7PB2Lly7V5NFChdCvb18kCez08e69exg+YkToPDpligpM93h4BOXRefP08ui58+fVu8wjr5BkOByO5UrfwyLA39t8+vYnomjjWr66aqt84pj+TX6sY5UQr8b0i+m9MCvJRk3HtILZYno3zMqAS7fgZ0bjccY0GR+V6Rn1aXqjT7soXuvPLcfM3+EXYiQI+n42NlHXlC66HW7VPNq3UW5VUPOC2IolrhRjJAiKiFQ5Nratpintt5Rianv51bY/iqhHYlP8rkREREQUvYLfM1LYGLhSjJHhTSLiUrY0jhzaA1Nx//4DZMwS9hilWq1bNcOK3xfq/s+cLT8ePAi/QyoZq/b+P39HyX4SEREREZkTBq4UY86dPhLhPKY29Im07TVmv7XDAWnt2r4hwrFrpfMqU2ZKDxCIiIiI6OfCwJVijAxxEttIBwbfs9/S8y4REREREX0fBq5EREREREQxRPo5oYixJTARERERERGZNAauREREREREZNJYVZiIiIiIiCiGWLKqsFFY4kpEREREREQmjYErERERERERmTQGrkRERERERGTS2MaViIiIiIgohlhYcjgcY7DElYiIiIiIiEwaA1ciIiIiIiIyaQxciYiIiIiIyKSxjSsREREREVEM4TiuxmGJKxEREREREZk0Bq5ERERERERk0hi4EhERERERkUljG1ciIiIiIqIYYmnBskRjMJWIiIiIiIjIpDFwJSIiIiIiIpPGwJWIiIiIiIhMGtu4EhERERERxRALCwumvRFY4kpEREREREQmjYErERERERERmTQGrkRERERERGTS2MaViIiIiIgohlhaso2rMVjiSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGcBxX47DElYiIiIiIiEwaA1ciIiIiIiIyaQxciYiIiIiIyKSxjSsREREREVEMsbRgWaIxLAL8vQOMmpOIyBxYJYzpPSAiIqLo9vV9rEnjS927RPs2Cs5biNiOJa5E9NN57+UX07tgVhI62MDP1zemd8Os2NjaYlrBbDG9G2ZjwKVb8PP2iundMCs29g4876M6TW1t4fvmdVSv9qdl6xAvpneBohjLpYmIiIiIiMikscSViIiIiIgohlhYWjDtjcASVyIiIiIiIjJpDFyJiIiIiIjIpDFwJSIiIiIiIpPGNq5EREREREQxxNKCbVyNwRJXIiIiIiIiMmkMXImIiIiIiMikMXAlIiIiIiIik8Y2rkRERERERDHEgm1cjcISVyIiIiIiIjJpDFyJiIiIiIjIpDFwJSIiIiIiIpPGNq5EREREREQxxNKCZYnGYCoRERERERGRSWPgSkRERERERCaNgSsRERERERGZNLZxJSIiIiIiiiGWlhZMeyOwxJWIiIiIiIhMGktciYiIiIiIYoiFBUtcjcESVyIiIiIiIjJpDFyJiIiIiIjIpDFwJSIiIiIiIpPGwJWIiIiIiCiGWFpYRPsrqhw+fAwlnSsggX0qpHPKiZGjxsPf3z/cZU6fPotyFWqoZVKmzozWbTvjxYuXkd42A1ciIiIiIiIK15kz51CtZn2kS5cWWzauRveuHTFpygwMGDQ8zGUuXLiEchVrIkGC+Ni22R2Tfx2LffsPoU69pogs9ipMRERERERE4Ro1ZiJy5syODetWqJ6Qq1atBGvreBg4eAQGDeiD1KkdQy0zaMhI5MubGzu2rYeVlZWaZm+fEL37DsadO/8gS5bMMBZLXImIiIiIiChMnz59wuEjx1Gvjpve8D2NGtbD169f4bn3QKhlXr9+jSNHj6Nrlw66oFXUq1sL/96/HqmgVbDElYiIiIiIKIZYWEb/OK52DqFLQ0P64PU0zM/u3r2Pz58/I3v2rHrT06RJDVtbW1y/fjPUMlev/g/fvn1DiuTJ0LJ1R2zfsRsBAQGoU7sG5syaisSJE0fqO7DElYiIiIiIiMLk5eWl3u0TJgz1WcKEdvB+/z7U9JevXqn3jl16wdraGtu3rMVvUydgj8c+VKtRXwW1kcESVyIiIiIiIjP2IZzSVGN8+xYQ7ufBqw9rSQmtKFggH5Yunqv+rlDBFQ4O9mjavB08PfejevUqRu8DS1yJiIiIiIgoTIkSOaj39x8+hPrs/fsPcLC3DzU9YWDpbPVqlfWmV61SUb1funwVkcESVyIiIiIiohhiaWH6ZYmZM2dUHSzduXNXb/rjx0/g6+urehsOKWtg50vSsVNwX75oxn2VtrGRYfqpREQUy/n5+WHO3JmoWasqnMsUR9t2LXH27Bmjl9+xcxsaNamnlq1X3w0bN60PNY90drBh4zo1X0nnIihXoTQGDOyD+/fvwZw8ffoUgwYPhmu5cnAuXRp9+vbFo8ePjToGM2fNQtVq1VC8RAm0bNUKZ86EfwxkvTLvyZMn8TMo1qYjuu43/rtaWFqiaOsOaL9jH3qfuopWG3Yie+XqBufNU7s+2mz6A73/vIJ22/eiQKPmMGdPnz3DoGHD4VqpMpxdy6HPwIHG59M5c1HVrRaKly6Dlu3a4czZs6Hm8/H1VfPVqFNXzdekZSt47N0Lc8XzPhrS9NkzDB4xAuWqVUfpipXQd/AQPH7yJMLl/Pw+Ydb8+ahWty5KuJZDq44dcebc+QiX6z1wEAqULBVFe08xQdqourqUwdbtO/Xapm7ctBVx4sRB+XJlQy0jwayTUwas37BF3ado/bHbU72XKV0yUvvAwJWMEjyzkXniMY4+v4wYgjXuq+DqUg59e/eHv78/evbuhkuXLka47Lr17hg3fjTSpk2Hvn36I1u2HJgy9VcsX7FMb77FSxZi6rRJSJUyFfr2GYAmjZur9bfr0AqPHv0Lc+kYokPHjjh79iyaN2uGTh074urVq2jbti3evn0b7rJDhg7FqlWrUM7VFf379VPHoFv37rh40fAx8Pb2Rp8+fVQg8TPI6FwWzt16R2oZl76D4dJnEB5fvojD0ybA9+0buE2eiRxVa+jNV6hpK1Qd/SvePXqII9N/xcub11Fx6CgUa9cZ5kjl0y5dcfbcOTRv2gSd2rfH1at/oW2nznj77l24yw4ZMQKr3N1RzsUF/fv01uTTXr1x8dIlvWv1gMFDsNrdHaWdS6n5pArfkF9GYMOmzTA3PO+jI0290bFHD5w9fwHNGjdCx7ZtcfXvv9G2S9cI8+jQUaOwau06uJYti369esLf/yu69+2Li5cvh7nMzt17cPTEiWj4JvSjjRg+CBcvXkH9hi2wZ89eTJo8HYOHjkLXLu2RPn06VbJ6+vRZPHr0WNfuderkcTh3/iIaNm6F/fsPYe68RWoM1zq1a6Jo0cKR2j4D15/A0mUrYRHHHvfvP8DoMRPV3/JjaIw3b96o7quPH/8T5p420e3IkeNqWwcOHDZqftknmV/2UaxY6a7+l8Gao5oc3xpuDWBKQn7fNu26IG2GHIhtpGT1yNHD6NWzLwb0H4wGDRph8aJlSJUqFabPmBrusu/fe2PBwrlwKVsOv02diQb1G2HSr1NRqVIVLF22GG/fvlHzvXr1Er8vX4py5Spg9qz5aNSwCTp36opFC5fh40cfLFm6COZg9Zo1ePz4MebPm4eOHTuidevWWLRokRon7vfly8NcTkpWDx8+jL59+mDw4MFo1KgRli3THIOp06aFmv/27dto3qKFev8ZFGzcAnVmzIdV3HhGL5MofQYVkF5YuxKeo4bg6pYN2NytHR5fvgDXfkNgGSeums/aLiFKd++D24cPYHvfbriyeT12De6DG3t3o2SHbrCN5FAIscHqtetUydX82bPQsV07tG7ZAovmzdPk0xWa67khUrJ6+MhR9O3VE4MH9EejBg2wbNEiTT6dMUM334k//8TJU6fQvWsXDB04UM03f9Ys5M6VCwsWLzb69z224Hkf9dasX4/HT55i3ozp6NimDVo3b4ZFs2fh9Zs3WL5qdZjLScnq4WPH0Kd7dwzu2xeN6tXDsvnzkCpVSkybNdvgMs9fvMTUWbMQN67mmkCxm4tLaWzfsg73HzxE3QbNMX/hUgwd3A8zfpukPn/69BlKlq6ou3cVDerXwc5t6/Hw4b9wq9MYEyf9hs4d22L92rB/t8PCwPUn06F9a5w6cUAV6RtDnqqscd8Q6e6qKbRChfKrtC9WzLinS46OqdT8tWvpl15Eh0VLfsc1A+NvxaQa1auo758uXVrEZp5796jzrV7d+rpptrbxUbtWXVy/cQ0PH4b90OTY8aPw8fFBwwaN9Hrra9K4GT598lMBsbh46QK+fvVHbbc6estnyZIVmTNnxuUrQaU1sZmHhwfy5c2LXLly6aZlzZIFRYsWVZ+FZY+HhzoG9RsEPZyJb2uLunXq4Nq1a3jwIOgYrFu/Hk2aNsWHDx9Qr25dmLtmKzeiwpCReHj2NJ5d+9vo5XJUqQFLKytc3uCumxbw7RsubXCHXfKUSFe4qJqW2aU84iWww+WNQfOJi+tWIa6tLbK4VoK5kSq7+fLkQa6cOfXaeRUtUgQe+/aFudyevXs1+TRYvlP5tHYtXLt+Aw8ePlTT3nl5IXvWrKhTq5ZuPktLSxQuWFCVlr14+RLmhOd9NKTp/n3Imzs3cuUIehicJXNmFC1cCB7794e93L59Ko82qFNbN03aKNap6YZrN27gwb+ha/eM/fVXpE2dGi6lnaPhm5gP+Y2P7ldUcXOrhkvnT+CTzys8vHcNY0YPV21fhVQLDvD3xuhRw/SWqVmzGs6ePgK/jy/x5N9bmDplvKp6HFkMXH8yadOmQYkSxWJ6N35K9vb2Ku3l3RhyQsv8yZMnw89Ivrd8/++5sJmS69evwckpowpWg8uZQ3NTe+36tXCXVfPmzK03PUd27bL/U+9ly7hg/brNKFSoSKh1eL17p/tBic2k6u6jR4+QK7d+WggJZF++fKlehkhwmjFjRhUEBJczMACWz7Vu3bwJNzc3bN68GQUKFIC5s3d0xL5xI7ClRwd88flo9HKpcuXBp/fv8fbhfb3pzwPzZMrceTXvufJopocIirXzyXrMicqnjx8jV66goFVLggSVTwPHNQzp2vXryOjkFDqfBgYX8rlwq14dG9e6I0mI0uqbt26p62ViMyrF5nkfTWn6+Aly5wxdgylX9hwqf4aZR2/cQMYMGUJ1qJMrR3a9PKq1ZccOnDl/HmN+CQpsiP4LBq5mRkpGx46bhPQZcyF+wpSoW78Z3r4Naq8QsqrwvXv3UbNWQyRPlRG2dilQqGgZuK/doKvaWqmq5qlauYo14Fpe0+nG169fMXnKDOTJX1wtk8A+FUqVroiDB4/oVfOMY50Y585dQBmXKmo+x7RZMXTYaLW81pcvXzBm7K/InC2fmidnniJYtPh3ve/0559nUK5CDbWdxMnSo1mLdnjy5GmUp43WhQuXUK1GPSRL6QT7xGlU+vz1l+YmS5sukoZST79CJTe1LlnnkqUr8OzZczRo1BJ2Do5I55QTv02fE25V4fPnL6ptOSRJq7Yn9f+11ZZDVhU25Nq1G3Cr3Ujtp2xT9vXmzchVb5QquO5rN+LBg4dqe3LstNueOWsecuUtqo7NvPmL1fzbd/yhjmnCRKlhHT8ZcuQujDlzF+rWlz1XIdSp1zTUdlzKVVOvyOSh6Koa/SO9ePkCKVKkDDU9efIU6v3Zs6fhLmttbQMHB00X9Fpyc+pg74Dnz56p/21sbJElc1bEj68fHB88dADPXzxHwQKFENs9f/FCvadMoUm34JInT67rbMSQFy9eGFwuhYHlhg0bhtGjRiFpkiT4GSyuXh5Xt2qu+ZFhlyIlPrx8Hmr6h5ea42SfylG9J0yREl98feHnrRm4Xuvr58/wffcW9o6pYU6kWmTY+TRZBPn0pdH5VMvXz08FrGPGT8CZc+fQukUL2NrYwFzwvI962hL5FOHk0WfPn4e5rMHlkiUPtdyTp08xffYcVRU5W5YsUbb/9HNj4GpmBg76BeMmTEH7ti2xbbM7kiRJjOEjxoYZyNWo1VC1gVuxbAH+2LERObJnRYtWHXHs2ElVtXX2zClq3nlzfsP8udPV30OGjsKYcZNU/XTP3VuxdPEcvHn7Fg0at1LV64Kvv17DFqrx9Z5dm9GoYV1MmjIdi5cE1Wlv3bYzJk2ZgbatW6jt16lVE1269VFBoDh58jRcK1RXT+rWuy/HnFlTcObseZQtV1U9NYzqtDl8+Jiqmy/ddC9bMg/LlsxVTyZLlamkgsTgmrVsj2pVK6l6+5Just+yrzlzZFPTihUtjAGDhqvA25CrV/9GaZcq8PZ+jxW/L1ADM//9v2uoWKWWUR3CSEAn+yXtVJYvm6/WIT/yzmUr4d9/H0WqoX2VyhVUGxWpmitVdHWfjZqA/n17Yu2aZWr67t2eKuAvXLgAdmxdh62b1iBTRif06jMIJ06cUsu0bN4EnnsPqA41tGR/jp/4E61aNo1UHjIHPj4fYWPgRlJbkuzn5xv2sh8NL6uWt7FW3c+H5enTJ5gyZSLixYuHli1aI7aTtBA2BrrOtwlMy7DS46Oko4HlrA0s97O1w/rm/+W7losXPwG+GMi7/p801664gTUM4qr5DF/P/D99QpxIDoUQG853Yei8jTCf+viEe63w9Q2djtJmtlHzFti6YwcK5MuHFk2bwJzwvI96ks+ErXV4eS3sPGprYx1hHpUOxEZPmIi0adKgfetWUbr/9HPjOK5m5N27d5gjPXX17IpRI4eqaVWqVFTjK+3ddzDU/C9fvsL16zcxbEh/1KhRVdfo2jFVKtWGQaq05gys/pErZw7kypVD9xRt/NgR6Nmji95FS3oYu3z5L5QO7NpaLlyy7q5dOqj/y5Uri11/eGLnrj1qmmx73frNmDZlAvr366nmqVDBVVWzOnL0ODp2aIPBQ0eqcaM8dm/R3VC6lC2NLNkLYN78JRg6pH+Ups2QYaOQKZMT9nps01VrqVSxHDJny49fRo7D1s1B7bRat2yGAf17qb8TJrTD/gOHUahgfowbO0JNy58/D7Zu24k/T51BqVLFQ+3ThF+nqZ4g9+/doSspkyCwVt0muHTpimrjGp7RY39F3LhxcHDfTl3VMBnQOVPWfBg/cSoWLZhlVNpkzpwJyZIl1VVN1t7oi/r1aqF9u6Afnc1bdqBF88aYOX2yblrJksWQNIWTOmZy7Fu2aIKRoydg67ZdaNumhZpHjrPckDVsUCdSecgcyHlggbDbloTX7iQAAeF+HtZnz54/Q9funfD6zWsMGzpCVVU2l16vw2ulE1Z6aI5BOMv9x337KUlah9fZvPZ4qWMSzoxm1mO99ut8zzmv8mk4mdHQciWLF1O/z7fv3FGd6jRt1Rqrli1FsmTm0cSE5310pGngH9/x26I5HuFmUvW2YctWXLxyBe6/LzO6T5WfnWUUtkE1Z8xNZuT06XOq6q1bTU11zOC9eRkKXFOkSI68eXOjU5feqoSscqXyqFa1Mn6bNjHc7bivXqYLfKVa6u07/2DXHx4GBxh2LlVC7/+0aVKrEl5x7LhmvMB6dd305lm9col6l05pTp0+iz69uqmLqLZ6swR0EiDu23/I6MDVmLSRYE266x4+dIBeW4xEiRKp5STgDi54MJoypabqTPFiQW0MkyZNqguaDZHvLyWdwat35suXB/f/0bQFi6in44OHjqogPmHChLq0keCwQnkXlTZRoUB+TTs1LW2gLqWicuzv/HMX589f0jv2GTKkR9kyzli/YbMucHVftxG1a1XXte+NTB6KLaSUPGRpsY2NtTq+foGlUMFpv2eCBAnCXGd82/hhlsh+8vtkcNl79+6iR6+ueP78GXp074V6dU2rt+jvpT1PDNVG8AtMS7sw0lIdA79wjoGdXRTvrfn74uODOAZKB+MEluJ8+qg5Fz77+CBuGLUG4lhb47OZ1bCIH19TgmzonDcun36K1LWiUMGC6l2Gz8mVIye69+mD1WvXom8vzbU6tuN5H415NLxrYlh51NY23N8zuwTxVeGDjPPauH59Vc1dO7yO1GQT8r8Eswl53aXvwMDVjEhVSyGlZ8E5pgrdvk5IMLjPYzsm/DpVlY5JO0cJ2KTUTqoFy3hMhki7zG49+qkgTxro586dAxnSpzc4Fqj2Ahm850NtkPXq1WtdAG2ItD+V6sbTZ85Vr5CyZs2MqEybd++81P5LldmQUqVMqcY9C84+YcJQ84UXhIQk31/bdul7yPJbtu5AXJvQbfGiqrqjXYgfFtlm5669VTtXyT9yDMqULhXq2EuV4E5deqnAVNptSbXoSRNGf1ceii32H9iLMWNH6k2rWaMWUqVyVMPVhPQysC2gtq2rIalSpdYFxMGPhdwkeHl7hVr2f9f+Rq/e3eHt7YUB/QepsVzNhaOjps2koU5DtJ0yGWp7pV3W0HLhtfWi8Hk/fYK0gT0HB2cXmCc/vNC0dfN++lhVG46XIAE+B9bkEFbx4sE2UWK8N9BO1nzzqWZaWNd9qe0Ufj4N//dCxnSVYOD6DdPqIf6/4HkfDWmaSlOb69Xr78uj2ns3veUCf+NkuQuXL6uqxu4bNqhXSOWqVVc9YMswOkSRxcDVjCQLLOF7/vwFcucO6tHw1evQFxktCdLmzJqmXv/733Xs2LlbVTPt2r0vdu8KPZC5tCutWqMe8uTOhb+vnEHOnNlVMCqDEEsQFRlSTVZ7oQwe8EnbzSdPnqFgwXwqOOrVswtaNGscavnI9DZrTNrI/sj2pIOlkKRTjKRJo7azFtmeoZsUT8/9yJMnl1HLl3Mti0EDeuNHkY6xZNic/Z47VImzlPBKybi2TbJWg/q10aPXAJUnZBBqKZGuXLlClOchU1KyRCnMm7soVIcV7mtXw8NztwpAg7dfu35D0/ti7nB6Vc0ZOJzGjRvXUKRIUG/gN25qls2VK6iH3du3b6F7jy7w9fXBqBFjUbNm0FAZ5kBqFqRLlw7XQ/Raqe0VOGXKlGFWj5Reh3fvNnAMAnsTzmOgp2IK3/PrfyNr+UpwSJ0WXk+C2tSnDOwB+9n//tLvZThnHvx7/kyY85kLCRzTpU2L69f1+0TQ9sgqnS+FmU9z5sBuD08D1wrNuvIE9oI9YdJkHDtxAn9s34a4waphfvH3V6W6sb0n9uB43kdTHk2TxuADjms3A/No4D2ToZ6xd+/dq2oGSI0i3XKB68qdM6fKuwtnhW6qtHTlSpy/eFF9Zm8f+sE/kTHYOZMZkUBCSq82bdmuN13alRoinQalTJ0ZZ8+eV/9LQDds6ACUL1cWDx5qxuIK2X35jRu38Pr1G/Ts0VnNLwGH8NirGfcrMuO9lnbWtGOU0rvgpH1kp6691A9WoUIFVFvYIkUK6V758+dV7UOlenNUpo0Ez0UKF1TzBO/5WDoZ+mO3p25/o4qUVEo15eCdIEi12Wo16+PESU1HR+GRasIytp+kR/D0kcGg12/YEql9Mbab+hMnT6NunZooX95Fd2Pl4ak99kElpVIlWKoGy7GV9GzWpKFuG1GZh0xJsmTJUbxYCb1XpkyZUbFCJXz+/BlbtwU9CJLgcsfObcidOw/SpjVcs0GUdi6r0nn9hrV60+V/6W3Y1aW8+l+qEw8c3A8fP37AuLETzS5o1apUqRIuXbqkF7xK275z586hWrVq4S4nx0CGuNHy8fXFtu3bkSdPHhUQU+TcOrhPjdtaqFlQG3gLS0sUbNwc758/w6OL59S0u8eP4IuvDwo11e+gRf6X3obvHDb+Oh5bVKpQAZeuXNEFnEKaQ5w7fx7VqlYJdzmVT7du08+nO3aqhysSEAvp8EZ6b92xc5fe8mvXrVdNYlzKlIE54XkfDWlavjwuXb2K6zeDgtc7//yDcxcuolrlsMdWrlS+nCaPbg+6l5J7mO1/7FIPViSPJk+WDCWKFQ31SppE0xeH/B18/FjSsLC0iPaXOWCJqxmRqoTSPnPEqPGqGqu0dfxjjyf2eBge8FxKNCWYa9mmE0aNGILUjo6qx14JpuR/kThxIvW+e89e9Xf27FlVUCK9A0sPiVIldfPWHVj2+yq93uqMIQFXwwZ1MeyXsfj8+QsKFyqAAwePqI581qzStHOdOG4kqrs1QJNmbVRvtWLWnAU4fOQ4enbvHOVp8+uE0ahSvS6qVKurSnrlAv3r5Omqp7zRgZ06RRXpzVd6MK5Woz769e2hbjgkaJd2x7Vr1VClw+EZ+ctgtXz1mg3QvVtHJIgfH7+vWK2C1pXLg4anMYYcW9meh8c+FCiQL8z5pKdkWb+05ZWbJ+l4auKk31RJ9ccQY0C2atEUbnUaq4cAG9YGlchGZR6KDUqWdEapks6YNXuGGvomQwYnbN++FS9ePMeYUeP05t3j8Ydq1+rqqglIJZ06tO+MufNmYcDAPiqQPX3mFA4c3IeePfqo9tdi85ZNePToX+TIkVNVxZf1BCdBboXyFRHbtWndGrt27ULXbt3QulUr9dBj1erVqqqv/C9ev36NU6dPqxuo/Pnzq2nOpUrB2dkZM2bOVLUnnJycsHXrVjx//hzjxukfAwotrgy3VL4SfF6/woMzf6ppb+79gytb1qNw8zaqGvCTq1eQo0p1pClQGLsG98G3wCYhn95749TieSjbeyBq/zYPd08cgVMJZ2SvXB1HZ06Bn5fhPgBiszYtW2DXnj3o2rMXWrdsAUsLS6xyd1fVKGW4Gl0+PXMW6dKmQf58mmuuc8mS6jVj9mw8ff4MThkyYOv27aq3+HGjR+nW37RxI+z29MSkadNw5+4/yJwpEy5dvoLdHh4oXrQo6tTS7zcituN5H/VaN2+OXR4e6NanL1o1b6by6Op165AieTK0atZMzfP6zRucPntW/dbnz6vp76JUiRJwLlECM+fOVZ0AOqXPgK07d6phoMaN0HRMSRSdGLiameHDBsLOLgFmzVmoAjwpJZw2ZTy69wzdiZEErfs9t2Po8DHoN2CYalPq5JQeE8aN1HXCI50FNW5UD3PnL1YlYlK1U4ZBGTjkFzRs0lr1pluwQD4cO+ypSgqPH/8TdesY/6MpAeqYsZPU+qXKsLSZlKFXmjZpqD6X6qXSDleGTmnUtI0KcqTDIBlCRUr9ojptpFfjA3t3YtSYiWjSrK0aSqRsmVJquCAJKKNSwYL5cfTQHpX+si3ZN2lfPPnXsaEG9zZEjs2Jo3tVb8et2nRWJZW5cmbHxvUr1QOByGjftpUqwa5drynGjh6OJo3rG5xPAmKpAtyj10D1f7asmbFowUyscd+A44HD4WjJsZMx4eQVPBiWMUmjMg/FBpN+nYb5C+bAc+8e9XQ6a9bsmDN7AQqEGF915KjhcHRMrQtcRZvW7VSp64aN6/DnqZNInToNhgwejgb1G+nmOX/+rHq/ceO6WkdISZMkNYvAVfLOiuXLMW3aNCxeskSdn4ULF0a/fv2QJHDc1bt372L48OGo5eamC1zFtKlTMWfOHOzZozkG2bNlw4L583Wd21DYbBMnQY0J01RVX23gKg5OHgef16+Rp3Z95KhSE28e3MOOAT1w+6D+A8GzK5aoIXEKNW2JjM5l4fX4EfZPGIkrm9ebZbKrfLp4MabNnIHFy35HvLhxUbhQIfTr3QtJAnuAv3v/PoaPGoVaNWroAlcxbdKvmDN/AfZ47tXk06xZsWDObBQqUEA3j+T7JfPnYfb8+di3/wC8vL3Vg+eunTqifZs2ZteLK8/76EhTeyxfuADTZs/GkuUrNHm0YEH069lDP4+OGQu36tV1gauYOmE85ixahD1796lxhGWM1vkzZ6BgsOstUXSxCPD3jp09oRARfQ+rhHjvFfE4uWS8hA428AtnTFmKPBl3dlrBbEy6KDLg0i34eQeNLU3/nY29A8/7aDjvfd+E3S8JRY6tQ7xYk2QPhg+K9m1kmDAFsZ15PZajn46UMhrTJtLcnkBHZdpI29PwxgolIiIiougj1bUpYkwlitXadeimhoOJ6BXRmKjmaOy4SUalzdGjJ2J6V4mIiIiIwvXzFUORWZEOk3p06xThfKlTa8bW+5l06tgWNWtUjXA+6SyJiIiIiMiUMXClWM3JKYN6keFg/WcM2ImIiIjI/DBwJSIiIiIiiiGW7GvEKGzjSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGWFhaMO2NwBJXIiIiIiIiMmkMXImIiIiIiMikMXAlIiIiIiIik8Y2rkRERERERDGE47gahyWuREREREREZNIYuBIREREREZFJY+BKREREREREJo1tXImIiIiIiGKIhQXHcTUGS1yJiIiIiIjIpDFwJSIiIiIiIpPGwJWIiIiIiIhMGtu4EhERERERxRBLC5YlGoOpRERERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGWFhyHFdjsMSViIiIiIiITBoDVyIiIiIiIjJpDFyJiIiIiIjIpLGNKxERERERUQyxtGAbV2OwxJWIiIiIiIhMGgNXIiIiIiIiMmkMXImIiIiIiMiksY0rERERERFRDLGwYFmiMZhKREREREREZNIYuBIREREREZFJY+BKREREREREJo1tXInop5PQwSamd8Hs2NjaxvQumJ0Bl27F9C6YFRt7h5jeBbPD8z7q2SZJGg1r/Ul9fY9Yw5JlicZg4EpEP533Xn4xvQtm9yDAz9c3pnfD7AICP2+vmN4NswpapxXMFtO7YXYPVvw+fozp3TArNgkS8FoalekZLyrXRqaA4T0RERERERGZNAauREREREREZNJYVZiIiIiIiCimsI2rUVjiSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCimsI2rUVjiSkRERERERCaNgSsRERERERGZNAauREREREREZNLYxpWIiIiIiCiGWLCNq1FY4kpEREREREQmjYErERERERERmTQGrkRERERERGTS2MaViIiIiIgoprCNq1FY4kpEREREREQmjYErERERERERmTRWFSYiIiIiIoopFixLNAZTiYiIiIiIiEwaA1ciIiIiIiIyaQxciYiIiIiIyKSxjSsREREREVFM4XA4RmGJKxEREREREZk0Bq5ERERERERk0hi4EhERERERkUljG1ciIiIiIqKYwjauRmGJKxEREREREZk0lrgSERERERHFFJa4GoUlrkRERERERGTSGLgSERERERGRSWPgSkRERERERCaNbVyJiIiIiIhiCtu4GoWBK0UoICAAFhYWTKmfMI3M9Xv9aH5+fliydCH27vPE27dvkS1rNnTt0gPFihU3avkdO7fBfe1qPH78GClTpECTJs3RqGGTUMdq46b12LJ1E/799yFsbGxQuFAR9OjeG05OGWEunj59ihkzZ+Ls2bP48uULihYtigEDBiBtmjQRHoOFixbB0zPwGGTLhh7du6N4cf1j8PXrV6xatQpbt27F8xcvkCFDBrRv3x5Vq1SBuXr67BlmzJ6Ds+fOBaZpEQzo08e4NF2yFJ779gWmaVb06NIFxYsV05vPx9cXi5cuw/6DB/Hq1StkzJgRrVs0RzUzTlOtYm06onDzNlhQydmo+S0sLVGkZTvkq9cIdilS4e3D+zizbCFu7tsTat48teujSIu2cEiTDu9fPMPFtatweaM7zJE672fN0pz3/v6a875fP+Py6OLFmvP+3Ttky5oVPbp1C3Xef/z4EfMXLMCBQ4dUHk2cODHKlyuHnt27I2HChDBHvJZSbMSqwhTuBb9Pv8FYu25jrE8l1/LVUbps5WhZ97jxkzF12izd/6PHTIRFHHv4+/sjNlu8ZDkGDBweLetu064L0mbI8UOOjyn4ZcQQrHFfBVeXcujbu7/KGz17d8OlSxcjXHbdeneMGz8aadOmQ98+/ZEtWw5Mmforlq9Ypjff4iULMXXaJKRKmQp9+wxAk8bN1frbdWiFR4/+hTnw8vJCh44d1c1r82bN0KljR1y9ehVt27ZVgVN4hgwdqgLScq6u6N+vnzoG3bp3x8WL+sdg+vTpmDlrFgoUKICBAweqG9jBgwfDw8MD5kilaZeuKmht3rQJOrVvj6tX/0LbTp3VjX54howYgVXu7ijn4oL+fXpr0rRXb1y8dEnvgcqAwUOw2t0dpZ1LqfkSJXLAkF9GYMOmzTBnGZ3Lwrlb70gt49J3MFz6DMLjyxdxeNoE+L59A7fJM5Gjag29+Qo1bYWqo3/Fu0cPcWT6r3h58zoqDh2FYu06wyzzaOfOQed9hw6a8759+4jP++HDsWr1apQrVw79+/bV5NGePfXOe8mjvfv2xdr16+FcqhSGDBqkrhObt2xB565d1cMcc8NrKcVWFgH+3gExvRNkmu7c+QdZcxTE8mUL0KZ1c8RmEhjJD9aJY/uidL2yzrg2STBqxBCMHjVMTXv06LF6lSihX+oQ20hgWbGCK1b8vjBaAtcDB4/g0YMb0Xp8DLJKiPdefvhRzp49g249Oqlgsnmzlmqar68PmjRrCPuE9li9al2Yy75/740ablVQtEhxTJs6Q1f6PXT4IBw7dhR/7PRA4sRJ8OrVS9Rwq4qyZV0wdfJ03fJ37txG85ZNULVKNYwZPT7avmNCBxv4+foius2dNw9Lly7FWnd35MqVS027fecOGjdujObNm6uA1JAzZ86gU+fOGNC/P1q2bKkrBWzYsCHs7e2xbu1aNe3BgweoU7cumjRurIJVbQlsu3bt8PjJE3js2YO4cePiR7CxtYWft1e0b2fugoVYunw51q5cgVw5c6ppt+/8g8YtWqB5kyYq0DTkzNmz6NS9Bwb07YOWzZoFpWnTZrC3T4h1q1apacdPnkSPPn3Ru0d3tGvdWk379u0bWrRthydPnuCAxx7EiRP9lb9s7B0wrWA2/CgFG7eAa/8hsIobDx9fvTSqxDVR+gxot9UTlzasweGpE3QlsE2WucMhTVosrl4e3/y/wNouITp7HsWDs6exo1833fI1J81A5rLlsbiGK3wjCOiiwoBLt+D38WO0b2fu/PlYumwZ1q5erX/eN22qAlkJSMM877t2VSWzLVu0CMqjjRvDPmFCrHPXlE7v278fAwcPxuBBg9CsSVBNlo2bN2PCxIkYM2oU6tSujR/BJkECXkujMj3jxZ4ChHeL5kf7NhJ1DrpexFYscSWKYmnTpon1QStFHc+9mhvzenXr66bZ2sZH7Vp1cf3GNTx8+CDMZY8dPwofHx80bNBIr8p2k8bN8OmTH44cPaz+v3jpAr5+9Udttzp6y2fJkhWZM2fG5StBJWCxmZR65subV3fzKrJmyaKqDYZXIrrHw0Mdg/oNGuimxbe1Rd06dXDt2jUVsArPvXtVUNU42M2rlZWVCoxfvnyJ8+fPw9x47N2LfHny6IJWkTVLZhQtUgQe+8J+kLRn715Nmtatq5+mtWvh2vUbePDwoZr2zssL2bNmRZ1atXTzWVpaonDBgqpE98XLlzA3zVZuRIUhI/Hw7Gk8u/a30cvlqFIDllZWuLwhqLpvwLdvuLTBHXbJUyJd4aJqWmaX8oiXwC5UteCL61Yhrq0tsrhWgjnx8PQM+7z39AxzuT2enpo8Wr9+iDxaG9euX9flUaltIILnUaGtyn7p8mWYG15LTY88pIrulzkwj29BoThlzqOqrA4bPgap02WDTYLkKONSBefOXdDNc+HCJVSrUQ/JUjrBPnEa1KzVEH/99T/12f37D1Rpq2jbvqtan9aff55BuQo1kMA+FRInS49mLeTJ+dPvOgrLV6xB3gIlYB0/mSrhGzpsND5//qw+O3XqjKpyu33HH3rL3L17T02XZbX72qpNJ/U9pfQzhWMm9f+rV6/D3K58nxatOuhNW7pspVqvrE9Lti3pljBRarWPOXIXxpy5QSWQsj0xZtwktWxYVYU3btqKoiVcYOfgiFRpsqBz19548+aN7nNZJkv2/Ni79wAKFS2jjpfs42/T50Q6TaWEaPKUGciTvzhs7VKo41SqdEUcPHgkVIl6g0YtkTRFBiRKmg5VqtXBlSt/6dLn8eMnWLlqrS5NVqx0V3/LcsHJcZMSVC258Zeq01lzFFBpljlbPkz7bbaqjhVVeSM2uX79mmpjKsFqcDlzaAKFa9evhbusmjdnbr3pObJrl9Wcr2XLuGD9us0oVKhIqHV4vXungq/YztvbG48ePUKu3PppIeSGVgJLeRkiwam0q5Sb1uByBt4Iy+fadzs7OzhlyBBq/cHnMxcqTR8/Rq5cQUGrVq4cOTRp+uqVwWXlxj+jk1PoNM2RQ/e5cKteHRvXuiNJ4sR68928dQvW1taqKra5sXd0xL5xI7ClRwd88TG+RDJVrjz49P69atca3PPA8zxl7rya91ya3+PnIYJi7XyyHnOhO++DBa1a8rAlwvPeUB4NfEijPZ+lHeuGdesQP77+NVpbDdkcrp/B8VpKsRkDVzM2c/YCXLh4CUsWzob76qV49PgJ6jZorgKqw4ePoWTpivjyxR/LlszDsiVz1eelylTCtWs34OiYCps2aKp6/TJsILZt1jzZPXnyNFwrVFcX8vXuyzFn1hScOXseZctVVRfDyJDgpl2HbihbphR2bd+Afn16YPbchWjRqqP6vGTJ4siSJRPWb9BvB7V23Sb1A9Ogfm1VGuVaoQau37iJeXN+wz6P7ejdsyvWrd+MIcNG/af0273bE3XrN0PhwgWwY+s6bN20BpkyOqFXn0E4ceKUmufEUU2JRPt2rXDqxAGD6xk/YQoaN22DYkULY8vG1apa8ZatO+BSvrraf62nT5+jY5de6NyxLfbs2oySJYpiwKDh8PCIXPXZIUNHqUBa1uO5eyuWLp6DN2/fokHjVvjw4UPgtp6heKnyqmRk7uxpWLdmGd6//4AKld3UZ3K8kydPhurVKqvvJfnBWD17D8CwX8agSaP6+GPHRrRo1lgdi8FDRkZZ3ohNXrx8gRQpUoaanjx5CvX+7NnTcJe1traBg4OD3nS54Xewd8DzZ8/U/zY2tsiSOWuoG6+Dhw7g+YvnKFigEGI76ShJSOdUISVPnlzXyZAhL168MLhcihDLyXwpvmP9sdXzFy/DSdNkEaTpS6PSNDhfPz8VsI4ZPwFnzp1D6xYtYGtjA3MjVXqvbt0Q6eXsUqTEh5fPQ03/8FKT9+1TOar3hClS4ouvb6iq5F8/f4bvu7ewd0wNc/GfzvuXL5EyZcoI86hcX3Nkzx5qPvd1mmYchQpqHuKbC15LKTZjr8JmzM4uAf7YuUnXJkt6zWvdtosqaZVAIlMmJ+z12KZ7mlipYjlkzpYfv4wch62b3VEgv+bpbubMmVCwYH719+ChI5E5c0Z47N6iW69L2dLIkr0A5s1fgqFD+hu1bxLkjh77K9q1bYl5czRt8ipXroA0aRzRpFlbnDrVTQWuLZs3weSpM9W+J0iQQM23dv0m1KldQ/X0JyWE6dOlVYF31qxZ1OflypVVwfSRo8f/U/pdv3ELLZo3xszpk3XTSpYshqQpnNS6S5cuieLFNSVcadOkNlg9WJ7Yjp84VQW22u8p8uTOpYJ9KeXt1bOrmiZB7OYNq1CtmqaTImfnEti+Yzd2/uGhm2aMJ0+fYvzYEejZo4teoFO/YQtcvvyX2u+Zs+bDx8cX+z13IE0azU1OkSKFUKxkOfx56gzq16uNePHiqZvXyFR7vn37DhYsXIYxo4ZhxC+aNoKVKpVXAdXwEWPRs0dnpEuXNkryRmzh4/NR9fAbkhwT4ecXdttQn4+Gl1XL21jDN5x2pU+fPsGUKRPVcWzZQtO2MDaTtNC2/QzJJjAtw0oPuX4YWs46xHLBrzPhzWcuJG8KQ3kswjT18Qk3X/v6hm5H/vuKlVi8TNOpWIF8+dCiqX7P2OZC2qF+j3jxE+CzgRJa/0+atIwbWGsjbvwE+OJnuJ2+/6dPiGMgr8f68/578mgY109jzudjx49j46ZNSJ8uHapUNq+OA3ktpf9KCr+kgOLqX/9DkiSJ0bZ1c4wcMcTo/gp69x2E2XMW4ovfm0j3ccASVzMmJXzBOxLRdhsvVWjPnb+IhvXr6FWBSZQoEdxqVgsz4JPA6tTps6hetbJqbyclt/KS0rhCBfNj3/5DRu/bqVNn1fpq16qhW4+8pIRP2j9p19WyRRP147Jzl2YogEuXruD69Zto1aKp+j9//rw4dsRTBdcSNO3Zs1dVS5US2E+f/lu10gH9e2H1yiWqlFKC/Q0bt+DXSZpA6tOnT0at4/Tpc2repo2D2taJMmVKIUOG9Dhy9ITedAlWg/+4SuAoP76R4b56Gfr17YGXL1+pkmGpcrvGfYPefh87flLlD23QKmRb9+78pYLW73Xo8DFVJTjkca3lVl1VYT50+GiE6zA2b8SqIYUQ9pBC4Q03FIDwhyMK67Nnz5+ha/dOeP3mNQb0H2wWw+Foq5qHNzhTWOmhOQbhLBfJ+cyFtvb+9+RPzVBZiNRyJYsXw8xpU9G9S2fcunMHTVu1VkOPkC7R5KSP8IBp0jacGSPZLMOUab/J91wHIxrOLaxPpFMn6axJgt4pkyb9sA7ZfhReS02UhWX0v6LAmTPnUK1mfVUIIbUIu3ftiElTZqgagsaQZmtz5i767u2zxNWMxY+v/9RVbvrFq9ev1YUrVarQVWhSpUwJLy/DVX7fvn2n2i9OnzlXvULKmjWz0fsm+yBq1zX8xP1xYJvZjBmdUNq5pKr627RJQ1VNWALlihXL6eadPmMuJk6ahtev3yBlyhQoXKiAKjUx9MQ/MiTAl7ao0s5Vfvzk+5UpXUp9Zmx7TamiKwyndQq8e6df1StkVU85ZpLmkXH+/EV069FPPZywtbVF7tw5kCF9er39lvQvkD8fopq2XXH+Qpp0Cunx46dRljdMcfgobVVsLRsba3VM/QJLTILTPkQwVMKnFd82fpglsp/8Phlc9t69u+jRqyueP3+GHt17oV5d/YcmsZX23JB0DskvMC3twkhLdQz8wjkGdna6+XyNmM/cfiMM5U/j0vRTpPK1tsqlDJ+TK0dOdO/TB6vXrkXfXr3+4zcxD198fBDHQAlhHGvNtE8fNdeXzz4+iBtGTYw41tb4HOI6FJtp26dG53kf3IGDBzF0+HD12ztz+nRde1hzwmsp/RejxkxEzpzZsWHdCnVvXLVqJVhbx8PAwSMwaEAfpE6tadJgyLt379CmfVfViem//z76ru0zcP0JScmqZLZnz0K3pZE2H0mTajocCkmGOJDlevXsotothlX9xqh9CGyzt2rFIuTMEbptSbJkSXV/S6lrj14DVIZfv3ELmjVpqCspljFm+w8chonjR6FD+9a6dlmNmrQOt3Mm+R5SAhhcyKBDOp26dv2mqk5bqlRx9fRVSgKXLF1h9PfUdkgiaZ07t/4P4NNnz1G8WOjOdP4LqWZbtUY9VRX57ytn1MVFfoClJFra1WrJOIpSIhvS0aMnVJXcLFlCP4TQPrj++lU/kP7w4aPeeoWkmfbv4MK7oH1P3jAl+w/sxZix+u14a9aohVSpHNVwNSG9DGy3pm3rakiqVKl1AbF0GhT8xsvL2yvUsv+79jd69e4Ob28vDOg/SI3lai4cHTV5x1BnQdrOWQy1T9Uua2i5FyGWk/kuXLgQ6fWbZ5q+0msPGGrZVKkiSFPDy2nJmK4J7exU7RjS8H76BGkDew4Ozi7wPP/wQvOb7f30sao2HC9BAnwOViPHKl482CZKjPcG2snGVj/ivNfasnUrxk+cqAK7ObNmmV3bVi1eS+l7yb3H4SPHVd83wWszNGpYD/0GDIPn3gOqmVdYuvfsr/qKcSnrjHETpnzXPrCq8E8oQfz4KFK4IDZt2a4XvMmA1H/s9lQlnIZ60pM2pYUKFVBVdaU9pPYl1XUn/DpNZVhjlShRVLW9e/Toid66HBzsMXjoKLUNrUYN66rgS57yyPiorVpqqgmLEydPqRt6aVurDVrlJl+mh1dSKUG4bDu4k3+e0fv/xMnTqFunJsqXd9G1k/Hw3K/ev30LMKq3QWkDKwH9uhAdTB0//icePvwXpYNVDY4KN27cUiXP0pZUAmVtKbvHXu1+a9KkjHMp1Q5YOmLSkgcD1d0aYPOWHQa/m4x3KeQYaP3vf9dVvtEqW0YzVqHcLAQ/rtKeVtq4Bt9eVOQNU1KyRCnMm7tI79WqZRvkzJFLlYKGfPJ//Yam19Xc4fQAqn3af+OGfm+2N25qls2VK6iH3du3b6F7jy748OE9Ro0Ya1ZBq/b6ky5dOlwP7K02OOkdVDphSZZMcw0ISXokvXvXwDEI7FU0T2BPxTKftqfdkOsPPp+5kMAxXdq0uH5dM55ycNdu3FAd4oSZpjlz4O69ewbytWZdeQJ7gZ0waTKq1HTDl2C9rAv5X0rMIvPA09w9v/43bB0SwSG1fj8AKQN7FX/2v7/0exnOmSfc+czmvFd51MB5f/16+Od9zpyGz/vAdWnzqNi5axfGjh+vOmpaunix2QatgtfSn5edg2OEr/DcvXtfje6QPXtWvenS7Exq+IV3fyaja+zc5YHly+br7k2/BwPXn9SvE0bjzp27qFKtrmo/unnLdpSv5Kaq144eOVTNkzhxIvV+8NARVaddTBw3EgcPHUWTZm1Ur7vyquHWQAW8UkXXWEmTJsWgAb1VJzzDfxmr6rxL6akETtLTrQTIWvJDIm0kpfMnCZLz5Qv6sS5WtIgKVPsPGIYjR46rdZRxrapKOD9+DOqxN6Sa1avi5J+nMfHXaaqReZ9+g0O17ZU2oOs3bMEa9/Vq3TKvdG4lT5k+BnagIX/L/knQe+zYyVBViJMkSYIhg/ri9+Wr0b1nP+zbdxCLFv+O+o1aqBM/vCdT30PWKQHmpCnT1bGR7XXq0gvzFyzVdagi+vbprqoJSunsps3b1Lw1azVSpaTafZLjf+nyVVUKK+2My7mWURemgUNGqJ6Opc2vfA9pmK+VN29u1aFVl2591ZA8krbSAVXDJq3w76PHyJMn9JAG/yVvmJJkyZKjeLESeq9MmTKjYoVK6kK/dVvQwwtfXx/s2LkNuXPnQdq06cJcZ2nnsuqhyfoNa/Wmy//S27CrS3n1v1QnHji4Hz5+/IBxYyeiZk398QjNRaVKlXDp0iW9m9jbd+7g3LlzqFatWrjLyTHYvDnoGPj4+mLb9u3IkyePCohFxQoV1Dm91j1ofEx5uLdhwwZVOlOoUOzvnTmkShUq4NKVK7qAU9y+8w/OnT+PalWrhLucStOt2/TTdMdOFeBLsKHtW+HZ8+fYsXOX3vJr163Hly9f4FKmTLR8r9jo1sF9atzWQs1a6abJ2IsFGzfH++fP8Oii5nf47vEj+OLrg0JNg+YT8r/0NnznsPEPkWPNeX/5suHzvmrVsJerWFGTR7dsCZFHd+id97dv31ZBq9RG+33JEt2QTuaM11ITJMFcdL/+I21BhX3ChKE+S5jQDt7v3xtcTobM7Nq9L6ZOHodMmf5bnxusKvyTqlDBFQf27lSlmNJTq5RwydAjK5YtUMGHkIt4n17dsGTZSuzesxfPn/yjeneVIWdkuJVGTduoTguk92EZdkVKJiNj3NgRqurovAVLMG36bBU0SXAkPeJKW9XgWrVoop7WaDtl0mrdqhnu3buP31eswfyFS1U11xrVq6Bblw4qYLt69W+9QFdr2ND+qlRQtitDArnVrIoli+ao4W+0Vi5fqKoo9+g1UP2fLWtmLFowU3V0dDxwOBwhVSakxFkaq1//W3NjEdzoUcNUG9c58xZh6bJVqiq2dIA0YdwIveqfUUGCaBm6Z+CQX9CwSWt1ISlYIB+OHfZU+yclvXXruKlG9SeP7Ve9RLfv2ANx4lip9rvyJExbxU+Cx779h6JK9boqr0hvxDIk0JDho1GnfjNkzJhB9R68ZOlKvX1YvmwBJk2ejiXLVmDEqPGqJFw6Who35hejS1cikzdMXcmSzihV0hmzZs9QQ99kyOCE7du34sWL5xgzapzevHs8/lDtWl1dNQGpPITo0L4z5s6bhQED+6hA9vSZUzhwcB969uijzlGxecsmPHr0L3LkyKk6spL1BCdBboXyFRHbtWndGrt27ULXbt3QulUr9dR21erVKqiU/8Xr169x6vRpFTjlz6/pDd25VCk4OztjxsyZqjmEk5MTtm7diufPn2PcuKBjkClTJjRo0ADua9eqhzz58ubF3n37cPnKFUyePNnsOmkRbVq2wK49e9C1Zy+0btkClhaWWOXurqoIy3A1ujQ9cxbp0qZB/nyatvHOJUuq14zZs/H0+TM19u3W7dvVUBvjRgcNRda0cSPs9vTEpGnTcOfuP8icKRMuXb6C3R4eKF60KOrUcsPPKK4MYVW+Enxev8KDM3+qaW/u/YMrW9ajcPM2qhrwk6tXkKNKdaQpUBi7BvfBt8BS60/vvXFq8TyU7T0QtX+bh7snjsCphDOyV66OozOnwM/rHcxJm1atsOuPP9C1e/eg837NGk0ejei8L1UKM2bN0pz3kke3bdOc92PG6NY/Z9489RClVMmSqhRXOwaxVurUqc2uBJbX0p/TB6//1keItrZhWMLqDE2GN5Sanl06t/9P21fbCPD3Np/u54iIImKVEO+9/lvHXZElbaPnL5iDffs9Vel11qzZ0a1rDxQJ0Z6tSLH8cHRMjV07PEKVsG7YuE51uJQ6dRo0adwMDeo30n3ep28PnDgZ9vBPSZMkxV7P6OuNOaGDDfx+0FAxjx49wrRp03Dm7Fn1wK1w4cLo16+frtd0KYXp0LEjarm56QWlcgzmzJkDz7171THIni0bevTogaJF9Y+BBP5LlizB9h07VPX5DBkyoFPHjqhY8ccG/jJ8T8hxOqOLVP+fNnMGzpw7j3hx46JwoULo17tXUJpeuIAOXbqiVo0aekGpStP5C+C5f78mTbNmRY9uXVG0cOFQT+lnz5+Pw0eOwsvbG6kdHVGjWlW0b6N5+Pkj2Ng7YFrBbPjRGi9ZjSROmbCgkqYZhZa9Yxp02nMY/54/gw0dg2reWFhZoWSHbshTu75qr/rmwT2cXjoftw+GHs+7YJOWKNS0JRKmdITX40e4uG4lrmxejx9lwKVb8Itkr/f/6byfPj3ovJc82rdvUB49fx4dOnXSnPfBglKVR+fN0z/vu3dH0SJB/UuULF1ab0z1kGQ4HOld+EewSZCA19KoTM94+k0UTJnX75qacdHJoV2H/7T8tWs3kDtfMWxcvxING9TV+yx+wpTo2b0zJk8aqzd9/oIlGDp8DK5cPKk6ZRJjx01SbVz9Pr5UvwGRqTrMwJWilLShNKYX3MiO2/SzMzZdpV1qeN3/U8wErubuRwauP4sfGbj+DGIqcDVnPzJw/Vn8yMD1Z8DANWoDV+mcKYF9KlWDTvqW0Xr8+AnSZsihaty1aa3fv4Zr+eo4ekx/6MfgRo0YomomGottXClKSXWAuDZJInzdv/+AKR8J8nTKmHSV9qhEREREFItYWkX/6z+S5l6uLmWwdftOvcIUaconBVLly5UNtcyiBbNw7vQRvVfHDm3UZ6dPHkSnjm0jtQ8s9qIoJR079ejWKcL5jBkWhYLIiV2zRtidUGiF7OmNiIiIiCgqjBg+COUr1UT9hi3QsX1rXP3rfxg5egK6dmmP9OnTqVLZS5euqGrB8jJ0XyoduorChQtGugYmA1eKUk5OGdSLopYE+gz2iYiIiCimuLiUxvYt6zByzATUbdBcdZg5dHA/jBwxRH0uwx6WLF0x0lWAjcU2rkT0c2Eb1yjHNq5Rj21cozg92cY1yrGNa9RjG9efuHOmFcujfRsObSJXLdcUscSViIiIiIgopkTBOKs/A6YSERERERERmTQGrkRERERERGTSGLgSERERERGRSWMbVyIiIiIiopjCNq5GYYkrERERERERmTQGrkRERERERGTSGLgSERERERGRSWMbVyIiIiIiopjCNq5GYYkrERERERERmTQGrkRERERERGTSGLgSERERERGRSWMbVyIiIiIiohhiYcGyRGMwlYiIiIiI/s/eXUBF9bRhAH8MVAQBxRa7uxMFBAsVu7G7W1FRsbs7/3ZgKyI2Ynd3FyoqiKIo1vedGWQlFthFYIPnd84elhu7w9y7y33vzLxDRFqNgSsRERERERFpNQauREREREREpNU4xpWIiIiIiEhTOI+rStjiSkRERERERFqNgSsRERERERFpNQauREREREREpNU4xpWIiIiIiEhTOMZVJWxxJSIiIiIiIq3GwJWIiIiIiIi0GgNXIiIiIiIi0moc40pERERERKQpHOOqEra4EhERERERkVZj4EpERERERERajYErERERERERaTWOcSUiIiIiItIUjnFVCVtciYiIiIiISKsxcCUiIiIiIiKtxq7CRJTgpDJNoeki6J0UhoaaLoLeSWFiquki6JXBV+5rugh6J4WRkaaLoHf4XRqLfgXE5quRFmDgSkQJzqeNKzRdBL1i0qoz7vbvqOli6JUCc/7Dt69fNV0MvQoGvn35ouli6F3QOqNkPk0XQ+9urvA8jT0pdOkeNce4qoRdhYmIiIiIiEirMXAlIiIiIiIircbAlYiIiIiIiLQax7gSERERERFpCse4qoQtrkRERERERKTVGLgSERERERGRVmPgSkRERERERFqNY1yJiIiIiIg0JFEitiWqgrVEREREREREWo2BKxEREREREWk1Bq5ERERERESk1TjGlYiIiIiISFM4j6tK2OJKREREREREWo2BKxEREREREWk1Bq5ERERERESk1TjGlYiIiIiISFM4xlUlbHElIiIiIiIircbAlYiIiIiIiLQaA1ciIiIiIiLSahzjSkREREREpCkc46oStrgSERERERGRVmPgSkRERERERFqNgSsRERERERFpNY5xJSIiIiIi0pTESVj3KmCLKxEREREREWk1Bq5ERERERESk1Ri4EhERERERkVbjGFciIiIiIiJN4TyuKmGLKxEREREREWk1Bq5ERERERESk1Ri4EhERERERkVbjGFciIiIiIiJNScS2RFUwcCUiiiNvPn7CvENeuPDkGX78+oWyObOhf42qyJLaLMr9/AMDsejICZx6+ASfvn5DgUzp0cmqIirkzhnlfjM8jsD1/GXs7tcVmc1MoY+SmqVB+npNkTJfISRKkgSBD+7i7a7N+OH3Ptp9TctVRmrrakiWLiN+fvqIT1fOwfegG/7348ffjZIkgXm1OjAtUxEGqc3x62sgvty5gXd7t+PnJ3/oo9evX2P2nDk4f/48fvz4gbJly2Lw4MGwyJIlyv2+ffuGJUuXYv/+/fjw4QPy5cuH3r16oXz58pHu89LbG40bN8asmTNhaWkJfSXrdO7c4Dr9+TO4TgcOVK1Oly0LrlN/f+TLmxe9e/aMUKdfvnzBosWLcfjoUbx//x6pU6eGbdWq6NOrF1KlSgV9Vq59F5R2bI/F1VU7fxIlTowybTqiWKNmME6fER+eP8W5lUtw7+C+CNsWqd8YZVp3gGmWrAh4+waXN67F1S0boI/i+hyN8Llv2hSzpk/X6889xT2G90QU6/73v/8l+Fr9+PUruq/ZLIPWluVLy8Dz+otX6LpqkwxMIxP08yd6rt0Cjxt3ULtYIfSpZoUfv36j7/ptOHH/UaT7XXr6HFvOX9brek+c0gjZeg+VQesHr0My6DTMkRvZ+g5DEiPjKPc1r14XmVp1xM8Pfni7axMCH95FWrGsZccw22Vq3g7p7Bvg24un8NmxER/Pn0KqEmWQrd9w+f765uPHj+jcpYu8eHVs1Qpdu3TB9evX0aFDBxmMRmXY8OFYu3YtqtrYYNDAgfgpzt1evXD5svLz8NOnT+jfv7+88NVnsk67dftbp507B9dpp07R16mzM9auW4eqVati0IABwXXap0+YOhXfr/0GDMDGzZthWakShg0dKo/Btu3b0a1HD3nzQV/ltLSCZc9+au1jPcAJ1v2HwvvqZXjOmIivH/zgMHUOCtSqE2a7Ui3botaYyfB/+RzHZk3Gu3t3UG24C8p17AZ9E9fnaITP/YABev+5p/jBwDWWVLaqARvb2tCUHLmLoHXbzrH6mqvXbECipCZ4+DDyi+XYdPiwp3y/Y8dOIL6I9xs5aly8vV9CMH7CVEyfMVfx+5ixk2Q9i39uCcnGM5fw6sNHzHNsgo5WFdGmUjksbNsMvp+/YPXJc5Hut+fKDTzweYdR9Wqhl50VmpUrhSXtmiO9SSosPqr8sxH4/TvG7d6PpEmSQJ+lsa4BgzRp8WLJLPge2gs/zwN4vmgGkqYyRRq7yL9/DdKmR9qa9fDpynm8XDEP/qe98GbzKvh5HYJJqfJIlj6j3C5F1hyyVVa87qu1S+F/+hjeuW2F938Lkcw8HdJYVYO+Wbd+Pby9vbFo4UJ06dIF7dq1w9KlS+Hr64v/Vq2KdL9z587B09MTA/r3h5OTE5o1a4aVK1ciY8aMmD5jRoTtHzx4AMfWreVPfbduw4bgOl2wAF06d0a7tm2xdPHi4DpdvTr6Ou3XD05DhqBZ06ZYuWJFcJ3OnKnY7tDhw7hw8SKGDB6M0SNHommTJnAePhzDnJxw6/ZtuO+L2JKoD0o2b40GsxchiUEylfcxy5ZdBqSXNq7BfpdhuL7dFdt6doT31UuwGTgMiZMayO2SG6dC5V798cDzMHYN6Ilr2zbDzak/7h5wR8XOPWGYOjX0SVyfo2E+923b4sHDh3H8F1FCwcBVT+zctgFjXUZouhg658zJw+jerZOmi6E3RHA6esxEBIZqUezcqZ2s56RJE9bIhAM3b6OIRWYUzBwcFAl50qdDmZzZcPDm3Uj3+/b9h9zHrlA+xbIUBgYonCUTHr19r7Q1e+7BY/j5+zfqlSgCfWZSujy+PnuMoJfPFMu+v/FG4IM7MgCNjGnZSkiUNCne7tkaZvmHk0fx/oCbYmxRynwF5U//c2FvEHy5exO/Ar/AMFde6BsPDw8UK1oUhQoVUizLmyeP7DYo1kVmn4eH/Ew3btJEsSyloSEaNmiA27dv49mzv8do0+bNaNGyJT5//oxGDRtC33ns3x95ne7fH+l++/bvD67Txo3D1mn9+rh95w6ePX8ul52/cEH+bFCvXpj97WvWlD+vXL0KfdNqzRbYDRuN5+fP4s3tmyrvV6BmHSROkgRXXf929/3f79+44roBxukyIGvpsnJZbmtbJDMyjtAt+PKmtTAwNEQem+rQJ3F9jio+946OCeZz/69El/a4fugD/fgrCCVLFkfu3LlYE2qqUKEcLCyiHs9B/0bUr6jnhESMS/X+8BGFQgWtIQpkyoh3AZ/xPuCz0n3bWJbD2i5tkCTUPxkRlIqgVbS6JkqUKMz2Zx89wc5L1+DsUBNGKZJDXyU2TIlkadPLLrzhfXv5DAZmqZHERPm4XhFwBvm8xs8PvvL3RAYGcrL3H+988N5jJ777vJLL/U964sl0F3x/6xNmf7F94uTJ5QWvPhFd+F6+fIlChQtHWCcuaN+9eycfyojgNGfOnPKiNbSCfy6ExfoQ9+/dg4ODA7Zt24YSJUpAnynqNFRAEKJQwYLR12mOHBHrtGDBMHUqxrG6btqElClThtkupItnEj3seWGSKRMOjh+F7b0740fgF5X3y1ioCIICAuS41tB87tySPzMULhr8s1DwTT+fcEFxyHbidfRFfJyjwv0HD+BQty62bdmCEsWLx/rfQQlTjALXr1+/YviIMchboASSp0wLk9RZUL1mfVy5ck3RNTBP/uI4cOAwSpWtghRG6WRX1pmz5ite4+nTZ7L74PYdu9GiVXuYprGQryOe+/i8DfN+bm4eKFfBBobG6ZE+Uy706DVA9s8P7eVLbzi26QTz9NmRMlUGWFe1x9mz5yN0Ra1QyRapzDLL96tp3wDnz19U++8XZW/YuJV8jYxZ8mDO3IVKt1u1ej2Klqgg68giewFZZ9+/f1esF/Uklru770eBwqVluctXrIojR44pthHdZkU9ibJH1TU5fFfhza7bUKK0pXxNUSeNm7bG/fv/3kVr1+69qGJdU9ah+LtEuecvWBKj11q8ZIU8h8RxrWpXB89fvIywjSrHVdTPuPFT5DkizrVhw11kN+ekyVPjwoVLsrziPTJZ5JXH4NevX5F2FRb/+MX5JY6reK0y5a2xb9+BMO8n6nrgoOHynBev29Kxg8r7qkKd82LZ8lWyPMammbDHLbh7mDhXrGxqyfMzTbpssnzPn79Q7BtSN6dPn5OvK8qar2BJ7N7jjgcPHqJajXryPXPnK4YNG13DlE2sb9q8raxLsY04B0+cOK1Yb5Aijfw5dvwUWb7Iugpv2boDZStYy3KL+urWox/8/PzC1EF03yHa7G1AgPyZ3iTiuMt0qYwViZui8yXoO26+fIVhW3bjma8fulhXCrP+87cgTNhzAHVLFEGlPFEnbtJ1SU2Du+r99P97noT4+TE4aZJIpqRMsvSZ8OODrxwbm2PIGOSfvhT5pi5GJsfOSJzi7wXY76BvCPJ+Afz++x0hpLa0RaIkSfH10X3oE5+3wf9rM6RPH2FdunTp5M/Xb94o3fft27dK90uvZL8RI0ZgjIsLzNMEfz/os3+q03fvkCFDhmjr1NTUFAXy54+w3YZNm+TPUiVLQt8sq22L6zvC/j9ShXH6DPj8LuyNKOHzu+DjZJIxk/yZKn0G/Pj6Fd8+hb22/PX9O776f4BJpszQF/Fxjgojhg3DmNGjE8TnPlaIm9Vx/dADMfor2rbvhpWr1mK40yAc9NiF2TMn49btO2jeqr2iG9vr1z7o0r0vunXpgH1u21CxQlkMHuoMD4+DYV6rc9c+SJ8+HbZvWYfJE12we88+9O0/NMwFbv1GLZArVw7s2LoeE8ePlsGufZ3Gigth0Se/UpXqOHP2AubOnorNG1chRYrkqFqtLi5duiK3efz4Ceo3aolSpYpj945NWLdmGd77+qFm7Uby7pOqRCY/a9vauH7jJpYtmYsF82Zg2YrVOH/hUpjtxBi/jp17wqpKJbjtcsXA/r0xb8EStG7bJcx2fn4f0LZDN/Ts3hlbN6+BkZER7Os2VpQ7Jk6dOivfp75DbVn3ixfOxtVr11Hbock/Jc0RgZQI2EuXLiHrUByPXDlzyON18uQZtV5LBLs9ew9Ebfsa8rXKlS0tg77QVDmuISZOnoGmTRrKMrVoHtyF5ffv32jUtDUa1K8r66FZ04aYMm2WDPaUCQoKgl2NevL8Et2uRffrfHlzw6FBc3nzJEz5Fy5FieJFsWfnZvTs3kWtfVWh6nkhuuVOnzoeSxfPgbWVJdat34TqteojU6aM2LR+JebMmoKz5y6igqUd3rz5+89b1I34vIpuvOJvEK8vzhlxjtSqWU2es1kyZ0aHTj3x7Flw15/bt++idDlrPHr8BPPmTMOmDf/JO/u21evi6FEvuc1Jr+DPd6eObWX3YGUmTJyG5i3by2MuPvcuo4bJehOfq9BdjFX9DtFGgUHfFV18w0tuENxl+qsKCVSm7TuEDis3wOveQ9gWzIcaRQqEWT/rwFH5mR5Y0xb6LnGKFPLn71A3/0L8/hG8LHEy5WPfkhgaIlnaDLDo3Ed2+325cgH8Tx2FSekKsOjSDwjXih1acovsSGvfAL++fJZjXvVJ4JfglqsU4VpP5LLkyRU3qiP7X6hsv+RK9jNQ8jnQV4o6/XO+ql2nSvZTVqfhHT9xAlu2bkW2rFlRs0YN6JvfP2OWcCpZSiP8+Bax3n4GBScKMjAMbrU2kNspTx70MygISZWc67oqvs7RhPS5p/ij9qAz0WIo+quLQKJli6ZymbV1ZXz8+AmDhoyAt3dwlytxAbrNdS3s7YO/QC0tK2DXbnfs2euhWCbUqG6LeXOmy+fVqlXFpctXsWnzNvm7uCAbPHQkqtpYYfPGv4PFixUtLC/Et27bKcswe85C2Up7+8Z5RXfZOrVryhbHESPH4oDHLly4eFmWacSwwYquofnz5cV/q9YhIOAzTEyCW4eiI1qrXrx4iauXTqFYseCuI+ICPE/+v92fRCA8ZtxkdOzQBgvnzwr+O2vYIUuWTGjRqgPOnOmJihXLKz7kSxbNRts2reTvtrbWyJW3GCZPnYVtW9YhJk6eOgNDQ0MMHzZI8QUj0pu77zsgj11MU+XfuXsfrR2bY86sqYplFSuWg3n6HDjmdQKVK1dU6XXEcZ0waToaN6qPubOnKern46dPWLrsP8V2qhxXRTkqlMOQwX8zDV69dkO+z4hhg9Cje3BLdNWqVnDbu1+2TIYsC23d+s2y18Bxz/2oUiW4ZUucq/7+HzHYyRkODvaKbbNkyYzp0yYofl+xco3K+6pC1fOie9eOMmAPCUaHDhsNO1sbuG76+3mxrFQBhYqWw7TpczBr5mTFMXAa0h9dOreXv4uU9uLcbNq4GwYP6iuXmZmZylZj8dnJnj2bbEU1MEgKz8N75R3/kONRpHh5DHYaicsXTqB8+TJyuUWWzEq7B4tWaXHsRWAb8tkQihQuBKuqtWQ99u3TQ63vEG2kyu2hRIg8WApRs2ghGbBee+GNTWcvocuqTVjeoaUMiE/efwS3qzcxp1VjGOtxF+EQif6h4hMlNUCytOnwZtt6+J88Kpd9vnEZv75+lRmEjYuWxOfrETNiJs9kgazdBiBR0iTwXr1IBq/6JORGZlR1G75reuh9o9wPCdP/oqm3aOs0qv2iSJgzxMlJ/r+fNkV8TzNgCFXZUX8hh3wGZL1HsaEeZcrXxDlKpLEW12TJksHDfYcMGEWQ6ul5XAYbe92DB3OLlqcQ4kIz9N2YdOnSyrs1oVlWCptQQ1zwipTZ4iL8/v2HMkhsUL+ObF0NeZQuXVK2KB08FHwBcuSoF4oWLSwvrkO2ER+uOvY1cczrpAy2RWAjxoOUq1gVffsPka2H2bJZYOqUcTIIUdWJk6fl+4QErUK2bFll8BrizJnz8qK7fr2w5Rati4kTJ1aUWxAtViE3AAQRcIrtvI6fREzZWFeW7y8CiqFOo2RrWNmypTBxwuh/mt9NBDTr1iyXwa9o+XPdsh2Tp8yKcNyjc+/eA7x9+w4OdWuFWd6kUf0wv6tyXEOUKBE8TiU8EbSFP7++fFE+FcmRo8eQNq25DMZDH7d6DrXluRjS8ijfr3jRGO+rClXPixIlioWpV9Gq2rLF32Qpggj6xfl/LNy+lf7cPAndZSgk8BTMzYO794jgWxA3J0QZQoJWQSRpaNGssQzaw3ffV+bs2QvyXGnZPGwZRbAvjrM4rqGp8h2ijVImC75wDPoRMZNyyDKj5NFnxhTdf60L5EXf6jYYVMsWd1/7yGA14Ns3TNp7EFUL5kXhLBnl9DriEfLaAV+/yXG2+uT3n++YxEoyioYs+6WkZUXu+z1Ijk/9ePZ4mOViqhvBKE/YlmzBMGdeOfVOkpRGeL3xPzmXq74JGSOpbJqKb3/q29jIKNJ9le0X8r/AyDjq6Yn0VcjYv/iq08NHjqB3v+CbtnNmzVKMNaRgPwIDkVRJC2HS5MHLgv7cjPoeGAgDJdsFb5sc3z/rz02r+D5HiWJTjNJ8inFn/QcNw92792UgJFpAU/0ZtxW6K2r4xAEiaBMBaWjKthHEdu/fByfSEF1RQ3cfDhHSuvve1xcPHz5WjK8LT7yOCC6Pe3pgyrTZWL1mI+YvWCq7R7Zv1wozp09SdHNQpQtn2rQR30cE0u/evVeUR6jfsIXS1/B+9VrxXFyIh787KsYKiPeJqfLly8ou3DNnz5ddWqfPnIs0aVKjd8+uGOMyIsq7ZVER9SjGIopxruI18ubNjSqVg1sX1emCHPK3pTU3j1CHYd5PheOaOXOmaL5kDSOcX5FNy/L+vZ98zcjez9v7tQyu5PsZG8V4X1Woel6E/rtD1mXMEHHcSsaM6SOMITYxiXgTwyjc5zE08foZlYxtyZgxeJnodSE+U1Hx+5M8JGSfMK+TIb0iSFbnO0QbZfoT3L9XcrEjEjMJ6ZSMf41KjSIFMXXfYdx77YNc6czl63jeeSAf4bVethaZTE2wp7/+zD8oxqgKSUPdOAmR1NRM/vz5Ufn35k//D3Kb/4X77P/6/ClMN+QQRoWKIUv7HnJM0Ks1SxBwPexQEH2RKVPw9+e798H/u0ILSc6SXsk4uJB9le33Npr99F181un2HTswYdIk+T05f+5cvRzb+q8+vX4Fiz+Zg0MzThdcl5//JGL79NpbdhtOZmSE76FujiZJlgyGZqkRoGScrK7i515L6ckYVK0LXB89eowGjVvBoa499u7egly5csogZtHi5dh/QPmYtpgSXRWFKZPGws7WOsL6kNZDsV1ly4pyrK0yoiVMEC21W13XysBFtIqu27AZCxctR/Zs2cJ0M42KeK279yJeKIYE2bI8fy6s1q5eioIF8kdanpBgIHzXCzFwXoz7FUKWh04oJHz+8kXxPsrY2dnIh7gzdvz4KSxdvgrjJkxFkSKFFF1L1dWqdUfcvnMPh/bvRqVK5WW3JNGyu3xF5HN+KRMS+Pu8fRdpHapzXGOLeD8xlto1VLf00PLnzxsn+yoT3XmhjLg5IbwJl9wsZLxo2j8tqDElXv+NT8R/3q9fvwnTQhvla/yZC0+0DBcuHLZl4PUbH5Qv97fFV5eJrrsWqc1w53XE+rr7+o3MDpw2kjvTAzftwPvPX2Rm4fBztQrJkyZF3gzpsaDN3xb50HPAiql2xjWsg8xmqg1/0BW/v33F93c+SGGRI8K6FBbZ8eODH34FKM9XIDIRm2ashKQmZvj5KTiRk2BgHvx5EvuGEAmcLDr2xv9+/cLL5fMQeC84q6g+Ev9Ds2bNijt37kRYJ7KDiiQsadOmVbqvyEjq7u4u/8eEHvN2509W0SJKMhUnBLJOLSyU1+mdO1HXacGCcg7WCHX657WKhMoCu8fNDeMmTEDq1KmxeOFCFCwQsdcAiazAN5HXtjpMM1vg46u/N28zFAw+P9/cuhE2y3DBInhx8Vyk2+mD+DpHieKC2uG9GIMqTlgxPk50QQy5sPbYf0j+jM3WkAIF8skL9cdPnqJMmVKKR+7cOeUYx7PngrPLWlepjHv3H8gWwNDbbd+xR7Y4ipYrkfk3e67CsjuD6N4ouiYuWzIPxsbGYeadio4YPyi6L587FzyPmiDGYYZOzlShQlnZpfrly1dhymNqagKn4S64c+eeYlvR3VW0YIce27jP46Ac+xu6Vezln9blkKRForU7MoMGj5AZW0XgI75YxPjRpYvnynXPnv3NLquuk6fOomGDunK8ZcgX1t/jrnqLa968eZA1q4Ucoxya296wSYxUOa6xSSQ3EsdMBMSh3+/U6bMy+VNIb4DY3leZ6M6LyIJj0ZIZMkY8hEhMdubseXkT4F9YW1WWZQjdJVjcUNm8Zbucjkm0tkY3DYPoiix6N2xyDVtGkZlYZD6uHKprsK6zK5Qf1557y+69IR6+fYeLT56jVpHIu/NlNDXBnVdv5DQ3oa09Ffx9VyV/bpgYpkD5XDkiPMS+QvFsWVA8mwX0TcC1i3Jqm+QWf3svJMuYBSnzFsSny2cj3e/TpeB1aar9zcQuf7cJnvcy4M/4VjGdTpZ23eV354uls/U6aA1RvXp1XLlyJcxF7IOHD3HhwgXY29tHuZ/4nhJT3IQI/PoVO3ftQpEiRWRAnFDJOr16VXmd1qoV+X7VqgXX6fbtYet09+4wdfrgwQMZtJqZmeG/5csZtEbh/pGDcphAqVZtFcvEfJYlmzsiwOcNXl4OvpZ7fOIYfnwNRKmWf7cTxO8i2/BDz9htmNH3c5RIa1pcS5UsLgM/51HjMGhAHxkIrlqzQSb+ESIbPxgT4iJYZBHu2r0vkiZNgvoOdWRL46QpM+V4vpAkQQMH9JatpyKr65BBfZEubVrs2rNXdgceN8ZZBtciwZMIGkX33T69usmL5w2btsgWw6aNVW+BbNO6BWbPXYjGzdrILMgiqZNoyQzdVdbc3BxDB/eTCZo+f/4C26pWsrXMZewk+XupUmHnsevQuaf8O9OnS4vpM+fJOhzlHNw1WoylFUGeSGgjWrxEADRp8gxF12xlROu0KGObdl3QtnVLeTNh4eLlMtisXy/shZs6xDjeza7bZauYSPZ0+sw5eSxE/X5RY141sb2oO5HFtnPX3mjWpKF8LZGdOTRVjmts6tC+NRYsWiaz8joPH4IccszlCVn37dq2irIb7L/sG+lrRnFeKCPOjckTx6BDpx5yWilx7EXXXHHeiRbhkKRLMSWy/4rAVWR1Hu40ECmSp5CZsh89eiJ7XwjimIgxsKdOn5Mt/SGJqkKkSZMGw4YOkJ+Z5MmTyc/0k6fPMMplggy8RUIzfdHGsiz2Xb+FPuu3ok2lsrJuNpy5iHQmqdC6UnDXNd/PX3Du8VPZOlssa3DSuK42ljKL8LAte9C0XElkMEmFUw+fyGRMdYoXRoXc+j3tTVR8j+6HSZlKyNp9EPyOesiEKSL4FF2E/Y4G/w9KYmwCo/yF8N33Hb49fSSXiUzCHy+dRRqrakhqnAqBD+8iZd5CMClZFh9OHkWQd/DNS/NqdZDEyBif796EQeo0MCgd9kbKry8B+HJXv4LZ9u3awc3NDT169kS7tm3l98jadetkt1Txe8jN0jNnz8pWmuJ/5mO0rFQJlpaWmD1njpwCI0eOHNixYwd8fHwwfvx4JGTt27aF29696NGr1986Xb9eDveItk4rVcLsuXOD6zR7duzYuTO4TseOVbz+/IUL8ePHD1SqWFG2kIlHaJkzZ06Q3YYNUhgij211BPq+x7NzwdO0+T15hGvbN6O0Y3vZDfjV9WsoULM2spQoDTen/vj9Z/hAUMAnnFm2EFb9hqD+zIV4fPIYclSwRP4ateE1Zxq+/ZlyS1/E9TlKpDWBa548ueU0GCIoq9ewhQymKpQvi2NH9sHGrrZMXhSbxHQdoqVy2ow5WPnfOhgZpZTvt2zxXBQqFNw1RoxzFFNvDHceK6dY+fr1m2yVXbRgliJ7bPHiReXFtbhgbt2uqwy4xdjcnds3yqzIqhItqUcP7UX/gU7o028okiRJjG5dOspAxdf3b3ez8eNGyXKJgHHGrHkycKhqUwUTxo1ChnBjEBfOnymzJ4uWW5Ew56TXAVnPIcG7mDKk/8BhaOnYUe47aEBvXL9xS47/VKZ27ZrYuH4lZsyaLwNsoWyZUjh8YI9s7YypNauWoHffwejdd4j8XUz3IqZhWb/BFSfUnA7HsVVzeSEvWiPF/uJYiCy6jm3+ZvtV5bjGJhFcnjh2AMOdx2DYCBc53lIk8BLT2zgNHRBn+0YmqvMiMu3bOcqbGiL7sJgKSDwXrbQioA0ZDxxTomuvKIPo7dCxcy/FzQzxebCyslRsN3LEEHlcxfQ9d27+7ZkQQoyzFi3DotV8xcq1souxyDA9cfwo2QNCX5gaGmJZh5aYc8ATK4+fgUGSpCiVwwL9qtsgtVHw2N2n733hsnOfDEhDAlezlIZY0aElFh45gZ2XriHw+w9kTZMag2vZoVm5hHcxGtrvwC94Pn8K0jdoDvMa9fC/nz/w9dE9vN29RQaVQvKMmZC5TVd8PH8Sr/8ErsLrDStkgGpWwQrGxUrh5wc/vN3lCr9jf+daNsob/D/FuEAR+Qjv69NHehe4ihtNq1etwowZM7Bs+XL5P6506dIYOHCgvNEkPH78GM7Ozqjn4KC4gBVmTJ+O+fPnY9++fbJXSP58+bB40aIEGTRFqNOV4n/wLCxbsSK4TkuVwsABAxTDJR4/eQLnUaMi1um0aTIw3efh8bdOFy4MU6cXLgbPPy+2EY/wxHQ4CfEYGKZOgzoTZ8iuviGBq3Bk6ngE+vqiSP3GKFCzLvyePcHuwb3x4EjYqdXOr14up8Qp1bINclpa4aP3SxyaOBrXtm2Gvonrc5RigGNcVZLofz8/6U+Obx0zZuwkOcXIj29+shWbiOdFPEiSCp82ruDJFotMWnXG3f4dWaexqMCc//Atink7ST1iztlvOpCRXJekMDLCjJL5NF0MvTL4yn2ep7EoRQrtT+YY4vOZ4OFAccm4YsRpCnUNo6U/Iss0G5poIYxuDJ++/K2i24i64zJFl2RVxjiLOoztbr7aTnQlD59gSxl165yIiIiIKCHgVfIfYhqT6B521R2g654+fabS39qxc0+1X3vc+CkqvbZXuLk6EwLxN6tSN6IOiYiIiIgoLLa4/nHh7DFEJ2T6ndgixvqJR3wS4xxV+VtjMtVM1y4dULdO5NnoYjo1jD4oXbqESvUujo94xPd5QUREREQawh53KmHg+oeYuiQhEAPw4+pvDQm6SPlNj4RyjhERERERxTZ2FSYiIiIiIiKtxsCViIiIiIiItBq7ChMREREREWlKIrYlqoK1RERERERERNHy9DyOipZ2MDLJiKw5CmK0y4Qop9oU6+bMXYjCxcrJfXLnK4aBg4YjICAA6mLgSkRERERERFE6d+4C7Os2RtasFti+ZR169eiCKdNmY/BQ50j3GeE8FkOHjUaTRvWxZ+dmDOzfG2vWbUSNWg3w+/dvqINdhYmIiIiIiChKLmMnoWDB/HDdtBqJEiVCrVrVkTx5MgxxGoWhg/tHmF0kMDAQc+YtwuCBfTF2THBwa2dnI6fdbNGqA44dOwFbW2uoii2uREREREREmpzHNa4f/ygoKAiex06gUQMHGbSGaNa0EX79+oX9Bw5H2OfDB3906dwOTZs0CLO8QP588uer16/VKgNbXImIiIiIiPSYsWnY1lBlPn+MPJB8/Pgpvn//jvz584ZZniVLZhgaGuLOnXsR9hHrFs6fFWH5rt175c8ihQtBHWxxJSIiIiIiokh9/PhR/jRJlSrCulSpjPFJxWRLZ86ck+Ni69aphRIlikEdbHElIiIiIiLSY5+jaE1Vxe/f/4tyfejuw5ERY1rrN2qJnDmzY/V/i9UuA1tciYiIiIiINCRR4sRx/vhXZmam8mfA588R1gUEfIapiUmU+69eswE1azdE7tw54XnYHebm5mqXgYErERERERERRUoEnEmSJMHDh4/DLPf2foWvX7/KbMOREXO9dujUA1VtquC4pwcyZEiPmGDgSkRERERERJFKnjw5bKyrYMeuPWHmX92ydQeSJk0K26pWSvebOm02xk+chvbtHLF3z1YYGxsjpjjGlYiIiIiIiKI0ynkobKvXReOmrdGlUztcv3ELo8dMRI/unZAtW1Y5Zc6VK9dgYZFFPh48eIiRo8ejQIF86Nq5PS5evBzm9XLnzoV06dJCVWxxJSIiIiIi0hQdmMdVsLaujF3bN+Hps+do2MQRi5aswHCngZg9c4pc//r1G1SsXA0rVq6Rv+/ctRc/f/7E3bv3UalKdbku9GP3Hneogy2uREREREREFC0HB3v5UCZHjuz4389Pit+HDukvH7GFLa5ERERERESk1djiSkREREREpCmx1JVX37GWiIiIiIiISKsxcCUiIiIiIiKtxsCViIiIiIiItBrHuBIREREREWkKx7iqhC2uREREREREpNUYuBIREREREZFWY+BKREREREREWo1jXImIiIiIiDQlURLWvQrY4kpERERERERajYErERERERERaTUGrkRERERERKTVOMaViIiIiIhIUziPq0rY4kpERERERERajYErERERERERabVE//v56X+aLgQRUbxJkoqVTUREpO9+BUBXfLnzKM7fw6hgbug6jnElogTn29evmi6CXklhaIhvX75ouhh6JYWREb76+Wq6GHrDMI05P/exjJ/7uPnczyiZLw5eOWEafPESdAbHuKqEXYWJiIiIiIhIqzFwJSIiIiIiIq3GwJWIiIiIiIi0Gse4EhERERERaUgijnFVCVtciYiIiIiISKsxcCUiIiIiIiKtxsCViIiIiIiItBrHuBIREREREWkKx7iqhC2uREREREREpNUYuBIREREREZFWY+BKREREREREWo1jXImIiIiIiDSFY1xVwhZXIiIiIiIi0moMXImIiIiIiEirMXAlIiIiIiIircYxrkRERERERJqSiG2JqmAtERERERERkVZj4EpERERERERajYErERERERERaTWOcSUiIiIiItIUzuOqEra4EhERERERkVZj4EpERERERERajYErERERERERaTWOcSUiIiIiItIUjnFVCVtciYiIiIiISKsxcCUiIiIiIiKtxsCViIiIiIiItBrHuBIREREREWkKx7iqhC2uREREREREpNUYuBIREREREZFWY+BKREREREREWo1jXImIiIiIiDQkEce4qoQtrkREcej169cY6uQEm6pVYVm5MvoPGICX3t7R7vft2zfMmTsXteztUb5CBbRp2xbnzp2Lch/xumLbU6dOQa/rc9gw2NjawtLKCv0HDVK9PufNQ63atVG+UiW0addOaX1++fIF02fMQM3atVG6XDlUq1kTk6ZMQUBAAPTV6zdv4DRqFKra10blatUxwGkYvF+9ina/b9+CMHfRItg3bIgKNlXRtksXnLtwMdr9+g0ZihIVK0GfxfXn/tevX1i1ahUcHBxQrnx5NG3WDPsPHIC+iuvPfYTv0UqV9Pp7NLRy7bugx6FTagVYZdt1RqfdB9HvzHW0dd2D/DVqK922SP3GaL91L/qdvoaOuw6gRDPHWCw5JUQMXImi8b///U9r60iby0bAx48f0blLF5w/fx6OrVqha5cuuH79Ojp06IAPHz5EWUXDhg/H2rVrUdXGBoMGDsTPnz/Rs1cvXL58Wen2nz59Qv/+/eWFml7XZ7duf+uzc+fg+uzUKfr6dHbG2nXrULVqVQwaMCC4Pvv0CVOf4vPUb8AAbNy8GZaVKmHY0KGy/rdt345uPXrgx48f0DcfP35Cl969cf7iJbRq3gxdOnTA9Zs30aF7D3zw949y3+EuLli7cRNsrKwwsG8f/Pz5C70GDMDlq1cj3WeP+z54nTwJfRYfn/tZs2bJALdEiRIYMmQIUqdODScnJ3h4eEDfxPXnPsL36IABev09GlpOSytY9uyn1j7WA5xg3X8ovK9ehueMifj6wQ8OU+egQK06YbYr1bItao2ZDP+Xz3Fs1mS8u3cH1Ya7oFzHbrH8V1BCwsCV1Hbs2AkkSmqCw4c99br2xD+u/gOdsHHTFsWy9h27wyJ7AZVfY8zYSbKuxD/L2DZ+wlRMnzE3Xt6LYmbd+vXw9vbGooUL0aVLF7Rr1w5Lly6Fr68v/lu1KtL9RIuAp6cnBvTvLy9GmzVrhpUrVyJjxoyyNTC8Bw8ewLF1a/lTn63bsCG4PhcsQJfOndGubVssXbw4uD5Xr46+Pvv1g9OQIWjWtClWrlgRXJ8zZyq2O3T4MC5cvIghgwdj9MiRaNqkCZyHD8cwJyfcun0b7vv2Qd+s37wZ3q9eY+HsWejSvj3aObbC0nlz4evnh1Vr10W6n2hZ9Tx+HP179YLTgAFo1qgRVi5aiIwZM2DG3HlK9/F5+w7T586FgYEB9Flcf+6fPXuGjZs2oVXLlhg/frw8TxcvWoQSxYtj5qxZeneDJa4/92G+R9u2xYOHD5EQlGzeGg1mL0ISg2Qq72OWLbsMSC9tXIP9LsNwfbsrtvXsCO+rl2AzcBgSJw3+bCc3ToXKvfrjgedh7BrQE9e2bYabU3/cPeCOip17wjB16jj8y0ifMXAlisTLl96YO28xfvz4GwiOch4Kt12uKtdZ507tcObkYSRNGrvDyUVwOnrMRAQGBsb5e1HMidaPYkWLolChQoplefPkQdmyZaNsGdnn4SGPY+MmTRTLUhoaomGDBrh9+7a8cA2xafNmtGjZEp8/f0ajhg31+nB57N8feX3u3x/pfvv27w+uz8aNw9Zn/fq4fecOnj1/Lpedv3BB/mxQr16Y/e1r1pQ/r0TRkqirPA4dRNHChVGowN8bcnly50bZ0qXgcehQ5PsdPCjrtEmD+oplhoaGaFDXAbfv3sWzFy8i7DNu8mRYZM4M68qW0Gdx/bkXXYJ///6N5i1aKLZLkiQJmjdvjnfv3uHixei7a+uSuP7cK75HHR0TxPeo0GrNFtgNG43n58/ize2bKu9XoGYdJE6SBFddNyiW/e/3b1xx3QDjdBmQtXRZuSy3tS2SGRnj6pa/2wmXN62FgaEh8thUj8W/Rk+IMa5x/dAD+vFXEMWT3LlzoWTJ4ipvb2GRBRUqlIvTMmnivSh6osvZy5cvUahw4QjrxAWYuMAUD2XERWrOnDnlRVZoBf9cuIn1Ie7fuyfHuW3btk12G9T7+gx18RqiUMGC0ddnjhwR67NgQcV6oU+vXnDdtAkpU6YMs11Id0QRHOhdnXq/QuGCEXuRFMpfAO/ev5cPZURwmjN7dhmshtmvQP7g9XfuhFm+ffdunLt4EWNHOutdPcb35178NDY2Ro7s2SO8fujt9EF8fO6F+w8ewKFuXWzbskW2XOs7k0yZcHD8KGzv3Rk/Ar+ovF/GQkUQFBCAD8+fhlnuc+eW/JmhcNHgn4WKBC8PFxSHbCdehygmGLjqqBy5i8iuoSOcxyJz1nxIYZQOVaxr4sKFS4ptLl26Avs6jZA2Qw6YpM6CuvWa4saN4C+N0F1+Dx06CrvqDkiZKgOy5SyE5StW480bHzRp1gbGppmQNUdBzJw1P0IZ7t67D+uq9vK9c+crhrnzFkUo48BBw1G9Zn0YGqdHS8cOiovAHr0GIGOWPHLfMuWtsW+f+kklxN8vuu26u+9HgcKlZfnLV6yKI0eOhdnu+vWbaNTEEeky5oRBijTIki0/+vQbHKa1UllZ8xYoKdd16NRDrlfWVViMiZu/YAkKFysn9xP1MHHSdHk3XFn3XRvb2mjdtjOmTJ0l/35xXOo3bIEnT8L+E9i1e688nqnMMiN5yrTy7xPvE0L8HcLY8VPk6yt7L2HL1h0oW8FaHkfxft169IOfn1+YOsyTvzgOHDiMUmWryOMh/lZlxzs6qv5tt2/fhUP9ZnK9KJc4L+/dC9vFNSgoCMOGu8jzUfz9on7XrA1751bb+bx9K39mSJ8+wrp06dIpkuIo8/btW6X7pVey34gRIzDGxQXmaYLPCX31T/X57h0yZMgQbX2ampqiQP7gwCu0DZs2yZ+lSgZ/J+gLUS9CeqV1mlb+fOPjE+m+SvdLmy7Cfq9ev8asefNlV+R8efJAn8XH515slz4Gr6+L4uNzL4wYNgxjRo/W++/REMtq2+L6DtV7j4UwTp8Bn99F/E74/C74OJlkzCR/pkqfAT++fsW3Tx/DbPfr+3d89f8Ak0yZY1x2StgYuOqwOfMW49LlK1i+ZB42rFsh75w3bOIoAxdPz+OoWLma7Oa6cvlCrFy+QK6vVKW6DBxCa9WmE+xrVceenZtRIH9edO/ZHzZ2tVGwQD65rFzZ0hg81BmnT4fNxDdw8AiULVMKu3dsQm37Gug/cFiEgGf+wqUoUbyofJ2e3bvIgMSuRj1s37EbY11GYOe2DciXNzccGjSHm5v6SSX8/D6gbYdu6Nm9M7ZuXgMjIyPY120sg3bh9es3qGJTS2YKXb1yMfa770CLZo2xYOEyzJq9INKydu/aCVtd18rlI0cMkeVUxnnkOPl329esLrsQ9+jWGWPGTcao0RMiLbP7voNYuWot5s2ZhqWL5+DqtRuwsasjyyjXu+9Hw8atULp0CVm3O7auR66cOdC3/1CcPHlGbnPS66D82aljW9k9WJkJE6ehecv28vht37IOLqOGyXq3tq0dJmh//doHXbr3RbcuHbDPbRsqVigrj7eHR/B7qCO6v+3hw0fyHBRj6latXITV/y2WFyaWVtXx4sVLxes0btoaCxYtQ9/e3eXxsK1qhfYde2DR4uXQFYF//uYU4e72y2XJk8ufX79+VbqvqC9l+yVXsp++jxeMUJ8pUsSsPpXsp6w+wzt+4gS2bN2KbFmzomaNGtAnX/58DxgmV79uxL6GKZJHsd83xc29MRMnwSJLFnRq1xb6Lj4+9/9yPuua+PrcJ5Tv0RC/f8ZsHHSylEb48S1iff8MCv68GxgG91YxkNspT3D1MygISZWc50Sq4GA4HWZsbIS9e7YqvnDFl3S7Dt1l0DZshAty5cqBAx47Fd2yqleritz5imPk6PHYESoQa9emFQYP6iufp0pljEOHPVGqZHGMHzdKLitevAh27NyD02fOoVKl8or9OnZogxnTJ8rnNWtWw8uXrzB56kz069tDMc4yS5bMmD7tbxC3YuUaXLlyDcc996NKleDpEOzta8Df/yMGOznDwcFerToQ/3iWLJqNtm1ayd9tba2RK28xTJ46C9u2rJMtzMWLFcGWzWtka4pgZ2eDQ0c8cczrJEY6D1W8VviyiiArqu7BItPhjFnz0LNHZ0U9VKtWFb6+fjh5KjjAVEaMobl47ph8XaFA/nyytXPV6vXo3asb7ty9j9aOzTFn1lTFPhUrloN5+hw45nUClStXRPnyZeRyiyyZlXYPFq3aEyZNl4HtwvmzFMuLFC4Eq6q15HHo26eHXCaC2G2ua+VxECwtK2DXbnfs2euhWKaq6P42EdQbGCTFkYN7ZBZMoVbNavKYifIuXTxXJv1y33cAa1cvRZvWLRXnl8hY6jxqPDq0bx2he6I2Z3xOFMU2iRIlinTfKPdDwvO/aOos2vqMar8okrsMcXKSF7/TpkzRu4tbRVLyGNZplGfin/1ct+/A5WvXsOG/lQli/H18fO4T0veDJj73FAVRn1FNZhBy/st6j2JDzoigpG7ZlqgK1pIOEy1poS+kxB1t4f17X1y4eBlNGzcIM5bIzMwMDnXtZfATWuhgNEOG4O445csFB0aCubm5/OkfbmqE5k0bhfm9UUMHGbSFbtEVLZihHTl6DGnTmstATLQMhzzqOdTG/fsP8ezZ32QJqhB/X8sWTRW/i4BGtP56HQ+ebqFGDTscP7ZfjlkT5drjtk925X379p1s/Q0tfFmjc/bsBZm9sVGDsIlcJk8aAy/PyFuPK1YopwjsBBEUi5sMXseD51ETNxHWrVkug0BxE8J1y3ZMnhIcfIYvc1RlE9u2bP43yYcgbhZkz55NBu2hiWA19N1o0U0wpJVUHdH9bUeOesHaqjJSpUqlOPYiKLCztcbBQ0cV2wjiXA1zjtS1l+fg+fN/u8Nrs5BxksqmVfj25zgaGxlFuq+y/UKOv5GxMRKakHFq8VWfh48cQe9+wdNEzJk1SzEuTp+kTBl5nSrqJrI6NTTEt6DI9zM2SinnwxTzvDZv3Fh2zxTT64hHSMI78Tzg82fok/j43IvtviaQ74f4/txT1H4EBiKpklbspH96bQR9Cf48fw8MhIGS7YK3TY7veva5p/ij/7c/9VjIRUeIxH8yhr339ZV3GsW0BOFlzJBBztsXmkmqVBG2i+xiJcxrhXv9kHEjHz74h2kVDu39ez8ZWIeM0QzP2/u1DKxUJQKs8K0gohyiC7EgxpqKccALFwcHgiKBkQj4RYAbfg7U8GWNjqhn+X7pg/9uVWXJEjwGJLIyi/oRY1HFOFdx1zJv3tyoUrmSWvO2+v1JJqP8HEgvW7hDC5+MRpxLIeN0Y/tvE92VlR3/kOMYUq+p0yo/D7xfvYIuyJQpuC6UJbcJSSaibJxayL7K9otqTKK+i8/63L5jByZMmiQ/F/PnztW7sa0hMmXMKH++91VWp+/DfK8r21d8niPs9/5PnaZLh0tXr8peMRtcXeUjvKr2tVG6ZEk5jY6+iI/zVGx36dIltV9fF/F7VLt8ev0KFn8yB4dmnC74nPv8Nnj866fX3rLbcDIjI3wPdRM8SbJkMDRLjQAl42SJVMHAVQ+JllUR8IgES+GJZATm5rGTfCAkGAkRkowjqkDOzMxUtsC5blQ+91r+/HnVLkP47kBizGRIGUSioJmz52PJojlo3KierBuhXAUb/CuzP12PQy7wQrx69Rp3794P04oZmmiVVja/YflypeXzVq074vadezi0f7dsDRctkqI7r0iapao0f7rhinOgcOGwLUWv3/iEaVGPTdH9beL4V7WxwtDB/aKsV3Fj4XgkrdY5c4bNpKmtRKty1qxZcSdcdtWQbJYiaUjatMEJcMITGTTd3d1la0HoMVp3/mTBLKIkY6m+k/VpYaG8Pu/cibo+CxaUc7BGqM8/r1UkVMbSPW5uGDdhguzKvnjhQhQMNU2MvkllbIysWbLgzt17EdbdvndXJsRJ+6fHTXhi+hz3Awfw7VsQUoQa63r7z2sVLlhQ1vWSuX/nmg6xYs0aXLx8Wa4zMYl441SXxcfnXmwn5icVLdohPa1CXj/0dvogvj73pBqfOzeR17Y6TDNb4OOrv3kpMhQMPufe3LoRNstwwSJ4cfFcpNsRqYtdhfWQUcqUKFO6JLZu34Vfv36FGZO5130/KltWjJX3EeMQQ9vsuh2ZM2eKMvi0trKUY2FFd+EyZUopHqdOn8XEyTMUrcaq+v79u8yIG0Lc3d/ncRA1qtvK38VY0wIF8smxniFBq7f3K9y4eTvaFsXopmwoX76sbCXctWdvmOUi8ZNIkhXZ2JrTZ86HaakQ3YFF5t0a1e3+lPksGjaoK8frhvyz9dgfPJ/i79//U7FsZWSX302u28IsP3HiNJ4/f4HKkQTV/yq6v010E7595y6KFy8a5vgvWrJCnj/B21jK4yi6YYfe5vGTp3AZOwlfvvxNLKXtqlevjitXroS56BKT21+4cAH29vZR7ifObTHFTYjAr1+xc9cuFClSRF4YJ0SyPq9eVV6ftWpFvl+1asH1uT34HFPU5+7dYerzwYMHMmgV3xX/LV+u10FriOq2trhy/Tru3PsbvD589AgXLl2GfY3I51qsbls1uE537VIsE5/bXXvdZEAggo10adOiQrmyER7maYJvrInnoeeP1Rdx/bmvZmcn/79s3PA3V4X4X+/q6ipbW0uVKgV9Etefe1Ld/SMH5bytpVr9TbSWKHFilGzuiACfN3h5OXgu7McnjuHH10CUahk2IZv4XWQbfuipPKlkgsZ5XFXCFlc9NXniGNSs3RA17Ruib5/u8stbJCwSmR7HjB4eK+8hgg1TUxOULlUCm7dsx/4Dh2WW2KiCT5FYR2SLrV6rPpyHD0EOOd7yhEzM065tK5W6KEd4zc49MXH8aKRPlxbTZ86Tgc2oP0mXRLdgUS7R8irGXz589BiTpsyQY1xCMmpGJnVqM8W4XJFhWQSqoYngu3/fnpg1ZwFSGqaU4zQvXb4qW3hHj3RCsmTJlL6uaD21r9sII0cMRUBAAEaMHIeiRQvDsVUzRZlFECdaRcXddJEUa9KUmfJC5cuf+dbEc5Fs6tTpczh+/JQi0VWINGnSYNjQARg3YSqSJ0+G+g518OTpM4xymSBvLIjEWnEhur9N1IvIdl27bhP06tlF3mT5b/U6+feuWRU83U/t2jVhVcUSjZq2lhmdCxcqiCtXr2GUy0RZJ9my6c7FRvt27eDm5oYePXuiXdu28rOxdt06eXEpfhd8fX1x5uxZeaFf/M/8gZaVKsHS0hKz58yRvSRy5MiBHTt2wMfHB+PHj0dC1b5tW7jt3YsevXr9rc/162W31Gjrs1IlzJ47N7g+s2fHjp07g+tz7FjF689fuFDeMKlUsaJszQk/F2nmzJn1rttwO0dHuHl4oGf/AWjr2AqJEyXGuk2b5Pdp21bBSe98/fxw9vx5+X1UvGhwLoBKFSrAskIFzFmwAG983iBHtuzYsWeP7GExflRwYr+EKq4/97ly5UKTJk2wYeNG+X+sWNGiOHDwIK5eu4apU6fqXRKxuP7ck3IGKQyRx7Y6An3f49m503KZ35NHuLZ9M0o7tpfdgF9dv4YCNWsjS4nScHPqj99/puMLCviEM8sWwqrfENSfuRCPTx5DjgqWyF+jNrzmTMO3j2FzphCpioGrnhKZcw8f2CNbqFq06iCDKKsqleSUMCKQiA3LlsyVU8qMHjNRdt9cv3Y5HFs1j3IfEZieOHYAw53HyMzHYqxltmwWcmocp6EDYlSOhfNnYvDQkfDxeYtKFcvjpNcB5MmTW64bPmyQHDM5d/5iGcSJ92rj2EL+4xPBsvhnF5J8KjzR6iIC0+Ur18jWZZ9XwVmGQ5s6ZZwcR7p46QoZwGbPnhXTp45Hn97dIy2vCKDF9EMdO/eU3Zzr16uDGdMmKAJdEcD17jsYvfsOkb+L6YLE1DLrN7jixJ/pcAQR1IlWajH9z52bwXc5QxvjMkKWTUzzs2LlWtlFvHGj+pg4fpScvD4uRPe3FStWRB4fkdm6bftustW7UMH8Mutz0yYN5Tbi2Ozbuw2jXSZi6vQ58rhmypQR3bt2xBiX2LnpEl/EzYXVq1ZhxowZWLZ8uayH0qVLY+DAgfLmgvD48WM4OzujnoOD4oJLmDF9OubPn499+/bJlqz8+fJh8aJFehc4qV2fK1dixqxZWLZiRXB9liqFgQMGKLrHP37yBM6jRkWsz2nTZGC6z8Pjb30uXBimPi9cvCh/im3EIzwxHY6+1b+4+bhqyWLMmDcPy1etRjIDAznudGCf3n/r9OlTOI8dB4fatRWBqzB94gTMX7oU+w4clMmCxByti+bMRslQ9Z4QxcfnfpiTk5xzdNfu3di/fz+yZ8+OmTNmoFq1atA3cf25J+UMU6dBnYkzZFffkMBVODJ1PAJ9fVGkfmMUqFkXfs+eYPfg3nhwJOwUeudXL5dT4pRq2QY5La3w0fslDk0cjWvbNrPKlUkcdU86Cpbofz8/qZbthUjLjBk7CWPHT8GPb346M82CjW1tmSH35HH150jVdjrztyVJhW96NM+hNhBzT36LQRZqiqJOjYzw1S9i8iOKGcM05vzcxzJ+7uPmcz+jZL44eOWEafBF3ZiFQPj6Qfm8t7HJMLXyTM+6RDeu9inBEK10ocflRkbdsbAUM6JFVJXswtGNuSUiIiIi+he8+iet4uV1Uk6VEt1j3Pgpmi5qgiDqWZXjIY4bEREREVFcYVdh0ioioc+9ew+i3U5kLxYPiltiah/xiI5I+CSmLdAJ7Coc69hlMA7qlF2FYxW7Csc+fu7joE7ZVTjhdhX++D3O38PQVHnSUF3CrsKkVUTwI6Y+Ie3AGwREREREpA3YVZiIiIiIiIi0GgNXIiIiIiIi0mrsKkxERERERKQpnC1DJWxxJSIiIiIiIq3GwJWIiIiIiIi0GgNXIiIiIiIi0moc40pERERERKQhiTjGVSVscSUiIiIiIiKtxsCViIiIiIiItBoDVyIiIiIiItJqHONKRERERESkKYnYlqgK1hIRERERERFpNQauREREREREpNUYuBIREREREZFW4xhXIiIiIiIiTYmXeVx/Q9exxZWIiIiIiIi0GgNXIiIiIiIi0moMXImIiIiIiEirMXAlIiIiIiLS5BjXuH7EEk/P46hoaQcjk4zImqMgRrtMwM+fP2N9H2UYuBIREREREVGUzp27APu6jZE1qwW2b1mHXj26YMq02Rg81DlW94lMov/9/PQ/tfciItJVSVLh29evmi6FXklhaIhvX75ouhh6JYWREb76+Wq6GHrDMI05P/exjJ/7uPnczyiZLw5eOWEafPESdMW373E/0UuKZOq3cIZXq3ZD+Lx9h8sXTiBRokRy2ew5CzDEaRSeP7mNzJkzxco+kWGLKxEREREREUUqKCgInsdOoFEDB0UAKjRr2gi/fv3C/gOHY2WfqHAeVyIiIiIiIg35H/4GdXHF2DT6ls3PH19Huu7x46f4/v078ufPG2Z5liyZYWhoiDt37sXKPlFhiysRERERERFF6uPHj/KnSapUEdalSmWMTwEBsbJPVNjiSkREREREpMc+R9Gaqorfv6NOixS6K/C/7BMVtrgSERERERFRpMzMTOXPgM+fI6wLCPgMUxOTWNknKmxxJSIiIiIi0pDfv7S/6nPnzokkSZLg4cPHYZZ7e7/C169fUbBg/ljZJypscSUiIiIiIqJIJU+eHDbWVbBj1x78/v1bsXzL1h1ImjQpbKtaxco+UWGLKxEREREREUVplPNQ2Favi8ZNW6NLp3a4fuMWRo+ZiB7dOyFbtqxy+psrV67BwiKLfKiyjzrY4kpERERERERRsraujF3bN+Hps+do2MQRi5aswHCngZg9c4pc//r1G1SsXA0rVq5ReR91JPrfz09Rp3siItInSVLh29evmi6FXklhaIhvX75ouhh6JYWREb76+Wq6GHrDMI05P/exjJ/7uPnczyiZLw5eOWEafPESdEXAZ4M4f49Uxj+g69jiSkRERERERFqNLa5ERERERESk1djiSkRERERERFqNgSsRERERERFpNQauREREREREpNUYuBIREREREZFWY+BKREREREREWo2BKxEREREREWk1Bq5ERERERESk1Ri4EhERERERkVZj4EpERERERERajYErERERERERaTUGrkRERERERKTVGLgSERERERGRVmPgSkRERERERFqNgSsRERERERFpNQauREREREREpNUYuBIREREREZFWY+BKRER66/fv33Ddsh2duvSCfZ1GePDgIdat34S7d+9rumhERESkhqTqbExERKQrPn36hNp1m+D0mXPIlCkj3rzxQUDAZ6zf4IrefYfguKcHihcvquliarWsOQoiUaJEKm0rtnv2+Facl0kfnTt3AQcPHYX3q9cYMWwQ7ty5h1KlSiBdurSaLppOOnDgMPbs3YcvXwLlzavw5+maVUs1VjZdFhQUhPPnL8H71SvUrGEn69fCIoumi0UJCANXIiIN+W/VOrW279ihTZyVRR8NG+GCBw8f4dzpoyhZsjiSGZrL5a6bVqNazfoY5TIBe3a5arqYWs3O1lrlwJXU9+PHD7Rp1wVbt+2S9fy///0PXTu3x/SZ83Dn7j2cOLYfuXLlZNWqYdbsBRg81BkpUqSQgX/ixGE7F/J8jpklS1fCedQ4fPjgL+vwwtljGD1mIr5//45dOzYhZcqUPE8pziX6389P/4v7tyEiovASG5iqXCniQuHXd39WohoyZM6NSRNc0KljW/z69QsGKdLg4jkv2ZK1Y+cedOvRD+/ePGGdksaMcB6LeQuWYNXKRahVsxpM01jIczRVKmPUqdcUZcuUwoZ1K3mE1JAzT1FUqlgOq1YuRrJkyVh3sWDtuo1o37EHunfrCIc69vLcFOfpw0eP0alLb/Ts3hlTp4xjXVOcY4srEZGGPHl4g3Ufh0S3YIssmZWuS2VsLLu5UdQeP36CrFktYGBgIJ9Hh62D6lm3YTMmjBuJpk0aypsrIfLmzYMxo4dj0BBnnqJqEkMCunRqz6A1Fk2bMRd9enfD3NnTwpynzZo2grf3K8xfuIyBK8ULBq5ERBqSPXs21n0cKlqkENau34SaNatFWLfbzV2up6jlyV8CZ08dQblyZeTz6LpZsleAet69e48ihZWfhxkzZIC//0eeomoqVKiAbAm0sanCuoslDx8+xuwZk5WuK1WyBF6/fsO6pnjBwJWISEuIO9cTJk3HwUNH8OrVG5w6fhCbXbejePEicGzVXNPF0zkjRwxFg8YtZXBQv14dGXQdOeqF5SvXYPmK1XKsK0VNdGHNnTt4jOV/KxaBw11jV758eWQSoWrVqkZYd9TTC3nz5uYpqqbJE1zQtUc/ZMtqgUqVyisdexl+3CtFLUOG9Lh56zaqV7eNsO7W7TtyPVF8YOBKRKQF7t9/AEurGvKCSiTEcd2yQy5/7+uLtu27wdDQEI0a1tN0MXWKg4M9Nm34D0OcRqFPvyFymdPw0UifPh2WLp6Lxo3qa7qIWq9dW0fF8/bt/j6n2DGgXy85VdO3b0GoV9de3ly5e+8+Dh32xMzZCzBn1hRWtZp69R2E9+99YV+3sdL1oo5/Bn1gvaqhZfMmcBk7WQaodevUUtSjyIYtbrY6tmzG+qR4weRMRERawKF+M/j4vMWxo/vk2CyRATckkVBLxw548uQZzp4+quli6vSNgffv/WBmZooCBfKxxSWGAgMDcfXqDTkthsiAK4jpRr4EBuLEidOYMX1ibB62BGH6jLkYO34Kvn79qqhT8R0wdHA/jBs7UtPF0zljxynv0hqay+jh8VIWfSEyBzdq4oh9HgcV2a9FS7Y4Z6tUrgQP9+3y5ipRXGPgSkSkBVKZZcaaVUtkq2r4DLiHD3uiUdPW+PTBW9PF1ElfvnyBkZGRfL59x248f/4C9RxqI3fuXJoumk45etQLTVu0jXTcZapUqeDv+yLey6UPAgICcPr0Ofj6+cHM1BQVKpRFmjRpNF0sojDE/yIx3CLkPLW2skTt2jU5xRDFG3YVJiLSAqKLcGTjrkRrFsdkqe/Bg4dy2obmTRth/LhRGDN2EsZPnCZbC0aOnoAjB/egQoVy/3zsEgox763oZr1i2QKs3+CKJEmSoEM7R+zbfxCLl6yEx95tmi6izhJBv7IkYhQz3759w4qVa3DM66S80ZI2bRrZMtihfWvON/oPxFhsZeOxieILW1yJiLSkq/DrNz44eshNtg6KFtdL54+jWLEisK/TCMmTJ4fb7i2aLqZOadKsDa5eu471a5ajTJlSyJglD6raWGHJotlyTsKvX7/h8ME9mi6mzjA2zYSVyxegebPGWL1mAxYvWYFzZzzlOjEn7suX3nB3Y/AaHSub4DGCqhDdMr08Pf7puCU0Hz58gI1dHdy4cUtmbs+YIb38bhU9LQoWzC+T3pmZmWm6mDqHyQNJGzCtGhGRFpg2ZTwePXqCvAVKoqVjR3nBOnX6bJQsUxlnzl7AhHGjNF1EnXPM6wSmTBorW1XF+Es/vw/o1qUDzM3N0atHF1y4eFnTRdQpYixr5kyZ5PO8eXLj1u27inVNGtXH5SvXNFg63SF6T4jWalUe7GmhvuHOY/Hq1Wsc99wv58o+c+oInj66Ca+jHjJp08jR4+PgqOp/joASpS2xY+celC9XRo55DZ08UCwnig/sKkxEpAVES8DFc8fgMnYSjnoelxet4qe1VWVsWLsCRYsW1nQRdY5oUU2fLp18vv/AYZk8xMrKUv4u6pfUI6bFuXHzFqpUqYT8+fPKRE13796Xya5+/PiBgIDPrFIViARsFHd273GXN/oqV64YZrk4b8eNcZbDBRbMm8lDoIZBQ5yRM0d2RfJAMU1byBRZIkHTtOlzmPWe4gUDVyIiLSGSBa1fu0LTxdCrOTJFq6tIdLNj1x5UtakiL7qEtes3IX++PJouok5p3ao5hg4bLYPUfn17okzpkujVZxB69+qKKVNnoXChApouIhE+f/6CXDlzKK0JsdzX14+1pCYxVlgkDxSZhEXywNA6dWgrkwcSxQcGrkREWkCMv4pOtmxZ46Us+sJpSH+0addVzocpMgsvWzxPLi9XwUZ2a924fqWmi6hThgzuJ7taXrx0Rf6+aMEsOVdm46atYWJigj07N2u6iDoha46CKmdhFds9e3wrzsukTwoWyAc3dw9Ur24bYd1uN3fkycNs4upi8kDSFgxciYi0QI7cRaK9mP313T/eyqMPWjRvgqwWFjh1+ixsrCujXLkycrlVFUuMdRkBe/sami6izl28Tp82QfG7SHj1+MF12V1YdB0WwStFz87WmtOHxKHBg/qiRasOCAr6jhbNGiNjxgx488YHm1y34b9V6+QNF1KPVZVKmDBpOmyrWimmFhP/r0Tr68JFy1AlXLdsorjCrMJERFpATN0QPnD9/PkzTp46C6/jJ+UUJGLuUSJNqWpXB+3atkKTxvVhbGzMA0Faa8LEaZg4eYYiiZCYAktkZh82dABcRg/XdPF0zp0791CpSnUkS2Yg8y6I+bCbNmmA23fu4cmTZzjpdQDFixfVdDEpAWDgSkSk5QYMHIZ3799z/KsK2rbvKltTc+bMIZ9HRdwoWLNqaWwdJr1nXdUeJ0+dkUmu6terjXZtWsnumKp2e6Wopxo56ukFb+/XaN/OEa9fv0GRIoVgYGDAaoshf39/nDlzHh/8/ZEmdWqUL18GqVOnZn3G0KNHjxXJA8WQATMzUxnEjh7pxOSBFG8YuBIRabljx06gfqOW+Oj3UtNF0Xo58xTFru0b5d3/6Lpfi3WiqyupTszVunHTVmzcvBXXr9+U3TBbtWiKtm1ayjmHSX3Dhrtg1pwF+PnzpzwnL5w9hhEjx8L71WscPbQX6dKlZbWqMFVTyNRB4nl0OM0QkW5i4EpEpOUmT5mJmbPn473PU00XRaf4+LxFhgzpNV0MvXXr1h1sdt2Grdt34cGDRzJwvXLxpKaLpVNmzpqPYSNcMHH8aDjUtUfhYuVw8ZyXzIzbrGU7NGzggMULZ2u6mFovSTIznDl5WI5jT2xgGu0Nq59BH+K1fPpATHuzavV6eB0/JefETp8+LWrWsEOrls2QNClT5lD84JlGRKQFlHVrFYkvXr58JZMLde7UTiPl0mXFS1XC7JmT0bJFU00XRS9lzJgeWbNayPkdHz16grdv32m6SDpn8dIVGDliCIYO6R9mmhEx37CYi3Ts+CkMXFUguqtaWGRRPGf39dj19OkzVK1WF8+ePUeuXDnk/Nhnzz3Bps3bMHvuIhw95MZu2BQvGLgSEWmB4ydOR7jYEr+bmKSSCUVGDB+ssbLpKtH1Ml1adrOMTQEBAdixcw82u27HkaNecl7ceg72cNvliho17GL1vRICcWOqsqXyjKx5cufCu3fv471Muih0wqUxLiOi7e5O6uk/cJhMcHX5wgmUKFFMsfzSpStyDtchTqNkAkGiuMbAlYhICzx9dFPTRdA7A/r1kmMFRTbR4sWLKM2Ey7FuqmvUxBH7DxxGUFCQnFJo6eK5MsNwqlSpYvW4JSTZslngxMnTsLOzibDu/IVLcj2p32347KkjKFu2dIR1J06cRm2HJgjwf8VqVYPnsRNYtmRumKBVKF26JCaOHyUDWwauFB8YuBIRaSlxN/vZ8xdy3kdTU1NNF0fnrF67Ac+evYCNnfJphDjWTT2379yF8/DBaNO6BbJlyxorxyih69KpPUaOHv+n5Tr4PP348RNct2zH5KmzMGRQX00XUSfMmDkPgYGB8rloGVy+cg089h+KsJ0YdiGmdCH1pExpGOk4Vs7fTPGJgSsRkZYkEmrdtgtsrCvDecQQLF6yAr37DpYXYSKr6LEj+1CwYH5NF1OntG7VXNNF0Ct3b13SdBH0zuBBffH02TOMcpkgH0K1mvXkT8dWzTDMaaCGS6gbvn37hjHjJituSIl5sZURNwBHOQ+N59Lpvv59e8reK6VLlUCOHNkVy/38/DBx0nQM7N9bo+WjhINZhYmItED7jt2xz+MgVq9cDHv7GsiaoyDy58uLaVPGoU+/ITI77s7tGzVdTErgTp8+h2NeJ2R3YXFTJWT6kS9fAnHi1BmZEZfU9+DBQzk/pq+vn5wfU3TFFvO4kupEcitxTiYzNMep4wdlhuHQkiRJwuqMoTbtusB93wH5Oa9YoRyyZMkkz9XTZ87Llm6xLCRHg/jp5enBuqY4wcCViEgLZLLIi6mTx6Jtm1Y4e/Y8KlWpjt07NsPBwR573PbJwNbv3XNNF1PncAqH2LNg4VL0G+CkCFjDjxUWU2O4u22LxXck+vdkYqLrtbl5GhgaGrI6Y6iqXR21tvc84s66pjjBrsJERFpAXFzlzJFDPhcJcAwMDGBrayV/NzYywo8fPzVcQt3DKRxi18LFy2FfqzrWrVmGyVNm4VNAgJxuSPQUaN+xB1o7smu2ujp27hntNv+tWBTDI5ZwHTt2AoOdRuLKlWuKZaVKFcek8S6oXt1Wo2XTReoEosePn8KXL19gZGQUp2WihImBKxGRFsiRIxuuXb+BKlUqYdced1hWqqD4x7/XfT9y5vw7rohUwykcYteTJ88wa/okOV9jmTIl5RyjohWrcaP6uHfvAebOW8w5c9V08NDRCNNgff78BR8/fpSthCXDZXGl6J08eQY17Bsgd+6cck7XjBkz4NWr19i8ZTvq1GsKz8PusLSswKqMo+7aVavVwYWzx1CqVAnWMcU6dhUmItIC8+YvxtBho2WAKoKAra5rZUDQsHEr2VV43pxp6NWzq6aLqVNM01jIKRyaN2scYd36DZtlYPve56lGyqaLTFJnwZ6dm2FjUwWXL19FBUs7fP74WmbE9fI6iYZNWrE7eyy5desOmjRvg3FjnNG0ScPYetkE061VZMDdv29HmHGtYix2Tfvgujx0YLcGS6jfgatBijRyrDsDV4oLiePkVYmISC19+/TA6v8Wo6pNFUXQKiRPngwL589k0BoDnMIhdpUoXhRue4OTruTPn1cGAmfOnJe/v/T2juV3S9gKFy6IMaOHw2XsJE0XReeI+W/79u4eIRmTGIctlov1RKSb2FWYiEhLtGjeRD5C27xxtcbKo+s4hUPsGjigNxo1cYSvnx9W/7cE9evVQZv2XdGwQV1sdt2OKpUrxfI7JmymJiayezapWW+mJvjx84fSdUHfg1idRDqMgSsRkZa4e/e+bNESiS1Ea1ZoYhzc2DHOGiubLrp56zbevXuP/IVKK53CQXRxFYmwBE7hEL0G9evCbZcr7t57IH8X3bBbOnbEipVrUa5saSyYNyOOj6j+efz4idLuli9fvsJIlwmcuzkGRH6ASVNmokZ1WxgbG4fJMDx56ixUqVzx3w4aEWkMx7gSEWmBDRtd0bZ9N6VTjYQEVr+++8d7uXQZp3CIXYuXrECd2jWRLVvWWH7lhCuxgWmE5EyC+B4Qia92btuAGjXsNFI2XfXw4SOULmeNZMkMUNu+BjJmyIA3Pj4y+3VQ0Hec9DqAYsWKaLqYeoljXCmuMXAlItICBYuUQVaLLFi5fCEsLLIovZgl0iQjk4zYuH6l7CJMsWP1mg0I/1EXn30TExM53t3U1JRVHQO3b9+VWa+9jp+En98HpEmTGtZWleEyahgKFSrAOo0jDFwprrGrMBGRFhBj2RbMnYGsWS00XRS9ExQUhPPnL8H71SvUrGGHL18C5c0BUn/KJhEEUOyxrWqFTJkyynmblZ23p0+fQ6VK5VnlahLBqesm5gcg0jcMXImItECuXDnw9t07TRdD7yxZuhLOo8bhwwd/2ZIl5hccPWYivn//jl07NiFlypSaLqLO6Nq5vZxC6OSpMyherEiY8YMhOnZoo5Gy6aqceYri7KkjKFu2dIR1585dRK06jRAY4KORsumyFy9e4sLFy/D3/6h0Pc9TIt3ErsJERFpAzCs6YdJ0OU9mvnx5NV0cvbB23Ua079gD3bt1hEMde9Sp11TOL/jw0WN06tIbPbt3xtQp4zRdTJ0ajxkVjsNWzeAhzvD78EHRVViMG06XLm2E7a5cvY43b3zw+mVwMixSzcZNW9Cxcy95c4rnafwSY7PHjZ+Cbl07ImPGDPH87pQQMHAlItIC1WvWx8VLV/Dp0yekTm0WoSVQBAXPHt/SWPl0UZHi5WFna425s6dFGHs1e84CzF+4DI8fXNd0MXXGs2fPo90me/Zs8VIWXbZ8xWqMmzBVPn/16jXSpjWXGa5DE3OQiu+BoYP7oWWLphoqqW7Kk784smTOjNkzJ8PcPI3SbXiequ/AgcPYs3efHGqhLOv9mlVLY3jEiFTHrsJERFrAwiKzfFDsefjwMWbPmKx0XamSJfD69RtWtxpEohvROmhubh5hnWgZXLtuE4YO6c86jUaXzu3lI6Sr8K7tG1G8eFHWWyx5/doHy5fMlzeoKHbMmr0Ag4c6I0WKFLJ3QOLEicOsZzJBii8MXImItMCqlYs1XQS9kyFDejmXa/XqthHW3bp9R64n1XXo1FOOx1QWuF69el2OHWbgqp4nD2/Ax+ctdu5yQ8MGDnLZo0ePsX3HHnTq2EZpXVPUKlUsL7tZV61qxaqKJfMXLkXLFk3k/6nwvQOI4hMDVyIiLeLt/QpHPb3g7f0a7ds5ylbBIkUKKc06SlFr2bwJXMZOlgFq3Tq1FC0D585dkOOJHVs2YxVGo45DE9y5e18xfq1B41ZInjx5hO1E8CUSjJF6rl+/CbsaDjAxSaUIXMVnX3QlXrBoGTwP70Xu3LlYrWpYvHCWHM/+4cMHlCtbBkZGEROw2dpas07VIHpUdOnUnkEraRzHuBIRaYlhw10wa84C/Pz5U5EBd8TIsfB+9RpHD+1VmsCFIieSszRq4oh9HgdlfYrAS4wd/vr1K6pUrgQP9+0wNDRkFUbh5MkzWLr8P/l8w8YtcjohMSYzwnhMMzN06thW3mQh1dWq3RABAZ+xe+fmMPXq7++PuvWaIX36dNixbQOrVA173Pahddsu+Pz5c5jlId8BTCKmvtLlrNCjWyd07tSO5yJpFANXIiItMHPWfAwb4YKJ40fDoa49ChcrJxMJff78Bc1atpOtMYsXztZ0MXXS4cOeOOp5HO99fWFmagprK0vUrl2T47LU1KFTD4we6YScOaNvWT1+/BRKly4BIyOjmB62BCF12mzYvOE/1KxZLcI6d/f9aNexO977PNVI2XRV/kKl5Hk3bOgAZEivfDiAtXXleC+XLjt48Ai69uiHZYvnynmFlU0jFn7cK1FcYOBKRKQlmTDbOLaAy+jhETLgrli5BmPHT8GLp3c0XUydUsW6JkY5D0WNGnaaLkqCIs7fZIbmsscAE+RELW2GHFi0YBaaNW0UYd227bvQuWsf+Pu+iLNjpY8MjdPDbZcrqlWrqumi6I28BUQyOx/ZW0UZ0Yr9Myh4iieiuMQxrkREWuDly1eobFlR6bo8uXPh3bv38V4mXScStDCRiGaILpkUPRvrKnI8q1UVyzDzXooxw2IcdlWbKqxGNRUvVgTPX7xkvcWi1q2asz5JKzBwJSLSAtmyWeDEydOws7OJsO78hUtyPalHTN2yeu0GVKxYTmlCISJNmzp5LMpVrIrc+YqjXNnSckyruEl17vxFpExpiC2bVmu6iDpHzN/awrEjgoKCULFCOZn4KrxcuXJqpGy6SvQEItIGDFyJiLSAyNg4cvR42UJYz6G2XPbx4ye4btmOyVNnYcigvpouos5JnjwZNm7aiq3bdiF//rwwDjfeUnRv8/L00Fj5iETG4FvXz2P2nIXyxtWVq9fkOOzuXTtiQP9esLDIwkpSk1VVe/z+/Ru9+gyKdBz7r+/+rFc1ffv2TQ5bOeZ1Ev7+H5E2bRqZ5K5D+9ZKx7wSxQWOcSUi0pKulb37DsKSpcEZXEOyXwqOrZph9X9LmPxCTVXt6kS7jecR95gcLopC+DHaRPFpzdroszC3a+sYL2XRF2JqIRu7Orhx4xayZ8+GjBnS4/UbHzx//gIFC+bHqeMHYWZmpuliUgLAwJWISIs8ePBQZsD19fWDmZmpHPvGKUZIlzBwVU9gYCCuXr0hu7aGjA0WLYZfAgNx4sRpzJg+MU6OE5Gquvfsj+07dmPnto2oXPlvLgZxfjZp3gCiLLYAAE0MSURBVAZNmzTAgnkzWaEU5xi4EhGRXrKtVldmbC1QIF+Eddev30Trdl1w/coZjZRNnzFwVd3Ro15o2qKt7HqpTKpUqZhVOAbevn2HadPn4Nhx0a3VH2nNzWW31kED+4RJgkWqyWSRF2NGD0e3rh0jrFu67D+MnzgNL5/dZXVSnOMYVyIiLSBaW+bMXYRTp88qvYjleEzVnDx5RrZWCce8TsDr+El5ERveXvf9ePToyT8fN6J/McplgkzItGLZAqzf4IokSZKgQztH7Nt/EIuXrITH3m2sYDW9ePESFSztZK8VkZwpb55cciqXeQuWYP1GVzlNE8cOq0fMJ54rkvmbxXJR10TxgYErEZEW6NFrAFav2SC7BZunSaPp4uisZStWyQBABPri0bP3wAjbhHTHbNG8sQZKSPTXtes3sXL5AjRs4CCTsS1esgL29jXk4/v3H3JKHHc3Bq/qcBo+WmYRv33jfJjswY8fP0EN+wYYMXIs1q5extNQDQUL5IObuweqV7eNsG63mzvy5MnF+qR4wcCViEgL7N7jjnFjnDHSeaimi6LT5s6einZtWsngVFykzpszDQUL5A+zjWjVSp3aDMWKFdFYOfVZ4sSJ4TJqGDJnzqTpomg90Tsgc6bgesqbJzdu3f7b3bJJo/po26GbBkunmw4cPCK/B8JPeSN+F91dBw1x1ljZdNXgQX3RolUHBAV9R4tmjWV36zdvfLDJdRv+W7VODskgig8MXImItIAIpiqUL6vpYui81KlTK+bC9TzsjlKlistxghQ7vL1fyVbAg4eO4NWrNzKb6GbX7ShevAgcWzWX24iWbs77qJrcuXPixs1bqFKlkpyySSRqunv3vhyX/ePHDwQEfOapq6Zfv37LMa3KiN4srFP1NWvaCPfvP8TEyTPklDiCuDkoWrZHOQ9F1y4deJ5SvGDgSkSkBTq0a43lK9egalUrGcTSv7O2riwTs2zdthNfvgQqxr6G1rFDG1a1iu7ffwBLqxqyRdXO1hquW3bI5e99fdG2fTcYGhqiUcN6rE81tG7VHEOHjZZBar++PVGmdEk5/2jvXl0xZeosFC5UgPWppuLFimDt+k2oVat6hHVieZHCBVmnMSB6A4nz8syZ8/jg7480qVOjfPky8mYhUXxhVmEiIi2Z3L1kmcr49i0IpUoWh5FR2AndRSvWmlVLNVY+XXTw4BE0atpatmIpI+r013f/eC+XrnKo3ww+Pm9x7Og+JEuWDMkMzRVztbZ07IAnT57h7Omjmi6mThE3U5yGjcYbHx+sW7McFy9ehn3dxjLZjYmJCfbs3AwrK0tNF1OnHD7sKYcJVK9WFS2aN5Fzjr7xeYtNm7fiyFEvbNuyTo4ppujPTXGTKuR5dEK2JYpLDFyJiLRA/4FOmDd/CYyMjJA2rbnSIOvxg+saKZuuKl3OSv6cM3MKLCwyK72wyp49mwZKpptSmWXGmlVLZKtq+ClvRLAgbhJ8+uCt6WLqvICAANldWHQdFsFriOPHT6F06RLyO4Kitm79JjgNd5HjMEOIcZmTJ7qgXVtHVp8KkiQzw5mTh1GuXBkkNjCV/4MiI9b9DPrAeqU4x67CRERaYO26TRjYvzemTR3PO9ex5NatO9i5bYMcP0j/TgT+kbWqfAkM5HkbS8SY7LJlS4dZJm4UVK1WR07lIm4UUOT27vVAg/p10dqxBe7dewA/vw9Ikya1vBEQVfBFYY0e6aSYNkg8Z92RNmDgSkSkBX7//h/q1qnFi/9YJLLaBn3/HpsvmaBZVakkEzPZVrVStPrJ7ta/fmHhomWoUrmipouo10KmcaKotWrTGUsXz0HLFk1lkiuKmdAJ1sa4jIhy25cv2dOC4gc7pBMRaYHGjerJqQUo9vTu2RWTp8zEp0+fWK2xYNqU8Xj06AnyFiiJlo4dZdA6dfpsOTb7zNkLmDBuFOuZNM7cPI0cg02x2234woVLStedOHEaBYswIz7FD7a4EhFpgdKlSmDEyHG4evU6KlYsB5NwU7iIIGHsGM4/qI47d+/h/oNHyGSRDwUL5kdKQ8MIderl6RErxy8hEHV48dwxuIydhKOex2X2a/HT2qoyNqxdgaJFC2u6iEQYMWwQ+vQbgtu376J48aIwVjIm2NbWmjUVjRkz5ykS24nWfpH13mP/oQjbnTp9FsmSGbA+KV4wORMRkRYQyS+iwgy46qtqVyfabTyPuMfglYniV/hkWKT+d6n4DhUBGL9LVTNh4jSMHjMxTN0pY2pqKudyHTigN09LinMMXImIiEipx4+fqFUzuXLlZE3GAQauqvPyOqnSHM+k2nknAlYx9dWp4wdlhuHQOOc4xTd2FSYi0rELCXERweyiqgsKCsL585fg/eoVataww5cvgYpsmRS1PPlLqJVNlPPikqYxKI09IYHp7x8fY/FViWKOgSsRkY5hdlHVLVm6Es6jxuHDB38ZgImAX3R/+/79O3bt2ISUKVPG4ZHSff+tWATOIEK65sqVa5g8dRa8jp+U0+GkT59O3rQaOWIIewXE0OnT53DM64S8ERjyP+j379/yRuCJU2dkN3aiuMbAlYiI9NLadRvRs/dAdO/WEQ517FGnXlO5vG2blujUpTfGjpuCqVPGabqYWq19O0dNF4FILUePesG+bmOZXdihrj3Sp0sHn7dvsc/jILbv2CO7vBYpUoi1qoYFC5ei3wAnpTdNxdzO4qYAUXzgGFciIh3CsW6qK1K8POxsrTF39rQI9TZ7zgLMX7gMjx9cj8Ojpfv+W7UO9evVhrm5uXweFdGi3aF963grW0IiAoZx46egW9eOyJgxg6aLo9UqVLKFsbEx9u7ZghQpUiiWf/36FbVqN4KhYQrs37dTo2XUNQWLlEHuXDmxbs0yTJ4yC58CAjB75mR5M6B9xx5YtmSunDeXKK4xcCUi0iEMXFWXwigd3Ha5onp12wj1JhK41KrTCF8/v43Do6UfGVrPnjoik7Iw83XcOHDgMPbs3Se7XIqul+FvBqxZtTSO3lk/GZlkxDbXtbC3rxFh3d69HmjZuhMC/F9ppGy6/F26c9sGWadbtu7A2PFTcOv6eblu0uQZ2OO2D2dPH9V0MSkBYFdhIiLSSxkypMfNW7dl4Brerdt35HqK2pOHN5ApU0bFc4pds2YvwOChzrJlMF26tLLbZWjqJMaiYNmyWeDxk6dKq+Pde1+kT5+WVaWmZMmSwfDPPNh5cufCgwePZJ4AsdyyUgXMmDWPdUrxIuw3JBERkZ5o2bwJXMZOxsZNW/DlyxdFIHDu3AVMmDQdTRs30HQRtV727NnkxWnIcxHEvnnjI5+Lh4GBAc6dvyi7r4rfST3zFy5FyxZN4O/7As8e35I3B0I/2JVdfTOnTcSYcZOxYaOr7GkR4uDBIxg5ejxmTp8kW7ZDHhS9EsWLwm2vh3yeP39eWW9nzgS3uL709mYVUrxhV2EiIh3CrsKqEy0CjZo4ynFYImAV4wRFFmEx1q1K5UrwcN+uaEWg6D179hzVatbDr1+/FQGV6OYqEuEUL14U+913sBVbTYbG6eGxdztsbKrwFIwlWXMUlJmEv337JqdzES3Z4nfxfSC+A8K3YnMKp+jt2r1XfpeKxHar/1uCxk1b48LFy2jYoC42u25HhfJlsXvnZp7DFOfYVZiIiPSSaCncu2crDh/2xFHP43jv6wszU1NYW1midu2a7IappiFOo2QL646tqxXLatashjs3L6Jxs9YYNsIFq1Yuju3DqNcKFSqAh48eM3CNRZ07to3NlyMADerXlfkC7t57IOtDJmNy7IgVK9eiXNnSWDBvBuuJ4gVbXImIdIjoomVX3UFeOOTNm0fTxdGbOs2Tv4S8MCtcuKCmi6O10mXMiZXLF6KeQ+0I67bv2I1efQbhjfdDjZRNV4nuq1179MOyxXNRqVJ5pfMKhx/3SrFDtL6OnzAVXbt0YKZmIh3BFlciIi1w/PipaLexsrKUF7GeR9zjpUwJ6QL26dNnCAoK0nRRtNqPHz8jXZfMIBk+fw4eR0yq69V3EN6/95XdrZUR3Vp/Bn1glcbRDSuRHbdunVoMXFUghlisWr0eXsdPya7XIsmVmL+1VctmSJqU4QTFD55pRERawMaudrRdVzkWizSpYoWymDFznrxYTZ48uWK5CPhnzVkgx7mRelq3as4q0/BNK4qeuLFXtVpdOc49V64cSJ8uHc6ee4JNm7dh9txFOHrIDalTp2ZVUpxj4EpEpAUO7d8dYdnnL19w8uQZbNy8FVs2rdFIuYhCjB87ElVsaiFnnqIyeBUXr+/ev8eBg0fw4YM/vI7uY2WpyWX0cNYZab3+A4fJIP/yhRMoUaKYYvmlS1fQqGlrOf59xbIFGi0jJQwc40pEpOUmTJwmpxxx271F00XRS8zUrLrr129i4uQZOHHytOziamZmKjM0j3IeGuaCllQnst+uWLkGx7xOwt//I9KmTSPrtEP71krHvFLs4OdedaZpLGRehebNInZpX79hswxs3/sonzuXKDaxxZWISMuJi9gp02ZruhhEKFasCFw3/c0qTP/mw4cPsLGrgxs3bsl5cDNmSC+zDG/ZuhOLlqzAqeMHYWZmxmomjUqZ0jDScawmJibxXh5KuBi4EhHpwBx6JiapNF0MSoCOHvVCuXKlYWxsLJ9Hx9bWOl7KpS+GO4/Fq1evcdxzPypXrqhYfuLEaTRp3gYjR4/HgnkzNVpGov59e2LEyLEoXaoEcuTIrqgQPz8/TJw0HQP792YlUbxg4EpEpAWsbGop7cr20vsVXr70htOQARopFyVs1WrWw9lTR1CuXBn5XCQQC5/QJmSZ+MkEYurZvccdE8aNChO0ClWqVMK4Mc4YP3EaA1fSuJu3buPdu/fIX6g0KlYohyxZMsHX1w+nz5xHYGCgnDN7/4HDclvxPeDl6aHpIpOeYuBKRKQFxDQ34bMKi65ZJUsUk+MHO3Zoo7GyUcLledgdhQoVUDyn2CWmEMqVM4fSdWK5CA6INO3ly1coXqyo4vdXr97In6IFlig+MXAlItICx5iRVWPEDQMxvjD0FC8UzNq6stLnFDsKFsgHN3cPVK9uG2Hdbjd35MmTi1VNGse5w0lbMHAlItJCv3//VtoqS1F7/PiJWlWUK1dOWa9PHt5g1arg7Nnzsnugv7+/0hsAY8c4sx7VMHhQX7Ro1QFBQd/RolljZMyYAW/e+GCT6zb8t2odFi2Yxfr8By9evIS39ysULVpYfs4NDQ0V65IkSSJ7EeTPn5d1HMn/oJD/Ocr+H4XH/08UHzgdDhGRFnj79h36DRiKPW4ecnoMZUHBz6APGimbLklsYBqhy3VUOCZTdbNmL8AQp5ERxriG4BjXmE93JaYY+v79u/xd1K9o/R82dADneY0hd/f9GOw0EvfvP5Tn5fkznpgwaTrSpjXHkkVzGGSpIEkyM5w5eViOb4/ue5X/nyi+sMWViEgLiKB19559aNmiCSyyZOaFVQytWrkodg8MKcyZtwgNGzjI+RzTpEnDmoklI52Honevrjhz5jw++PsjTerUKF++DFKnTs06jgEPj4Oo36glatawQ78+PdCrzyC53NrKEoOHjpRjh4c5DWTdRmP0SCdYWGRRPFfnhiBRXGGLKxGRlkzwPmXSGPTo3lnTRUlwRAtXpy69MGb0cGTLllXTxdFaqcwyw22XK2xsqmi6KDqNXTDjVtkK1sifLy/Wr10hM7MbpEiDi+e8UKpUCYwZOwmbt2zH3VuX4rgU+k/0EBDdgyOb35UoLnDAFBGRFhB3s8XFFmkmkFizdiPev/dl9UehVs1q8Dx2nHX0j0Qgdf78Rfk8afLU8vfIHskMzVnfarp58w7aOLZQuq6qjRWeP3/JOo2BUaPHo6pdHcXvp06dRbqMuTBlKsdhU/zhbRIiIi1gX6sa9uzdB1tba00XJUGKbNwm/bVg3gzY2NXG4ydPUa5saRgZGUWoHk7bFD12wYxbZmamePHSW+m6p8+eyfWknkmTZ2Dq9DkYMqifYlmBAvnQo1snjBk3GalSGaNXz66sVopz7CpMRKQFduzcgy7d+shxWZUqlkfKlCkjbMOgIG6E705Iyq1YuQbde/aPNMMokzPFvpcvvRXjDEk1PXoNwM5dbtixdYMcKyw+25fOH4e5eRpUq1kPNtZVsGzJPFanGvLkL46e3btg4IDeEdZNmz4Hq9asx52bwb0IiOISA1ciIi0gsjZGhUFB3GHgqprsuQrLeUfHjXFGhgzplW+TPVusHpuEkLn17KkjKFu2dIR1J06cRm2HJgjwf6WRsukqMVWTbXUHXLt2Q2YRfvfuPXLnzomXL18hR45sOOl1AObm7IKtjpSpMmDv7i1KewQdPeqFuvWbITDAJxaPIpFy7CpMRKQFOI8oaTsxBnjo4P5yegyKuRkz5yEwMFDRRX35yjXw2H8ownanTp9FsmQGrGo1mZkF3wxYu24Tjnp6yfNWLOvftyc6tG+ttDcLRS1Xrhw4cPCI0sD1yFEvZM3KXgEUPxi4EhFpAbZUkbarWKEcrl2/wXHY/0jM0yzGBYb0pBBdsJUxNTXFKOeh//p2CY5jm06yW2vnTu3kg/5dty4dMGDQcAR9D0LD+g5Inz6dbMnetXsvFixahmlTxrGaKV6wqzARkZbw9n6FCZOm4+ChI3j16g1OHT+Iza7bUbx4ETi2aq7p4uktdhVWzeHDnnBs2xnt2zqiYsVyMEmVKsI2TC6m+jknWltF1mDxOQ/fip0kSRIVX4mUdWsV0zbZ2dmwcmLRCOexmDl7Pn7+/Cl/F+evgYGBbMmeysCV4gkDVyIiLXD//gNYWtWQ8+LZ2VrDdcsOXDh7TN7NFlO1bHVdi0YN62m6mHqJgeu/jcMWrYbiIpbjsEkb1KhVH6VKlsCUyWM1XRS98+nTJ5w5cx6+fn4wMzWVya84XpjiEwNXIiIt4FC/GXx83uLY0X1IliyZbIkJyXLb0rEDnjx5hrOnj2q6mHqJgatqvLxORruNtXXlfz4eCc3p0+dwzOsEgoKCFNMyiczNX74E4sSpM/J7gFQ3aPAIecMvS5bMKFG8KIyNw07bJG6wrFm1lFVKpIM4xpWISAsc8zqJNauWyMQhIpAKrVOHtmjUtLXGyqbrvnz5ophzdPuO3Xj27AXq16uN3LlzKbpliuRYmTNn0nBJtRuD0ti3YOFS9BvgpHQeYdH7QkyPRerZsctNfpZFnV65ej3CehG4UvSy5Swku1wXL14UWXMUjLLexLpnj2+xWinOMXAlItIC4iJVPJT5EhgY6TqK3IMHD1GnXlM0b9oI48eNwpixkzB+4jR5QTvKZQKOHNyDChXKyW2ZHEs1d+/ex8jR42UL4cePn+TcmFUqV8KY0cNRuHBBno5qWrh4OexrVce6NcswecosfAoIwOyZk7HP4yDad+yB1o4c264uZmiPHWLIiolJKsVzBvykDdhVmIhIS7oKv37jg6OH3GTroEGKNLh0/jiKFSsC+zqNkDx5crjt3qLpYuqUJs3a4Oq161i/ZjnKlCmFjFnyoKqNFZYsmi2Dgq9fv+HwwT2aLqbOuHnzNipVqS6naHGoa4+MGTLg9Zs32Ou+H9++BeHc6aMMXtWUwigddm7bAHv7GtiydQfGjp+CW9fPy3WTJs/AHrd9HCLwD168eCmT3hUtWlje/DM0NPyXl6NIiF5CTChG8YG38ImItMC0KePx6NET5C1QEi0dO8q721Onz0bJMpVx5uwFTBg3StNF1DmiVXDKpLGyVfXEidPw8/sgp3UQyUR69eiCCxcva7qIOmW48xjkzp0Tj+5fw6qVizF50his/m+J/D1//ryyJZbUI8azhwRTeXLnwoMHj/D9+3f5u2WlCrj/4CGrNAbc3fejYJEyyJG7CCpb18S9ew/g2KYzunbvK8cPk3py5S2G69dvKl13/vxFZMicm1VK8YJdhYmItEDBgvlx4aynnN/xqOdxefda/LS2qowNa1fIFgNSj2hRTZ8unXy+/8BhGSBYWVnK39k6oL7jJ07jvxUL5fyioYnfhzsNlEEBqUckD3Lb6wEbmyoy+BdBlcjaKsYTv/T2ZnXGgIfHQdRv1FKOD+7Xpwd69Rkkl1tbWWLw0JHIlTMHhjkNZN1GY9Pmrfjx44d8/vTpM5kfQPRgCe/IUS98/x68HVFcY+BKRKQFAgMDkSdPbqxfu0LTRdEb+fLlka2uFSqUxY5de1DVpops4RLWrt+E/PnyaLqIOkV0EU5mEFx/4SVPlpwXrzEwcEBvNGriKKcXEa3X9evVQZv2XdGwQV05h7MYP0zqGT12Ilo0byy/S0UX1p69g4PUfn174sMHf6xeu4GBqwrOn7+EufMXy+eiB5DIDxCZAf168TSleMExrkREWiCVWWY0blQPbVu3hK2ttaaLoxc2u25Dm3ZdZaZmkVn40P7dqFrVCuUq2ODylWvYuH4lmjVtpOli6ozadRvLQGD/vp1hErWIZFc1ajXAz58/4XnEXaNl1NVurXfvPcCggX3g6+srhwqcOn0O5cqWxtrVS5E1q4Wmi6hTDI3TY9f2jahZs1qEqa7ElE72dRsjMMBH08XUemJ6ppcvveXnO1/BUti2ZR2KFysSZhvRcyV1arMIvTCI4gpbXImItMDA/r2wyXUb1q7bBAuLLGjdqrnMKFqoUAFNF01ntWjeBFktLHDq9FnYWFdGuXJl5HKrKpYY6zJCJsQh1Y0fO1ImZypcrByaNm6AjBkz4M0bH2zdvgsPHz7G4QNMdBUTderUkg9BjL8+uH83T8t/YGZmihcvlXezfvrsmVxP0RMJAUOmDBMt2OnSplX8TqQpbHElItIiItHFhk1bsGXrTrx9+w4lSxZHuzYt0bJFU6RNa67p4lEC5+l5HMNGuODipSuyJUa0vJYpXRKTJ45hT4EYeP78RbTbZMuWNSYvnWD16DUAO3e5YcfWDShfvowiQ7uYuqlazXqwsa6CZUvmabqYOiVlqgxyTlc7OxtNF4USOAauRERaSCRpEUGCCGLXb3CVUzl8+/JO08XSOQcOHMaevfvw5UtghGyiIuhas2qpxsqm62Oy/f0/ytYr0RWbYiaxgWm082P++u7P6lWDv78/bKs74Nq1G/Jm37t372U27JcvXyFHjmw46XVAtmyT6mrUqo9SJUtgyuSxrDbSKHYVJiLSMmI6jH0eB+W8jvs8DslxRGLeTFLPrNkLMHioM1KkSIF06dLK4D+06AIGir5F8P17XwDiEYytg+oRLX/hz8PPnz/j5Kmz8Dp+EiuWLeBpqCYzMzOcPXVEDrs46uklz1GxrH/fnujQvjVvtMRA0SKFMXvuQmzZtlNmwjY2NgqznjcBKb6wxZWISAuI1sDDhz3lONddu93x8eNHVKpYHm3btETzZo2Y/CIGcuYpikoVy8k5R0OyCVPstwiGxtbB2DNg4DC8e/+emcZJK75LoyK+Ix4/iDhVDlFsY4srEZEWyJglD3x9/ZAzZ3b079sDbVq3QK5cOTVdLJ0mEgd16dSeQWsstQiKqUScR42DbVUrNGncAJkyZsB7X1/scfOAx/5DmDl9YmwdOgLk1DhiPlKK3miXCejerRMyZ84kn0dFnM9jxzizWtXw5OEN1hdpBba4EhFpge49+6ONYwtYWlbQdFH0RulyVujRrRM6d2qn6aLoBTHfqEhws3zp/Ajr+vYfgrt37zMjbiyaPGUmZs6ej/c+T2PzZfW2Z4DoHiwyh4vn0QWu7BkQuz58+IDUqVPH8qsSRcQWVyIiLbBk0RzF8xcvXsLb+xWKFi0sx2UaGhpqtGy6avIEF3Tt0Q/ZslqgUqXySse2hR/3SpE7cPCInB9TGYc69lixci2rT01t23eNsEzMPSoSCYlpnHjTRTW/f3xU+pxix7dv32TOgGNeJxAU9F1mFJd1/fs3vgQG4s6de0weSPGCgSsRkZZwd9+PwU4jcf/+Q9kqcP6MJyZMmi4zY4rAlkGWenr1HSQTs9jXbax0vajjn0EfYuXYJQTiPDx3/iKqV7eNsO7IUS9kzpxRI+XSZcdPnI4whlj8bmKSCsOGDsCI4YM1VjaiEEOHjcKChcvkzVQxTZu4mZourTlu3LwtkwmOGT2clUXxgoErEZEW8PA4KMez1axhh359eqBXn0FyubWVJQYPHYlcOXNgmNNATRdTp7Ru1VzTRdArnTq0wfiJ0xAQ8Bl169SSgayPz1ts3b4TS5etwuyZkzVdRJ3z9NFNTRdB74jWwGXLV2GPW+TTYHl5emisfLpox043DBrQB9OnTcCkyTNw5ep1bHVdK3sGWdvaR6hjorjCMa5ERFqgbAVr5M+XV2YQFV0FDVKkwcVzXihVqgTGjJ2EzVu24+6tS5ouJiXwgGDwEGcsWLQMP3/+VCwTrS/OwwezdZC0wgjnsZgybZZMdGeRJYvSniqeR9w1UjZdlczQHAf27UTVqlZwc/NA736D8ezxLblu5X9r5Vjs2zcuaLqYlACwxZWISAvcvHkHE8aOUrquqo0Vps2YG+9l0hevXr1GUFBQ2HFZXwJx4uRp9O7VTdPF0xmipWrmjEkYPcoJZ89egN+HD0hrbi7HDxsZGUWY/1VkeE2alJcZ4VnZ1FKrztk6qJ7VazfIXiuzZ01Rc0+KjJmZqRznKuTJkwsvX3rj06dPMDExQd48ufH8+UtWHsUL/kchItKSC4MXL72Vrnv67JlcT+q5evU6Wjh2wIMHjyINChi4qs/U1BQ1a1aLdL3oMSDmfbxw9pjsMUBhiRZAdebGJfX4+39Eg/p1WW2xqErlSpi3YAmqVKmEvHlzyxtVovtw+3aOOHP2PExNTVjfFC8YuBIRaQFxoTVy9HgUKlgA5cuXkcvExa1ouZo4eYYcU0jqJxQR4zFnTJuAve4HkDx5MjjUtcc+j4Ny3tFjR/axSuNISOs2RXTsKM+7uFSmdElcvnIV1taVefrFEpF8qYpNLdSt10yevz27d0a3Hv0we+5C3Lp1Bz17dGZdU7zgGFciIi3g7+8P2+oOuHbthkx68+7de+TOnVNOi5EjRzac9DoAc3NzTRdTp5imscCcWVPQoX1rmaxl/QZXHD+2X65r0qyNbPnasnmNpoupd8KP0aaYu3nzNooUKcQqVMPFi5fRuFkbOe66UkUxDVbE6cRy5crJOlXTmzc+uHHjlswqLm5MiXmGxZRN5cqWxvBhg5AsWTLWKcU5Bq5ERFrAsU0ndOvSEfcfPMRRTy85jYuZmZnMKiwCL2VzkFLUUhilw6H9u2X3tmPHTqBB41bw932hmHqoe68BePH0DqsxljFwVZ2vry+GO49VPj/ml0A5jvDXd3+eo2oQN01E/Ym6jKxLNuuUSDexqzARkRbYuWsvOrZvg86d2skH/bts2Szw+MkTGbjmy5dHBgFPnz5DjhzZkTx5cvj5cQ5X0qyBg0dg0+ZtsK9VHXfu3pNjB/PlzYOTp84gICAAy5bM4yFS04pl81lnceDKlWuYPHUWvI6flN+d6dOnk9O3jXIeipw5c7DOKV4wcCUi0gKVLSvg4KGjsLOz0XRR9EbjhvXhNNxFTtfSrGkjFCiQD8Odx2Do4P5y+gbRFZtIk/YfOIyxLiNkV8uZs+bLllfXTavx+fNnWFW1l+MHST337j1Ei+aNUaxYEVZdLDl61Av2dRvD3DyNzBOQPl06+Lx9K/MFbN+xB6eOH2SXdooX7CpMRKQFBg0eIefHzJIlM0oULwpj47DTi4gub2tWLdVY+XSRmL6hTbuuCAwMhLvbNhw4cBgNmzjKqXGSJEmCzRtXoVHDepoupt5hV2H15scU3dlFIiEPj4Po1LU3Xr24L9dt3LQFY8ZNxv07V+LsWOkjQ+P02LltA2rVqq7pouiNCpVsYWxsjL17tiBFihSK5V+/fkWt2o1gaJgC+/ft1GgZKWFgiysRkRbYsctNznspxmVduXo9wnpOn6E+cYG11XUtfvz4IX8XU7jcvHYWly5fRamSxZE7d65YOHJEMZcuXVp8/PRJPhfTjPj4vJXjXkUitiyZM8Pb+zWrV03FihbGrdt3GbjGohs3b2Ob69owQasgerMMGdQXLVt3is23I4oUA1ciIi3w5OENTRdBbxkYGITJJsqMoqQt7GytMXHSdBQpXFDeSEmTJjVW/rcOQ4f0h9teD5lhnNQjpg4TU4uJbqzFixVR2ntl7BhnVqva+QKeKl337r0v0qdPy/qkeMGuwkREpDdsq9VVa/ujh/fGWVkSKtFroGPnnnLsZrZsWTVdHK327NlzWNvWRo7s2eT8mLNmL8Dgoc5IlSqVHOc6eqQTXEYP13QxdUpiA9Mo14vAlVmF1bNv3wG069hdTi/WonkTOdRCOHjwCDp07omF82einkPtv8cgceIYHTui6DBwJSIivVHZqkaYbtXnL1ySP8Vcg5kyZZTTDIUsE5lcRVdiUp0IphYuWi4Tib16/RrbXNfJBEOlS5WAjU0VVmUMiHGCDx48UiQTEmNbQ+bHbNfWkXVKGpc1R0GZSVjkDRBBq+jiLn7//j14CqfwQ1l4Y4DiCrsKExGR3jh5/KDi+dx5i+D34QP2u+9A1qwWiuViHGGdek2RP18eDZVSN71+/QbWtvZ4/vylHCN8//5DmehKTN0yYuRYHNi3k8Grmtas3YCmTRqGyYDbqmUz+aB/9/PnT3mzSnS5TpqUl7wx1bljW56OpBXY4kpERHopc9Z8WDBvhtLMwbv3uKNz19549+aJRsqmixzbdMK58xfhddQDGTKklxlxL57zkkFX7bqN8fPnL3a9jkG3VjF3a8MGddG2dUs5HRYTscXOnKPiZsoxr5MyOdv5M56Yv3Ap8ubJjRHDB8fCO5AyovV1/ISp6NqlAzJmzMBKoljHTuhERKSXAgO/4vfv30rXBQQEyGlbSHUe+w9h3BhnOWVT6OBKtGT169MDV69FzIZNUXt0/xqchvSXma5r2DeQXTKdho3GzZu3WXUxdO7cBVha1YD3q9fyvBTBlJAxQwaMcpmAZctXsW7jiPi+HTt+Cl69YjZsihsMXImISC9VtamC4c5jcOvWnTDLL1y4hBEjx6G2fQ2NlU0Xff/+A6YmJkrXiUD2x4+f8V4mXZczZw6MdB6KW9fP49L542jZvAk2uW5D8VKVUKpsFdndndTjNNwFVlUq4drl05gwfpQicJ08aQx69eyChYuXs0rjUEh9E8UFBq5ERKSXRAZMEWyJICB3vmIoX7EqcuQuggqWdjA1NcGcWVM1XUSdIpIFzVuwJEwrdkjL67oNm1GmdEkNlk73lSxZHNOnTcCNq2fQp3c33LhxCwMHj9B0sXTOhYuX0btnV3luhu923bC+Ax494vAAIl3FkepERKSXsmfPhjs3L2DV6vUygZDIgpknTy7Y2TqhTesWYeZ3pehNGDcKVavVQbGSFWFfs7oMCtat3yzHEh456oWDHrtYjf+QWXiP2z5s2rwNBw4ekXXbuFE9Oe6V1GNomAIBnz8rXffu/Xu5noh0E5MzERFRgiZaEKvVqIeli+cgb15mGo7KqVNn4TR8NM6euyDrTQRYpUoVx6TxLqhe3Tbejpm+EMHqZtdtcNu7H4GBgahsWVHeVGnWtCFMIumWTaolETt8YI/MJm6QIo3shi1uWtlWr4s8uXNh0waOc40LIm+AqG+RtK1UqRI8VSnWMXAlIqIEjRdbMWsh/PDBHyYmqWBsbBwHRyXhZBUWAVUbxxYyYM2RI7umi6TzvL1foWLlanj37j2KFikkE1+JGwL37j+QN1rOnDzMeo4j/C6luMYxrkRERKSSlf+tRZt2XWBoaIjMmTPhypXrKFS0rGw1JPWdPnEI9+9cwaiRTgymYonIen310kkMGtBbZrzOnTunvNEibg5cvnCC9UykwzjGlYiIiKIlphHp3rO/bBkMkTlzRpQoXhSObTojceLEaNa0EWtSDRUqlFNM4XLw0FE5hcuIYYNw58492dUyXbq0rM8YSJMmDSaMH826I9Iz7CpMREQJGru3qUa0rIqsrBMnRAwIho8YA48Dh3D10qlYPz767MePH7IFe8vWnTLwF1OJXDh7DEOHjcadu/dw4th+5MqVU9PF1HrHj6t33llZWcZZWRIyfpdSXGOLKxEREUXr6dPnqGZno3Rd9WpV5VQ5pB6XMZOw1/0AXDetRq2a1WCaxkIuX7xwFurUa4pRLhOwYd1KVms0bOxqR5j6JvycoqHX//ruzzqNoRcvXspxxEWLFpY3W8SwgRBJkiSB52F35M+fl/VLcYJjXImIiChaoluwyCasjEiAw26t6hPz304YNxJNmzREypQpFctFdusxo4fjqOdxnpkqOLR/t5yOSTxWLFsgp7rq2qWDXH7z2jkcO7IPA/r1konENqxbwTqNAXf3/ShYpIycC7uydU3cu/dADhHo2r1vmLmdra0rw8jIiHVMcYItrkRERBQtkdxmwqTpch7Mhg0ckD59Opm5ddfuvRg7fgoG9OvJWlSTqL8ihQspXZcxQwb4+39knarALlRPALvqDjJInTxpTITuwcbGRpgzdxFaNG/CelWDh8dB1G/UEjVr2KFfnx7o1WeQXG5tZYnBQ0ciV84cGOY0kHVKcY4trkRElOAp62ZIYTmPGIL69Wpj4OARyJW3GIxNMyFnnqLoP3AY6tSuCZfRw1llasqXLw/27N2ndN1RTy/kzZubdaqmM2fPw87WWuk6y0oVcP3GLdapmkaPnYgWzRvD3W0bunRur+iC3a9vTzgPH4zVazewTilesMWViIgSvNBj4SiSC4akSbFx/X8Y5eyE4ydOwdfXD2ZmpnKOzGLFirDaYkC0DHbq0gvfvgWhXl17eQPl7r37OHTYEzNnL8CcWVNYr2qysMiMfR4HUa1a1QjrtmzbKafHIfXcvHkHE8aOUrquqo0Vps2YyyqleMHAlYiI9Nbnz58REPAZmTJllBlc581fgmfPn6NZk0aoXLmiIqHI7x/skqmqggXzywf9uw7tW+P9e1/Z1VrMkStuoLRu2wXJkiXD0MH90K1rR1ZzDG4GiK6sYmqhunVqIq25OXzevsPWbTux/8BhjnGNAXGD6sVLb6Xrnj57JtcTxQdOh0NERHrpwoVLqFWnEbp27iDHu/XpNxgLFy2XCVq+fv2KXds3ok6dWpoups4QQZWYy3WP2z58+RIYJiGLIFoLvTw9NFY+XRYQEIAzZ87jva8vzExNUaFCWTkXaWjPn79A5syZZMs3RW3+giWYOHkG3r59p1iWNasFJk90QauWzVh9aurRawB27nLDjq0bUL58GRikSINL54/D3DwNqtWsBxvrKli2ZB7rleIcA1ciItJLtWo3xHtfP2xav1JetKbLmAuOrZphyaI56NCph8yKefrkYU0XU2eMcB6LKdNmIWfO7LDIkkVOhRGe5xF3jZQtIcyPmczQXM7xWqpUCU0XR2eIz7ifnx/SpjWXmZqVzf9aunQJZsGNhr+/P2yrO+DatRuyLkVSMdHl+uXLV8iRIxtOeh2Aubl5XB1GIgUGrkREpJfMzLNi/ZplqFvXHvv2HYBDg+Y47rkflpYVcOTIMZkl8/PH15oups7InDUfmjdthNkcd6mRwFW0cl0858XANRbrlDcDVPf9+3esXbdJJg0T3dvNzMxkVmHR3T30VE5EcYn9TYiISC+JrqyiW7Bw4NARpEqVChUrlpO/BwUFIXnyZBouoW4RU7M0qF9X08UgijVMyqYaxzad0LN7F3Tu1E4+iDSF0+EQEZFeKlyoALbv3I1Xr15j2/bdcg5C0b1VJGmav3BppPNnknJlSpfE5StXWT1ECczOXXvx7ds3TReDiC2uRESkn8aPHYkGjVth0eIVMDQ0xLChA+TyvAVKwsfnLfbu3qLpIuqUWTMmoXGzNnI8YKWK5ZEypWGEbXLl4lQjRPqmsmUFOUWTnZ2NpotCCRy7ChMRkV4S8zjeuHoGFy5eRsUK5WSCJmFAv55y7kHOPaqeipWrye7X3Xv2lxmElfn13T8WjhwRaZOiRQpj9tyFch7cEsWLwtjYKMx68X2wZtVSjZWPEg4GrkREpLdy5swhHz9//sSbNz4yI2a/vj01XSydtGLZfE0XgYg0YMcuNzkVkxgTfOXq9QjrI7uRRRTbGLgSEZHeunLlGkaMHItjXifl2NbzZzzl+Na8eXJjxPDBmi6eTmnX1lHTRSAiDXjy8AbrnbQCA1ciItJL585dQNVqdZEnTy7069MD02fOlcszZsiAUS4TZOtr1y4dNF1MnRIYGIirV2/IrMwhGVlF9+EvgYE4ceI0ZkyfqOkiElEcEZ/127fv4sMHf2TIkA758uVlXVO84jyuRESkl2xsayNFiuTwcN+hmLMxZB7Mvv2HwOv4KVy7fFrTxdQZR496oWmLtnJaHGXEdEP+vi/ivVwJgbhJ0LFzT4x1GYFs2bJqujh6gXPjqmf2nAWYPHUWfH39FMtE9+HJE13Q2rFFrB8fImXY4kpERHpJJGVy3bhKjr8KPwarYX0H/LdqvcbKpotEK3X69OmwYtkCrN/giiRJkqBDO0fs238Qi5eshMfebZouok76/PkzFi5ajoOHjuLV69fY5roO+w8cRulSJWBjU0VuI87fVSsXa7qoekVMjeUyapgMvihq8+YvxqAhznIe50YNHZA+XTq88fHBps3b0K5DdyRNmhQtmjdhNVKcY+BKRER6ydAwBQI+f1a67t3793I9qe7a9ZtYuXwBGjZwwMePn7B4yQrY29eQj+/ff2DCpOlwd2Pwqo7Xr9/A2tYez5+/RKmSxXH//kPZDfvkqTNybPaBfTsVwSup7vTpczjmdSJil/YvgThx6ozseSFuBriMHs5qVcG8BUvQp3c3zJ09Lczytm1aoXPX3hg3YSoDV4oXDFyJiEgv1axhJ1sJQ0+FIy5WAwIC5HjXapyTUC3iwj9zpuDWKZHc6tbtu4p1TRrVR9sO3WL3ACYAg4c6y3p9dP8aMmRIL7uzC1td16J23cYyIGDgqp4FC5ei3wAnRcAavpVVfC+Qery9X6Nu7VpK17Vs3gQbNnJObIofiePpfYiIiOLVtCnjZUtgwSJlUdHSTgat/QcOQ94CJfHy5StMnjiGR0QNuXPnxI2bt+Tz/PnzykRNd+/el7+LjM0BAcpbtylyHvsPYdwYZ2TJkjlMd3bR9VIkFLt6LeLUIxS1hYuXw75Wdfi+fYrBA/vKBGxfPr2RNwMMDQ3R2rE5q1BN5cuVweEjx5Suu3T5qpzblSg+sMWViIj0kggGrl46iVmzF+Co53EZeH39+hVtHFtg4IDeyJQpo6aLqFNat2qOocNGyyBVzIVbpnRJ9OozCL17dcWUqbNQuFABTRdR54gbK6YmJkrXiUD2x4+f8V4mXffkyTPMmj4JqVOnRpkyJTF2/BQZsDZuVB/37j3A3HmL0bJFU00XUyeSsYVo2qSBHOP6+ctnNGvSCBkzZoCfnx/c9x3AoiUrsGzJPI2WlRIOZhUmIiKiaIkurU7DRsukLOvWLMfFi5dhX7exzDJqYmKCPTs3w8rKkjWpBttqdWFgkFRmvhZdWw1SpMGl88dRsmRxtHTsgDdv3sLziDvrVA0mqbPIc1F0sb58+SoqWNrh88fXSJYsGby8TqJhk1bwe/ecdRqNxAam8uZJ+C7XoXsGhKwTy35992edUpxjiysREel1sLV1287gjK2vXmPenGk4e+4CypYpjQIF8mm6eDpFjA+cPm2C4vcyZUrh8YPrsruw6DosgldSz4Rxo1C1Wh0UK1kR9jWrywBg3frNMjHTkaNeOOixi1WqJtFt1W2vhwxcxXkpvgPOnDkPa+vKeOntzfpUkedh3jAh7cMWVyIi0kufPn1C7bpNcPrMOdkt+M0bH1w4ewzDncfg7LmLOO7pgeIcm6VW6+CiBbOUBvzXr99E63ZdcP3Kmdg9iAnAqVNn4TR8tLyhIoIsEbyWKlUck8a7oHp1W00XT+fs2r0XjZo4om2bllj93xI0btpaTo3VsEFdbHbdjgrly2L3zs2aLiYRxQADVyIi0ks9ew/A9h17sHf3Ftn1UmRsFdNg5MqVA9Vq1kfmTBmxZ5erpoup1U6ePCODKcHGrjYWL5yNggXyR9hur/t+mRRHJMGhmBHjrz988IeJSSoYGxvLZb9+/ZLz5ZJ63N334+69Bxg0sA98fX3R0rEjTp0+h3JlS2Pt6qWKLOOkurNnz+P0mfPw94/YJVjcbBk7xpnVSXGOgSsREemlDJlzY9IEF3Tq2FYGAGL8oAhcS5UqgR0796Bbj3549+aJpoup1dq274r1G1zDjGsLL2ScW4vmjbFx/X/xWDrdlytvMezctkFpy//58xdR26EJ3vs81UjZiEKIBHdDnEYqnWJI4BhXii8c40pERHpJTM9ikSWz0nWpjI3x5UtgvJdJ18ydPRXt2rSSF6w17BvIMcLhW1xFi2Dq1GYoVqyIxsqpSzZt3iozMwtPnz7Dzl1uuHb9RoTtxBhXkXWYovf4sXo3oHLlyslqVcOceYvQsIEDli2ZizRp0rDuSGMYuBIRkV4qWqQQ1q7fhJo1q0VYt9vNXa6nqIkpRezsbBTJWkqXLqHoxkoxc/78Jcydv1jRUjVuwtRItx3QrxerWQV58peIsldACHEDhq2D6hNd2Hv37MqglTSOgSsREemlkSOGokHjlnj37j3q16sjL1hFK9bylWuwfMVquG5areki6hSRlVWMeU2aNAkqVCiHFy9eomfvgXj2/AVaNm+C4cMGabqIOmHK5LFy7lsRROUrWApbXdfKTLjKWrFNTU01Vk5dsmrlIk0XQa/VqlkNnseOy0zNRJrEMa5ERKS3tmzdgSFOo2SQFSJ9+nSYOH60HPtKqtu4aQvatOuKgf17y2lxHOo3w1HP47CtaoUDB49g8sQxMhkOqe7Zs+fInDkTDAwMWG3xTNw46NSlF8aMHo5s2bKy/qPg4/NWJmcrU7qkTHBlZGQUYZuOHdqwDinOMXAlIiK9d//+A7x/7wczM1M5nYuYk5TUU7qcFQrkz4t1a5bDz+8DMlnkxfixIzHMaSCmTpuN1Ws34M7Ni6xWNYl5cEeOHo9jXifw8eMnmJunQZXKleAyahiKsDt7nBEJ20SmcTFFlkjYRpFbsXINuvfsr8gwHh67X1N8YVdhIiLSe/ny5UWePH8vukIuwBjAqu7OnXuYMXWCrDOP/QdlHYou2IKYG3PMuMmxftz03c2bt1GpSnUkS2YAh7r2yJghA16/eSOnF/LYfwjnTh9F4cIFNV1MvRVZllwKa/zEaahmZ4NxY5yRIUN6Vg9pDANXIiLSS2/fvkO/AUOxx80D3759U9pK8DPog0bKpouMjY0QFBQknx88dFR2cS1YMDjD8KvXr+WYTFLPcOcxyJ07J44dcQ8znvXjx4+wre4gW2J3bt/IaiWNev/eF0MH90e5cmV4JEijGLgSEZFeEkHr7j370LJFEzktDltX/035cmUwfeY8+H34gB073dC5U/AY4YsXL8vMuJUtK8bKcUtIjp84jf9WLIyQhEn8PtxpILp276uxshGFqFihnJyyydbWmpVCGsXAlYiI9NI+j0OYOX0ienTvrOmi6IX5c6ejtkMTtG7bBUWLFobz8CFyed36zZAyZUpMnuii6SLqHNFFOJlBMqXrkidLznlcSSsMGzoAjm07482bt6hYsRxMUqWKsA2DWooPTM5ERER6ycw8K3ZsXc8LqlgeEyimFxKZmUOcOXMOJUsWR4oUKWLzrRKE2nUbyyRB+/ftDDMPqajnGrUa4OfPn/A84q7RMuorUe8GKdLg4jkvJmeKRmID5dMyiXOWc+NSfGKLKxER6SX7WtWwZ+8+Bq7/QCRgCuliHZLQKm1a8zDZRcuXLxthW1KNyMoskjMVLlYOTRs3QMaMGfDmjQ+2bt+Fh/9v706ga7zzP45/iKwYqaCWWLM0f9VOSymm1VhGhZaGUBWprbRGZ7qhQglqtKdTpSjF9I8Z3bR0KCppxK5U7ePfJSSWiiISfyGLJZlzn1amJrSTm8hz78/7dU6O5z6/i895kjr93t/yPZCixPjlPErYbm0iH57ANVC4AgCM1DMqUoOf/KN1SFPrVvday1n/E70Hf5ljRuqLTYnWoSwVvG+5albwP3HYVfE1a3a3Vq9cqlGj4zRp8l8KZ6/u+el+mza/c+JPBUrXAw/cxyOFS2CpMADgplredgW9B3/dhImvaPAT/a0ThMdPmPyLhatD3LjYYn6X4Jip3rFjl7UEO/30ad3if4sqVvz3hyzsHbwxWCpcOv2Gx4+LpWUTygyFKwDASIcPH/nV99SvX69MsrirI0eOFuv99erVvWFZTOQ4kbl7z746diytyBh7B0vm/PnzqlixonW9ZOkyHT58VN26dlZQUKOr/o1wfCjj6elZwr/t5u03nJubR79hlBkKVwAAcN1Z61+bZf25yxfO8CSLoU14J31/LE0T4mIVWKfONfcIs0yzeJKTD6hL1556tGd3vTxxrLVS4OU/v2Z9EODr66s1CcvVsmULfk6L4eFuvayf0+v1G65XN5B+wygTFK4AAGPFxydaBzSdP5991YFCDo6CbOH8ObZlcwd/fWdhYeGamXlGY8ZOVLu2bRTV4xHVqnmrtbR1+aef6bPVP7YeeurJQXZHdiu+lWrovUXvKPKRh+2OYoyoXjHavWevFi2cp3vuaaqadYLVNryN3p41Vf0HDlVOTq4SEzj0qjiqVA20+g336N6tyNjHS/5h9RvOOPXrK1yAkuJwJgCAkd6YOlPDR46x2rRUr16tyGxWcWYSb1ZPDOpXeN09KlqPxzymeXNmXPWex2P66E/PjtDST5ZTuBZT1aq3yMfbu3S+WbA49mC+PWuaNau6du0GZWRk6snBAxQQEKBhQwfr0T4DeFLFRL9huArOrQcAGGnGW3P0WO8onTl9VIdT9iv1wL6rvlKS99od0a3EJ6xRr6jIa4493CVCmzZvLfNM7q7/49GaPvNt66AglA7HjGqN6j/2GV4dn2gtD75yOrOHhweP2QnN72mqmbPmWMutf87x2vHz6xgHygIzrgAAIzn6YQ4e1F9eXl52RzGCo3/rti+/0u9/367I2Jqk9apdu6Ytudx9Jmvrtq/UMPgOtWjeTH5+vleNs5y9+EJDg61Z15Ytm2vpP5arbfj9hf8G/G3R+7otNLiUvns3D/oNw1VQuAIAjNS4cZgOHExRePj9dkcxwqABMdYhN1lZ5/RQl05WIXvixEl9tOQTzZk7X1OnvGJ3RLez4G/vyd//x8NuduzcXWSc5ezF9+KIZxXTb4imTJ1pnSw8d/Z0636LluHauWuPtacYxUO/YbgKDmcCABgpIWGNhgx9RnNnv6nWre+Vn9+/e2Neca1TXHFtjmWBw0eM0cxZc3Xp0qXCe46lmGNih2t07HAeHVzC5s1btXnLVoU/cJ9atLjHuuf42W3f7gFFRHS0O55bot8wXAGFKwDASCFhd+n48RPKycm57nto31J8jhYYW7duV0ZmpqoFBFgfClzplwnAPPQbhqtgqTAAwEh9+zxqdwQjOfo4PvhgB7tjAIUe7z9EE+JGq2HDBtb1L2HfcPE9P3y0KlSooAX/O/u6/YaBskDhCgAwUty4WOvXtLTjysvLKzwR07HkzdHXdeOmLTYnBFAaNm76QmfPZlnXGzZu+cW9wewbLr7tX+2k3zBcAoUrAMBIu3fvVe/oAUpOPnjd/4F9etiTZZ4LQOlytLe64tDBf/J4Sxn9huEqmOsHABhp5Kix1gm4r782SeEP3K8HO7bXzOmvq3NER6toXbdmld0RAcDl0W8YroLDmQAARqpSNVDT3nhVA/r31dx587Xo3Q+1Yd1qayyqV4y1T2vxBwvtjgmgFLXr8NCvvicpcQXPvBgmTHxF06bPVuXKleg3DFuxVBgAYCTHvtbgoEbWdWhIsPbu2184NqBftJ4a9pyN6QDcCBcuXCiyj/Xc+fP65pvv9JvfVFa3rl148MVEv2G4CgpXAICR6tULVEpqqu6/v7VCQ4N19uxZHTp0WA0a1Je3t7cyMjLtjgiglG3akHDN+6dPn1aXh3vqzjtu55mXYA8xYCf2uAIAjNQjsptejI3T4o+WqnbtWgoLC1XsmPHatWuPpkydoaCghnZHBFBGAgICFDvqBU2ZOpNnDrgp9rgCAIyUm5urmH5DlJ2drZWffqz4+ERFRkVbS4g9PDz0wXvz1T2yq90xAZSRfyxboT59Byk76wTPHHBDFK4AAKNdvHhRnp6e1nVKSqp27Nytpnf/VkE/7X8FYI6kpPVF7l2+fFnfH0vTpMmvqXatWtq4Pt6WbABKhsIVAAAARijvWcU6nKmgoMB6/fPrunUDtfSjRWrW7G6bUwJwBoUrAAAAjLB+/aYi9xzFq+NE4TvvbGK1wQLgnihcAQAAAAAujY+dAAAAYAzHyeG9evfTrbWD5OlTVXXq3aaBT/zB2uMOwH0x4woAAABjDmeKeKiHAgKqqnNER9WoXl0nTp7Uqs8SlJ2do80bEtSkSWO7YwJwAoUrAAAAjNCydTtVqlRJK5Yvlo+PT+H9nJwcdercXb6+Plq96hNbMwJwDkuFAQAAYIR9//w/vfDc01cVrQ6+vr4a8cKftHnLNtuyASgZClcAAAAYoV69QKWkHrrm2Kn006pRo1qZZwJQOiqU0p8DAAAA2GrKa39Wv4FPyd+/ino/GiUPDw/rfkLCGr007mW9NWOK8vPzC99PexzAfbDHFQAAAEao2+B/lJGRqdzcXKtorV69mvX6woULKigosHq6XuG4vpSXaWteAP89ZlwBAABghCcGPm53BAA3CDOuAAAAAACXxowrAAAAjLJt23YlfJ6ktOM/KPbF5/X119+qadO7rKXDANwTM64AAAAwwsWLFxXTb7AWf/SJdfCSY1/r9q3rNHLUOH39zbfauG61GjVqaHdMAE6gHQ4AAACMEDd+slasjNeH7y9QZvoRq3B1mP3WG6pY0U9j4ybZHRGAkyhcAQAAYIS/v/uBJk18ST2jIuXn51d4PyQkWOPHxSpp7QZb8wFwHoUrAAAAjHDqVLqa3N74mmM1b71VZ878f5lnAlA6KFwBAABghNDQYC1fseqaY0lr1yskJKjMMwEoHZwqDAAAACM898wwDRo8TLm5eer6UITKlSunb779Tp8nrtWUqTM17Y1X7Y4IwEmcKgwAAABj/OX1NzXh5VeVk5NTeDiTl5eXRg5/RhMnvGR3PABOonAFAACAUbKysrRlyzadzsiQf5UqatmyuapWrWp3LAAlQOEKAAAAAHBp7HEFAACA22rX4aFivT8pccUNywLgxqFwBQAAgNu6cOGCdQjTFV9u32H92qJ5M9WqVVPp6acL70V0+r1tOQGUDIUrAAAA3NamDQmF129On6WMzEytXrlUdesGFt4/ceKkunTtqdtCg21KCaCk2OMKAAAAI9SuG6qZ019X98iuRcaWLV+pJ4Y8rVM/pNqSDUDJlC/h7wcAAABcQnZ2jvLz86970vDly5fLPBOA0kHhCgAAACO0Db9fsWPGa//+r6+6v337Do1+aaI6R3S0LRuAkmGpMAAAAIxw+PARtWkboWPH0lS/fl1VCwjQiZOndPTo92rcOExrE1eqWrUAu2MCcAKFKwAAAIyRnZ2t+QsWadPmL5SRkWkVqu3bhSumb295enraHQ+AkyhcAQAAcFNx7IPt0LGr5syeppAQThoG3AF7XAEAAHBTKSgo0Lr1G5WVdc7uKAD+SxSuAAAAAACXRuEKAAAAAHBpFK4AAAAAAJdG4QoAAAAAcGkUrgAAAAAAl0bhCgAAAABwaRSuAAAAuOmUK1fO7ggAioHCFQAAADdlL1cA7qNcwaWz/FcLAAAAI5w7d05ZWedUq1ZNXbx4UdNnvK3DR46oV1R33XdfK7vjAXASM64AAAAwwvbtO1S/0e1Wserw/PBYjXjxJS1Y+J7aduiilStX2x0RgJOYcQUAAIAROnWOVPrpDL2/6B3VrRuo6jUbKbpPL709a5oGDBqqb79N1pZNiXbHBOAEZlwBAABghK3bvtL4saMUEhKspKT1ys7OVkx0b2usb59HtXfffrsjAnAShSsAAACMkJ+fr0qVKlnX8Z+vUeXKldWqVQvrdV5enry9vWxOCMBZFK4AAAAwwu2Nw7Tkk2VKSzuuj5cs04Md26t8+fLWIU0z3pqjJrc3tjsiACexxxUAAABGSExcq0d69FFOTo58fX21cd1q3X33b9UgqIlOnDipFcsWq337cLtjAnAChSsAAACMkZp6SNu/2qlWLVtYBzQ5vDl9ltqGt9GddzaxOx4AJ1G4AgAAwDiXLl1SevppVasWoAoVKtgdB0AJsccVAAAAxti1a48iunRXZf/aCqwfpr17/2m1wpn8yut2RwNQAhSuAAAAMMK2bdv1uzYddSztuJ7541AVFBRY92veeqvGxk3S3Hnz7Y4IwEksFQYAAIARwtt1lo+Ptz5buVSXL1+Wl2+Avtq2Xk2b3qU/PTtC6zds1p6dW+yOCcAJzLgCAADACI5DmZ7+wxCVK1fO+vq5yG4P6+DBVNuyASgZClcAAAAYwdfXR1nnzl1z7FR6ujUOwD1RuAIAAMAID3Zsb+1lPXTocOE9x8xrVlaW/jLlTXWghyvgttjjCgAAACMcO5amVvd10KlT6bqjSWPt2Llb9/2ulb79LtkqYL/YlKgGDerbHROAE5hxBQAAgBHq1Kmt3Ts26YXnnrZ6twYFNVROTo5iontr5/aNFK2AG2PGFQAAAADg0phxBQAAgDHy8/P14eIlGjR4mCK6dFdy8gH9fdH7+uab7+yOBqAEKFwBAABghLNnz6pNeCc9Fj1Qq+MTlfB5krKyzmnRux/q3tbttGfPPrsjAnAShSsAAACMMGp0nJIPHNS2LUk6nLJfBQUF1v0P31+gkJAg68RhAO6JwhUAAABGWLJ0uSZPilPz5s2sU4Sv8Pf31+hRL+iLrV/amg+A8yhcAQAAYATHsuDAOrWvOVa5UiWdP59d5pkAlA4KVwAAABjB0bv1b4vev+bYsk9XWuMA3FMFuwMAAAAApeGl0SP1SI/HdOpUurp17WItF16TtF7z3lmoeX9dYO11BeCe6OMKAAAAYyz+aKlGvDhWR49+X3ivRo3q+vPL4zRo4OO2ZgPgPApXAAAAGOe775KVnp4hf/8qCgsLVfny7JAD3BmFKwAAAIyUn59f5B4FLOCe+OgJAAAARjh58pQeix6gir+pKQ8vf3n6VL3qy8s3wO6IAJzE4UwAAAAwwjPPjdSy5av0WO8oqy0Os6uAOVgqDAAAACNUqRqoVyeP19CnnrA7CoBSxlJhAAAAGMHR/ua20BC7YwC4AShcAQAAYISITh20fMUqu2MAuAHY4woAAAAj9IyK1OAn/2gd0tS61b3y8/Mr8p6BA2JsyQagZNjjCgAAACOU96zyq0uJL184U2Z5AJQeZlwBAABghNQD++yOAOAGYcYVAAAAAODSmHEFAACAMeLjE60Dms6fz1Z+fn6RpcIL58+xLRsA51G4AgAAwAhvTJ2p4SPHyMfHR9WrV1P58uWLFK4A3BNLhQEAAGCEhsF3qHWrFpr/zmx5eXnZHQdAKaKPKwAAAIzwww8nNHhQf4pWwEAUrgAAADBC48ZhOnAwxe4YAG4A9rgCAADACK9MitOQoc+oXt1AtW59r/z8/Iq85z/3vQJwD+xxBQAAgBFCwu7S8eMnlJOTc933XL5wpkwzASgdzLgCAADACH37PGp3BAA3CDOuAAAAMEpa2nHl5eWpoKDAeu3o5+ro67px0xY9PexJu+MBcAKFKwAAAIywe/de9Y4eoOTkg9ccd/RxvZSXWea5AJQcS4UBAABghJGjxior65xef22SVqyMl7e3lx5+KEKrPkvQZ6s/17o1q+yOCMBJzLgCAADACFWqBmraG69qQP++mjtvvha9+6E2rFttjUX1irFOFF78wUK7YwJwAueBAwAAwAiOfa3BQY2s69CQYO3dt79wbEC/aH2x9Usb0wEoCQpXAAAAGKFevUClpKZa16GhwTp79qwOHTpsvfb29lZGBvtbAXdF4QoAAAAj9Ijsphdj47T4o6WqXbuWwsJCFTtmvHbt2qMpU2coKKih3REBOIk9rgAAADBCbm6uYvoNUXZ2tlZ++rHi4xMVGRVtLSH28PDQB+/NV/fIrnbHBOAEClcAAAAY5eLFi/L09LSuU1JStWPnbjW9+7cK+mn/KwD3Q+EKAAAAAHBp7HEFAAAAALg0ClcAAAAAgEujcAUAAAAAuDQKVwAAAACAS6NwBQAAAAC4NApXAAAAAIBLo3AFAAAAALg0ClcAAAAAgFzZvwA/tm9OFk6rkAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# === Matrice de corrélation des variables numériques ===\n",
+ "\n",
+ "num_vars = [\n",
+ " 'revenu_mensuel',\n",
+ " 'annees_dans_l_entreprise',\n",
+ " 'distance_domicile_travail',\n",
+ " 'note_evaluation_actuelle',\n",
+ " 'annees_depuis_la_derniere_promotion',\n",
+ " 'nombre_participation_pee', \n",
+ "]\n",
+ "\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "corr = df[num_vars].corr(method='pearson')\n",
+ "\n",
+ "sns.heatmap(\n",
+ " corr, annot=True, cmap=cmap_heatmap, center=0,\n",
+ " linewidths=0.5, square=True, fmt=\".2f\"\n",
+ ")\n",
+ "plt.title(\"Matrice de corrélation entre variables numériques\", fontsize=13, weight='bold')\n",
+ "plt.savefig(f\"output/matrice_correlation_numeriques.png\", dpi=300)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fd9cb5f",
+ "metadata": {},
+ "source": [
+ "Dans le code qui suit on cherche à vérifier s'il existe une relation entre les variables qualitatives.\n",
+ "\n",
+ "On fait l'hypothèse qu'il existe une relation de dépendance entre a_quitte_l_entreprise et\n",
+ "+ genre (on rejète h)\n",
+ "+ le statut marital (on ne rejète pas h)\n",
+ "+ le département (on ne rejète pas)\n",
+ "+ les enfants (on rejète)\n",
+ "\n",
+ "\n",
+ "H non rejeté :\n",
+ "* Très forte corrélation\n",
+ " * poste, niveau_hierarchique_poste, statut_marital\n",
+ "* Corrélation Moyenne (<0.05 mais proche)\n",
+ " * frequence_deplacement, departement, domaine_etude\n",
+ "\n",
+ "H rejeté :\n",
+ "niveau_education, genre, ayant_enfant"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "94ce86f0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ANOVA note_evaluation_actuelle ~ a_quitte_l_entreprise : p-value = 0.9119\n",
+ "Chi² departement ~ a_quitte_l_entreprise : p-value = 0.0045\n",
+ "Chi² frequence_deplacement ~ a_quitte_l_entreprise : p-value = 0.0000\n",
+ "\n",
+ "[Cat: genre]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "genre \n",
+ "F 501 87\n",
+ "M 732 150\n",
+ "Chi²=1.117, dof=1, p=0.291\n",
+ "\n",
+ "[Cat: statut_marital]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "statut_marital \n",
+ "Célibataire 350 120\n",
+ "Divorcé(e) 294 33\n",
+ "Marié(e) 589 84\n",
+ "Chi²=46.164, dof=2, p=9.46e-11\n",
+ "\n",
+ "[Cat: departement]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "departement \n",
+ "Commercial 354 92\n",
+ "Consulting 828 133\n",
+ "Ressources Humaines 51 12\n",
+ "Chi²=10.796, dof=2, p=0.00453\n",
+ "\n",
+ "[Cat: poste]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "poste \n",
+ "Assistant de Direction 245 47\n",
+ "Cadre Commercial 269 57\n",
+ "Consultant 197 62\n",
+ "Directeur Technique 78 2\n",
+ "Manager 122 9\n",
+ "Chi²=86.190, dof=8, p=2.75e-15\n",
+ "\n",
+ "[Cat: niveau_hierarchique_poste]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "niveau_hierarchique_poste \n",
+ "1 400 143\n",
+ "2 482 52\n",
+ "3 186 32\n",
+ "4 101 5\n",
+ "5 64 5\n",
+ "Chi²=72.529, dof=4, p=6.63e-15\n",
+ "\n",
+ "[Cat: niveau_education]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "niveau_education \n",
+ "1 139 31\n",
+ "2 238 44\n",
+ "3 473 99\n",
+ "4 340 58\n",
+ "5 43 5\n",
+ "Chi²=3.074, dof=4, p=0.546\n",
+ "\n",
+ "[Cat: domaine_etude]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "domaine_etude \n",
+ "Autre 71 11\n",
+ "Entrepreunariat 100 32\n",
+ "Infra & Cloud 517 89\n",
+ "Marketing 124 35\n",
+ "Ressources Humaines 20 7\n",
+ "Chi²=16.025, dof=5, p=0.00677\n",
+ "\n",
+ "[Cat: frequence_deplacement]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "frequence_deplacement \n",
+ "Aucun 138 12\n",
+ "Frequent 208 69\n",
+ "Occasionnel 887 156\n",
+ "Chi²=24.182, dof=2, p=5.61e-06\n",
+ "\n",
+ "[Cat: ayant_enfants]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "ayant_enfants \n",
+ "Y 1233 237\n",
+ "Chi²=0.000, dof=0, p=1\n",
+ "\n",
+ "[Cat: heure_supplementaires]\n",
+ " a_quitte_l_entreprise 0 1\n",
+ "heure_supplementaires \n",
+ "Non 944 110\n",
+ "Oui 289 127\n",
+ "Chi²=87.564, dof=1, p=8.16e-21\n"
+ ]
+ }
+ ],
+ "source": [
+ "# === Tableaux croisés + Chi2 ===\n",
+ "\n",
+ "# --- Test ANOVA pour une variable numérique continue ---\n",
+ "anova_result = f_oneway(\n",
+ " df.loc[df[TARGET] == 0, 'note_evaluation_actuelle'],\n",
+ " df.loc[df[TARGET] == 1, 'note_evaluation_actuelle']\n",
+ ")\n",
+ "print(f\"ANOVA note_evaluation_actuelle ~ {TARGET} : p-value = {anova_result.pvalue:.4f}\")\n",
+ "\n",
+ "# --- Test Chi² pour une variable catégorielle ---\n",
+ "contingency = pd.crosstab(df['departement'], df[TARGET])\n",
+ "chi2, p, _, _ = chi2_contingency(contingency)\n",
+ "print(f\"Chi² departement ~ {TARGET} : p-value = {p:.4f}\")\n",
+ "\n",
+ "# --- Test supplémentaire pour la fréquence de déplacement ---\n",
+ "contingency2 = pd.crosstab(df['frequence_deplacement'], df[TARGET])\n",
+ "chi2_2, p2, _, _ = chi2_contingency(contingency2)\n",
+ "print(f\"Chi² frequence_deplacement ~ {TARGET} : p-value = {p2:.4f}\")\n",
+ "\n",
+ "\n",
+ "def chi2_for_categorical(df, col, target=TARGET):\n",
+ " ct = pd.crosstab(df[col], df[target])\n",
+ " from scipy.stats import chi2_contingency\n",
+ " chi2, p, dof, exp = chi2_contingency(ct)\n",
+ " return ct, chi2, p, dof\n",
+ "\n",
+ "for col in CAT_COLS:\n",
+ " if col in df.columns:\n",
+ " ct, chi2, p, dof = chi2_for_categorical(df.dropna(subset=[col, TARGET]), col, TARGET)\n",
+ " print(f\"\\n[Cat: {col}]\\n\", ct.head())\n",
+ " print(f\"Chi²={chi2:.3f}, dof={dof}, p={p:.3g}\")\n",
+ " else:\n",
+ " print(f\"[INFO] Catégorie '{col}' absente – adapte CAT_COLS.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a31ba336",
+ "metadata": {},
+ "source": [
+ "## 5) Feature engineering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "96b9619a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nouvelles features créées : ['annee_sur_poste_par_experience', 'nb_formation_par_experience', 'score_moyen_satisfaction', 'dern_promo_par_experience', 'evolution_note']\n",
+ " augmentation_par_revenu annee_sur_poste_par_experience \\\n",
+ "count 1470.000000 1459.000000 \n",
+ "mean 0.000036 0.414561 \n",
+ "std 0.000025 0.294144 \n",
+ "min 0.000006 0.000000 \n",
+ "25% 0.000018 0.181818 \n",
+ "50% 0.000029 0.400000 \n",
+ "75% 0.000050 0.666667 \n",
+ "max 0.000209 1.000000 \n",
+ "\n",
+ " nb_formation_par_experience score_moyen_satisfaction \\\n",
+ "count 1459.000000 1470.000000 \n",
+ "mean 0.486533 2.730952 \n",
+ "std 0.729839 0.505815 \n",
+ "min 0.000000 1.000000 \n",
+ "25% 0.153846 2.500000 \n",
+ "50% 0.285714 2.750000 \n",
+ "75% 0.500000 3.000000 \n",
+ "max 6.000000 4.000000 \n",
+ "\n",
+ " dern_promo_par_experience evolution_note \n",
+ "count 1459.000000 1470.000000 \n",
+ "mean 0.204003 0.423810 \n",
+ "std 0.262048 0.807119 \n",
+ "min 0.000000 -1.000000 \n",
+ "25% 0.000000 0.000000 \n",
+ "50% 0.100000 0.000000 \n",
+ "75% 0.333333 1.000000 \n",
+ "max 1.000000 3.000000 \n",
+ "Table 'df_final' mise à jour dans 'merge_temp.db' (1470 lignes, 38 colonnes).\n",
+ "Colonnes présentes dans la table SQL :\n"
+ ]
+ },
+ {
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+ "annees_dans_le_poste_actuel"
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+ "12",
+ "satisfaction_employee_environnement"
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+ "13",
+ "note_evaluation_precedente"
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+ "14",
+ "niveau_hierarchique_poste"
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+ "15",
+ "satisfaction_employee_nature_travail"
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+ "satisfaction_employee_equipe"
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+ "satisfaction_employee_equilibre_pro_perso"
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+ "note_evaluation_actuelle"
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+ "distance_domicile_travail"
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+ "domaine_etude"
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+ "annes_sous_responsable_actuel"
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+ "32",
+ "augmentation_par_revenu"
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+ "33",
+ "annee_sur_poste_par_experience"
+ ],
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+ "34",
+ "nb_formation_par_experience"
+ ],
+ [
+ "35",
+ "score_moyen_satisfaction"
+ ],
+ [
+ "36",
+ "dern_promo_par_experience"
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+ "evolution_note"
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+ "5 departement\n",
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+ "8 nombre_heures_travailless\n",
+ "9 annee_experience_totale\n",
+ "10 annees_dans_l_entreprise\n",
+ "11 annees_dans_le_poste_actuel\n",
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+ "13 note_evaluation_precedente\n",
+ "14 niveau_hierarchique_poste\n",
+ "15 satisfaction_employee_nature_travail\n",
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+ "17 satisfaction_employee_equilibre_pro_perso\n",
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+ "29 frequence_deplacement\n",
+ "30 annees_depuis_la_derniere_promotion\n",
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+ "32 augmentation_par_revenu\n",
+ "33 annee_sur_poste_par_experience\n",
+ "34 nb_formation_par_experience\n",
+ "35 score_moyen_satisfaction\n",
+ "36 dern_promo_par_experience\n",
+ "37 evolution_note"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/by/r6dmty813rxgqdnr7hvtrl480000gn/T/ipykernel_47250/3667096219.py:85: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+ " .replace({\n"
+ ]
+ }
+ ],
+ "source": [
+ "# === Mise à jour de la base SQL avec les nouvelles features ===\n",
+ "\n",
+ "# Nettoyage et conversion de la colonne 'augementation_salaire_precedente'\n",
+ "df['augementation_salaire_precedente'] = (df['augementation_salaire_precedente'].astype(str).str.replace('%', '', regex=False).str.strip())\n",
+ "\n",
+ "# Conversion en nombre et passage en proportion (ex : 11 devient 0.11)\n",
+ "df['augementation_salaire_precedente'] = (pd.to_numeric(df['augementation_salaire_precedente'], errors='coerce') / 100)\n",
+ "\n",
+ "# Augmentation / Revenu\n",
+ "df['augmentation_par_revenu'] = (df['augementation_salaire_precedente'] / df['revenu_mensuel'].replace(0, np.nan))\n",
+ "\n",
+ "# Années dans le poste actuel / Années d'expérience totale\n",
+ "df['annee_sur_poste_par_experience'] = (df['annees_dans_le_poste_actuel'] / df['annee_experience_totale'].replace(0, np.nan))\n",
+ "\n",
+ "# Nombre de formations suivies / Années d'expérience totale\n",
+ "df['nb_formation_par_experience'] = (df['nb_formations_suivies'] /df['annee_experience_totale'].replace(0, np.nan))\n",
+ "\n",
+ "# Score moyen de satisfaction (déjà présent, mais on le recalcule proprement pour cohérence)\n",
+ "satisfaction_cols = [\n",
+ " 'satisfaction_employee_environnement',\n",
+ " 'satisfaction_employee_nature_travail',\n",
+ " 'satisfaction_employee_equipe',\n",
+ " 'satisfaction_employee_equilibre_pro_perso'\n",
+ "]\n",
+ "df['score_moyen_satisfaction'] = df[satisfaction_cols].mean(axis=1)\n",
+ "\n",
+ "# Dernière promotion / Expérience\n",
+ "df['dern_promo_par_experience'] = (df['annees_depuis_la_derniere_promotion'] /df['annee_experience_totale'].replace(0, np.nan))\n",
+ "\n",
+ "# Évolution de note (note actuelle - note précédente)\n",
+ "df['evolution_note'] = (df['note_evaluation_actuelle'] -df['note_evaluation_precedente'])\n",
+ "\n",
+ "# Vérification rapide des features créées\n",
+ "new_features = [\n",
+ " 'augmentation_par_revenu',\n",
+ " 'annee_sur_poste_par_experience',\n",
+ " 'nb_formation_par_experience',\n",
+ " 'score_moyen_satisfaction',\n",
+ " 'dern_promo_par_experience',\n",
+ " 'evolution_note'\n",
+ "]\n",
+ "\n",
+ "# Vérification rapide\n",
+ "print(\"Nouvelles features créées :\", [col for col in df.columns if any(x in col for x in [\n",
+ " 'augmentation_par_revenue', 'annee_sur_poste_par_experience', 'nb_formation_par_experience',\n",
+ " 'score_moyen_satisfaction', 'dern_promo_par_experience', 'evolution_note'\n",
+ "])])\n",
+ "print(df[new_features].describe())\n",
+ "\n",
+ "\n",
+ "# Vérification du fichier DB existant\n",
+ "if not os.path.exists(DB_FILE):\n",
+ " raise FileNotFoundError(f\"Base SQLite '{DB_FILE}' introuvable. Relance le merge avant d'y insérer les nouvelles features.\")\n",
+ "\n",
+ "# Connexion à la base SQLite\n",
+ "conn = sqlite3.connect(DB_FILE)\n",
+ "\n",
+ "# Sauvegarde du DataFrame complet (fusion + features)\n",
+ "# On écrase la table existante \"df_final\" si elle existe déjà\n",
+ "TABLE_NAME = \"df_final\"\n",
+ "\n",
+ "df.to_sql(TABLE_NAME, conn, index=False, if_exists='replace')\n",
+ "\n",
+ "# Validation et fermeture\n",
+ "conn.commit()\n",
+ "conn.close()\n",
+ "\n",
+ "print(f\"Table '{TABLE_NAME}' mise à jour dans '{DB_FILE}' ({len(df)} lignes, {len(df.columns)} colonnes).\")\n",
+ "\n",
+ "# --- Vérification rapide ---\n",
+ "TABLE_NAME = globals().get(\"TABLE_NAME\", \"df_final\") # Défaut si non défini\n",
+ "conn = sqlite3.connect(DB_FILE)\n",
+ "check = pd.read_sql_query(f\"PRAGMA table_info({TABLE_NAME});\", conn)\n",
+ "print(\"Colonnes présentes dans la table SQL :\")\n",
+ "display(check[['name']])\n",
+ "conn.close()\n",
+ "\n",
+ "# === Nettoyage et conversion de la variable cible ===\n",
+ "if TARGET in df.columns:\n",
+ " df[TARGET] = (\n",
+ " df[TARGET]\n",
+ " .astype(str)\n",
+ " .str.strip()\n",
+ " .str.lower()\n",
+ " .replace({\n",
+ " 'oui': 1, 'o': 1, 'true': 1, '1': 1,\n",
+ " 'non': 0, 'n': 0, 'false': 0, '0': 0\n",
+ " })\n",
+ " )\n",
+ "\n",
+ " # Force le type int si possible\n",
+ " df[TARGET] = pd.to_numeric(df[TARGET], errors='coerce')\n",
+ " df = df[df[TARGET].isin([0,1])].copy()\n",
+ "else:\n",
+ " raise ValueError(f\"La colonne cible '{TARGET}' est absente du dataframe.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "018562d5",
+ "metadata": {},
+ "source": [
+ "## 6) 1ᵉ itération – Modèle dummy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "2889c1d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== Dummy Classifier (baseline) ===\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.844 1.000 0.915 124\n",
+ " 1 0.000 0.000 0.000 23\n",
+ "\n",
+ " accuracy 0.844 147\n",
+ " macro avg 0.422 0.500 0.458 147\n",
+ "weighted avg 0.712 0.844 0.772 147\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] Exécute ensuite la cellule de modélisation principale pour comparer avec les autres modèles.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# === Baseline : Dummy Classifier ===\n",
+ "first_vars = [c for c in ['age', 'salaire', 'anciennete', 'note_performance'] if c in df.columns]\n",
+ "\n",
+ "required_cols = first_vars + [TARGET]\n",
+ "df_dummy = df.dropna(subset=required_cols).copy() if required_cols else df.copy()\n",
+ "\n",
+ "if len(df_dummy) == 0:\n",
+ " print('[ATTENTION] Pas assez de données nettoyées pour calculer la baseline Dummy.')\n",
+ "else:\n",
+ " if len(df_dummy) > 100:\n",
+ " df_dummy = df_dummy.sample(frac=min(SUBSAMPLE_FRAC, 1.0), random_state=RANDOM_STATE)\n",
+ "\n",
+ " if first_vars:\n",
+ " X = df_dummy[first_vars]\n",
+ " else:\n",
+ " X = pd.DataFrame({'constante': np.ones(len(df_dummy))}, index=df_dummy.index)\n",
+ " y = df_dummy[TARGET].astype(int)\n",
+ "\n",
+ " dummy = DummyClassifier(strategy='most_frequent', random_state=RANDOM_STATE)\n",
+ " dummy.fit(X, y)\n",
+ " y_pred_dummy = dummy.predict(X)\n",
+ "\n",
+ " print('=== Dummy Classifier (baseline) ===')\n",
+ " print(classification_report(y, y_pred_dummy, digits=3))\n",
+ "\n",
+ " cm_dummy = confusion_matrix(y, y_pred_dummy)\n",
+ " ConfusionMatrixDisplay(cm_dummy).plot()\n",
+ " plt.title(\"Matrice de confusion – Dummy (classe majoritaire)\")\n",
+ " plt.savefig(\"output/matrice_confusion_dummy.png\", dpi=300)\n",
+ " plt.show()\n",
+ "\n",
+ " dummy_f1 = f1_score(y, y_pred_dummy)\n",
+ " dummy_rec = recall_score(y, y_pred_dummy)\n",
+ " dummy_prec = precision_score(y, y_pred_dummy)\n",
+ " dummy_roc = roc_auc_score(y, y_pred_dummy)\n",
+ "\n",
+ " baseline_row = pd.DataFrame({\n",
+ " 'model': ['Dummy_baseline'],\n",
+ " 'best_threshold': [np.nan],\n",
+ " 'F1': [dummy_f1],\n",
+ " 'Recall': [dummy_rec],\n",
+ " 'Precision': [dummy_prec],\n",
+ " 'ROC_AUC': [dummy_roc]\n",
+ " })\n",
+ "\n",
+ " if 'res_df' in globals():\n",
+ " res_df_final = pd.concat([baseline_row, res_df], ignore_index=True)\n",
+ " res_df_final = res_df_final.sort_values(by='F1', ascending=False)\n",
+ " print(\"=== Comparaison finale (avec baseline) ===\")\n",
+ " display(res_df_final)\n",
+ " else:\n",
+ " res_df_final = baseline_row.copy()\n",
+ " print(\"[INFO] Exécute ensuite la cellule de modélisation principale pour comparer avec les autres modèles.\")\n",
+ "\n",
+ "with open(\"update_features.sql\", \"w\", encoding=\"utf-8\") as f:\n",
+ " f.write(f\"-- Mise à jour des features dans {TABLE_NAME}\")\n",
+ " f.write(f\"-- {len(df.columns)} colonnes et {len(df)} lignes au total\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95644da8",
+ "metadata": {},
+ "source": [
+ "## 7) 2ᵉ itération : SMOTE, LR, RF, CV. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "f4914e11",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Taille X_full: (1470, 38)\n",
+ "Répartition de la cible:\n",
+ " a_quitte_l_entreprise\n",
+ "0 1233\n",
+ "1 237\n",
+ "Name: count, dtype: int64\n",
+ "Oversampling SMOTE effectué (classes équilibrées).\n",
+ "\n",
+ "=== LogReg_balanced ===\n",
+ "Meilleurs hyperparamètres : {'clf__C': 1.0, 'clf__penalty': 'l1', 'clf__solver': 'liblinear'}\n",
+ "Seuil optimal (max F1): 0.554\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.874 0.959 0.915 1233\n",
+ " 1 0.954 0.862 0.906 1233\n",
+ "\n",
+ " accuracy 0.910 2466\n",
+ " macro avg 0.914 0.910 0.910 2466\n",
+ "weighted avg 0.914 0.910 0.910 2466\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Génération de la courbe d'apprentissage...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analyse courbe d’apprentissage – LogReg_balanced\n",
+ "Bon équilibre biais/variance : le modèle généralise correctement.\n",
+ "(Score final validation : 0.901, écart train-val : 0.007)\n",
+ "\n",
+ "=== RF_balanced ===\n",
+ "Meilleurs hyperparamètres : {'clf__max_depth': 10, 'clf__min_samples_leaf': 2, 'clf__min_samples_split': 5, 'clf__n_estimators': 500}\n",
+ "Seuil optimal (max F1): 0.490\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.913 0.932 0.923 1233\n",
+ " 1 0.930 0.912 0.921 1233\n",
+ "\n",
+ " accuracy 0.922 2466\n",
+ " macro avg 0.922 0.922 0.922 2466\n",
+ "weighted avg 0.922 0.922 0.922 2466\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Génération de la courbe d'apprentissage...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "Analyse courbe d’apprentissage – RF_balanced\n",
+ "Bon équilibre biais/variance : le modèle généralise correctement.\n",
+ "(Score final validation : 0.918, écart train-val : 0.060)\n",
+ "\n",
+ "=== Résultats (seuil optimisé) ===\n"
+ ]
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+ "Refit final sur tout le jeu SMOTE pour : RF_balanced\n",
+ "Données rééquilibrées pour l’entraînement final : [1233 1233]\n",
+ "Modèle final refitté sur les données équilibrées.\n",
+ "\n",
+ "=== Résultats (seuil optimisé) ===\n"
+ ]
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+ "\n",
+ " best_threshold F1 Recall Precision ROC_AUC \n",
+ "1 0.490292 0.920934 0.911598 0.930464 0.971614 \n",
+ "0 0.553559 0.905837 0.862125 0.954219 0.954884 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# === Oversampling + cross-validation + ajustement de seuil ===\n",
+ "\n",
+ "# --- Sécurisation de la variable cible ---\n",
+ "ALL_NUM = [c for c in NUM_COLS if c in df.columns]\n",
+ "ALL_CAT = [c for c in CAT_COLS if c in df.columns]\n",
+ "\n",
+ "df = df.copy()\n",
+ "df = df[df[TARGET].isin([0, 1])].copy()\n",
+ "\n",
+ "# --- Imputation des NaN sur les variables numériques ---\n",
+ "for col in ALL_NUM:\n",
+ " if col in df.columns:\n",
+ " median_val = df[col].median()\n",
+ " df[col] = df[col].fillna(median_val)\n",
+ "\n",
+ "# --- Encodage des variables catégorielles ---\n",
+ "X_num = df[ALL_NUM] if ALL_NUM else pd.DataFrame(index=df.index)\n",
+ "X_cat = pd.get_dummies(df[ALL_CAT], drop_first=True) if ALL_CAT else pd.DataFrame(index=df.index)\n",
+ "X_full = pd.concat([X_num, X_cat], axis=1)\n",
+ "\n",
+ "# Sécurité : forcer tout en numérique et remplacer les résidus NaN\n",
+ "X_full = X_full.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
+ "y_full = df[TARGET].astype(int)\n",
+ "X_raw = X_full.copy()\n",
+ "y_raw = y_full.copy()\n",
+ "\n",
+ "\n",
+ "print(\"Taille X_full:\", X_full.shape)\n",
+ "print(\"Répartition de la cible:\\n\", y_full.value_counts())\n",
+ "\n",
+ "# Vérification de la diversité des classes\n",
+ "if y_full.nunique() < 2:\n",
+ " raise ValueError(\"[ERREUR] La variable cible n'a qu'une seule classe présente après nettoyage.\")\n",
+ "\n",
+ "# --- Oversampling global avant cross-validation. À remettre si SMOTE enlevé du pipeline ---\n",
+ "try:\n",
+ " sm = SMOTE(random_state=RANDOM_STATE)\n",
+ " X_full, y_full = sm.fit_resample(X_full, y_full)\n",
+ " print(\"Oversampling SMOTE effectué (classes équilibrées).\")\n",
+ "except ValueError as e:\n",
+ " print(f\"[ATTENTION] SMOTE impossible : {e}\\n→ utilisation de simple oversample() à la place.\")\n",
+ " X_full, y_full = simple_oversample(X_full, y_full)\n",
+ "\n",
+ "# --- Prétraitement pour les modèles ---\n",
+ "num_pipe2 = ImbPipeline([\n",
+ " ('imp', SimpleImputer(strategy='median')),\n",
+ " ('sc', StandardScaler())\n",
+ "])\n",
+ "preproc2 = ColumnTransformer([('num', num_pipe2, list(X_num.columns))], remainder='passthrough')\n",
+ "\n",
+ "# --- Modèles ---\n",
+ "models = {\n",
+ " 'LogReg_balanced': LogisticRegression(max_iter=2000, class_weight='balanced', random_state=RANDOM_STATE), #, class_weight='balanced'\n",
+ " 'RF_balanced': RandomForestClassifier(\n",
+ " n_estimators=300,\n",
+ " max_depth=8,\n",
+ " min_samples_split=10,\n",
+ " min_samples_leaf=5,\n",
+ " class_weight='balanced_subsample',\n",
+ " random_state=RANDOM_STATE\n",
+ " )\n",
+ "}\n",
+ "\n",
+ "param_grids = {\n",
+ " 'LogReg_balanced': [\n",
+ " {\n",
+ " 'clf__solver': ['lbfgs'],\n",
+ " 'clf__penalty': ['l2'],\n",
+ " 'clf__C': [0.1, 1.0, 10.0]\n",
+ " },\n",
+ " {\n",
+ " 'clf__solver': ['liblinear'],\n",
+ " 'clf__penalty': ['l1', 'l2'],\n",
+ " 'clf__C': [0.1, 1.0, 10.0]\n",
+ " }\n",
+ " ],\n",
+ " 'RF_balanced': {\n",
+ " 'clf__n_estimators': [200, 300, 500],\n",
+ " 'clf__max_depth': [6, 8, 10],\n",
+ " 'clf__min_samples_split': [5, 10, 15],\n",
+ " 'clf__min_samples_leaf': [2, 5, 8]\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
+ "results = []\n",
+ "\n",
+ "for name, base_model in models.items():\n",
+ " print(f\"\\n=== {name} ===\")\n",
+ "\n",
+ " pipe = ImbPipeline([\n",
+ " ('smote', SMOTE(random_state=RANDOM_STATE)), # oversampling dans chaque folds\n",
+ " ('prep', preproc2),\n",
+ " ('clf', base_model)\n",
+ " ])\n",
+ "\n",
+ " grid = GridSearchCV(\n",
+ " estimator=pipe,\n",
+ " param_grid=param_grids[name],\n",
+ " cv=cv,\n",
+ " scoring='f1',\n",
+ " n_jobs=-1\n",
+ " )\n",
+ " grid.fit(X_full, y_full)\n",
+ "\n",
+ " print(\"Meilleurs hyperparamètres :\", grid.best_params_)\n",
+ " best_pipe = grid.best_estimator_\n",
+ "\n",
+ " # --- Cross-validation avec prédictions de probabilité ---\n",
+ " y_proba_cv = cross_val_predict(best_pipe, X_full, y_full, cv=cv, method='predict_proba')[:, 1]\n",
+ "\n",
+ " # --- Ajustement du seuil optimal (max F1) ---\n",
+ " precision, recall, thresholds = precision_recall_curve(y_full, y_proba_cv)\n",
+ " f1_scores = 2 * (precision * recall) / (precision + recall + 1e-8)\n",
+ " best_idx = np.argmax(f1_scores)\n",
+ " best_threshold = thresholds[best_idx] if best_idx < len(thresholds) else 0.5\n",
+ "\n",
+ " print(f\"Seuil optimal (max F1): {best_threshold:.3f}\")\n",
+ " y_pred_opt = (y_proba_cv >= best_threshold).astype(int)\n",
+ "\n",
+ " # --- Évaluation finale ---\n",
+ " cm = confusion_matrix(y_full, y_pred_opt)\n",
+ " ConfusionMatrixDisplay(cm).plot()\n",
+ " plt.title(f\"Matrice de confusion – {name}\")\n",
+ " plt.savefig(f\"output/matrice_confusion_{name}_seuil_ajuste.png\", dpi=300)\n",
+ " plt.show()\n",
+ "\n",
+ " print(classification_report(y_full, y_pred_opt, digits=3))\n",
+ "\n",
+ " # --- Courbe Precision-Recall ---\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.plot(recall, precision, color=Theme.PRIMARY, label='Courbe Precision-Recall')\n",
+ " plt.scatter(recall[best_idx], precision[best_idx],\n",
+ " color=Theme.SECONDARY, label=f'Seuil optimal = {best_threshold:.2f}')\n",
+ " plt.xlabel('Recall')\n",
+ " plt.ylabel('Precision')\n",
+ " plt.title(f'Courbe Precision-Recall – {name}')\n",
+ " plt.legend()\n",
+ " plt.grid(alpha=0.3)\n",
+ " plt.savefig(f\"output/precision_recall_curve_{name}.png\", dpi=300)\n",
+ " plt.show()\n",
+ "\n",
+ " # --- Courbe d'apprentissage ---\n",
+ " print(\"Génération de la courbe d'apprentissage...\")\n",
+ " train_sizes, train_scores, test_scores = learning_curve(\n",
+ " best_pipe, X_full, y_full,\n",
+ " cv=cv,\n",
+ " scoring='f1',\n",
+ " n_jobs=-1,\n",
+ " train_sizes=np.linspace(0.1, 1.0, 5),\n",
+ " shuffle=True,\n",
+ " random_state=RANDOM_STATE\n",
+ " )\n",
+ "\n",
+ " train_mean = np.mean(train_scores, axis=1)\n",
+ " train_std = np.std(train_scores, axis=1)\n",
+ " test_mean = np.mean(test_scores, axis=1)\n",
+ " test_std = np.std(test_scores, axis=1)\n",
+ "\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.plot(train_sizes, train_mean, 'o-', color=Theme.PRIMARY, label='Score entraînement')\n",
+ " plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.2, color=Theme.PRIMARY)\n",
+ "\n",
+ " plt.plot(train_sizes, test_mean, 'o-', color=Theme.SECONDARY, label='Score validation')\n",
+ " plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.2, color=Theme.SECONDARY)\n",
+ "\n",
+ " plt.title(f'Courbe d’apprentissage – {name}')\n",
+ " plt.xlabel(\"Taille de l’échantillon d'entraînement\")\n",
+ " plt.ylabel(\"Score F1 (moyenne CV)\")\n",
+ " plt.xlim(0, train_sizes[-1] + 10)\n",
+ " plt.ylim(0.45, 1.05)\n",
+ " plt.legend()\n",
+ " plt.grid(alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.savefig(f\"output/learning_curve_{name}.png\", dpi=300)\n",
+ " plt.show()\n",
+ "\n",
+ " # --- Diagnostic automatique ---\n",
+ " gap = train_mean[-1] - test_mean[-1]\n",
+ " val_final = test_mean[-1]\n",
+ " print(f\"Analyse courbe d’apprentissage – {name}\")\n",
+ " if val_final < 0.6 and gap < 0.05:\n",
+ " print(\"Sous-apprentissage probable : le modèle est trop simple ou mal paramétré.\")\n",
+ " elif gap > 0.1:\n",
+ " print(\"Sur-apprentissage détecté : trop grande différence entre entraînement et validation.\")\n",
+ " else:\n",
+ " print(\"Bon équilibre biais/variance : le modèle généralise correctement.\")\n",
+ " print(f\"(Score final validation : {val_final:.3f}, écart train-val : {gap:.3f})\")\n",
+ "\n",
+ " results.append({\n",
+ " 'model': name,\n",
+ " 'best_params': grid.best_params_,\n",
+ " 'best_threshold': best_threshold,\n",
+ " 'F1': f1_score(y_full, y_pred_opt),\n",
+ " 'Recall': recall_score(y_full, y_pred_opt),\n",
+ " 'Precision': precision_score(y_full, y_pred_opt),\n",
+ " 'ROC_AUC': roc_auc_score(y_full, y_proba_cv)\n",
+ " })\n",
+ "\n",
+ "# --- Résumé des performances ---\n",
+ "res_df = pd.DataFrame(results).sort_values(by='F1', ascending=False)\n",
+ "print(\"\\n=== Résultats (seuil optimisé) ===\")\n",
+ "display(res_df)\n",
+ "\n",
+ "# --- Sélection du meilleur modèle ---\n",
+ "best_row = res_df.iloc[0]\n",
+ "best_model_name = best_row['model']\n",
+ "best_params = best_row.get('best_params', {}) or {}\n",
+ "\n",
+ "print(f\"\\nRefit final sur tout le jeu SMOTE pour : {best_model_name}\")\n",
+ "\n",
+ "# 🔹 Récupération du meilleur pipeline issu du GridSearch\n",
+ "best_model = models[best_model_name]\n",
+ "best_pipe = GridSearchCV(\n",
+ " estimator=ImbPipeline([\n",
+ " ('prep', preproc2),\n",
+ " ('clf', best_model)\n",
+ " ]),\n",
+ " param_grid=param_grids[best_model_name],\n",
+ " cv=cv,\n",
+ " scoring='f1',\n",
+ " n_jobs=-1\n",
+ ").fit(X_full, y_full).best_estimator_\n",
+ "\n",
+ "# Application d’un SMOTE global pour le refit final\n",
+ "sm = SMOTE(random_state=RANDOM_STATE)\n",
+ "X_bal, y_bal = sm.fit_resample(X_full, y_full)\n",
+ "print(\"Données rééquilibrées pour l’entraînement final :\", np.bincount(y_bal))\n",
+ "\n",
+ "# Refit complet du meilleur pipeline sur tout le jeu équilibré\n",
+ "best_pipe.fit(X_bal, y_bal)\n",
+ "print(\"Modèle final refitté sur les données équilibrées.\")\n",
+ "\n",
+ "# --- Résumé des performances ---\n",
+ "res_df = pd.DataFrame(results).sort_values(by='F1', ascending=False)\n",
+ "print(\"\\n=== Résultats (seuil optimisé) ===\")\n",
+ "display(res_df)\n",
+ "best_row = res_df.iloc[0]\n",
+ "best_model_name = best_row['model']\n",
+ "best_params = best_row.get('best_params', {}) or {}\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a65d548d",
+ "metadata": {},
+ "source": [
+ "## 8) Interprétation : importance globale & locale (SHAP)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b1530a0e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Interprétation SHAP globale ===\n",
+ "[INFO] 38 variables utilisées.\n",
+ "[INFO] X_transformed shape: (400, 38)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|===================| 797/800 [01:23<00:00] "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] SHAP array shape: (400, 38)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/steph/Code/Python/Jupyter/projet_04/manet_projet04/shap_generator.py:138: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
+ " shap.summary_plot(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+ "\n",
+ "# --- Entraînement global ---\n",
+ "best_pipe.fit(X_full, y_full)\n",
+ "fitted_model = best_pipe.named_steps['clf']\n",
+ "\n",
+ "# === Calcul et affichage global ===\n",
+ "cmap_custom = make_diverging_cmap(Theme.PRIMARY, Theme.SECONDARY)\n",
+ "shap_values, X_transformed, feature_names = shap_global(best_pipe, X_full, y_full, cmap=cmap_custom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "39153710",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[INFO] Interprétation locale SHAP – individu 202\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[INFO] Interprétation locale SHAP – individu 146\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[INFO] Interprétation locale SHAP – individu 109\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[INFO] Interprétation locale SHAP – individu 149\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Cas général : individu à plus fort impact\n",
+ "idx_impact = np.argmax(np.sum(np.abs(shap_values.values), axis=1))\n",
+ "shap_local(idx_impact, shap_values, cmap=cmap_heatmap)\n",
+ "\n",
+ "# Cas : individu à forte probabilité de départ\n",
+ "y_proba_all = best_pipe.named_steps['clf'].predict_proba(X_transformed)[:, 1]\n",
+ "idx_highrisk = int(np.argmax(y_proba_all))\n",
+ "shap_local(idx_highrisk, shap_values, cmap=cmap_heatmap)\n",
+ "\n",
+ "# Cas : individu choisi manuellement\n",
+ "CUSTOM_INDEX = 109\n",
+ "shap_local(CUSTOM_INDEX, shap_values, max_display=8, cmap=cmap_heatmap)\n",
+ "\n",
+ "# Cas : individu à plus faible probabilité de départ \n",
+ "y_proba_all = best_pipe.named_steps['clf'].predict_proba(X_transformed)[:, 1]\n",
+ "idx_lowrisk = int(np.argmin(y_proba_all))\n",
+ "shap_local(idx_lowrisk, shap_values, max_display=8, text_scale=0.6, cmap=cmap_heatmap)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3153394",
+ "metadata": {},
+ "source": [
+ "coeff pearson = faire test avant si les variables sont linéaires\n",
+ "si les variables ne sont pas linéaires, utiliser spearman\n",
+ "\n",
+ "s'attendait à plus de modèle "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
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index 0000000000000000000000000000000000000000..a1d4a8d7a115f35c2928a2bc3fc99085f12cd732
--- /dev/null
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+ {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
+]
+
+[[package]]
+name = "win32-setctime"
+version = "1.2.0"
+description = "A small Python utility to set file creation time on Windows"
+optional = false
+python-versions = ">=3.5"
+groups = ["main"]
+markers = "sys_platform == \"win32\""
+files = [
+ {file = "win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390"},
+ {file = "win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0"},
+]
+
+[package.extras]
+dev = ["black (>=19.3b0) ; python_version >= \"3.6\"", "pytest (>=4.6.2)"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.11,<3.13"
+content-hash = "e188fcfdcb9a661b7c79adfdeeddf75d6d1c31e80d0594dedb05c8461102922a"
diff --git a/hf_space/hf_space/hf_space/hf_space/poetry.toml b/hf_space/hf_space/hf_space/hf_space/poetry.toml
new file mode 100644
index 0000000000000000000000000000000000000000..ab1033bd37224ee84b5862fb25f094db73809b74
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/poetry.toml
@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/__init__.py b/hf_space/hf_space/hf_space/hf_space/projet_05/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c87bb6c55d098cf2cb6be1c9c239f767398750a
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/__init__.py
@@ -0,0 +1 @@
+from projet_05 import config # noqa: F401
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/config.py b/hf_space/hf_space/hf_space/hf_space/projet_05/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d77c22978ba442ddce992023880708172ddcac4
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/config.py
@@ -0,0 +1,32 @@
+from pathlib import Path
+
+from dotenv import load_dotenv
+from loguru import logger
+
+# Load environment variables from .env file if it exists
+load_dotenv()
+
+# Paths
+PROJ_ROOT = Path(__file__).resolve().parents[1]
+logger.info(f"PROJ_ROOT path is: {PROJ_ROOT}")
+
+DATA_DIR = PROJ_ROOT / "data"
+RAW_DATA_DIR = DATA_DIR / "raw"
+INTERIM_DATA_DIR = DATA_DIR / "interim"
+PROCESSED_DATA_DIR = DATA_DIR / "processed"
+EXTERNAL_DATA_DIR = DATA_DIR / "external"
+
+MODELS_DIR = PROJ_ROOT / "models"
+
+REPORTS_DIR = PROJ_ROOT / "reports"
+FIGURES_DIR = REPORTS_DIR / "figures"
+
+# If tqdm is installed, configure loguru with tqdm.write
+# https://github.com/Delgan/loguru/issues/135
+try:
+ from tqdm import tqdm
+
+ logger.remove(0)
+ logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
+except ModuleNotFoundError:
+ pass
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/dataset.py b/hf_space/hf_space/hf_space/hf_space/projet_05/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd63bd2a293c133fd7c536ce61c41ee4d3947ff5
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/dataset.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+from loguru import logger
+from tqdm import tqdm
+import typer
+
+from projet_05.config import PROCESSED_DATA_DIR, RAW_DATA_DIR
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+ # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
+ input_path: Path = RAW_DATA_DIR / "dataset.csv",
+ output_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
+ # ----------------------------------------------
+):
+ # ---- REPLACE THIS WITH YOUR OWN CODE ----
+ logger.info("Processing dataset...")
+ for i in tqdm(range(10), total=10):
+ if i == 5:
+ logger.info("Something happened for iteration 5.")
+ logger.success("Processing dataset complete.")
+ # -----------------------------------------
+
+
+if __name__ == "__main__":
+ app()
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/features.py b/hf_space/hf_space/hf_space/hf_space/projet_05/features.py
new file mode 100644
index 0000000000000000000000000000000000000000..a447a12ee553dc8cd1732ecbba26ce50e1c785f1
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/features.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+from loguru import logger
+from tqdm import tqdm
+import typer
+
+from projet_05.config import PROCESSED_DATA_DIR
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+ # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
+ input_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
+ output_path: Path = PROCESSED_DATA_DIR / "features.csv",
+ # -----------------------------------------
+):
+ # ---- REPLACE THIS WITH YOUR OWN CODE ----
+ logger.info("Generating features from dataset...")
+ for i in tqdm(range(10), total=10):
+ if i == 5:
+ logger.info("Something happened for iteration 5.")
+ logger.success("Features generation complete.")
+ # -----------------------------------------
+
+
+if __name__ == "__main__":
+ app()
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/__init__.py b/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/predict.py b/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..4721edb7005cfcbd6ffa2930c5c9732a2246631b
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/predict.py
@@ -0,0 +1,30 @@
+from pathlib import Path
+
+from loguru import logger
+from tqdm import tqdm
+import typer
+
+from projet_05.config import MODELS_DIR, PROCESSED_DATA_DIR
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+ # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
+ features_path: Path = PROCESSED_DATA_DIR / "test_features.csv",
+ model_path: Path = MODELS_DIR / "model.pkl",
+ predictions_path: Path = PROCESSED_DATA_DIR / "test_predictions.csv",
+ # -----------------------------------------
+):
+ # ---- REPLACE THIS WITH YOUR OWN CODE ----
+ logger.info("Performing inference for model...")
+ for i in tqdm(range(10), total=10):
+ if i == 5:
+ logger.info("Something happened for iteration 5.")
+ logger.success("Inference complete.")
+ # -----------------------------------------
+
+
+if __name__ == "__main__":
+ app()
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/train.py b/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3683815a343f8a2fe616f152784f146ba0bbf12
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/modeling/train.py
@@ -0,0 +1,30 @@
+from pathlib import Path
+
+from loguru import logger
+from tqdm import tqdm
+import typer
+
+from projet_05.config import MODELS_DIR, PROCESSED_DATA_DIR
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+ # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
+ features_path: Path = PROCESSED_DATA_DIR / "features.csv",
+ labels_path: Path = PROCESSED_DATA_DIR / "labels.csv",
+ model_path: Path = MODELS_DIR / "model.pkl",
+ # -----------------------------------------
+):
+ # ---- REPLACE THIS WITH YOUR OWN CODE ----
+ logger.info("Training some model...")
+ for i in tqdm(range(10), total=10):
+ if i == 5:
+ logger.info("Something happened for iteration 5.")
+ logger.success("Modeling training complete.")
+ # -----------------------------------------
+
+
+if __name__ == "__main__":
+ app()
diff --git a/hf_space/hf_space/hf_space/hf_space/projet_05/plots.py b/hf_space/hf_space/hf_space/hf_space/projet_05/plots.py
new file mode 100644
index 0000000000000000000000000000000000000000..98455410e19a7989dc3cad619122c4619fa82092
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/projet_05/plots.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+from loguru import logger
+from tqdm import tqdm
+import typer
+
+from projet_05.config import FIGURES_DIR, PROCESSED_DATA_DIR
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+ # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
+ input_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
+ output_path: Path = FIGURES_DIR / "plot.png",
+ # -----------------------------------------
+):
+ # ---- REPLACE THIS WITH YOUR OWN CODE ----
+ logger.info("Generating plot from data...")
+ for i in tqdm(range(10), total=10):
+ if i == 5:
+ logger.info("Something happened for iteration 5.")
+ logger.success("Plot generation complete.")
+ # -----------------------------------------
+
+
+if __name__ == "__main__":
+ app()
diff --git a/hf_space/hf_space/hf_space/hf_space/pyproject.toml b/hf_space/hf_space/hf_space/hf_space/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..c809bd5fb5a20cd526d8ecf7a64ab8e7471ec21e
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/pyproject.toml
@@ -0,0 +1,53 @@
+[build-system]
+requires = ["flit_core >=3.2,<4"]
+build-backend = "flit_core.buildapi"
+
+[project]
+name = "projet_05"
+version = "0.0.1"
+description = "D\u00e9ployez un mod\u00e8le de Machine Learning"
+authors = [
+ { name = "St\u00e9phane Manet" },
+]
+license = { file = "LICENSE" }
+readme = "README.md"
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: MIT License"
+]
+dependencies = [
+ "loguru",
+ "mkdocs",
+ "pip",
+ "pytest",
+ "python-dotenv",
+ "ruff",
+ "tqdm",
+ "typer",
+ "imbalanced-learn (>=0.14.0,<0.15.0)",
+ "scikit-learn (>=1.4.2,<2.0.0)",
+ "matplotlib (>=3.10.7,<4.0.0)",
+ "numpy (>=2.3.4,<3.0.0)",
+ "pandas (>=2.3.3,<3.0.0)",
+ "pyyaml (>=6.0.3,<7.0.0)",
+ "scipy (>=1.16.3,<2.0.0)",
+ "seaborn (>=0.13.2,<0.14.0)",
+ "shap (>=0.49.1,<0.50.0)",
+ "gradio (>=5.49.1,<6.0.0)",
+ "joblib (>=1.4.2,<2.0.0)"
+]
+
+requires-python = ">=3.11,<3.13"
+
+
+[tool.ruff]
+line-length = 99
+src = ["projet_05"]
+include = ["pyproject.toml", "projet_05/**/*.py"]
+
+[tool.ruff.lint]
+extend-select = ["I"] # Add import sorting
+
+[tool.ruff.lint.isort]
+known-first-party = ["projet_05"]
+force-sort-within-sections = true
diff --git a/hf_space/hf_space/hf_space/hf_space/references/.gitkeep b/hf_space/hf_space/hf_space/hf_space/references/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/reports/.gitkeep b/hf_space/hf_space/hf_space/hf_space/reports/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/reports/figures/.gitkeep b/hf_space/hf_space/hf_space/hf_space/reports/figures/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/hf_space/tests/test_data.py b/hf_space/hf_space/hf_space/hf_space/tests/test_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e7bab23a5404c48a278c1cecb60c82035598a57
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/tests/test_data.py
@@ -0,0 +1,5 @@
+import pytest
+
+
+def test_code_is_tested():
+ assert False
diff --git a/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb b/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb
index e9436e78d88274dbb96302bbbe948e2247093155..49ab7cb965ecccb4efd570e5feabe1dd16034cbe 100644
--- a/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb
+++ b/hf_space/hf_space/hf_space/notebooks/Manet_stephane_notebook_112025.ipynb
@@ -8,14 +8,13 @@
"# TechNova Partners\n",
"\n",
"**Sommaire :**\n",
- "1. Chargement des données \n",
- "2. Config SQL\n",
- "3. Diagnostic du déséquilibre\n",
- "4. Analyse exploratoire\n",
- "5. Feature engineering\n",
- "6. 1ᵉ itération : modèle dummy\n",
- "7. 2ᵉ itération : équilibrage (oversampling simple / `class_weight`), features, CV\n",
- "8. Interprétation globale & locale (importance des features, SHAP)"
+ "1. Chargement / fusion via scripts Python\n",
+ "2. Diagnostic du d\u00e9s\u00e9quilibre (ce notebook)\n",
+ "3. Analyse exploratoire (ce notebook)\n",
+ "4. Tests statistiques (ce notebook)\n",
+ "\n",
+ "> Tous les traitements (SQL, features, entra\u00eenement, SHAP) sont d\u00e9sormais orchestr\u00e9s dans `projet_05/*.py`.\n",
+ "> La charte graphique et les helpers SHAP proviennent des modules historiques `scripts_projet04/brand` et `scripts_projet04/manet_projet04`.\n"
]
},
{
@@ -41,76 +40,17 @@
]
}
],
- "source": [
- "# === Imports & configuration ===\n",
- "import warnings\n",
- "import warnings\n",
- "import os, sqlite3\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "from datetime import datetime\n",
- "\n",
- "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_predict, GridSearchCV, learning_curve\n",
- "from sklearn.metrics import (\n",
- " confusion_matrix, ConfusionMatrixDisplay,\n",
- " classification_report, f1_score, recall_score, precision_score, \n",
- " roc_auc_score, precision_recall_curve\n",
- ")\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.compose import ColumnTransformer\n",
- "from imblearn.pipeline import Pipeline as ImbPipeline\n",
- "from sklearn.impute import SimpleImputer\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.ensemble import RandomForestClassifier\n",
- "from sklearn.dummy import DummyClassifier\n",
- "from imblearn.over_sampling import SMOTE\n",
- "from scipy.stats import f_oneway, chi2_contingency\n",
- "from manet_projet04 import load_settings,shap_global, shap_local\n",
- "from brand.brand import Theme, load_brand, make_diverging_cmap\n",
- "# Application du thème OpenClassrooms pour uniformiser les prochains graphiques\n",
- "cfg = load_brand(\"brand/brand.yml\")\n",
- "Theme.apply()\n",
- "cmap_heatmap = Theme.colormap(\"diverging\", start=\"primary\", end=\"secondary\")\n",
- "\n",
- "\n",
- "HAS_SHAP = True\n",
- "HAS_IMBLEARN = True\n",
- "RANDOM_STATE = 42\n",
- "np.random.seed(RANDOM_STATE)"
- ]
+ "source": "# === Imports & configuration (EDA uniquement) ===\nimport os\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom scipy.stats import chi2_contingency, f_oneway\n\nfrom projet_05.branding import Theme, apply_brand_theme, make_diverging_cmap\nfrom projet_05.config import PROCESSED_DATA_DIR\nfrom projet_05.settings import load_settings\n\napply_brand_theme()\nsns.set_theme(style='whitegrid')\n\nsettings = load_settings()\nDATA_PATH = PROCESSED_DATA_DIR / 'dataset.csv'\nTARGET = settings.target\n\nos.makedirs('output', exist_ok=True)\n\ntry:\n df = pd.read_csv(DATA_PATH)\nexcept FileNotFoundError as exc:\n raise FileNotFoundError(\n f\"Dataset trait\u00e9 introuvable ({DATA_PATH}). Ex\u00e9cutez `python projet_05/dataset.py` puis `python projet_05/features.py`.\"\n ) from exc\n\ncategorical_cols = [col for col in settings.cat_cols if col in df.columns]\nif not categorical_cols:\n categorical_cols = [col for col in df.select_dtypes(exclude='number').columns if col != TARGET]\n\nnumeric_cols = [col for col in settings.num_cols if col in df.columns]\nif not numeric_cols:\n numeric_cols = df.select_dtypes(include='number').columns.tolist()\n\npalette = Theme.PALETTE\ncolor_primary = Theme.PRIMARY\ncolor_secondary = Theme.SECONDARY\ncmap_heatmap = make_diverging_cmap(Theme.PRIMARY, Theme.SECONDARY)\n"
},
{
"cell_type": "markdown",
"id": "016da610",
"metadata": {},
"source": [
- "## 1) Paramètres & chemins"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "ebd95229",
- "metadata": {},
- "outputs": [],
- "source": [
- "CFG = load_settings('manet_projet04/settings.yml')\n",
+ "## 1) Pr\u00e9paration et entra\u00eenement (scripts)\n",
"\n",
- "RANDOM_STATE = CFG[\"RANDOM_STATE\"]\n",
- "PATH_SIRH = CFG[\"PATH_SIRH\"]\n",
- "PATH_EVAL = CFG[\"PATH_EVAL\"]\n",
- "PATH_SONDAGE = CFG[\"PATH_SONDAGE\"]\n",
- "COL_ID = CFG[\"COL_ID\"]\n",
- "TARGET = CFG[\"TARGET\"]\n",
- "NUM_COLS = CFG[\"NUM_COLS\"]\n",
- "CAT_COLS = CFG[\"CAT_COLS\"]\n",
- "SAT_COLS = CFG[\"SAT_COLS\"]\n",
- "FIRST_VARS = CFG[\"FIRST_VARS\"]\n",
- "SUBSAMPLE_FRAC = CFG[\"SUBSAMPLE_FRAC\"]\n",
- "SQL_FILE = \"merge_sql.sql\"\n",
- "DB_FILE = \"merge_temp.db\""
+ "Les modules `projet_05/dataset.py`, `projet_05/features.py` et `projet_05/modeling/train.py` g\u00e8rent respectivement la fusion SQL,\n",
+ "l'ing\u00e9nierie de variables et l'entra\u00eenement.\n"
]
},
{
@@ -118,156 +58,9 @@
"id": "1d373963",
"metadata": {},
"source": [
- "## 2) Chargement SQL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "26b72a3b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Tailles initiales : 1470 1470 1470\n",
- "\n",
- "=== Vérification des clés id_employee avant fusion ===\n",
- " sirh : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
- "evaluation : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
- " sond : 1470 lignes | 1470 identifiants uniques | 0 doublons\n",
- "Merge réussi — shape finale : (1470, 32)\n",
- "Aperçu des identifiants : [1, 2, 4, 5, 7]\n",
- "Effectifs par genre :\n",
- "genre\n",
- "M 882\n",
- "F 588\n",
- "Name: count, dtype: int64\n",
- "\n",
- "Répartition en % :\n",
- "genre\n",
- "M 60.0\n",
- "F 40.0\n",
- "Name: count, dtype: float64\n"
- ]
- }
- ],
- "source": [
- "# --- Config ---\n",
- "DB_FILE = \"merge_temp.db\"\n",
- "SQL_FILE = \"merge_sql.sql\"\n",
- "COL_ID = \"id_employee\"\n",
- "\n",
- "# --- Fonctions utilitaires ---\n",
- "def safe_read_csv(path):\n",
- " \"\"\"Lecture sécurisée d’un CSV.\"\"\"\n",
- " try:\n",
- " return pd.read_csv(path)\n",
- " except Exception as e:\n",
- " print(f\"[ATTENTION] Lecture impossible {path}: {e}\")\n",
- " return pd.DataFrame()\n",
- "\n",
- "def clean_text_values(df):\n",
- " \"\"\"Nettoie les valeurs texte incohérentes ou équivalentes à NaN.\"\"\"\n",
- " df = df.copy()\n",
- " to_replace = [\n",
- " '', ' ', ' ', ' ', 'nan', 'NaN', 'NAN', 'None',\n",
- " 'JE ne sais pas', 'je ne sais pas', 'Je ne sais pas',\n",
- " 'Unknow', 'Unknown', 'non pertinent', 'Non pertinent', 'NON PERTINENT'\n",
- " ]\n",
- " df = df.replace(to_replace, np.nan)\n",
- " for col in df.select_dtypes(include='object'):\n",
- " df[col] = df[col].replace(to_replace, np.nan).str.strip()\n",
- " return df\n",
- "\n",
- "# --- Lecture des fichiers ---\n",
- "sirh = safe_read_csv(PATH_SIRH)\n",
- "evaluation = safe_read_csv(PATH_EVAL)\n",
- "sond = safe_read_csv(PATH_SONDAGE)\n",
- "\n",
- "print(\"Tailles initiales :\", len(sirh), len(evaluation), len(sond))\n",
- "\n",
- "# --- Harmonisation des clés ---\n",
- "if 'id_employee' in sirh.columns:\n",
- " sirh['id_employee'] = sirh['id_employee'].astype(str).str.extract(r'(\\d+)').astype(int)\n",
- "\n",
- "if 'eval_number' in evaluation.columns:\n",
- " evaluation['eval_number'] = evaluation['eval_number'].astype(str).str.replace(r'[^0-9]', '', regex=True)\n",
- " evaluation['eval_number'] = evaluation['eval_number'].astype(int)\n",
- " evaluation.rename(columns={'eval_number': 'id_employee'}, inplace=True)\n",
- "\n",
- "if 'code_sondage' in sond.columns:\n",
- " sond['code_sondage'] = sond['code_sondage'].astype(str).str.replace(r'[^0-9]', '', regex=True)\n",
- " sond['code_sondage'] = sond['code_sondage'].astype(int)\n",
- " sond.rename(columns={'code_sondage': 'id_employee'}, inplace=True)\n",
- "\n",
- "# --- Nettoyage des valeurs texte ---\n",
- "sirh = clean_text_values(sirh)\n",
- "evaluation = clean_text_values(evaluation)\n",
- "sond = clean_text_values(sond)\n",
- "\n",
- "# --- Vérifications ---\n",
- "print(\"\\n=== Vérification des clés id_employee avant fusion ===\")\n",
- "\n",
- "for name, df_ in [('sirh', sirh), ('evaluation', evaluation), ('sond', sond)]:\n",
- " total = len(df_)\n",
- " uniques = df_['id_employee'].nunique(dropna=True)\n",
- " doublons = total - uniques\n",
- "\n",
- " print(f\"{name:>6} : {total:5d} lignes | {uniques:5d} identifiants uniques | {doublons:4d} doublons\")\n",
+ "## 2) Chargement SQL\n",
"\n",
- " # Liste les ID en doublon si nécessaire\n",
- " if doublons > 0:\n",
- " dup_ids = (\n",
- " df_['id_employee']\n",
- " .value_counts()\n",
- " .loc[lambda x: x > 1]\n",
- " .index.tolist()\n",
- " )\n",
- " print(f\" ⚠️ Attention : {len(dup_ids)} doublons détectés dans {name} → ex: {dup_ids[:5]}\")\n",
- "\n",
- "if 'id_employee' not in sirh.columns:\n",
- " raise ValueError(\"La clé 'id_employee' doit exister dans SIRH après harmonisation.\")\n",
- "\n",
- "# --- Merge via SQL (traçable et sécurisé) ---\n",
- "if os.path.exists(DB_FILE):\n",
- " os.remove(DB_FILE)\n",
- "conn = sqlite3.connect(DB_FILE)\n",
- "\n",
- "sirh.to_sql(\"sirh\", conn, index=False)\n",
- "evaluation.to_sql(\"evaluation\", conn, index=False)\n",
- "sond.to_sql(\"sond\", conn, index=False)\n",
- "\n",
- "sql_query = f\"\"\"\n",
- "SELECT *\n",
- "FROM sirh\n",
- "INNER JOIN evaluation USING ({COL_ID})\n",
- "INNER JOIN sond USING ({COL_ID});\n",
- "\"\"\"\n",
- "\n",
- "with open(SQL_FILE, \"w\", encoding=\"utf-8\") as f:\n",
- " f.write(sql_query)\n",
- "\n",
- "df = pd.read_sql_query(sql_query, conn)\n",
- "conn.close()\n",
- "\n",
- "print(f\"Merge réussi — shape finale : {df.shape}\")\n",
- "print(\"Aperçu des identifiants :\", df['id_employee'].head().tolist())\n",
- "\n",
- "df['genre'].value_counts()\n",
- "\n",
- "# Comptage\n",
- "genre_counts = df['genre'].value_counts()\n",
- "\n",
- "# Pourcentage\n",
- "genre_pct = (genre_counts / genre_counts.sum() * 100).round(2)\n",
- "\n",
- "print(\"Effectifs par genre :\")\n",
- "print(genre_counts)\n",
- "\n",
- "print(\"\\nRépartition en % :\")\n",
- "print(genre_pct)\n"
+ "Cette \u00e9tape est d\u00e9sormais automatis\u00e9e dans `projet_05/dataset.py`.\n"
]
},
{
@@ -275,7 +68,7 @@
"id": "be1facc5",
"metadata": {},
"source": [
- "## 3) Déséquilibre de la cible"
+ "## 3) D\u00e9s\u00e9quilibre de la cible"
]
},
{
@@ -294,7 +87,7 @@
"1 237\n",
"Name: count, dtype: int64\n",
"\n",
- "Répartition (%):\n",
+ "R\u00e9partition (%):\n",
" a_quitte_l_entreprise\n",
"0 83.88\n",
"1 16.12\n",
@@ -313,42 +106,27 @@
}
],
"source": [
- "# === Diagnostic du déséquilibre de la cible (version robuste) ===\n",
- "if TARGET not in df.columns:\n",
- " raise ValueError(f\"Cible {TARGET} absente. Assurez-vous qu'elle est dans le fichier sondage.\")\n",
- "\n",
- "# Normalisation des valeurs de la cible (convertit oui/non, True/False, etc.)\n",
- "df[TARGET] = df[TARGET].astype(str).str.strip().str.lower().map({\n",
- " '1': 1, '0': 0,\n",
- " 'oui': 1, 'non': 0,\n",
- " 'True': 1, 'False': 0,\n",
- " 'quitte': 1, 'reste': 0\n",
- "})\n",
- "\n",
- "# Suppression des valeurs non mappées\n",
- "df = df[df[TARGET].isin([0, 1])].copy()\n",
- "\n",
- "# Vérification du contenu\n",
- "if df.empty or df[TARGET].nunique() < 2:\n",
- " raise ValueError(f\"La variable cible {TARGET} ne contient pas de valeurs valides (0/1) après nettoyage.\")\n",
- "\n",
- "# Comptage et affichage\n",
+ "# === Diagnostic du d\u00e9s\u00e9quilibre de la cible ===\n",
"counts = df[TARGET].value_counts().sort_index()\n",
"ratio = (counts / counts.sum() * 100).round(2)\n",
- "print('Effectifs par classe:\\n', counts)\n",
- "print('\\nRépartition (%):\\n', ratio)\n",
"\n",
- "# Bar plot sécurisé\n",
+ "print('Effectifs par classe:\n",
+ "', counts)\n",
+ "print('\n",
+ "R\u00e9partition (%):\n",
+ "', ratio)\n",
+ "\n",
"if counts.empty:\n",
- " print(\"[INFO] Aucun effectif valide à afficher pour la cible.\")\n",
+ " print('[INFO] Aucune donn\u00e9e disponible pour la cible.')\n",
"else:\n",
" plt.figure()\n",
- " counts.plot(kind='bar', color=Theme.PALETTE[:len(counts)])\n",
- " plt.title('Répartition de la cible (0=reste, 1=quitte)')\n",
+ " counts.plot(kind='bar', color=palette[: len(counts)])\n",
+ " plt.title('R\u00e9partition de la cible (0=reste, 1=quitte)')\n",
" plt.xlabel('Classe')\n",
" plt.ylabel('Effectif')\n",
- " plt.savefig(f\"output/repartition_cible.png\", dpi=300)\n",
- " plt.show()"
+ " plt.tight_layout()\n",
+ " plt.savefig('output/repartition_cible.png', dpi=300)\n",
+ " plt.show()\n"
]
},
{
@@ -369,9 +147,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Âge moyen : 36.9\n",
- "Ancienneté moyenne (années) : 7.0\n",
- "Taux d’attrition global : 16.1%\n"
+ "\u00c2ge moyen : 36.9\n",
+ "Anciennet\u00e9 moyenne (ann\u00e9es) : 7.0\n",
+ "Taux d\u2019attrition global : 16.1%\n"
]
},
{
@@ -419,49 +197,47 @@
"output_type": "stream",
"text": [
"\n",
- "INSIGHTS CLÉS :\n",
- "• Les employés du département commercial présentent le taux d’attrition le plus élevé.\n",
- "• Les employés à faible satisfaction environnementale quittent plus souvent l’entreprise.\n",
- "• La corrélation entre revenu mensuel et ancienneté semble faible et non linéaire.\n"
+ "INSIGHTS CL\u00c9S :\n",
+ "\u2022 Les employ\u00e9s du d\u00e9partement commercial pr\u00e9sentent le taux d\u2019attrition le plus \u00e9lev\u00e9.\n",
+ "\u2022 Les employ\u00e9s \u00e0 faible satisfaction environnementale quittent plus souvent l\u2019entreprise.\n",
+ "\u2022 La corr\u00e9lation entre revenu mensuel et anciennet\u00e9 semble faible et non lin\u00e9aire.\n"
]
}
],
"source": [
"# --- Dossier de sortie ---\n",
- "os.makedirs(\"output\", exist_ok=True)\n",
- "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
"\n",
"# --- Statistiques descriptives ---\n",
- "print(\"Âge moyen :\", round(df['age'].mean(), 1))\n",
- "print(\"Ancienneté moyenne (années) :\", round(df['annees_dans_l_entreprise'].mean(), 1))\n",
- "taux_attrition = df['a_quitte_l_entreprise'].mean() * 100\n",
- "print(f\"Taux d’attrition global : {taux_attrition:.1f}%\")\n",
+ "print(\"\u00c2ge moyen :\", round(df['age'].mean(), 1))\n",
+ "print(\"Anciennet\u00e9 moyenne (ann\u00e9es) :\", round(df['annees_dans_l_entreprise'].mean(), 1))\n",
+ "taux_attrition = df[TARGET].mean() * 100\n",
+ "print(f\"Taux d\u2019attrition global : {taux_attrition:.1f}%\")\n",
"\n",
- "# === GRAPHIQUE 1 : Taux d’attrition par département ===\n",
+ "# === GRAPHIQUE 1 : Taux d\u2019attrition par d\u00e9partement ===\n",
"plt.figure(figsize=(8,5))\n",
- "dept_attrition = df.groupby('departement')['a_quitte_l_entreprise'].mean().sort_values(ascending=False) * 100\n",
- "sns.barplot(x=dept_attrition.index, y=dept_attrition.values, hue=dept_attrition.index, palette=Theme.PALETTE)\n",
- "plt.title(\"Taux d’attrition par département\")\n",
+ "dept_attrition = df.groupby('departement')[TARGET].mean().sort_values(ascending=False) * 100\n",
+ "sns.barplot(x=dept_attrition.index, y=dept_attrition.values, hue=dept_attrition.index, palette=palette)\n",
+ "plt.title(\"Taux d\u2019attrition par d\u00e9partement\")\n",
"plt.ylabel(\"Attrition (%)\")\n",
- "plt.xlabel(\"Département\")\n",
+ "plt.xlabel(\"D\u00e9partement\")\n",
"plt.xticks(rotation=45)\n",
"plt.tight_layout()\n",
"plt.savefig(f\"output/attrition_par_departement.png\", dpi=300)\n",
"plt.show()\n",
"\n",
- "# === GRAPHIQUE 2 : Distribution de l’ancienneté selon l’attrition ===\n",
+ "# === GRAPHIQUE 2 : Distribution de l\u2019anciennet\u00e9 selon l\u2019attrition ===\n",
"plt.figure(figsize=(8,5))\n",
"sns.kdeplot(\n",
" data=df,\n",
" x='annees_dans_l_entreprise',\n",
- " hue='a_quitte_l_entreprise',\n",
+ " hue=TARGET,\n",
" fill=True,\n",
" common_norm=False,\n",
- " palette=Theme.PALETTE\n",
+ " palette=palette\n",
")\n",
- "plt.title(\"Ancienneté et probabilité de départ\")\n",
- "plt.xlabel(\"Années dans l’entreprise\")\n",
- "plt.ylabel(\"Densité\")\n",
+ "plt.title(\"Anciennet\u00e9 et probabilit\u00e9 de d\u00e9part\")\n",
+ "plt.xlabel(\"Ann\u00e9es dans l\u2019entreprise\")\n",
+ "plt.ylabel(\"Densit\u00e9\")\n",
"plt.tight_layout()\n",
"plt.savefig(f\"output/anciennete_attrition.png\", dpi=300)\n",
"plt.show()\n",
@@ -470,39 +246,39 @@
"plt.figure(figsize=(8,5))\n",
"sns.boxplot(\n",
" data=df,\n",
- " x='a_quitte_l_entreprise',\n",
- " hue='a_quitte_l_entreprise',\n",
+ " x=TARGET,\n",
+ " hue=TARGET,\n",
" y='satisfaction_employee_environnement',\n",
- " palette=Theme.PALETTE\n",
+ " palette=palette\n",
")\n",
- "plt.title(\"Satisfaction environnement de travail selon l’attrition\")\n",
+ "plt.title(\"Satisfaction environnement de travail selon l\u2019attrition\")\n",
"plt.ylabel(\"Satisfaction environnement\")\n",
"plt.tight_layout()\n",
"plt.savefig(f\"output/satisfaction_env_attrition.png\", dpi=300)\n",
"plt.show()\n",
"\n",
- "# === GRAPHIQUE 4 : Lien entre revenu et ancienneté ===\n",
+ "# === GRAPHIQUE 4 : Lien entre revenu et anciennet\u00e9 ===\n",
"plt.figure(figsize=(6,5))\n",
"sns.scatterplot(\n",
" data=df,\n",
" x='annees_dans_l_entreprise',\n",
" y='revenu_mensuel',\n",
- " hue='a_quitte_l_entreprise',\n",
+ " hue=TARGET,\n",
" alpha=0.7,\n",
- " palette=Theme.PALETTE\n",
+ " palette=palette\n",
")\n",
- "plt.title(\"Lien entre revenu mensuel et ancienneté\")\n",
- "plt.xlabel(\"Années dans l’entreprise\")\n",
- "plt.ylabel(\"Revenu mensuel (€)\")\n",
+ "plt.title(\"Lien entre revenu mensuel et anciennet\u00e9\")\n",
+ "plt.xlabel(\"Ann\u00e9es dans l\u2019entreprise\")\n",
+ "plt.ylabel(\"Revenu mensuel (\u20ac)\")\n",
"plt.tight_layout()\n",
"plt.savefig(f\"output/revenu_anciennete.png\", dpi=300)\n",
"plt.show()\n",
"\n",
- "# === Insights automatiques à commenter ===\n",
- "print(\"\\nINSIGHTS CLÉS :\")\n",
- "print(\"• Les employés du département commercial présentent le taux d’attrition le plus élevé.\")\n",
- "print(\"• Les employés à faible satisfaction environnementale quittent plus souvent l’entreprise.\")\n",
- "print(\"• La corrélation entre revenu mensuel et ancienneté semble faible et non linéaire.\")\n"
+ "# === Insights automatiques \u00e0 commenter ===\n",
+ "print(\"\\nINSIGHTS CL\u00c9S :\")\n",
+ "print(\"\u2022 Les employ\u00e9s du d\u00e9partement commercial pr\u00e9sentent le taux d\u2019attrition le plus \u00e9lev\u00e9.\")\n",
+ "print(\"\u2022 Les employ\u00e9s \u00e0 faible satisfaction environnementale quittent plus souvent l\u2019entreprise.\")\n",
+ "print(\"\u2022 La corr\u00e9lation entre revenu mensuel et anciennet\u00e9 semble faible et non lin\u00e9aire.\")\n"
]
},
{
@@ -523,9 +299,9 @@
}
],
"source": [
- "# === Visualisation des variables numériques selon le statut de départ ===\n",
+ "# === Visualisation des variables num\u00e9riques selon le statut de d\u00e9part ===\n",
"\n",
- "# Choix de variables numériques pertinentes à comparer\n",
+ "# Choix de variables num\u00e9riques pertinentes \u00e0 comparer\n",
"num_vars = [\n",
" 'revenu_mensuel',\n",
" 'annees_dans_l_entreprise',\n",
@@ -543,13 +319,13 @@
" x=TARGET, y=var,\n",
" ax=axes[i],\n",
" hue=TARGET, \n",
- " palette=[Theme.PRIMARY, Theme.SECONDARY]\n",
+ " palette=[color_primary, color_secondary]\n",
" )\n",
- " axes[i].set_title(f'{var.replace(\"_\", \" \").capitalize()} selon le statut de départ')\n",
+ " axes[i].set_title(f'{var.replace(\"_\", \" \").capitalize()} selon le statut de d\u00e9part')\n",
" axes[i].set_xlabel('')\n",
" axes[i].set_ylabel(var.replace('_', ' ').capitalize())\n",
"\n",
- "plt.suptitle(\"Comparaison des variables numériques selon le statut de départ\", fontsize=14, weight='bold')\n",
+ "plt.suptitle(\"Comparaison des variables num\u00e9riques selon le statut de d\u00e9part\", fontsize=14, weight='bold')\n",
"plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
"plt.savefig(f\"output/variables_numeriques_statut_depart.png\", dpi=300)\n",
"plt.show()\n"
@@ -573,7 +349,7 @@
}
],
"source": [
- "# === Matrice de corrélation des variables numériques ===\n",
+ "# === Matrice de corr\u00e9lation des variables num\u00e9riques ===\n",
"\n",
"num_vars = [\n",
" 'revenu_mensuel',\n",
@@ -591,7 +367,7 @@
" corr, annot=True, cmap=cmap_heatmap, center=0,\n",
" linewidths=0.5, square=True, fmt=\".2f\"\n",
")\n",
- "plt.title(\"Matrice de corrélation entre variables numériques\", fontsize=13, weight='bold')\n",
+ "plt.title(\"Matrice de corr\u00e9lation entre variables num\u00e9riques\", fontsize=13, weight='bold')\n",
"plt.savefig(f\"output/matrice_correlation_numeriques.png\", dpi=300)\n",
"plt.show()\n"
]
@@ -601,22 +377,22 @@
"id": "9fd9cb5f",
"metadata": {},
"source": [
- "Dans le code qui suit on cherche à vérifier s'il existe une relation entre les variables qualitatives.\n",
+ "Dans le code qui suit on cherche \u00e0 v\u00e9rifier s'il existe une relation entre les variables qualitatives.\n",
"\n",
- "On fait l'hypothèse qu'il existe une relation de dépendance entre a_quitte_l_entreprise et\n",
- "+ genre (on rejète h)\n",
- "+ le statut marital (on ne rejète pas h)\n",
- "+ le département (on ne rejète pas)\n",
- "+ les enfants (on rejète)\n",
+ "On fait l'hypoth\u00e8se qu'il existe une relation de d\u00e9pendance entre a_quitte_l_entreprise et\n",
+ "+ genre (on rej\u00e8te h)\n",
+ "+ le statut marital (on ne rej\u00e8te pas h)\n",
+ "+ le d\u00e9partement (on ne rej\u00e8te pas)\n",
+ "+ les enfants (on rej\u00e8te)\n",
"\n",
"\n",
- "H non rejeté :\n",
- "* Très forte corrélation\n",
+ "H non rejet\u00e9 :\n",
+ "* Tr\u00e8s forte corr\u00e9lation\n",
" * poste, niveau_hierarchique_poste, statut_marital\n",
- "* Corrélation Moyenne (<0.05 mais proche)\n",
+ "* Corr\u00e9lation Moyenne (<0.05 mais proche)\n",
" * frequence_deplacement, departement, domaine_etude\n",
"\n",
- "H rejeté :\n",
+ "H rejet\u00e9 :\n",
"niveau_education, genre, ayant_enfant"
]
},
@@ -631,23 +407,23 @@
"output_type": "stream",
"text": [
"ANOVA note_evaluation_actuelle ~ a_quitte_l_entreprise : p-value = 0.9119\n",
- "Chi² departement ~ a_quitte_l_entreprise : p-value = 0.0045\n",
- "Chi² frequence_deplacement ~ a_quitte_l_entreprise : p-value = 0.0000\n",
+ "Chi\u00b2 departement ~ a_quitte_l_entreprise : p-value = 0.0045\n",
+ "Chi\u00b2 frequence_deplacement ~ a_quitte_l_entreprise : p-value = 0.0000\n",
"\n",
"[Cat: genre]\n",
" a_quitte_l_entreprise 0 1\n",
"genre \n",
"F 501 87\n",
"M 732 150\n",
- "Chi²=1.117, dof=1, p=0.291\n",
+ "Chi\u00b2=1.117, dof=1, p=0.291\n",
"\n",
"[Cat: statut_marital]\n",
" a_quitte_l_entreprise 0 1\n",
"statut_marital \n",
- "Célibataire 350 120\n",
- "Divorcé(e) 294 33\n",
- "Marié(e) 589 84\n",
- "Chi²=46.164, dof=2, p=9.46e-11\n",
+ "C\u00e9libataire 350 120\n",
+ "Divorc\u00e9(e) 294 33\n",
+ "Mari\u00e9(e) 589 84\n",
+ "Chi\u00b2=46.164, dof=2, p=9.46e-11\n",
"\n",
"[Cat: departement]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -655,7 +431,7 @@
"Commercial 354 92\n",
"Consulting 828 133\n",
"Ressources Humaines 51 12\n",
- "Chi²=10.796, dof=2, p=0.00453\n",
+ "Chi\u00b2=10.796, dof=2, p=0.00453\n",
"\n",
"[Cat: poste]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -665,7 +441,7 @@
"Consultant 197 62\n",
"Directeur Technique 78 2\n",
"Manager 122 9\n",
- "Chi²=86.190, dof=8, p=2.75e-15\n",
+ "Chi\u00b2=86.190, dof=8, p=2.75e-15\n",
"\n",
"[Cat: niveau_hierarchique_poste]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -675,7 +451,7 @@
"3 186 32\n",
"4 101 5\n",
"5 64 5\n",
- "Chi²=72.529, dof=4, p=6.63e-15\n",
+ "Chi\u00b2=72.529, dof=4, p=6.63e-15\n",
"\n",
"[Cat: niveau_education]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -685,7 +461,7 @@
"3 473 99\n",
"4 340 58\n",
"5 43 5\n",
- "Chi²=3.074, dof=4, p=0.546\n",
+ "Chi\u00b2=3.074, dof=4, p=0.546\n",
"\n",
"[Cat: domaine_etude]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -695,7 +471,7 @@
"Infra & Cloud 517 89\n",
"Marketing 124 35\n",
"Ressources Humaines 20 7\n",
- "Chi²=16.025, dof=5, p=0.00677\n",
+ "Chi\u00b2=16.025, dof=5, p=0.00677\n",
"\n",
"[Cat: frequence_deplacement]\n",
" a_quitte_l_entreprise 0 1\n",
@@ -703,42 +479,42 @@
"Aucun 138 12\n",
"Frequent 208 69\n",
"Occasionnel 887 156\n",
- "Chi²=24.182, dof=2, p=5.61e-06\n",
+ "Chi\u00b2=24.182, dof=2, p=5.61e-06\n",
"\n",
"[Cat: ayant_enfants]\n",
" a_quitte_l_entreprise 0 1\n",
"ayant_enfants \n",
"Y 1233 237\n",
- "Chi²=0.000, dof=0, p=1\n",
+ "Chi\u00b2=0.000, dof=0, p=1\n",
"\n",
"[Cat: heure_supplementaires]\n",
" a_quitte_l_entreprise 0 1\n",
"heure_supplementaires \n",
"Non 944 110\n",
"Oui 289 127\n",
- "Chi²=87.564, dof=1, p=8.16e-21\n"
+ "Chi\u00b2=87.564, dof=1, p=8.16e-21\n"
]
}
],
"source": [
- "# === Tableaux croisés + Chi2 ===\n",
+ "# === Tableaux crois\u00e9s + Chi2 ===\n",
"\n",
- "# --- Test ANOVA pour une variable numérique continue ---\n",
+ "# --- Test ANOVA pour une variable num\u00e9rique continue ---\n",
"anova_result = f_oneway(\n",
" df.loc[df[TARGET] == 0, 'note_evaluation_actuelle'],\n",
" df.loc[df[TARGET] == 1, 'note_evaluation_actuelle']\n",
")\n",
"print(f\"ANOVA note_evaluation_actuelle ~ {TARGET} : p-value = {anova_result.pvalue:.4f}\")\n",
"\n",
- "# --- Test Chi² pour une variable catégorielle ---\n",
+ "# --- Test Chi\u00b2 pour une variable cat\u00e9gorielle ---\n",
"contingency = pd.crosstab(df['departement'], df[TARGET])\n",
"chi2, p, _, _ = chi2_contingency(contingency)\n",
- "print(f\"Chi² departement ~ {TARGET} : p-value = {p:.4f}\")\n",
+ "print(f\"Chi\u00b2 departement ~ {TARGET} : p-value = {p:.4f}\")\n",
"\n",
- "# --- Test supplémentaire pour la fréquence de déplacement ---\n",
+ "# --- Test suppl\u00e9mentaire pour la fr\u00e9quence de d\u00e9placement ---\n",
"contingency2 = pd.crosstab(df['frequence_deplacement'], df[TARGET])\n",
"chi2_2, p2, _, _ = chi2_contingency(contingency2)\n",
- "print(f\"Chi² frequence_deplacement ~ {TARGET} : p-value = {p2:.4f}\")\n",
+ "print(f\"Chi\u00b2 frequence_deplacement ~ {TARGET} : p-value = {p2:.4f}\")\n",
"\n",
"\n",
"def chi2_for_categorical(df, col, target=TARGET):\n",
@@ -747,13 +523,13 @@
" chi2, p, dof, exp = chi2_contingency(ct)\n",
" return ct, chi2, p, dof\n",
"\n",
- "for col in CAT_COLS:\n",
+ "for col in categorical_cols:\n",
" if col in df.columns:\n",
" ct, chi2, p, dof = chi2_for_categorical(df.dropna(subset=[col, TARGET]), col, TARGET)\n",
" print(f\"\\n[Cat: {col}]\\n\", ct.head())\n",
- " print(f\"Chi²={chi2:.3f}, dof={dof}, p={p:.3g}\")\n",
+ " print(f\"Chi\u00b2={chi2:.3f}, dof={dof}, p={p:.3g}\")\n",
" else:\n",
- " print(f\"[INFO] Catégorie '{col}' absente – adapte CAT_COLS.\")"
+ " print(f\"[INFO] Cat\u00e9gorie '{col}' absente \u2013 adapte categorical_cols.\")"
]
},
{
@@ -761,557 +537,7 @@
"id": "a31ba336",
"metadata": {},
"source": [
- "## 5) Feature engineering"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "96b9619a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nouvelles features créées : ['annee_sur_poste_par_experience', 'nb_formation_par_experience', 'score_moyen_satisfaction', 'dern_promo_par_experience', 'evolution_note']\n",
- " augmentation_par_revenu annee_sur_poste_par_experience \\\n",
- "count 1470.000000 1459.000000 \n",
- "mean 0.000036 0.414561 \n",
- "std 0.000025 0.294144 \n",
- "min 0.000006 0.000000 \n",
- "25% 0.000018 0.181818 \n",
- "50% 0.000029 0.400000 \n",
- "75% 0.000050 0.666667 \n",
- "max 0.000209 1.000000 \n",
- "\n",
- " nb_formation_par_experience score_moyen_satisfaction \\\n",
- "count 1459.000000 1470.000000 \n",
- "mean 0.486533 2.730952 \n",
- "std 0.729839 0.505815 \n",
- "min 0.000000 1.000000 \n",
- "25% 0.153846 2.500000 \n",
- "50% 0.285714 2.750000 \n",
- "75% 0.500000 3.000000 \n",
- "max 6.000000 4.000000 \n",
- "\n",
- " dern_promo_par_experience evolution_note \n",
- "count 1459.000000 1470.000000 \n",
- "mean 0.204003 0.423810 \n",
- "std 0.262048 0.807119 \n",
- "min 0.000000 -1.000000 \n",
- "25% 0.000000 0.000000 \n",
- "50% 0.100000 0.000000 \n",
- "75% 0.333333 1.000000 \n",
- "max 1.000000 3.000000 \n",
- "Table 'df_final' mise à jour dans 'merge_temp.db' (1470 lignes, 38 colonnes).\n",
- "Colonnes présentes dans la table SQL :\n"
- ]
- },
- {
- "data": {
- "application/vnd.microsoft.datawrangler.viewer.v0+json": {
- "columns": [
- {
- "name": "index",
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- "9",
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- ],
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- "10",
- "annees_dans_l_entreprise"
- ],
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- "11",
- "annees_dans_le_poste_actuel"
- ],
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- "12",
- "satisfaction_employee_environnement"
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- "13",
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- ],
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- "14",
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- "15",
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- "heure_supplementaires"
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- "35",
- "score_moyen_satisfaction"
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- [
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- "dern_promo_par_experience"
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- "37",
- "evolution_note"
- ]
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- "9 annee_experience_totale\n",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/var/folders/by/r6dmty813rxgqdnr7hvtrl480000gn/T/ipykernel_47250/3667096219.py:85: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
- " .replace({\n"
- ]
- }
- ],
- "source": [
- "# === Mise à jour de la base SQL avec les nouvelles features ===\n",
- "\n",
- "# Nettoyage et conversion de la colonne 'augementation_salaire_precedente'\n",
- "df['augementation_salaire_precedente'] = (df['augementation_salaire_precedente'].astype(str).str.replace('%', '', regex=False).str.strip())\n",
- "\n",
- "# Conversion en nombre et passage en proportion (ex : 11 devient 0.11)\n",
- "df['augementation_salaire_precedente'] = (pd.to_numeric(df['augementation_salaire_precedente'], errors='coerce') / 100)\n",
- "\n",
- "# Augmentation / Revenu\n",
- "df['augmentation_par_revenu'] = (df['augementation_salaire_precedente'] / df['revenu_mensuel'].replace(0, np.nan))\n",
- "\n",
- "# Années dans le poste actuel / Années d'expérience totale\n",
- "df['annee_sur_poste_par_experience'] = (df['annees_dans_le_poste_actuel'] / df['annee_experience_totale'].replace(0, np.nan))\n",
- "\n",
- "# Nombre de formations suivies / Années d'expérience totale\n",
- "df['nb_formation_par_experience'] = (df['nb_formations_suivies'] /df['annee_experience_totale'].replace(0, np.nan))\n",
- "\n",
- "# Score moyen de satisfaction (déjà présent, mais on le recalcule proprement pour cohérence)\n",
- "satisfaction_cols = [\n",
- " 'satisfaction_employee_environnement',\n",
- " 'satisfaction_employee_nature_travail',\n",
- " 'satisfaction_employee_equipe',\n",
- " 'satisfaction_employee_equilibre_pro_perso'\n",
- "]\n",
- "df['score_moyen_satisfaction'] = df[satisfaction_cols].mean(axis=1)\n",
- "\n",
- "# Dernière promotion / Expérience\n",
- "df['dern_promo_par_experience'] = (df['annees_depuis_la_derniere_promotion'] /df['annee_experience_totale'].replace(0, np.nan))\n",
- "\n",
- "# Évolution de note (note actuelle - note précédente)\n",
- "df['evolution_note'] = (df['note_evaluation_actuelle'] -df['note_evaluation_precedente'])\n",
- "\n",
- "# Vérification rapide des features créées\n",
- "new_features = [\n",
- " 'augmentation_par_revenu',\n",
- " 'annee_sur_poste_par_experience',\n",
- " 'nb_formation_par_experience',\n",
- " 'score_moyen_satisfaction',\n",
- " 'dern_promo_par_experience',\n",
- " 'evolution_note'\n",
- "]\n",
- "\n",
- "# Vérification rapide\n",
- "print(\"Nouvelles features créées :\", [col for col in df.columns if any(x in col for x in [\n",
- " 'augmentation_par_revenue', 'annee_sur_poste_par_experience', 'nb_formation_par_experience',\n",
- " 'score_moyen_satisfaction', 'dern_promo_par_experience', 'evolution_note'\n",
- "])])\n",
- "print(df[new_features].describe())\n",
- "\n",
- "\n",
- "# Vérification du fichier DB existant\n",
- "if not os.path.exists(DB_FILE):\n",
- " raise FileNotFoundError(f\"Base SQLite '{DB_FILE}' introuvable. Relance le merge avant d'y insérer les nouvelles features.\")\n",
- "\n",
- "# Connexion à la base SQLite\n",
- "conn = sqlite3.connect(DB_FILE)\n",
- "\n",
- "# Sauvegarde du DataFrame complet (fusion + features)\n",
- "# On écrase la table existante \"df_final\" si elle existe déjà\n",
- "TABLE_NAME = \"df_final\"\n",
- "\n",
- "df.to_sql(TABLE_NAME, conn, index=False, if_exists='replace')\n",
- "\n",
- "# Validation et fermeture\n",
- "conn.commit()\n",
- "conn.close()\n",
- "\n",
- "print(f\"Table '{TABLE_NAME}' mise à jour dans '{DB_FILE}' ({len(df)} lignes, {len(df.columns)} colonnes).\")\n",
- "\n",
- "# --- Vérification rapide ---\n",
- "TABLE_NAME = globals().get(\"TABLE_NAME\", \"df_final\") # Défaut si non défini\n",
- "conn = sqlite3.connect(DB_FILE)\n",
- "check = pd.read_sql_query(f\"PRAGMA table_info({TABLE_NAME});\", conn)\n",
- "print(\"Colonnes présentes dans la table SQL :\")\n",
- "display(check[['name']])\n",
- "conn.close()\n",
- "\n",
- "# === Nettoyage et conversion de la variable cible ===\n",
- "if TARGET in df.columns:\n",
- " df[TARGET] = (\n",
- " df[TARGET]\n",
- " .astype(str)\n",
- " .str.strip()\n",
- " .str.lower()\n",
- " .replace({\n",
- " 'oui': 1, 'o': 1, 'true': 1, '1': 1,\n",
- " 'non': 0, 'n': 0, 'false': 0, '0': 0\n",
- " })\n",
- " )\n",
- "\n",
- " # Force le type int si possible\n",
- " df[TARGET] = pd.to_numeric(df[TARGET], errors='coerce')\n",
- " df = df[df[TARGET].isin([0,1])].copy()\n",
- "else:\n",
- " raise ValueError(f\"La colonne cible '{TARGET}' est absente du dataframe.\")\n"
+ "## 5) Feature engineering (voir `projet_05/features.py`)\n"
]
},
{
@@ -1319,107 +545,7 @@
"id": "018562d5",
"metadata": {},
"source": [
- "## 6) 1ᵉ itération – Modèle dummy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "2889c1d2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "=== Dummy Classifier (baseline) ===\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.844 1.000 0.915 124\n",
- " 1 0.000 0.000 0.000 23\n",
- "\n",
- " accuracy 0.844 147\n",
- " macro avg 0.422 0.500 0.458 147\n",
- "weighted avg 0.712 0.844 0.772 147\n",
- "\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[INFO] Exécute ensuite la cellule de modélisation principale pour comparer avec les autres modèles.\n"
- ]
- }
- ],
- "source": [
- "# === Baseline : Dummy Classifier ===\n",
- "first_vars = [c for c in ['age', 'salaire', 'anciennete', 'note_performance'] if c in df.columns]\n",
- "\n",
- "required_cols = first_vars + [TARGET]\n",
- "df_dummy = df.dropna(subset=required_cols).copy() if required_cols else df.copy()\n",
- "\n",
- "if len(df_dummy) == 0:\n",
- " print('[ATTENTION] Pas assez de données nettoyées pour calculer la baseline Dummy.')\n",
- "else:\n",
- " if len(df_dummy) > 100:\n",
- " df_dummy = df_dummy.sample(frac=min(SUBSAMPLE_FRAC, 1.0), random_state=RANDOM_STATE)\n",
- "\n",
- " if first_vars:\n",
- " X = df_dummy[first_vars]\n",
- " else:\n",
- " X = pd.DataFrame({'constante': np.ones(len(df_dummy))}, index=df_dummy.index)\n",
- " y = df_dummy[TARGET].astype(int)\n",
- "\n",
- " dummy = DummyClassifier(strategy='most_frequent', random_state=RANDOM_STATE)\n",
- " dummy.fit(X, y)\n",
- " y_pred_dummy = dummy.predict(X)\n",
- "\n",
- " print('=== Dummy Classifier (baseline) ===')\n",
- " print(classification_report(y, y_pred_dummy, digits=3))\n",
- "\n",
- " cm_dummy = confusion_matrix(y, y_pred_dummy)\n",
- " ConfusionMatrixDisplay(cm_dummy).plot()\n",
- " plt.title(\"Matrice de confusion – Dummy (classe majoritaire)\")\n",
- " plt.savefig(\"output/matrice_confusion_dummy.png\", dpi=300)\n",
- " plt.show()\n",
- "\n",
- " dummy_f1 = f1_score(y, y_pred_dummy)\n",
- " dummy_rec = recall_score(y, y_pred_dummy)\n",
- " dummy_prec = precision_score(y, y_pred_dummy)\n",
- " dummy_roc = roc_auc_score(y, y_pred_dummy)\n",
- "\n",
- " baseline_row = pd.DataFrame({\n",
- " 'model': ['Dummy_baseline'],\n",
- " 'best_threshold': [np.nan],\n",
- " 'F1': [dummy_f1],\n",
- " 'Recall': [dummy_rec],\n",
- " 'Precision': [dummy_prec],\n",
- " 'ROC_AUC': [dummy_roc]\n",
- " })\n",
- "\n",
- " if 'res_df' in globals():\n",
- " res_df_final = pd.concat([baseline_row, res_df], ignore_index=True)\n",
- " res_df_final = res_df_final.sort_values(by='F1', ascending=False)\n",
- " print(\"=== Comparaison finale (avec baseline) ===\")\n",
- " display(res_df_final)\n",
- " else:\n",
- " res_df_final = baseline_row.copy()\n",
- " print(\"[INFO] Exécute ensuite la cellule de modélisation principale pour comparer avec les autres modèles.\")\n",
- "\n",
- "with open(\"update_features.sql\", \"w\", encoding=\"utf-8\") as f:\n",
- " f.write(f\"-- Mise à jour des features dans {TABLE_NAME}\")\n",
- " f.write(f\"-- {len(df.columns)} colonnes et {len(df)} lignes au total\")"
+ "## 6) 1\u1d49 it\u00e9ration \u2013 Mod\u00e8le dummy (couvert par `projet_05/modeling/train.py`)\n"
]
},
{
@@ -1427,692 +553,7 @@
"id": "95644da8",
"metadata": {},
"source": [
- "## 7) 2ᵉ itération : SMOTE, LR, RF, CV. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "f4914e11",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Taille X_full: (1470, 38)\n",
- "Répartition de la cible:\n",
- " a_quitte_l_entreprise\n",
- "0 1233\n",
- "1 237\n",
- "Name: count, dtype: int64\n",
- "Oversampling SMOTE effectué (classes équilibrées).\n",
- "\n",
- "=== LogReg_balanced ===\n",
- "Meilleurs hyperparamètres : {'clf__C': 1.0, 'clf__penalty': 'l1', 'clf__solver': 'liblinear'}\n",
- "Seuil optimal (max F1): 0.554\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " precision recall f1-score support\n",
- "\n",
- " 0 0.874 0.959 0.915 1233\n",
- " 1 0.954 0.862 0.906 1233\n",
- "\n",
- " accuracy 0.910 2466\n",
- " macro avg 0.914 0.910 0.910 2466\n",
- "weighted avg 0.914 0.910 0.910 2466\n",
- "\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Génération de la courbe d'apprentissage...\n"
- ]
- },
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Analyse courbe d’apprentissage – LogReg_balanced\n",
- "Bon équilibre biais/variance : le modèle généralise correctement.\n",
- "(Score final validation : 0.901, écart train-val : 0.007)\n",
- "\n",
- "=== RF_balanced ===\n",
- "Meilleurs hyperparamètres : {'clf__max_depth': 10, 'clf__min_samples_leaf': 2, 'clf__min_samples_split': 5, 'clf__n_estimators': 500}\n",
- "Seuil optimal (max F1): 0.490\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " precision recall f1-score support\n",
- "\n",
- " 0 0.913 0.932 0.923 1233\n",
- " 1 0.930 0.912 0.921 1233\n",
- "\n",
- " accuracy 0.922 2466\n",
- " macro avg 0.922 0.922 0.922 2466\n",
- "weighted avg 0.922 0.922 0.922 2466\n",
- "\n"
- ]
- },
- {
- "data": {
- "image/png": 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hodD7/VdvI1o5Re5dREQrs/pxRIAcVmwl/pN+GEyRyRHdYMe9TmLd+s3ywBOWusVHDGWfOX0i7O3t5fq798LPdKwetZcxQ3qdYCN0N5IguhFiIqKWLtHqILhmzaIzX1HYYEiMKLp563asTDgqkmUF53Rpw80PJUbuiS9w0XKS6Ud3hrpVJrTBQ0YhUSJbjBwxONyX/K1bt2Wd1qhRVS7CmTPnZUuYSESPKJgSyeUi6BK/7NWtT6HfL1Gen7WkhFawYD7ZvRQ2QIyKaN3YsO5POaO+SNoXB+MiP5L5RZlES5IIFEN33wh79hyQicGCaCEUHjx8hAKhkteFTl1+R6GC+ZE/X14ZSPXr2yvcIIiYdFNGRLRihH3dERGtgDGVMWMGrF21VCZSN27aRk4Oq/4/M6af/V+Jz4wIpH5r3AAb16/QCWR+1potBmGIwFx0QYf+/1y/YfMvlVXdsire/7DEwITHT55g5vRJsqtSdAeH/R4QrZOnTp9F9uyumh9XEQXXouWNTB9zpsjkdOzQRn7BDBg0XI7SC2vsuMkyV6FZ00Yyp0d0lYiDofilHnq4vzjYT5ysyoXxrFVdXqq/RK9dv6nZTgRW22OYUyWG/l+4cElzWwxfXrx0hQwUxBxY4iAsRniJgDD0MHkRCIpRPOKXcEQjoQxN/CIWJkyarvMLV5SpTv2mGDRkpDwgiZYHUTcrVq3V+XUsulBFUPTy1etwjy32E118bdp31WnZK1KkoGxZiqxVr+6PMo2fOE1n/eXLV+QoxerVKkc5kjMsMT2GOFCFXiKanyws8XpFrpTo5mnfqYfmlDLq8o0dP1lne/F+123QVNaHIFo3ROAn5kUL/frFdsuWr4K3tw8+/BiqL1p+Qtu37yDu33+o12ls1K1tP1vKlCn1S48v9u3WtYP8DIjPSWz42f+Vujs3V87sOoGU+J44+WN6g8jqVLwXIhAO3bUq/vcXLFwW5X5R1b8IwDdv2SFzJNU+f/4sW2f//feBbE2rVbOazK8L/bqEkaPHy9nlxahfoUG92nj06IkcVasmWufF6EAyfWyZIpMjDsQ7tq6Xv4rdylaWCZ5ihmYfH1/s239I/porVqww5s+drtln7uypcgi/GIIshtqLlg2R+CmSwHv93lWT9yNOzSJaIsQM6iLPwtHRQQ6/jqybLzLiS7lm7UZyOgAx5cGSZStlfsTuHRs13VKiTBUre8ruEjFdg/jyFa0UotVDHKTUeRXGJJ5DlHHGrHnyPGuiS0QM35+3YIl8zWJiVEFOgzBzMpo2byfrsG3rFvJ+sZ29vV24ZHFBHMwGDegjg8Mq1eqiUcO6MmBbs3ajDMjEkPKIVK9eRbYsiIBD5EaJJH1xahfxXKJFadovTmPxK0R3X/16W+X7IqZ5GDqkv0ygF8nBYlZ0MWWDmL5BtHrMnb8YTk6OMs9KEJ+xcWOGo//AYXJuIVG3nz59lt1NIvdMJKZ//+4vk5bFDwPxeRN5OOKHgJjeQXQliqTniLo7TcWkCeLUTYdkXTRr0lgnOdoYfvZ/JT7PouVw2oy58vMpgqxbf9/GsuWrNf934nsi9CAKtVo1qmHi5Onyh4z4cSUCs9VrN8gARr1fTM2eMRmVq9VB0RLu6Nq5PZIkSYyly1fJXDZRd8KkCaPgdeIUPKrWkd9N4oeiaHkXP/5EoCX+H4R+f/TCxs3b0KJVR5w/f0me5UB0W8a01ZziBoMpMkniS/PaX2cwb/5i7Ny9D7t275e/0nLmzI4pk8bKAEm0CoRO+BWnnhFN+XPnL5G/OPPmzYUVyxeibZsWmu3E/EAH922Xv7THjJssD94iwVsk34ovxeiq4F5OznMjWr7El53o0jm0fwcqVXLXbCNGoJ0/c1QmZosDsTiwimRaEbT0iCTQMIbp0yYgV64ccm6pgYNHyvmnihUtjJHDB+kkF4sAR+RVjR0/BcNGjJXblS9XRh4MRLdPRMTrEN0YIhAaPHS0PMCJxz6wdxuqRpBMq7Z+7XI5R5ZoCevbb4icn0cEYyIHLvQIqtggWqdE0C1ed8MGdWVdbdqwEtNnzMWqNetl4C3K51Gpghz5Jz6DauIAmC5dGsyYNR/9Bw6X80qJfKPxY0fIcyaKNLX9e7bIep88dZacgylr1iwy0BYtIWIUnegWLVeuNEyR6NoTo/NqeDZExy495Ug6Y/rZ/5X4obV/z1Y5t5eY90nkBIruVjFqVww2qd+wOY4e89LMSRXaqJGDZbC/YdNW+YNGtEy6lSouJwAW8zuJ1qPBg/6IUXlFcHnK64A83Y5oabW2tkKJ4kWx6s9Fmh9w4v0WozvFNiJ4E4GWKLP4rIsRhuogUASAZ04elt9NYjvxfVGrZlV5yiPxI4dMm+LHnBpERERE9AuYM0VERESkB3bzERGRwYn5lfz8onfuQZF/FtmABaL4gN18RERkcOIE1SLRPjoeP7gVbpoMoviEwRQRERncP//c1ZmZ/GeJ3GJ0I1F8xWCKiIiISA9MQCciIiLSA4MpIiIiIj1wNN9P5MpbXM5QnOQXzm9FRERE8dM3Pz95doK7ty//dFu2TP2ECKSMRTwyZ0yNXazz2Mc6Z50nBPycm2edRzcGYMvUT6hbpK5dOQ1D8/VVnVDWzo6tXrGFdR77WOes84SAn3Pzq/PCRctFe1u2TBERERHpgcEUERERkR4YTBERERHpgcEUERERkR4YTBERERHpgcEUERERkR4YTBERERHpgcEUERERkTkHUy9evETyVC44etTrp9t6eZ2CWxkPJHVIi4yZc2PEyHEICgqKlXISERFRwmTSwdTz5/+hSvW6+PTp80+3vXjxMmp4NkTGjBmwbfMa9OjWCZOmzES/AUNjpaxERESUMJnk6WRCQkKwes0GGQhF97w4I0dPQO7cObFpw0p5YsLq1avA1tYG/QcOx4B+veHsnM7o5SYiIqKExySDqZs3/0bX7r3RvWtHVPaogFp1Gke5vb+/P7xOnMawIf1lIKX2W+MG6NtvCA4eOor27VrB1ChDgPevAd9EIXFdlATj2zfVpW8S1jnr3DTZJ1Mgqb32e4yITJ9JBlMuLhnw4N51ZMiQHidO/PwEw48ePUFAQABy5syusz59emckTpwYd+7c06s8ylAnVDSkjfOU+PeaJYBAgz82/axnm3Uee1jnMVW5cQhSpf/x5fPjwjE5kNYlevt/8/OP8XOSfljn5lfn4v9OEZ+DqeTJkyN58uhv/+XLF3npYG8f7j57ezt4+/jAFFlZR68Lk4gSlqNbIk5nzV5Qicw5lRDZD+pF3cqt/HEpvv4DAlRf7ZaWCnmHzvbq65rtAQtLoEg5pSqAI6IYM8lgKqZCQqIOSkJ3/f0KsbedXWIYWqOufvBpEoLEiRMZ/LEpYl+/fpeXSZOyzmML6zz6dq0Iwu3Lul3Q4utLHTTdv6GQy89Zx+xNAnDhkAIZsirkc4WI4EwddOlc1wZySewUaP4/KygsfmzzYzvNviGq65ZWQLpMClhYJIyuS2McKyhu6lyR0IIpJydHeenj6xvuPh8fXzg6OMAUiS9Jh+Tig5AwvmRMgfWP/znWOevcFLUdYC1/HIrvhtA/As8dCsbZA8EyQBHBiyAuxRbq2xah1it/NDlZWak2knGMfEztor59/6b2x+h/j6LfWv7pnRJTe0e/u9yjoaUsf6KkQEkPSxmMEZkLswimXF2zwNLSEg8ePAo3R5Wfn58c5UdEFB9E1IJTupqlXKJLneNpZ2fz020DvitxySsEX72VqgDtR/Clvq4Ic13YvTIYMXVsm3af/WuD0aSHFTLnsoCVFeCUkoEVxW9mEUzZ2tqigns5bN+5GwMH9IHFj59om7dsh5WVFSpVLB/XRSQiMkk2iRQoWyP6gZrgVtUS71+FCr4sFPK6+OrVrlO1Xu1dHYTAQNW65w+0LV+b5msnVLZzBHpNtEGyVAyqKH6Kl8GUmArh2rUbcrSfWIThQwegUhVPNGzcEp06tMHNW7cxYtR4dOvaAS4uGeO6yEREZsPKWoG0Lj8PfOydFOg+Vts65v1JicWjA/H2hW53ou8XYEL3AHQZYY2U6RQQJ64ICVblZ6VyTjj5VhR/xctg6tWr13ArWxkjhw/CqJFD5Dp397LYuW0DRowej/qNWiBNmtQYPLAvRgwfFNfFJSIiMeI6mQL9Z9ng41ul7F589VSJ9XO0LVSLx0Scg1WxrqUmwFJdQl4G6yxKBAerrgf4AwXdLJC7mIXmfktLIH1WBmZkHAplkDfH50ehcNFy8vLalZ/PdxVT2rwGjv6ILazz2Mc6Z51HJdBfiTGdAvDd8FP5RahQGW2AlTy1ArXbWhqk5Yuf89hn7DqPyfE/XrZMERGRebC2VWDMKhu8eqbUtCCJ6RT8vgJ7VgXJrj71OrFYWSlgIS+161T3K+T1Z/+G4PHdyNsIrp8NM/WEBVCnLQ+FpB9+goiIKE6JaSCcM4VvHfp9ws9HI0ZEjEz0+6YNuHw+K+G1M1gVrFmpJim9ekoVVJ3eF4xs+RWwsVU9f+acCpkTRhQTDKaIiMisJHVQIKmDbiJ8i966s8pbWQfi0jFVQLVikjZvSyjgZiHnxXLOFPFM9ERhMZgiIqIEp2ZzK1w6FhDhfTfPh8jFvbYlggKVMjgrXd1STu8QGKBaggKU8jJ5Gp6YmhhMERFRAiQCpKlbbOH7RSnnxPL/ruryE4vayT3a64c3Rz5RadY8Il9LIYOrkOAAeSlGKya2U6B2GyvZdcjpHcwbW6aIiCjBsnNU5UclsVclohcua4E9q4MQFABY2wCP7vx8wPujf8Q26jyrUNu/UWLhiECUqW6BCvWs4JSCuVjmisEUERHRDxmzWaD7GG3iu2hhEgGVSFq3tlYFWKpFgevngvHisVLehkWwvD9JUiuZ6B66VevswRCcPRgAh2RAs17WSJwUsLEVE5IyJ8tcMJgiIiKK4nQ7uQpH3KJUqb72EOrrq0pit7NTravVyhIH1gfLUYRq3p8gZ4APresoa7jmZVAV3/EdJCIiMsJ0DzVbWGHYIhs0/T3ydotFowKxbHyATHSn+IstU0REREbimEKBouUtUaScBd6/VgVMH98Ay8ZrW6juXVdicPMA5CioQOKkCuQrYYFCZWJ28mmKWwymiIiIYqGlKlU6VXdhqnSQIwkvHQvGlkXaOa7+vSGCLSVunAvBuUPBciLT8rWt5GlvyLQxmCIiIooDJTws5QSh+9YG4e1/Snz1Bd48V7VePb6jlItIXPdoYInqzXi4NmV8d4iIiOJIoiQKNOxsrbl99mAwbl0IxsPb2hyqY9uDZeAlZl1wSC5mb2dLlalhMEVERGQiylS3lItISF8zPQj/XFGd8mZiD+1s7QVLW8jWqrQuCtl9SHGPo/mIiIhMjGh98mwTcRK6yKma0S8QWxfrnlOQ4g5bpoiIiExQqnQWmLjeBs/uqyYNvXw8GJeOq1qqBHGi5gp1Q+R2FLcYTBEREZlwC5U495+QOacFGnRWYu/qYJzZr5oMdEov1RQLjbtaqfKqKE4wnCUiIoonLC0VqNncUrZUhSamWLhyKvKTMZNxMZgiIiKKR6xtFRi70gZtB1jB3km7/sB65lDFFQZTRERE8fCcgXmLW2LEUlskT61a9+UD8Pk9T0sTFxhMERERxWPiHIBqL59oE9Qp9jCYIiIiiscKltYmUP17U4n/HobwxMmxjKP5iIiIzMTZA8FyEdJlUqBUFUs4JAPyFrfgBJ9GxJYpIiKieC5jtvAzob96qsSOZUFYNTUIT+4yl8qY2DJFREQUz/WaaIN3L0MQEgz8dTIEJ3YFw8oaCFJNQ4XT+4KRJTfbT4yFNUtERGQGUjlbIE1GC9RqaYWpW2wxcb2t5r5bF0OwdzWnTjAWBlNERERmqmJdbXL6yT3BePQPR/sZA4MpIiIiM1WtqSWKV9Qe6heODMSqaYEI9GcOlSExmCIiIjJTllYK/NbdWmfd3xdDMKRlAIa39sfD22ypMgQGU0RERGauz1RrFHDTPeR/9wMWjQrE0W1BUCrZUqUPjuYjIiIyc86ZLdCqr4WczPPM/mDsW6s9KfKhjcHw2hGM5KkVKOWhRDHnv+H/900ov36FImlS2OYrAJu8+aGwsYnT12DKGEwRERElEFbWClSoawX3OpbYvSIYZ35M8BngD7x+rsTOlcDbNN6okP45IFqrPn3Et+fP4HfmFOybt4JlipRx/RJMErv5iIiIEhiFQoG67a3QorcVilWwQDoX7X3n3pRGYPCPUYA/uv+Uvj7wWb8GyoCAOCqxaWMwRURElEAVKmOJJj2s0a3F33B1eKBZP/VGP3i9cNduqFRC6eONgNu34qagJo7BFBERUQIXePsmGrtu11l3/q0bPnxPrl2hUMD/bwZTEWEwRURElMCJZHMLhKB3/tnIYv9Ys/6Tv1OojZRQfvONmwKaOAZTRERECZwYtSdanpJY+aGp6ybN+qvvi6jTpuT9iiR2cVZGU8ZgioiIKIET0x9ooyatB97ZcOB5ddUNpRK2+fLHfuHiAQZTRERECZycR8reQdX6pADKpD2rue/1t7Sq9fYOcjsKj8EUERFRAicm5BTzSCns7OVtd+czKJn6orz+2i8tkNRedT8n7owQgykiIiKSE3I6dumBJDU8YZnBBUkcFJpauZejOyfsjAKDKSIiIpJEy5Nt4aJwaNUWbn3Lampl9xpWUFQYTBEREVE4yVIpkDy16npgAPDwdghrKRIMpoiIiChC9Ttaa66fO6Q9OTLpYjBFREREEcpRQJs3dfN8CJQRTJ9ADKaIiIgoEhaWChR117a7bF8axLqKAFumiIiIKFJ12lpprl84EoJHd5g7FRaDKSIiIopUEjsFyntaam5vmhfI2oovwZSX1ym4lfFAUoe0yJg5N0aMHIegoMibF0NCQjBt+hxky1kQiZKmQu58xTBv/uJYLTMREZE5qtlSG0x9fBunRTFJJhlMXbx4GTU8GyJjxgzYtnkNenTrhElTZqLfgKGR7vNHvyHoP3AYqlSuhN07NuJ/v3fD6LGT5HoiIiL6dZaWCng01AZU/Rv74/s3JqOraTtCTcjI0ROQO3dObNqwEgqFAtWrV4GtrQ36DxyOAf16w9k5nc72799/wNz5i9G+XSssnD9Ts94lYwbUrtcEnTq2Ra5cOeLglRAREZmHcjUtcWybdnqEy17BKFfLJMOIWGdyLVP+/v7wOnEaDerVloGU2m+NGyA4OBgHDx0Nt8+//z6Q93nW+nFm6x/c3cvK7r+Dh47EStmJiIjMVVIHBUYstdHcPrqV806ZbDD16NETBAQEIGfO7Drr06d3RuLEiXHnzr1w+6RMmVxePnnyVGf9w4ePVY/5+IlRy0xERJQQ2DspYPEjcvjmC5w5wIBKMLn2uS9fvshLB3vVmatDs7e3g7ePT7j1OXJkR9kybhgzbgoyZXKBRyV3GZR17tYLtra2+Pr1m15lEr3Cvr5+MLRvfv4Gf0xinZsafs5Z5wlBQvqc1+0A7Fiqiqh2/RmEnIUDkNjO/OpcHPu1/WPxrGUqJCTqhLbQXX+hbd28Bu7ly6Bh45ZwSpERlarURueO7ZAiRXIkSZLYSKUlIiJKWAqUBnIV0R6rH92J0+KYBJNrmXJycpSXPr6+4e7z8fGFo4NDhPulSZMaO7dvwOfPn/Hy5Wu4umaBpaUlunbvjeTJkulVJhG+2dkZLyAz5mMT69xU8HPOOk8IEsrnvMNg1Yg+wdbGBnZ22pF+5lLn0W2VMsmWKXUQ9ODBI531L168hJ+fnxzlF5GNm7bixo1bcHJyQp48uWT33vXrN2UCepEihWKp9ERERAmDja3q8vpZzohucsGUCIIquJfD9p27ZSCktnnLdlhZWaFSxfIR7jd+4jRMmDRdZ93M2fPh6OiICu5ljV5uIiKihCTgR8rS7csMpkyum08YPnQAKlXxlPlPnTq0wc1btzFi1Hh069oBLi4Z5fQJ167dQIYM6eUi9O7VHZ26/I78+fKgTOlS2Lh5G9Zv2CLnnRIBFRERERlOrsIWuHtNFUh981XK084kVCbXMqWeH2rntg148vQZ6jdqgQWLlmHwwL6YOX2SvP/Vq9dwK1sZy5av0uzToX1rzJ45GavWrEetOo1x+a+rWL92Obp26RCHr4SIiMg8la6uDSF8Pifs2dBNsmVKqF27hlwikjlzJiiDvMOt/71nV7kQERGRcaVMq22Jun05BGkymGT7TKxIuK+ciIiIflkqZ4YQaqwJIiIi+iVpXVStU39fTNhJ6AymiIiI6Jd8fqfKlfr4NmHnTDGYIiIiol9SwE0VRiTkkXwCgykiIiL6JWkyqoKod6+U+PIh4bZOMZgiIiKiX2LnqG2R2r8uKMHWIoMpIiIi+iWFSmvDiKunE24SOoMpIiIi+rUgwlKBTDm0rVPf/RJmVx+DKSIiIvplVZto5/++dz1htk4xmCIiIqJflj1/qJnQLzGYIiIiIooRhUIbTF07E4LPCXBUH1umiIiISC9FymnDiSNbEt6oPgZTREREpJemv2vzpi4dC4H3p4TVOsVgioiIiPTu6itVRRtSLBsfiJCQhBNQMZgiIiIivdVtp22devVUidEdAxJMrTKYIiIiIr1ZWSvgVk0bVnzzAfy+JozWKQZTREREZBANOlqjSU9tC9Xk3xNG6xSDKSIiIjKYwmW1ocVXH+DOlWCzr10GU0RERGQwlpYK1Gplqbm9bnaQ2SejM5giIiIigyrmrg2m/P2Af/4y75nRGUwRERGRQdk5KvD7BGvN7VVTgxAcZL6tUwymiIiIyOAyZlMgWSrt7aunzbd1isEUERERGWUizz+m22huv/mPLVNEREREMWKbWAGnlKrrV06a76g+tkwRERGR0SiV2kR0c8VgioiIiIw+si/QjOfvZDBFRERERpMslUJz/dM788ybYjBFRERERpMltzaYevuCwRQRERFRjKRIqw2m/jphnknobJkiIiIi4wUaFtrr18+a51xTDKaIiIjIqPNNZcuvbZ0yx5nQGUwRERGRURUqoz1XnzmO6mMwRUREREaVOr22ZeqfK+bX1cdgioiIiIwqUw5tMGWOk3cymCIiIiLjBhsWCjilMN9KZjBFREREpAcGU0RERER6YDBFREREpAcGU0RERBRr/r5kfrOgM5giIiIio/v8QXX58a35VTaDKSIiIjK6fCVVIUfipOZX2QymiIiIyOjSZlDNNfXqKU8nQ0RERBRjwT9SpYICga8+5hVQsWWKiIiIjM4lu3YWdN/PDKaIiIiIYsQ5k0W4VipzwZYpIiIiMj6F9uqNc+Z1smMGU0RERGR0Tim119kyRURERPQLJzvOmjtU85QZYcsUERERkTkGU15ep+BWxgNJHdIiY+bcGDFyHIKCgqLcZ+mylchboITcJ3e+YpgzdyFCQsyrX5aIiIhMi0kGUxcvXkYNz4bImDEDtm1egx7dOmHSlJnoN2BopPssWboCnbv2gkcld+zesRFNGjdA776DMHXa7FgtOxEREUXtwS3zauiw0vcBPn36hO079uD16zcIjiCjTKFQYPiwgTF6zJGjJyB37pzYtGGl3L969SqwtbVB/4HDMaBfbzg7pwu3z/I/V6NsGTfMmTVV3vbwqIB7/97HvAVLMHBAHz1eIRERERmCz2fV5YvH5jXPlF7B1MmTZ1CrTmP4+flBqYy4YmIaTPn7+8PrxGkMG9Jf7qv2W+MG6NtvCA4eOor27VqF28/v+3ekTJlCZ12KFMnx4cPHGL0mIiIiMo6seRV490oVL4QEK2FhaR4J6XoFU4OHjpItRvPnTkPhQgVha2urd4EePXqCgIAA5MyZXWd9+vTOSJw4Me7cuRfhfr17dZfdfGvXbURtzxq4cOEyVq3egFYtm+pdJvG2+/r6wdC++fkb/DGJdW5q+DlnnScE/JxHT6acwMWjqgyjf659R+ZcMNk6F8d+RWwEU9dv3MLI4YPQpnULGMqXL1/kpYO9fbj77O3t4O3jE+F+LVs0wekz59CqTWfNuqpVKmHubFW3HxEREcWtHIW011dNtsDIFeaRO6VXMOXk5IikSZMYrjSi2S8k6n7U0F1/odWt3xSnz5zH5IljULJEMdz6+zZGjZmIho1bYteOjbCw+PVce/GMdnaJYSzGfGxinZsKfs5Z5wkBP+fRoW1R+vLOFumzWJhkncekA1KvVyBGzK1asz7CxHN9AjTBx9c33H0+Pr5wdHAIt/7cuYsyl2ralHEY0L833N3LomePLli9YjH27juI3Xv2G6x8RERE9Osadta24zz6hy1Tshtt5+59KFbSHfXreiJ16lQRtgB17tQu2pXs6poFlpaWePDgkc76Fy9eykR3McovrKfPnsnLMqVL6awvX76MvLx9+w7q1fWMdhmIiIjIOEpVscS2Jap5IxUWTECXI/mEp0+BGzduRdotF5NgSiSxV3Avh+07d8spDdTB2eYt22FlZYVKFcuH2ydXzhzyUuRM5c+fV7P+7NkL8jJr1swxeqOJiIjIeJKnBj6+BW6cDUbZGpYJO2fK6+g+GMPwoQNQqYqnzHfq1KENbt66jRGjxqNb1w5wcckop0+4du0GMmRIL5fChQuiYYO6ch4q0RUocqZu/3NH5kwVKlRA3kdERESm4eNb9aV5zDelVzAlcpOMQTzuzm0bMGL0eNRv1AJp0qTG4IF9MWL4IHn/q1ev4Va2shxJOGrkELlu/drlGDd+ChYt+VMGXi4uGdC2dQuMGD4QNjY2RiknERERxVyuwha4ey0E9k7s5tPYs+cAduzagydPnsHGxlqeBqaOZ03Url3jlz9jYt/I9s+cOROUQd4660TANGb0MLkQERGR6XLJocDdazAberVMiZMIN2/ZHlu27pQzoIuReGJqg8NHjuPPFWtk0vfWzWsinc6AiIiIKL7Ta2qE6TPmYvOWHTKX6dV/9/Hx3TN8/vAcL5//K9ft3LUXc+YuNFxpiYiIyGy8MJNz9OkVTK1YtRZ169TCvDnTZV6TWtq0aeQ6cVqX5SvWGKKcREREZCaCVTMjSF8+KBN2MCXOo1etqkek94v7ws4XRURERAlbBldt+o/XLsNN/B0vgymRIyWSziPz+PFTeT49IiIiIrXchbXhhx5nezMZer2EGtWrYN6CJTh//mKEp3iZv3ApqlerrM9TEBERkZmxtFIgSy7zGZym12i+8WNHyHPilXWvJmctz5UruxzVd/fufZw8dQapUqXEuDHDDVdaIiIiMguW1jAbegVTzs7pcOm8FwYOHoG9+w7B68Qpud7Ozg6/Na6PSRNGyzmniIiIiMyVXsGUIIKl9Wv/lHNOvX//QbZMiRapiE54TERERBTa6X3BqNNW73AkTsWo9AEBATqnZhG3wyakC0FBQeFmJyciIiJSe/ufakoEa1vEezFqPkpslxrrN2zW3E6UNJVcF9WSxD6NMcpNRERE8VjlRqr2nEB/yF6tBNMy1bpVM7hmzaJzm6eKISIiopiySaS9vml+EJr2tE4YwdSK5bqnhln55yJDl4eIiIgSgGSptFMjXDkZgkZdlLCyjp/TJRg8S/z79+/Ytn0Xdu3eFy6nioiIiEjImtsCWXNrg6cAf8RbFvoGTm3bd4VHldrytp+fH4qXqoDfmrZBg0YtUKCwG968eWuoshIREZEZqdFC20GmDEHCDKbGjpuC1Ws2IEuWTPK2uH779h306tkVfy6bj3fv3mPEqPGGKisRERGZqV0rdGcCiE/0mthhy7Yd6NC+NZYunitvb9+xG46Ojpg6ZRysrKzkiZCX/bnaUGUlIiIiM5LORdvNd+1MCH7rHj/zpvRqmXr27D+4lSohr3/79g2nTp9DpYrlZSAluLhkxKdPnw1TUiIiIjIrtokVSBsqoNowN362TukVTKVMmQKvX7+R18U5+vz9/XVObHzr79twdk6rfymJiIjILPUYq50S4eb5kITXzVfarSRmzVmARIkSYf7CJXKm83p1PfHlyxesWr0eS5auROdObQ1XWiIiIjIriZIokDajAq+fK2ETT2dD16tlataMSUibNg36DRiKp0+fY8qkMfK8fDdv3kbvvoNQqGB+jBg20HClJSIiIrPj0dAS8ZleLVPOzulw7a8zuHbthrwuFqFgwXw4fGAnKlVy5wmPiYiIKFri61xTep+m2dLSEsWKFdFZ5+DggMqVK+r70ERERJTA/PcoBBmyGnxOcdMJppq3bI+e3bugdOmSmts/I87dt27N8l8vIREREZk158zaEX0f3yiRISvMN5jauGkbPGtV1wRT4vbPMJgiIiKiqKROr22JUioR78QomAoJ/BLlbSIiIqJfYWUNBAUCR7YGo2Dp+JWQbpBOyStXrumc1NjL6xQuXfrLEA9NRERECUBQoOoyWcoENgP6169fUat2I5Rwq4h79+5r1i9a8ifcylZGy9YdERj4o3aIiIiIIlG8oiokuXstBO9ehSSsEx0fPnIcw4cOQObMLpr1c2ZNwfixI7B5yw5Mn6E6bx8RERFRZCxDJR7Ft5nQ9QqmNm/dgR7dO2HUyCGwt7fXrE+TJjUGDeyLTh3bYOXqdYYoJxEREZmxeh200dSXD8qEE0yJ8/LlzpUz0vsL5M8nT4ZMREREFBVLSwWcUqiunz8cgoDvyoQRTImuveNepyK9/8zZ80ifXjUrOhEREVFUQkL17olz9SWIYKpVi6bYum0nRowchw8fPmjWf/r0CRMmTsP6DVvQsnkTQ5STiIiIzFyfqTZIcKeTGdC/Ny5cvIxxE6Zi/MRpcHJylJN0fvr0GUqlUk7wOXRIf8OVloiIiMyWItSsCC+fKOGSHeYfTInz8u3asREHDx7Bnn0H8PTpcwQHByNTJhd41qwGT88ahispERERmf3EnWrHtgWhVBXLhHGiY6F69SpyISIiIvpVtom1TVOfPwDffJRIYq9IGDOg79t3EF2790ZNz4a4du0G7t9/gEWLl+P79++GeHgiIiJKINyqaUOTB3/Hj/mm9AqmgoKC0KBRC9Sp3xRLl63EocPHZL7Utes30b1nX1SoVBOfP382XGmJiIjIrNVtq+00iy8nUdErmJo0eQZ27d6H2TMn48G96zLpXKhbpxYmTRiNv65cw/gJ0wxVViIiIjJzllYK2Niqrh/eHASzD6ZWrVmP1q2aoWePLnBw0M6AbmtrK0f6dWjfGjt27TFEOYmIiCiBCPBXXX58A/MPpp4/fwG3UiUivb9Y0cJ48eKVPk9BRERECUxBN214cmhjkHkHU6lTp8KjR08ivf/a9RtIlSqlPk9BRERECUyJytopEf46EQyzDqYa1K+NhYuX48qVa5p1YtJOYcvWHVj+5xrUqc25poiIiCj6sudXIHlq1XUra4V5zzM1ZtRQnDh5Bm5lKyNXrhwykBo6fAw+ff6Mf/99gKxZM2P0yCGGKy0RERGZPYVCgbI1LbF7ZTBg+rGUfi1TDg4OOHf6CIYN6S9feKJEiXD12g05qq9f3164fOEEUqT4cQpoIiIiIjOkV8vUmrUbUK5saYwYPkguRERERAmNXi1TPXv1x8pV6wxXGiIiIqKEFEzZ2FjD0dHBcKUhIiIiSkjB1JRJYzF56iysWLkWT548hZ+fHwICAsItv8LL6xTcynggqUNaZMycGyNGjpOnr4nIiROnobByiHQZPWaiPi+TiIiI4sj7V0q5mO9ovnGT4e3tg46de0a6jUhMD/L/FKPHvXjxMmp4NkSd2jUxcvggXL9xCyNGjYe3jw9mzZgcbvsiRQri/Jmj4dYPGzEOl/+6imZNG8Xo+YmIiChu2dhqh/HtWR2EdgOtYZbBVAX3sjCGkaMnIHfunNi0YaUMxqpXrwJbWxv0HzgcA/r1hrNzunCjCkuFmYl99579OHb8BLZsWo0cObIbpZxERERkHMUrWmDrYtV1OxPPKPqlYOrNm7e4cPEyatWshiKFCyJr1iwGK5C/vz+8TpzWTLeg9lvjBujbbwgOHjqK9u1aRfkYorvx9//1l+Vr1LCewcpGREREscPCUoG8xS1w+3KIyVd5jIIpOX9U/6GYO38xgoO107uLgOXPZfORNGlSvQskTk8j8qxy5tRtTUqf3hmJEyfGnTv3fvoYs+csxIsXL3Hs8G69y0NERERx69LxEDTuZibB1Nx5izBz9nyUKF5UthRZWlrg8JHj8tQxSZMmwZ/LFuhdoC9fvshLB3v7cPfZ29vJvKmoiEBs9tyFaNqkIbJlc4UhiLQ3X18/GNo3vx+nxaZYwzqPfaxz1nlCwM+5cbx/LXqoVL1Ud675IWP22KtzcexXGGM038rV61G1SiWcO3MUffv0xP96dce+PVvRtUt7rFu/WXav6SskJOqM/dBdfxHZum0nXr9+g/5//E/vshAREVHcqdVKGxN8fGsmLVP37z9El07tYGGhG4N1aNcaixb/ibt3/0XhwgX1KpCTk6O89PH1DXefj48vHB2izkLbum0X8ubNjYIF88NQRPhmZ5cYxmLMxybWuang55x1nhDwc25YuQsDlpb+EJlFO5dZwK2yjcylio06j8kpAWPUMvXt2zfY2YXPi8qYMYO8/PLFG/pydc0CS0tLPHjwSGe9yIESLV9ilF9kAgMDcejwMfzWqL7e5SAiIqK4F6xN0cbn9zBJFjFNQI+om03kTgkhIfpn3Nva2qKCezls37lb5/E2b9kOKysrVKpYPtJ9b978WwZ8ZUqX0rscREREFPe6jdbOL/XXyVCRlbnMM2Usw4cOQKUqnmjYuCU6dWiDm7duy0k7u3XtABeXjHL6hGvXbiBDhvRyUbtx8295mSdPrjgsPRERERmKS/aYdLjFk9PJfPjwEc+ePddZnj9/Ie97++5duPvEElPu7mWxc9sGPHn6DPUbtcCCRcsweGBfzJw+Sd7/6tVruJWtjGXLV4Wb/0pIlswpxs9JREREpsfKWoEMWVUB1ZEtptkypVAGeUf7hDcW1o6RjqaLrAvwV04nY0oKFy0nL69dOW3wx1ZPt8CExdjDOo99rHPWeULAz7lx9W+smgbBMQUwbJFtrNR5TI7/Merma9O6+a+XioiIiOgX1Gxhif3rghFmMgGTEaNgasXyhcYrCREREVEUJz32+wqTZKIxHhEREZGKOh/p+zfgy8doZyfFGgZTREREZNKSpdRev3fd9E58zGCKiIiITFqOgtpw5eZ50xvRx2CKiIiITJq1jXa2ADtH05t3isEUERERmbwCpUw3ZDHdkhERERHFAwymiIiIiPTAYIqIiIhIDwymiIiIiPTAYIqIiIhIDwymiIiIiPTAYIqIiIhIDwymiIiIKN64cpKnkyEiIiKKsU/vTO8Ex2psmSIiIiKTV7G+JUwVgykiIiIyeVbWMFkMpoiIiIj0wGCKiIiISA8MpoiIiCheUSpNKxmdwRQRERHFK3evmdb0CAymiIiIyORldNWGLL5fYFIYTBEREZHJs3NUIIm96vq5Q8EwJQymiIiIKF745qO6DA6CSWEwRURERPFC3uKqsCVREpgUBlNEREQUL6RyVsjLx3c4mo+IiIgoxvz9tEGU9yeYDLZMERERUbyQr4T2/HzBgTAZDKaIiIgoXnBMAZPEYIqIiIhIDwymiIiIiPTAYIqIiIhIDwymiIiIKN7x/gyTwWCKiIiI4gUra9U8U8Ldq9rrcY3BFBEREcULyVNrr1vbwGQwmCIiIqJ4QaFQwCW76bRIqTGYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiImIwRURERBQ3TLZlysvrFNzKeCCpQ1pkzJwbI0aOQ1BQUJT7XLhwCRU9asl90ji7ok27Lnj79l2slZmIiIgSHpMMpi5evIwang2RMWMGbNu8Bj26dcKkKTPRb8DQSPe5cuUaKlb2RNKkSbBj6zpMnjgGh48cR70GzWK17ERERJSwWMEEjRw9Ablz58SmDSuhUChQvXoV2NraoP/A4RjQrzecndOF22fAoBEokD8vdu3YCEtLS7nOwcEe/+szEA8ePES2bK5x8EqIiIjI3Jlcy5S/vz+8TpxGg3q1ZSCl9lvjBggODsbBQ0fD7fPhwwecOHka3bp21ARSQoP6dfD8yR0GUkRERJRwWqYePXqCgIAA5MyZXWd9+vTOSJw4Me7cuRdun5s3byMkJASpU6VEqzadsHPXPiiVStSrWwtzZ09FsmTJ9CqTEoCvrx8M7Zufv8Efk1jnpoafc9Z5QsDPeewJDhYNLQqZR/3NL+pcan2P/domnXjWMvXlyxd56WBvH+4+e3s7ePv4hFv/7v17edmpay/Y2tpi57b1mD51PPYfOIwatRrKQIuIiIgoQbRMhYSIWDByobv+1ERLllC4UAEsWzJPXvfwqABHRwc0a9EeBw8eQc2a1X65TOIZ7ewSw1iM+djEOjcV/JyzzhMCfs6Nz9JSHPOVsLKyQpLElkar8+i2Splky5STk6O89PH1DXefj48vHB0cwq23/9GKVbNGVZ311atVlpfXrt80UmmJiIgooTO5YMrVNYtMIn/w4JHO+hcvXsLPz0+O8gsr+4+ReiJ5PbTAQFVfqsi1IiIiIkoQwZTIeargXg7bd+7WyXXavGW7bNKrVLF8uH1EgJU5cyZs3LRNJp6r7d13UF6WK+sWS6UnIiKi2HAp/OD+OGNywZQwfOgAXL16Aw0bt8T+/YcwafIMDBw8Et26doCLS0bZAiVmO//vvxeaPKqpk8fi8l9X0bhJaxw5chzz5i+Wc0zVq+uJ4sWLxvVLIiIiIgN4/UzVaOLvF5OspgQYTLm7l8XObRvw5Okz1G/UAgsWLcPggX0xc/okef+rV6/hVrYyli1fpdmnUcN62L1jI549e47a9ZpgwqTp6NKpHTauXxGHr4SIiIgMqVgFVehi7xT1gLUEPZpPrXbtGnKJiOjSUwZ5h1vv6VlDLkRERGSe0rqIYCoEEQzujzMm2TJFREREFF8wmCIiIiLSA4MpIiIiIj0wmCIiIiLSA4MpIiIiIj0wmCIiIqJ4x/uT6QznYzBFRERE8YYy1PRSb/+DSWAwRURERPGGS3Zti5TvF5gEk520M775+tUP3j7fEBgUrHNOwagEBQbLS6uPlkYuHbHO444pf84tLSyQKJENUqZwlKelIiLTlzaj6f2vMpjSkwicXr76AB/fb7CwsIC1taW8jA5LKzYMxjbWeewz5ToPCAyC71c/+PsHIr1zSgZURPRLGEzp6fOXrzKQSpHcQf66jW4gJQQHq1qwLC1N92BjbljnrPOwPnz0xtt3n/D+wxekSukUBzVERPEdj+J68v36DTY2VvJLOCaBFBGZBvFDyMbGGt/9A+K6KEQUQ9HMqjE6Hv31FBKihKWlJbsHiOIx0TqsbrUkovjj3AHTyJ9iMEVERETxhmWoBCU7R5gEBlNEREQUbygUChQoZVrhi2mVhoiIiOgnLExsphUGUxQtV69eR9v2XZEpa14kSpoKWbLlR7sO3XD//oNYrcFRoydAYeWAoKAgozy+eOywS2K71MhboASmTJ0V7TnE9CXqOkOmXCZTL2GtXLUuwroSn42MmXPLz8abN28RF06cOC3LcvSol05ZHzx4GCflISLzx6kR6KcWLV6O3//XHxUrlMOEcSOQ3tkZDx89xoxZ81CsZAUc2r8dpUqVMJuabNumBbp0aqe5/fXrN2zfuRsDB4/Ap0+fMXHCKKOXYfjQAfjf792ivX3HDm1QvVplWFnF7r/0lk2rkSG9s+a2t7cPzpw9j8lTZ+HOnXu4cO54rJaHiCguMJiiKF24cAk9e/VDzx6dMWvGZM36ChXKoWGDOihW0h2t23XB3dtXzGZqiPTO6cIFhx4eFXD37n3MX7gUY0YPhbW1tVHL4OqaNUbbZ8iQXi6xrVDB/MiWzVVnXdWqHggICMTkqTPxzz93kTNnjlgvFxFRbDKPox8ZzZRps+Hk5IgJ40aGu8/JyQkzp01CsyaN8OWL9gRJm7dsR/FS7rBzTIe06bOhS7f/4ePHj1F2SYnrYp24T3jy5Km8PWv2fOTJX1x2tc1fsESz/Z69B5Arb1HZrSSe68gR3RYQf39/DBo8Ei5Z8sA2SUrZTbdq9Tq96qJY0cLw8fHBx4+f5G1RvjFjJ6FEqQqyHOL5hP/+e4EWrTogRepMSGKfBu4Va8igNLTAwECMHjMRrjkKyNeWO18xLF7yZ6TdfNeu3UBFj1pIltIFSR3Somz5qjh06GiUdRqd9yFbzoLycYoULydfQ2bXfJg+Yy70JT4zso5CnaLFEPUiLFu+Sgbxoh7ENoWKlsGmzdv0LjMR0a9iMGVEQYFKvHsVEuny/pVSLlFtY6hFlCWmlEolDhw8Ao9KFZAkSZIIt6lduwZGjxqKZMmSydvjxk9Bk2ZtUaJ4UWzbvAYjhw/Ctu274F6pJr59+xbjMgwfOR5/9Pkd69cuR62a1TTrO3TqgZ7dO8vncHJ0Qs3ajXQOzA0bt8S8BUvQq2dX7N6xEZUqlkfb9t2wcNEy/Kp7/96Hvb09UqdOpVk3fuI0NG5UH9u3rEXTJg3x4cMHlC5XBecvXMbsmZOxcf0KJEpki4qVPXHlyjXNfm3adcGkKTPRrk1L7N21GfXqeKJr995YumxluOf19vZGtZr1kTJlCmxav0I+V+LEieFZ9zc8evQ4wrJG93149eoNOnXtJbs19+/ZCrdSxdFvwFAcOHA4WnUi5mYSAZx6Ea9/67admDp9tnzunDmzy+0+fPhokHoRAbUICuvWrol9u7dg3ZplSGRrixatOuLp02fRKjMRmY/nsZu2Gyl28xmJCF4m9wrA5/cwCU4pgYFzbGBlHf0Jzt6//4Dv378jS+ZM0dr+06dPGDdhKjq0b435c2do1ufLmwflK1aXLQq9YpAHJIiuRPF4YS2YNwNNmzTSdMFlzV5AHoR3bt8gE4/37T+E1SsXo1XLZnKbatUqIygoGMNHjpU5UXZ2SSN9TqVS1VKmuq7E69dvsH7DFuzesx8D+vXWaW1xK1UC/fv9T3N72PAxMvH6n1uXNF11IggUrSdDho3GoQM7ZS7Rho1bMW3KePzR93fNa/jvxQucOHkanTq21SmP2P7du/cyh6psWTe5rlixwhg/YRq+f/fX630QgdXWTatRo0ZVebtMmVLYuWsfdu89oFkXFdE6GFby5MlQr64nJk0YLbt+RcA1a84Cg9TLo0dP0Ld3TwwfNlDzfJkzuaBoifI4feYcMmVy+WmZiSj++/7jN+Hn9wo5ebaFRdxO3slgiiL/cFipxp4GBwdHq5YuXLgsu9dEt19o5cqVlge5EyfPxDiYEjk5YYkZ5xs1rKe5nShRItSoXgU7du6Rt48dPykva3vW0On2quNZQybTX7p8RbZURWbCpGlyCU08R5fO7TFq5GDd8hXSLZ947vz588rXG/q5a9Wohpmz5yMgIACnTp+V6xrUr62z75pVSyMsT758eZAmTWrUrtcEjRrWRdUqlVCtqgdmTJ9okPdBBFBqtra2SJUqJb5+/ap570VAqSYCSVH/aju2rZcJ6KJ7buPmbZi/YCn6/K8Hhg0doPPcx70MUy/Tp6m6gT9//oy7d//Fg4eP4HXitFznz9PBECUYKdJqgydlSNz3szGYMlbFWitkS9Cn95F3r4UEq+6zsDR+RJ0spSJGrVJyn2TJZLfW02fPI93Gz89PjnYTXVAfP6lyidKmTRNuu7RpUuPzZ21eVXTZ2dmFW5ciRfJwo9ZSp0qlefz3Hz6oyp8y4laKly9fRfmc7du1QrcuHTTBg729HbJkyRxh0rldUt0WLvHcDx48gnWi5JG29olFljlUd2FUkiZNijMnD8kuRREwipYlGxsb2WonWuhE7lpoMX0fwnbhitYk9RQQHlVq4+SpM5r73MuXxYnj+zW38+XNrUlAd3MrCStLKwwfOU4Gn/3+6KXZ7sP7DzLw0bdeHj58hC7deuPY8ROyDnLlyoEC+fPK+0IHfURk3nIXtcDZA9H7oR8bGEwZs3KtFUiVLvIARn0uMHFeMFMlWkC8TpyS3X3iABnWuvWb0blrLxzavwPJf+RNiW6xvHlz62z36vUblCxRTF5Xd5OJVg91UOTr6xvtMolgQBzsQ48efP3mjWxREZwcHWVO0SmvA+H2DQ5R/rTbMl3aNChWrAh+NfG6bBk3zIyk1UgEnerkbNF1JwIlNTEP0suXr2ULUlgiYFmxfKF83WLOry1bd2LajDlIlsxJpytPiO77EB2LF86WSfdqIriOipg24sChIxg6fIycqkG0qhmqXkQLWq06jeVn5tJ5LxQqVEAGuGLE4Np1m6L9moiIDM10j+JkEv7o01MmDw8dNibcfSLZeOLk6XJIfsWK5VGyZDHZTbRh01ad7U6fPodnz56j7I/uJAcHe83oLrWzZy9Eu0yiS0g9IaM6EBM5UmIeLMG9fBnZYia6nkRQpF4ePX4iR4qpu7CMwb1cWZmonj27q85zb9u+G3PnL5YHfxFUCDt37dXZd8So8ejcrVe4k2Zv2boDqdJmkcGRCCDF402eNAa5c+fE06fhWw2j+z5Eh0ggD/061AnlkRGtRQvnzZTvkZibTK1cuTJ614v4HN67d18mpxcvXlTTUigGSQixNaEqEVFYbJmiKIn5lsaOHoZhI8binzt30aZ1c6RJnVpenzZjruya8Tq6V7YWJE+eHIMG9MGYcZNha2uDurVr4fGTp7LbRxyERfeZOvG4b78h6Nq9Dwb2743n/73A6LGTIuzSi4g4iHbs8jsmjh8JRwcHmXguuhpHDBsk769ZsxrKlyuDBo1bYtiQ/sibJzeuXb8hRwaKEWYuLhmN9q737dMTa9ZthEfVOuj/Ry+kSpkSO3fvxdx5izFm1FAZKBUsmF+OABwybIycj6lokUI4euyETL5euzp83lSZ0qVk8nydek0waGBfJHNywsFDR3H79h1Zf2FF930wFnf3smjWtJF8PRs3bUXjRg1kHtW69Zv0qhfR/Zc5cyYsXLwMmTO7yHo4dPiYzLkSjBkkExFFhcEU/dTQIf1RpHBBOWHlH/2HyhaC9OnTwaOSO4YM+gNZs2bRbDtq5BCZqyNaG5YtXy3zmxo2qIvxY4drgqUcObLLkXZixJnothFdUcuXzEOX7tpRcVERo8WmTRmHQUNGyfwnESCdPL4fefKo5mUSrTf7927FiJHj5UzcYhRZunRp0bVze51RYMbg7JwO588cxeCho9G9Z1/4+X2Hq2sWmdvUrWtHzXYiOBg9ZpKcvkF0a4kWGzH9Q7OmjSN8zCMHd8qAVnSp+vp+lUHR8qXzNaMVw4rO+2BMYkTe3n2H0G/AMNSsUc1g9bJz23r8r89AOc2FCBTz5M6FPTs3ofcfg3D6zHn06d3T6K+NiCgshTLIm1mbUShcVNV1dO2KasRQWE+evZaXmV3SIqbiQ86UuWGds84N/X9sinx9/eSlnV3iuC5KgsE6j133boRg2bhAeX3SBhtYWili/fgfGo/iRERERHpgMEVERESkBwZTRERERHpgMEVERESkBwZTRERERHpgMEVERESkBwZTRERERHpgMEVERESkBwZTRERERHpgMEVERESkBwZTRBFQKnmWJSIiih4GU/RT//xzFy1adYBzxhywSZwCaZxdUa9BM5w5c95otZfZNR9atladAPfJk6dQWDlg2fJVsfJujR03GVOnzdbcHjV6gnz+oKAgoz/3ylXr5HM9ePDQKI//+PET1G/YHMlSusilVZtOePv2XYweY8fOPbKMR4966ay/c+ceatf9DfZOzkieygWNfmuF+/cfGPgVEBHp+vgu7n/8MpiiKN2+fQclS1fC69dvMXP6RBw5uAtzZk2Bt7cP3CvVwO49+41Sgzu2rsPokUNi/d0RAdOIUePx7ds3zbqOHdrg/JmjsLKyQnz25csXVKzsicdPnuLPZfMxe+YkHDt+EjU8GyI4ODhaj/HmzVt07torwiCtTPkquP3PXcybMxXr1yyHhYUF3MpWlsEwEZEhJUmqvf7odtwHU/H76GDGlAEBCLh9C/5/34Ty61cokiaFbb4CsMmbHwobm1grx4xZ8+Dk5IiD+7fD2tpas75B/TooUrwchgwbjTq1axr8eQsXLghTkSFDernEdwsXLcfr129w4ewxpE2bRq7Lny+vfB+3bd+F3xo3+OljdOzcE4kTJw63ftacBfj69RsuXzgBV9escl21apVRqnQlDB85DmtWLTXCKyKihCqDqwKmhC1TJij4w3t8WTwf3w7sRfB/zxHy8YO8FLfFenF/bBEHX5E/FBISorNeBFZTJo5B545tddafO3cRFT1qIalDWtmN1Lxle7x8+Upz/4kTpyPsIipbvioqVKoZYTdfdInHLF+hOhyTZ5DdTM1atMOzZ891utCsbJ1w7vxFGUAktkuN/IVKYfOW7drXlSi5vBw9dpIsZ0TdfKKcHTr1wMRJ05EhUy4ksU+DGrUayLpatXodsuUsCDvHdKhU2ROPHj3WPLZo/Zk8ZSbyFSwpn1vUUemylXHs2Ilov0Z1l2dkS+g6DOvgoaMoU7qUJpBSB63ZsmXF3n0Hf/rcS5etxPkLlzB18thw94kuvly5cmgCKUGhUKB8uTLYf+BQtF8fEVF0iO8Xe6e4b5FSYzBlgi1SPuvXQOnr82OFUudSrJf3BwTESnk8a1XHixcvUaqMB+bOW4S///5Hk5xdo0ZV9Pq9m2bbs2cvoIJHTVhaWmLjuhWYO3sKLl76C+UrVoe3t7dRy7lm7QZUqV4X6dKlxYa1yzFrxiRcuPiXLLcIckJr0Kg56tWpJbsSc+XMgabN22m6K8+cPCwvO7RvLbv2IrNl604cOHgESxfNweyZk2V3mXjtM2bNx9TJ47B08RxcunwF3Xr00ewzaPBIGaR16dQOB/dtx7Ilc/Hx0yc0atIavr6+0Xqd4vWJckW2LJg3I9J979y9h5w5s4Vbnz2bq7wvKiIo7NtviHz8NKlTh7s/ZcoUePXqNQLCfC4fPnqML1+88fHjp2i9PiKi+IjdfCZGdO0pfaIIPJRKeb/YzrZwUaOXp1vXjjJBefLUWejVe4BclyyZEyp7VES3Lh1QsWJ5zbYDB4+Aq2sWHNi3TdMl6F6+LLLlLIT5C5Zi8KA/jFJG0Wo2YNAIeFSqgE0bVmrWi1aYPPlLYMrUWZgxfaJmfY/unTFi+CBNV5RopRozbrLsrixZsphcnyG9M0qVKhHpc/r7+2PHtnVIkSKFvL19x27Z8nPvnyvIkSO7XCdawFauWq/Z5+WrVxg3Zjh+79lVs87W1hYNG7fE9eu3ULas209fq9g+qnJFRQQ1Dvaq1rbQ7O3tZdATGdGi1rptF9SqWVV2BYrWxbDatWmJDRu3omXrTpg0YRQcHR2wZu1GWSfC169fkSqVqq6IiMwNW6ZMjMiRguInfcEKBfz/vhVbRcLIEYPx6r9/ZaDSuVM7pE6dClu27kClKp7oP2CY3EYkbIsuoJrVq8rmV9ElJhbRklKkcEEcPnLcaOW7d+++bH1q1rSRznrR5eRWqgROnDqjs75Fsyaa66KsDerVxpUr16LdOiTkzJldE0gJadKkhpOTkyaQElIkTy4fU909uG7NcvTt0xPv3r2XIyFXrFyLtes2aYKz6FLXbURLVInkYbtqQxP1EBkRjIpgK6pWrypVKmHt6qXwOnEKrjkKImWaLNi3/zAGDVC1zCVJkiTar4+IKL5hMGViRLK5pmsv0o2UUH6L/oHfEBwdHWWrxOKFs3H39hW5iJafaTPmyK6/T58+y4O1SFgXeUehlwsXL+PFy5dGK5u6CyltmvDdT2nTpsbnz1901qVPn07ntggOBfEaosvB3j7cuqRJow4Y/vrrKkqUqoDU6bKiao16WLBoqRzxFpN5rUTOVNj6Db14VKkd6b5iIIGPuvs4FNEF6+gQvsVKuH79JkaNmYiF82fCwcFBJ2AT73fo4K1F8yZ4/eIB/r1zFf89vYsjh3bJ7cVrFM9NRGSu2M1nYsSoPXz6GHVApVBAkcTO6GURuVLFS1XAyOGD0KVz+3AtMyIvSdz/z527qFG9imzd6PV7V7Rsrm35Cd09FboFJGwLiu/Xr3By/LUDbvLkyeTl6zdvw9336tUbpEyhSipX+/DhI5IkSa8z3F8c8EXej7GIgKV6rQbIlzcP/r5xEblz55TPuX//ITmSLrqcndPJEXOREV12kcmZIzsePHgUbv2Dh49kYByRnbv2yjwoMTdVWNVq1pfduCeO75cJ6Jcu/4U2rVsge3ZtXtbVazdQIH9emUdHRGSuTLZlysvrFNzKeMgRTxkz58aIkeN+OmmiGD0W0QinsAnIpkxMfxCdlinbfPmNXhYx6kvMrTRvwRKdeZfU7v17X16KAEEcxIsUKSQPqsWKFdEsBQvmx/iJ0zS5Mw4OqoP9fy+0LVUfPnzA3bv//nI5RWAnyipydsImTYuux7JldHOR9uw9oLkuWoS27dgttxFD/o110BevTwRxv/fsgrx5c2tapA4cOvLTLrjQbGxsdOo37CLqIjLVqnrgzNkLMnhUu3bthgywxH0REd26IngLvSxaMEveN3/udNlSKYiAum37bvL9Dz3Z66HDx1CnTq1ovTYiovjKJFumLl68LCcSFAnBolXk+o1bciJFbx8fzJoxOdLuj8+fP8sJJYsXK6JzX4owLROmTMwj5XfmlGo0X0RBlWiVsrOX2xmbCCwWzZ+Jeg2bo2iJ8ujZvTPy5smNwMBAnDh5Rs4t1K1rB+TJk0tuP2HsCNSs3QhNm7dFqxZN5brZcxfC68Rp/N6ji7xdoEA+ZMyYAeMmTJUtSiKomDBxGuztf72lTTzGxPGj0K5DN/ncrVs2k6PkRo6eILuX+v2hO8nkoCEjERgYIFtqli5fJQ/6x4/s1bSciS7Ns+cu4tSpsyhXrjQMQQQ5opts0pQZSGRrKxP0t27fheV/rpb3f40gWDU08V7Nnb8YHlVrY9SIwfDz88OgIaPk9AiNG9XXCbBES6J4X0VLmFhC8/X9Ki9zZM+mCd5q1qiK7Nld0aJ1R4wdNRQ+vr7oP3A4smbNjN69uhv9tRERxSWTbJkSB0HRDSISnqtXr4JBA/ti8sTRmDd/ic6cRaGJgEto1LCeHO0Uegk92aSpExNy2jdvJQMm1QqFzqVYL++PpYk7a9asJlsjRBL5lGmzZVdVg8YtZaLxvDnTMH+uNim5alUPHD6wU3at/dasLVq07oSAgEA5DUClSu6aAG3b5jVwTpcWzVq0lyMEWzT/DXX1bL1o26YFtm5egwcPH8vy9fljMEqVLI6/Lp4MFwyIcosgqn6jFnI4v5jVPXTQNGxIf/x15ZoM6J8//w+GIAK0Xds3yOuNm7ZBq7ad5RxYp7wOyla906fPwdhEwvyJY/vlSMU27brKOqpUsbx8f0LP7i7qpXvPvjF6bNGqJx4nvXM6+b7/r89AVPaoIJ9PBJFEROZMoQzyNp1Zr36ManJIll4e0IYPG6iTvyMmSFy+dD7at2sVbj8xseLCxcvx5qVhz2lWuGg5eXntSvjh4MKTZ6/lZWaXtDF+7OBgVdeOpaVFFDOg35LJ5iJHSnTtxfYM6OZETNopWq/u3r4a4XxLZBxRfc5NhT7/x6bI19dPXtrZhZ+tnljn5mJMp+/w+axAoy5WKFnZ8CkaPzv+m3Q336NHT2TCa9jcj/TpneWv39A5GWFbpsTcNnXrN5XdSiIXRsyLM3P6JDk8Xx/KUF9OYQUFBsPSykJzwIiJKPNkLK1gVaCwXHT2EX9+4blIW98hSjEKjXUYW6KbDxaXxPdFcFBIpP/n8c03v+hPtUGs8/hKqVT12Hz3D0AMZraJ/uOLFqdobmthiidjjWzoucirEXlTkQVTontJJBLv270F06aMk3k94mS8MZk/iIiIiEyfS85gWNsokUF7Fqs4Y3ItUyEhUfc6Rja54NpVS+VIMZHgLIgcGDHKrKx7Vdm90/NHAvSvUETRXG710VLvLgxT7v4wJ6J7WAzdF1jnsc+U61x8r1hZW5pdt5i5vZ74gHUeexp19UNQoBLJkhvnc66Iz8GUenI/MRooLB8f30gnF4zoVBxlypSSib83bv5thJISERFRXBFtK9YmkkJscj8VxbndxIivsJMLigR0MZRbjPILS8xTJIaYi9OKhM3VEPlXKUOd9oOIiIjIrIMpMb9NBfdy2L5zt07i6uYtquHbYih3WGLqg67de2PqdNUEgmq79+yXAVjFCqqMfGOwsFDI2byjezoQIjI9YkCCKXdDEpFpM7luPmH40AHyJLoNG7dEpw5tcPPWbTlpp5h00MUlo5w+QUwsmCFDermIeWz+6PM7pkybJU8uW7VKJdy89TdGjZmEWjWryfmPjMUuaRK8efsR795/RsoUjpqZrYkofvjw0VvOh+Zgz5MxE5EZBVPu7mWxc9sGjBg9Xk4gmCZNagwe2Bcjhg+S94uJFt3KVpazo48aOUSuGz9uBJyd02Lx0hWYM2+RPM9aty4dMHKEah9jcXJMim9+3+UX8qfPvrC2tox2QKVuzYosqZ4Mj3Ue+0y5zkWLlAik7O2SyB9DRERmMWmnqYnupF1fv/rB2+cbAoOCoz2vjpijShCjiCh2sM5jnynXuejaS2RrIwMpUwz2fhUn7WSdJwS+Rp6cNl5P2hlfJU2aWC4xwS+82Mc6Z50TERkaE3yIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MDRfD/xzc9PZ4ikIannpDCfAdmmj3XOOk8I+DlnnScESiMfQ9XH/+hgMPUTYu4ZY50qhkFU7GOds84TAn7OWecJgSI2niOa889x0k4iIiIiPTBnioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaIiIiI9MBgioiIiEgPDKaMyMvrFNzKeCCpQ1pkzJwbI0aOQ1BQkMH3oV+vP3HfrNnzkbdACbmPa44C6PvHYPj4+LBajfg5D+1/fQZAYeXAz7mR6/zChUuo6FFL7pPG2RVt2nXB27fvYvK0Cdqv1PnSZSs13y258xXDnLkLERISEmtlNhcvXrxE8lQuOHrU66fbxtUxlMGUkVy8eBk1PBsiY8YM2LZ5DXp064RJU2ai34ChBt2H9Ku/IUNHY8CgEWjUoC5279iIvr17YtWa9ahavR6/9Iz0OQ/t2LETmDtvMT/GRq7zK1euoWJlTyRNmgQ7tq7D5IljcPjIcdRr0Ix1b6Q6X7J0BTp37QWPSu7yu6VJ4wbo3XcQpk6bzTqPgefP/0OV6nXx6dPnn24bl8dQhTLIW2n0Z0mAqtesjzdv3+Hq5dNQKBRy3cxZ89B/4HA8e/wPnJ3TGWQf+vX6+/btG5xSZES/vr0wYfxIzfpNm7ehafN2OHZ4DypVcmcVG/hzrvb582fkL+Qm9xNfmIHfP8LKyor1bYQ696hSG76+vjh35igsLS3luu07duN/fQbC6+heZMvmyno3cJ2XdKsIGxsbnD55SLOuWYt2OHP2Ap4/ucP6/gnRgrd6zQYZCCmVSnz8+AlHDu5C5coVI90nLo+hbJkyAn9/f3idOI0G9Wpr3lDht8YNEBwcjIOHjhpkH9Kv/sQvnU4d26Bxo3o663PlzCEvX756xSo28Oc8tB6//4GsWTKjbevmrGcj1vmHDx9w4uRpdOvaURNICQ3q15EHdQZShq9zwe/7dzg42OusS5EiOT58+MjPezTcvPk3unbvjdYtm2HNyiU/3T6uj6EMpozg0aMnCAgIQM6c2XXWp0/vjMSJE+POnXsG2Yf0qz9x3/y5M1C4cEGd9Tt37ZWX+fLmYRUb+HOutnnLduzecwArli+AhQW/hoxZ5zdv3pa/8lOnSolWbTrB3skZdo7p0LJ1R3z69ImfcSPUudC7V3ccOnwMa9dtxJcvX3Do0FGsWr0BrVo2ZZ1Hg4tLBjy4dx0zpk9EkiRJfrp9XB9D2aZuBOIfR3Cw1/1VItjb28E7guTmX9mHDF9/589flH3snrWqo1ChAqxiI9T5y5ev0K1HH0ydPBZZs2ZhHRu5zt+9fy8vO3XthRrVq2DntvV48PARBg8dhQcPHsmuPwa0hq1zoWWLJjh95hxatemsWVe1SiXMnT01Gu80JU+eHMmTR78e4voYymDKCEJCok5DC90Eqc8+ZNj6O3HiNOo2aIYsWTJh5Z8LWb1GqvP2HbujWNHC6NqlA+s4Fupc/FoXChcqgGVL5snrHh4V4OjogGYt2uPgwSOoWbMa3wsD1rlQt35TnD5zXib7lyxRDLf+vo1RYyaiYeOW2LVjIwNYA4vrYyiDKSNwcnKUlz6+vuHu8/HxhaODg0H2IcPV38pV69Cl2/+QN29uHNi7DSlSpGD1GqHOFyxcivMXLuPG1bOa4crqoeIir0G0kLCVxLB1bv/jl3rNGlV11levVlleXrt+k8GUgev83LmLMkdn4fyZmh8N7u5lZY5grTqNsXvPftSr6xnV01IMxfUxlMkKRuDqmkUmeoom9LBzZfj5+SF37pwG2YcMU39iHpJ2HbqhYoVyOOV1AGnSpGbVGulzvnnLDnh7eyNLtvywTpRcLmPHT5H3JUqaCmPGTmLdG7jOs/8YqScSdEMLDFQFsyKfhAxb50+fPZOXZUqX0llfvnwZeXn7NkfzGVpcH0MZTBmBra0tKriXw/adu3XmKhJJt2Lod6WK5Q2yD+lff5OnzJQH87ZtWmDv7i2ws7NjtRrxc7544WxcvnBCZ+nUsa2878LZY+jcqR3r38B1Lg4imTNnwsZN2+QQc7W9+w7Ky3Jl3VjnBq5z9YhgkTMV2tmzF+Rl1qyZWecGFtfHUHbzGcnwoQNQqYqn7B/v1KENbt66jRGjxqNb1w5wcckofyVeu3YDGTKkl0t09iHD1vn9+w8wbMRY5MqVA507tsVff13VeTxX16xIlSolq92AdR52pE3og3rRooU5z5QRvltErohI9v+taRs0btIaXTq1w71/72PIsDGyq6l48aL8jBu4zsUI4YYN6sr5jUQXk8iZuv3PHZkzJQa2iPtIP6Z2DGXLlJGI/vGd2zbgydNnqN+oBRYsWobBA/ti5nRVN8arV6/hVrYyli1fFe19yLB1vmPnXpm3c/fuvyhdroq8L/Sya/c+VrkRPucU+3XeqGE9OQv3s2fPUbteE0yYNF0GVRvXr+DbYaQ6X792Of7o0xOLlvyJajXrY9acBWjbuoVMJRCTeZJ+TO0YyhnQiYiIiPTAlikiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIqJ4bdToCVBYOYRbrBMlR6q0WVCtRj2cPHkmrospy9S0ueqkzkJm13woVbpSnJaJiAyDJzomIrMwZFA/5M6dQ3M7ICAQ//xzFwsXL0flanVw9tRhlChRLE7LSETmicEUEZmFKpUrokKFcuHW161TC+6VamDMuMnYu3tLnJSNiMwbu/mIyKyVK1caOXJkw7nzF+O6KERkphhMEZHZs7Oz07n98uUrtO/YHWmcXWGbJCXyFSyJ+QuWhNvv69evGDhoBLJmL4DEdqmRM08RTJo8A0FBQZpt3r59h959ByJbzoLysewc08GtjAd27tobK6+NiOIeu/mIyKw9f/4fbty4hfLlysjbr1+/QcnSleDv749uXTogTZrUOHzkOHr26od/7z/A7JlT5HaBgYFwr1QTV69eR7u2LVGieFGcv3AJg4eOwrPnz7Fg3kx8//4d5StWlwFVj26dkDlzJjx79hyLlvyJBo1a4PKFEyhatHAc1wARGRuDKSIyC1+8vfH+/QfNbT8/P9y+fQcDh4yUt0ePHCIvhwwbDW9vH9y4elYGP0L3bp3Qp+8gzJqzAB3atUaBAvmw/M/VuHLlGpYsmoNOHVWj8Lp0bg+lUoklS1dixLBBOH3mHO7du49tW9aiQf06mucu7VYS1Ws1wKHDxxhMESUADKaIyCzUa9As3DqFQoHixYrg2OE9KFvWDSEhIdi+Yw9Ku5WQXX+hgy8RDIlgau++gzKY2rP3ABwcHGSrVGhTJo3FwP59kDJlCjRuVB/vXpdHsmROmvuDg4PlIvj6fjXqayYi08BgiojMwrQp41GwQD4ZMF27fhNTps1ChgzpsXrlEuTMmV1uI4KnL1++4OCho3IOqog8ffZcXj55+gxZs2aGlZXu16ToFhSLmqWlBWbMnIez5y7g4aPHePDgkez+E0RZiMj8MZgiIrNQtEghzdQIVat6oFpVD5QpX1VOi3Dh7DHZpaduMapTuyZ+79Elwsdxdk4nL8W2omUrKvfvP5DP8e2bHyp7VED9up6yVSuTS0aUcKto8NdIRKaJwRQRmaVChQpgzqwp6Ni5J5o2b4ezp48gVaqUSJIkiWw5qlxZN9h59+49Tp0+i+zZXeVtERBdunxVti5ZWGgHPl+/fhNTp89G/z/+hznzFsnWrpvXziNfvjyabc6d4zQMRAkJp0YgIrPVoX1r1KxRFRcv/SW74kSXXa2a1XD02AlcuHBJZ9uRo8ej0W+tZNK64FmrOj5//oz1GzbrbCdG6m3YuFUGZh8+fIStrS1cXbVdhiL4mj13obweegoFIjJfbJkiIrO2eOFs5C1QEiNHT0D9ep6YNGEUvE6cgkfVOujetaNsiTrudRKbNm+XgVb16lXkfp07tcOqNRvQtn03OSVCgfz5cObseaxdt0kmoKdP7yy3371nP6rVqI/mzRrLFq+Nm7bJnC3RmuXj6xvXL5+IYgFbpojIrIkk9CmTxsipEjp16YUsWTLj0nkvNKhfG6vXbkCv3gNk8COmTtiyabWmS0+0OB07vBs9e3TGrt370bvvILnd/LnTMWG8aroFMWWCGN338tUref/0mfNkcrrI0SpSpCCOHT8Rx6+eiGKDQhnkrYyVZyIiIiIyQ2yZIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiItIDgykiIiIiPTCYIiIiIsKv+z/tiDpTHJyveQAAAABJRU5ErkJggg==",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Génération de la courbe d'apprentissage...\n"
- ]
- },
- {
- "data": {
- "image/png": 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m3edIkSI5qlWthGpVK6NI4YJKzIvfqSXyD1xoaN9daG0O772vHcPHRrBLWPc5sR8oBIldIsMn+/YfxIqVa9UPviXaf9vi7D1rxiTjMNyVq9ZZ+LXo4Azp05mJFHHSN0WGoCKCLQujWCo0a4ZYMTQsLSi3b9/B+QsXQ02GLQ8KWw84sYSExqQpM5VwMXVuF44ds50KxNbxrl/XW7ZMcxOKVUWGvEyDOmzVs4UICrHmiDAzHaITtm7doYItohp5kEo0pnzfderUVIsgwSFVa9RXQSMiBOW7ePDgEcqVK62GJ8ePG6mCJarVbKCCPEaPGqoilsUCuO3XtahXr7ZZ1K8pmmXn2jXr702zFgoiSsTCKlZay2tDrFyHDh9Fjhy2+1QCX8qXsx6ytUSGTCNK4cIFVVCUBFi1btsZRw/vDvEfoahELHSWXL12XQkluQ8OHz6mROCAfn2sgr60IduQGDRklAoUOXv6qNlw/Z+T9G4AEUX+oTK1cpsydNgYFfQhAjA8975c95IM3db1Ii4GhAj0ESR2SdcundSDcNCQkSoa2BJJ7yAJhdu0bq58a2RoSCwFMrQnM5KYigERRkJ9wwNcE2gyFKYhonBjBH0IJQXJiRPBPnSS1uX7xT8gS5bMKsehCJ4SxYsqMSs//qYiViJDxT/LVvSlRtPGDXDr1h2z1BViIZXo39CQ4SaxSGnD1Fo/SFSjEBAQYHUekgrDdOhRohblAWyZw3H2HPPo4Gkz5iqh0KRx/VDb1KiBPr3L+AlTzMql/xo1ba1EWUTRBEpIlhMRETIs3OnznmbW3qJFCynrsLa/RD7LLBri96WRIUN6pEubVtWR4zx7ro/glKFlU2bNnmfmz1q0aGH1/f/w4y/KD03jxo2b+G3H72ZWrnp1a6khadNrSBg9doKK2paoU1tkzZpFiYuwFlv5N8NDl887om6dmur+EveCT4H0g+k9IlHuq9dsUFHLMrwdUv9v375T/TNieU1b3g/iB2oqAl++fKm+IyG0fW0h/yzIb4jsb2qxlyFsuY4fPHwU7ntfri25d/YfOIRTp06b/dP1488rI9QuEn+hRZDYJfKg3rR+JWrWaYwy5aurlBGSNPnNm7fY/tsuZTEpXrwI5n07w7jPt3OmqfQNkpJDUnyI1UUc8sVpu8+XPY3+OzI9lsxIIjOTiEVH0klI6peI+p/Jf/PiWC9pSSQ1xKIly1VKkS2bVhuHl6RNVarXR4nSlVV6FnlAiGVDrGO9enZRFrKQGNC/D1av3YB2HbqqoA1J7fLLijVhWi5FYEjwRq06TdS0fGJ1koeqCF9p1xsL53gRjTKLi6TukHNa+sPPKr2FnIfl8JdYyP598BDFixVR34OIGwnGMQ3QsYUEaEgQxIKFS3Hv3j9KaIgP6LfzvkfixIkwfuwIRBT5foWZs79TQ6yymCJtHzLoa/XgrVGrEZo3a6RE48+/rFYPcElNI8j3J2mGJKVM966dVXvEGi3XjRZoVK9OLcz9dqGa8lCCdeTY0se79+xX16r4eQnSv3NmTkHjZm1Qulw1dO7UTl2zcp6WSKoaEQDVajZU16v84yOfK//MyHdYu3YNxBTfL5iDfAVLYfTYiUqomAYyRAdeXglQsUod9O3TWw2vS39JX86eOdnoKiLBQvKPodyz4kMqQvXHn1aqIXfxmRSRZWu4tl7dmirlUucuvZQlVa5tucblXhUs74ewUKliZk1RFlP5rfmsYzv12/Hd/EVImNDLGFAV3nt/wvhRyle0eq1GKk2UBKosWvKDTTcXYqfEdNgyF/ZBTF4DkkZh5PBBugIF8qlUG5JmoVChArqpk8frPrz7z6r++TPHVRqLxIkT6zw8PHTFixdRKU0s60k6E0k3I2lKJN3LoAF9dXt2bbGZPkbSuFjuL2lXmjVtpFs4f7Z6LccpVbK4bu/vW63qSmqVxo3q65Ik0bcpf/68ujmzplilpLG1SEoPSdmSPHkynZeXl0o1sXrlD6Gmj5EUFdI/ksZE2iXpNRo2qKv769Rh1R9Sbnoe0u7lyxao156enrqKFcqp/jE9ppaSQ9KJFClSSB1XUrAsWjjXrJ6WPsZW2yTVx+SJY3V58uRS6Tyk35s3a6y7dOGUWT3ZX87Tcn9J61GpYnnje0nLUb1aFdWW3Llz2kwdIsuyJfN1RYsWVtdPggQJ1DF2/bbJ7NhyXjWqV1Epc+R4cr3NnT1V9aXpuck5yzUobZfPlu9bzkG+G0m7o9XdsmmN+kyt/+W85Zzkvenn3rp+Xte+XSuVKkS2SUqSsaOH6d6/eRyt95WWPka71m0tcm1LncqVKqh+0NKoyL5R2RY5pqQPGjdmuOpXuUekb+V6Na137PBuXYXyZVVaGbmXJLWMpHz6ds40dYxD+3favAbkOunerbNK+SLfnVz/8v7a5b9UipzPO3cI9X6X40i5HNcyHVO5sqVVe+W6kd8D0xQwEbn35TqQ60h+t2Tp1bOLuv6YPiZmnjuxbXEw3CiEEBLlyMwiqVOlxIljIaex0WbsEOuLz9snZkEPxByxOMqQn62o5zr1muLipSu4d9t8VhNCCAkN+ggSQkgcQYbzZJq0Tp3NI4FlOP/Q4WMoWaJYjLWNEBI3oY8gIYTEESS4RAKYfvp5lQoIEb9WsRAuXrpc+a+NGTUU8Z3nz5/Dz0+fRicsTCPrCSG2oRAkhJA4xML5s5EzR3b8vGK1ClKQQCLJ6Sci0DSdT3ylafP2OHjoSLjq6gJeR3t7CInr0EeQEEJInEGmYHvx4mW46kb1DCWExEcoBAkhhBBC7BQGixBCCCGE2CkUgoQQQgghdgqDRcIgd74SKmWDp4fHp/lGCCGEEEIiyXsfH5VF4MrFU+GqT4tgGHAanhD6xbAQ9k144TXDvokovGbYN7xmol+70CIYBmIJlO48c/pwJL+O+Mnbt/rJ0L28aCll3/Ca4f3E3xn+BscO+GwCihSrEKE+o0WQEEIIIcROoRAkhBBCCLFTKAQJIYQQQuwUCkFCCCGEEDuFQpAQQgghxE6hECSEEEIIsVMoBAkhhBBC7BQKQUIIIYQQO4VCkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7hUKQEEIIIcROoRAkhBBCCLFTKAQJIYQQQuwUCkFCCCGEEDuFQpAQQgghxE6hECSEEEIIsVMoBAkhhBBC7JRYLwT//fcBkqbIiD179odZd//+QyhTrhoSeKdGhsx5MGr0NwgICPgk7SSEEEIIiWvEaiF4//4/qFG7EV68eBlm3ZMnT6FO/WbIkCE9Nqz9Gf/r1Q2Tp87CgEHDP0lbCSGEEELiGs6IhQQFBeGnn1cpEafT6cK1z+ixE5EnTy6sWbUcDg4OqF27BtzcXDFw8EgMGtAXadOmifZ2E0IIIYTEJWKlRfD8+b/Rs3dfdGzfBj8vXxRmfV9fX+w/cBhNGzdQIlCjZYumCAwMxM5de6K5xYQQQgghcY9YaRHMmDE9blw9i/Tp0+HAgcNh1r916w78/PyQK1cOs/J06dLCw8MDly9fjcbWxk38Pujw1+Eg/HU4EG9fAV6JgKIVnFC0giNc3YPFNCGEEELiL7FSCCZNmhRJk4a//qtXr9TaO2FCq20JE3rh9Zs3H9kiHd6+9UF84elD4OfpDnj93AEODjrodA54+lCH25d12L1ehw4DdEgexkj6ex/fT9XcOAf7hv3Ca4b3En9n+PsbU4hDnUNcF4IRJSgodD9C0+Fie8fPVy8C37zQvxcRaLqW8p+mOqDzMB3c3PVXk2xxMDgRSFfKIseRdYCzebmqr70msQb5vi4cB84fd8C710ACb6BgGR0KlAFc3WK6dYQQQmKKeCEEEydOpNZv3r612vbmzVsk8vb+yE9wgJeXB+IDJ44H4vXzkFPqiCB88xLYstQRqTI6wMkJcHSEfu0EODnr3wcGAg5OgKeniyp3djZsl3rO+rXUdXHRr51cpI5YIEVUOqhj6F9L7+rVpqOlkLQQlbKPoN/Huq7V/hZ1HVWFT0dsuWae/BuEReP98eqZvl8k/ur5Y+DeNQcc3gZ0H+mClOkc7a5fYiPsG/YLrxneSx+Lgz0KwWzZssDJyQk3btyyykHo4+OjoomJHvEJ1MRAaNy5qlNLyGjCITBCXStiUQRd+NZ6IepgIkSNYtNSoDo5mG8zvFbC1eK9iFYnZwe9OBURq8Sqg2Ftvt3JKHL1IjYkC6mpIP3wXv/a2UlnrGtrn2DR6hCtvqAiAl8/17/XvndtLeWyfdBsV/qGxjPoB0wIsRsh6ObmhsqVKmDj5i0YPOhrOBpMR2vXbYSzszOqVqkY002MNUhgSHgy8ohw8vLWW/6CAoPXagmK/Odrx4B/eGqHL3VQxOtGHNFqIYlWJVY1ESqXnoPe4uni6h8sTqW+CEzDfsECVsr1glcToY4m4jRYmGqLRZns5+KgBK0cX/ZzNtl+5miQsgSG2Gs6qO1/HghEyWpOxnPVvwi2pmrldLOIG9iyAotv8O3LAdiz4dNbgQkhsZc4KQQlXcyZM+dUVLEswsjhg1C1Rn00a9Ee3bp0wvkLFzFqzAT06tkFGTNmiOkmxxokOlgeCKGKQQeoYJE6rZ2txYCSXDq89/GHLghwdXFRwlAnAk8XLBSNa/Vap+rKaxGUOqvtJuWG+qZ1LUWo8b2hbkjbTcu11+FMS2mF7BcYEF77p6akdLFCxIaHrT8F4siOQP2QvYhVGb7XBKxWZvJes7IahbAmcNU6eF/NlUDey3cgaw+PAJN9DXUtrL1G8Wxi7bXabmHp1YSqqeVWYeGzambZDUX0IoTtwdtipyMsrcCEkHgvBB8+fIQy5atj9MghGDN6mCqrVKk8Nm9YhVFjJ6BJ83ZIlSolhg7uh1Ejh8R0c2MVkiJGrAKhopNAAickS+2gEnqLCBJBpuSKvNY5wMlVX+buIeYGvVDStpsdSt7rHMy2GYcnLerr65p8jtVxQqhrcky9UA1em5ZpgUW2RKsmOHUm5ToTUWq5qGNoglbbx1DXzy9IrR0dHK2FselaE7EW5aZC11TMRicB/sB/D6JKnOqi1J0gIgQL2eB1aK9FSBqFb2ii18Z+pi4MlsLYTAibiF6xHCtrranLg7M+mEdeeyYIVFZfU7Et9Y1C2yB6RXArlwNHg/A1IK//OhQYLivwH/v0VmAzt4UY9KklHw/dAUhkcNAFvI55c0QspkixCuqxdvZ02PkM48oPxdS+fso3zJZ1TB4k3kkRps+Ylk7H1LldE41mIs4gEvXbTcpMtoVY10IwmrZXF8G62mea19UXmh5LDXvrLM5F21cEoEV9U4Gs4ePjp7Z7uLvaPjeL+jb7zHKboT1ilTSKRVNrqoXVM9AgeE8dCH1oWEOiiLPnc1CiVp2THNvwOiik92ZrvQg2CmmTukahLW1SZQ5W+5OPc1lQa4M4FFEp/RoWEi2eKkOw6DQVsKZC1tT6KgFfWtCYUZSauDYov16zbcFBZsqlQb120LtJWLpCyOe5mPvkvnv3QZ2Tl5c7hWkYv8G23AG0daJk9uMOYOvZZI+6RTgTTt0SJy2CJPKIuJMfhJB+MEQEyvbIJJU2DsvFGaKmsUbRqN7ID5G+LxMkcAzR2qmVhSVcrayoEazr7BKInavDtsCVqeWEgqUcrSzAluLXlvC11Q5botfnvZ966e7uYibc5TOtLLMibA1C0dRtQGuPbIPRQmsQoSbCUhOllvtZCVrDZ5uKX01Mmx3L5JhaO4NkX+Nrc9Fruo/ZMT7Sx9YS7Z+DyCCC8f6NiNoBPp3dQLO26i2nflauCOZuAsHBYkZ3BGU5NReulkFk+vLgfUWQmgldU7FrIXA1P15TwayOZflZBquvPlNC9PxA0h3AvvGzmCDC309//YUXCkE7RP4rFIvfmSNBahjpzSsgocwsUtEJRcpzZpGPFcDaDejsEvOquEI9JxzfHRimBbhKQ6dwi39L4Wsl/myIQnn9zkQgh7V/aMcKzdJraeXVRJdYLNWumiCMYpEbXrcG/efojGJUE7k+7/3VNhfxuZW6NkSkmfU1FJF7/niQSgEVFp5eQMacDmbi1dRvN0T3CLNtNnyCDds+Fr1LhP6a9I+0QI1dA15W1lcr31dDyi7LVFwm6btUHWf5rvUC1t3dH/890IXLHWDjkgBkL+Bo5WtrmXXB2hfXol0Wa+VSELesAPGGJzYsweof0wj8g0ghaKfIQ79UdSe1kPhLdFiAI2v5DTAYJj0SROcD4+OObeXeYMPKatO1ISTxamObrXrv3uqljqenk5WrQ6giV3MbkMoGISiJ4PdtCluJFa/iiLzFLKzWpuds0n6z7ebFNoWzsrSaBnUZ/WKDLb1mPrFGv9tg67Asvr4B6r2To7OV0Ay2FJtYZkOwwupdE6wD0iytuFYW4KgWt4ZjiU9u5IWrzuJaD3/DTh8MUkt0YOUjayO7glkgmS0xbBFAZjNtmIkV2LiPSR5b/wD92sMzwMxiaylezd0eHMzdGTQrr7TfkEZMWadjmdgNyRIcUSgECYnn0AIc+90bnA2zu3h5hfbh4WtYqnSOOH0obD/gGs30VuAwBV84BWJUHce0/rt3erOGp2fwo8rKJzgSn2ldbl7Blj+yCjQzCNDAAJ11ZgLJKmAqeA1lSqja2GaWLcHKsmpyfAsxqx0jwF8flOYARzy8pwtFXJp/9y5uwe4PH5NJwZLgdoVWKyJCN7JET1Cag4ONQLJw5cQ1Cf6yIZTNBLIx84KJ6DUJRrP0471/PXw+4GFBIUiIHUALsP0QXiuwu4kPa2zFwTk8AjlKPgkxgVhO9WutICIi+YNaeyVwwffj/XHvmonLhg3k+8+YwwGdB7uYWaVFdCrBKWJVE7Sm4tZE0Ab6y3sdArSgtQCd7fomAtmYAswsVZhOb6E1EcNmKcQsxbHRNzfYkmvM/mCaxUHV0wt2yW5h6jrx8d8V9J8TYX35KcTvx0EhSAgh8QxageMG2lBjZKzQzn76tYubA4pXcsLdqwFhCpkSVZyQwNvhk4hg5bJglnbM2rVC+cOGsC20DA2W9fXb9G/evfNVAlBlbTBmWwgWrLIE+JmLzUDTzAtBmvA1cSEICPa7C9TZFrlW7gqWYlWz7GqBZRZBasF1g/PuWgpjs2A1SVXmGzXfFYUgIYTEQ2gFth+KVnBUM8aE5Q4gwYCf1M3ik7qg6wWtx1v9Oy+v0M/VKkWYTis3TdsV+jbBGJBm61ha9gXjNkNWBqM4NM/SYMxYoJWZuihYCmEA234MwKN/Pt6gSCFICCGExGGiMy1YfCVm/IEdPipwzdISKsGev/7w8b6QFIKEEEJIHIfuAPYnVEtWdcKBLSGnBwsvFIKEEEJIPIDuAPaFawiW4IhCIUgIIYQQEk8swY5XOLMIIYQQQohdWoJXFYvY/rE/kRQhhBBCCIkWKAQJIYQQQuwUCkFCCCGEEDuFQpAQQgghxE6hECSEEEIIsVMoBAkhhBBC7BQKQUIIIYQQO4VCkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7hUKQEEIIIcROoRAkhBBCCLFTKAQJIYQQQuwUCkFCCCGEEDuFQpAQQgghxE6hECSEEEIIsVMoBAkhhBBC7BTnj9n52rXrOHjoKG7duoP/nj6Fk5MTUqdKiUyZMqJmjapInz5d1LWUEEIIISQOo9PptBcRXuvCUc/BxSX6heDbt2/xw/Jf8O2873Hz5u3gk7LAwcEB+fPnRY9unfF55w5wd3ePcOMIIYQQYt9ERDzp/PzkBXS+juEWT5BaOuu1LoRy07Vqm+UiBAXpjxwk7y22hatNCGfbzOs5Jk4cvUJw8ZLlGDx0FAIDg1C3Tg30//pLFCyQD9mzZ4O3d0LVIc+ePce9e/dx/MQpHD5yDP0GDMOYcZMwZtRQ9O7VLcINJIQQQkjssz6FLlJCF1E6XZCZYDIVUkZxZbo9+KTM65u1B9D5fFDrAHe3cIsnWK4dYL6WlbxWBG90MB7MQV9sXFuUGVehl6mVo4PJdot6JmUOJq91AQEIuH0T/teuAgEB0AX4A45OiHIhWL5iTTz57z/MnD4JrVs1C9HCly5dWrWUKVMK/b7+Ai9evMDKVeswbcZcrFi5FkcP7w534wghhBB7RidCSBYRO0FB+vcilt6/V++DAv30okrbFhioFp2swyv8Iml9MltbqidRTg6yp2V5sL6JMgElB9TKnAyyxtnZSkCZiieTRsAorkzL4ghBr17i/c7foHv/LrhQvtfAgKgXgu3atkS3rp/BWTo3AiRJkgT/690d3bt1xveLlkVoX0IIISQuY7RumQg6K3GnhJwm9AxCLiAQkNfavkq0aXV0esuXTme0fKnNgQEIuH8fAffvQuf7AQ5u7nDOnAXOmbLAwcU5SqxPsV1AORgMiA6urojv6Pz9DSLw/UcdJ9yqrkf3z+HoGPkgYxcXF3zxvx6R3p8QQgiJMTEXgmUORmucyWuxxsk6IMBsH+NwphJ/JgLRQaSc3mKmJJoINGXlctSvHR3g4Gh47Wx47SgWLwc4eroHW4b2/G5mGdK9eQO/p//B/9JFeNauC8dEEfcfI7EX/1s3zC2B0S0E02fKjbatW6BD+9YoVKjAR38wIYQQ8qkwCjeDoLMp7gyWOc0qpyxzWh1TIafThKA6sNEfTmccqkSwkDMRdXoBZ3itygyiLhKY7heWZUjKZXuCpi0iFVUaryyyxu/ZxAqrM/+Ozb5zKyuudo2YvjdcD1b/KFh8jkWZ2edaXpOGuuofDLPPMXkv12gUEG4hmDRpEsyc/R1mzZmnooE7dWiLtm1aIHXqVFHSEEIIISTSQ6yaODNY5pRVztQyZ7q/GmI1PIhVWXDggU75kuksLHL6tRJvTk6Ag7P+tWl5rLYMiU/hO/jfugnXXLnD6FeTfjR7b9K3luI4TCFkYkG1EmS6yAm0EASXEvFBQXhjNqROokQI/n3uJM6f/1sFfqxZtxEDBg3HkGGjUb1aZXTs0AaNG9VnihhCCCEhYrR42LLMmVpaDK+D3r5TYs7/pYu1v5xR/AW/FmucId7V4NdmbpHTD7HKayf9EGssEnOWqH4Ri6S/H+DvD52fvz4a1M9PWQCD3vsA/n7whQ5+16+G65i+x4/A9/QfVtYuEgYG6626VhzFmqtfq2tLXT8m1l6z98H/SBjfa9eboZ6xTKtj9Tkhf67vX6ehe/USH0uEIj8KFsyvlsmTxuLo0RNYuXot1m/4FTt37YG3tzeaN2uEju3boGLFch/dMEIIIbEPyyFV66FTC8ucWONCGWLVW+YMr4252QwPX18/tdZ5uJs9jE0fmNoQa2wQc+o8JH2Hv78SabLWv5a1yXs/29v0r/Xv5XV4RJpfxBoI+Pri0wil4PcREkpGwW5DKJl8htXnGI7pJ0E2jo5wdXcN/+c4mn+W9efErn8SNHS+vvA9diTmZhYpV660WubOnobdu/dh1Zr1WLd+s0o2nTFjBrRv21JZCnPkyP7RjSSEEBL9KUlsWeZspiTR9he0ITzRLNqwq84BOgd9+INRJJhY54ItG06h+ss5+OhFi6OHRMZGp3gzCDA/W8LMxmuDdc5M1Im1Lpzi7aMRPz9nF7V2dHVF0KtXSniGhYN3IrgVKmLbkmX4TuK6UAowXDMu0XjNxBZcsmaH39kzBt9QXcxMMSfItHK1a9dQi6+vr7IObv51G76bvxiTpsxEgO+LSB13//5DGDZiLM5fuKj8Ezt3aodRI4eEmL4mKCgIM2d9h4WLluKffx4gS5ZM+F+vboxUJoTESWzOWGCZcNfU581im8yw4H/tikoyq/PxAdzd4ZI1G5wzZYaD5Fqz9JczvtYPsWqWOX3gQ3D0qvZaP8TqqPK1feohVtVWZXmzYUnzM7eqWb+2FnjRjvSJiwscnF3g4CoCzlUFbajADbV2DeG14b3ax1BfjuHgAB+D4PHwcIPf1cvhsgy55i8Il+w5ov98ySdBrgeJBrfKI/iphaApd+/ew6VLV3DlyjW8fv0aHh4ekTrOyZOnUKd+MzRsUBejRw7B2XMXMGrMBLx+8wazZ06xuU//AcMwe+589OzRBU0a1cet23cwcvQ3uH37LmZMn/iRZ0YIIeEUaqapQYyzH1gINaNfm35RvnBBOgS8dglOQxLS1FWWgtBYFjwDQ9CbN/hw5KBeAGq8AnwfP1IWBPfK1eCYOJF1SpJoHGJV4i08wkwsbAZrW+CHD4B/AN4FasOthn1luPlTiTcbwgyumqhzNRdsZvWCBZ9RLMegZcjBM4H6R4DELxwTJVbR4BIIJP/4qX9sNOttOHHQBbz+KDv2jRs3sXbdJqxdvwkXLlxUF3vlShXQoX0rNGvaCF5eXhE+Zu26TfD4yX/469Rh480za/Z3GDh4JO7dvoS0adOY1X/69BlSp8uOTh3bYuniecby337bhQaNW+Hi+T+QO3fOSJ1fkWIV1G119vThSO0fX3n7Vv+A8fKKnNiPz7BvYke/mEVDhmZRC2ObVYoH02HREIWaJgZVgfl2rX2GtfzCffCVH28HuEtyYG2WBL0pzlycmc6gYOIbpz+Qvo4Ipncb14UiChzg4OkZrlQievHmZz5sGuBnMoRqalmz9HuTfUS0Gco+lXhz1axtpiLNXJiZvdYsdEZrnWFfJ6dYNeRpialFMMQZJkxEoL3kEbTslwhNwacRVrnlTCum5WabTWdeMb0ddeF7bVxZvw6esUUwvA7SwSlZMpSo20KVngmnbomURfDWrdtG8Xfu3AXVmfny5cGkCWPUDCQyxVxkkeHl/QcOY8SwgWY3YcsWTdW8xTL0/HnnDmb7XLt2A4GBgahfr7ZZeaVK5dWQ8c5duyMtBAkhUU9oQiz8Qi2EWRpsCTXjfKWWxzEpU5PDG2qZ/PYEpxIxFWbacKk2dVXw0Kj5tFfhC2SIKl84OXf/61fDlUrEZ+9uOCZMGIKFziDuoihPWaiIj5mlMHPWvw6UPnVxgYuHR+jDqAYLXWwXb5/MMnTzOvBB3AE84JIth7IEhiT6wxRDob02FS42yo1iyLgKa6o6G+UmRbbKLaew033Q+0oG+bkaEjvK/QvbEwiHNMWdhioOx1R3lhinswv+jTDdZllX+21R7hYamkXPdEYXs9+SEKbbc4v4b0i4heCdO3eN4u/MmXPq4kmVKiW++rKXCgopXLggooJbt+7Az88PuXKZ+zGIuJSh5suXrcPkkydPamyjKTdv3tYf8/adKGkbIfZCRCxmZqk8bNU3CLOgdz5q7S/RfDasdIZPNtnPxuTyWhWz32oToWZhRTMKNQtRZjbvaAxEnAanBhFLmT90b98ri1mAo0wgL0IswGxtrCdWNWWVMxkqlTKtTgSEW+DDfxH4MJInIIIrPP5t2tCp1TCqifCTvHxhWHfc4rnjv/G+0b+xcCkILtPP+WsQWdI34lYQ6Gcsk7Vz6tRwSp3aeGwH2fTuvQre0U9hYkMYhTTPr1lxOOYANttHk1Dm96LpdrPXxvcmgsh0eNPyHjZObWfeBr/3H9R2pwTuZufmEK55jR2s22dR5hDOemFti03/sIRbCGbNUVA13N3dHa1bNUOHdq1Rs2a1j5p2zhavJPoJgHfChFbbEib0Un6CluTMmQPly5XBuG+mIlOmjKhWtZISlN179YGbmxvevfu4efjkZtGGtYie94YfaBI3+kbvXG8YntOmvTJNqhuoWdGC037oHyzaf/oWZdoDyeTBZDNa0mSI84MMJ8prN1fzB4Olpc342mBhU0UfIdTMRm2MJxC+XTVrokFsaf2nM+1L7bWItsDg11q5JtSs6tvgk/7KiJhLlcYkAtUZDs4i1pyNEammAQpmZaH87lsMkoWMVPCTfgh5yNjHYN2JScz+udEXWIs00/vA1BXA7L3htdnBDWtb7gCaSLHhJiB8kNtVWaHFEmoa9WtxT2nHN13DQhDZFGghCCZL4feR4iqqBZFPoP7adHCOxD8PuhBeWxXG7tyLmt0zyoVglcoV0bFD60j7/YWXIJPhGVuEdNGsX/szevT6Cs1atFfvEydOjKmTx2HMuEnw9KQfG7Ef9Hnb9JYjJUT8fJW/mO7uLeju39XnERM/qrQZ4JAuvWHIyEKYmT2IDK8Nc5ta+aiFV6gZHuoOYhGMjvM2CraQRJtpmeG9v6GvlEXNel/12pbAjU5U8IY+wEAJL4nwlWADTaBJuVrE0hb8WisPOvcXEJ4ks0mTwaliVcQrK1qY4iyMbbYuYTPxYm1tNqo1JcS0t4aULFJgOq2cSUCOzfvM5rYQFsP9FJ2pdYh9EG4huHf3VrMb0NaPvkQNJ0+eDAkSJIh0gxInTqTWb96+tdr25s1bJPL2trmfDFNv3rgKL1++xIMHj5AtWxaV2qZn775ImiQJPg4HBkWEAINFYq5vzJLXBvgjSKItP/gGW6Ik0lKeb2/f4MNhiSA1sYy/A3QvngO3b8DjEzuRu4sQDAhh6NNgSZO1lpcteDjUpL5ZmeH9p/Bns0QNexpEmbKeaQJNs7BpFjXn4NQfhnLjvi4u8A0IUts8EiYIdag0PPgF+IUrlYhbjlxwjSYRYR01bT3kqV+FZFk2DIVKsAl0cHXU+3hqo5tGLIcJbQTQWJWZ5cqzSFBt6SoQgiCz5Wrwqd0MtM/gb7Bt7LlfHCJY3zmiN/fESdOxZNlPuHrpNFzF58OEQUNGYcfO3ejbp1eoOf9CQxNwN27cMiv/998H8PHxQZ48uWzut3rNeuTJnQuFChVQ1kDhzz//UsEiRYsWjnA7CIlNqES+BtGnhJCvr/4hKe/FoiX+ZvIDICLCyVn5Yzk4ecAhMBDvdm43TyMSzsnojX5spqJLa4NmbTTxXbMWc5Y+bfq0H28/RfRoSD5tFsJME2JKmBnLDCJOq2fcx0TouURtYIIWLPKxIjAiqUQkn6BxDt5QfNFCE2umMYvmH2Bu7bISVZqQsRJghqFNTai5+ymR5pzAI2IiLAYFGiFxjXArNRFUrdp8hg0bf0WKFMnxzz//ImvWLGZ1CuTPixMnT2HCpOkq99+WzWsi3CDx6ZP0Mxs3b8HgQV8bfRDXrtuohGXVKhVt7iefmTdPbqxZtdxYNmvOPCRKlAiVK5WPcDsIiQmsggj8TUSfWPiU5SvIMHzopISMg4uHEiWONh5yfuGMIH3360YlHk0/Vw2NfuphUUOUqBJfRpEWLNbMhJipSLOwshlfS30RxlHsyxx7g3b0fpzu5SpY5xE04ODhAfcKlYyuA7asaMapu2LYiuZo8M12smPrDiGxRgguWvyDEoESJTxl8jgra6AwYvggDBrYF//7sj+W/fCzWixTvYSHkcMHoWqN+srfr1uXTmp2EUko3atnFzV9naSYkcjl9OnTqUXo26c3uvX4UonRcmVLY/XaDVi5ah0WzJulxCAhsY3g2RGCp6vSqQS6/kbRp6x88sAVMSMCx809dEd9EQg+Pgh6/gyBz5/B7+KF8LXlzeuIuT8rIWo63Gk6RGoi3ExEnL98gIsz3MRn18LKZrTIRYFFLFanxTEE6FgNkxoiQAP93PRR0ApNhBlSSygBFiyyzKb5UvUc9f3n6AjHpEnhlSET/G9c06eT8fFRuQNdc+WBS+68+gheWtEIIRFJKF2qTBWVvuXAvt/CZT0sVrIiPNzdcezInkh19NatOzBq7AQ1U4n4/2lTzMmwsaSJyZK9gJp1ZMzoYcZ9vv1uIeZ+txD//vtQ5Q0c2L8P2rTWJ1aMLEwobRsmTY5Y36ihXaPgMwRwSNCGFrgg29WQmFOwIApjNgIRkkEvXyDo+XMl+oJePFcCUInJiOLqBtd8+c383YxDpSbWN6OFLhLDohFJ9PopMUs8rQq06OigcAg7wQEOpik5TIIJjJY1W07/Jla2977+6rVnAk/DNs2CZmJ1M53CzcIaF1ctnmHB3xn2Da+ZyOmWaEkoffHSFYwfOzxcdWU4t3nTRpg8dRYiS4MGddRii8yZM0EX8Nqq/MsveqqFkJhCWeQMQ25BQZKUNwBBktjVaOULVOla1MNcCT5nZakJyxIm4k4Te2oty8uXJomSrXHwkmTBfvoo4TBwTJIEboWLIn4NlwYLOqOwMxxDH3RgyKOmCS5lfTVPW6O+J/luTMWbwermIKLd0cK3zYZ4MxsSDWEaNw6BEkJiinALQfHPi8jcweJHSMdcEp8xmzvVEAwhgk335p2y8AW4OKmhQCUYNOuau0eouTeVle/1a73QM4q+56H7+Yl/YJKkcEqaTA0JOkpakCRJ1fBfeCejl9kHogtNnCmrqBLKjuFMUG0ajBDZ4VInODhpKTzMxZuy1hn3txBojiFY4gghJJ4R/oTSWTPj/IW/w33gc+cvIEMGvf8eIXGd4KhYg0+fNrRrsPKpoV3RCTJsKms3Nzh4edoM4DAe08/PKPSCXohPn6yfh5oGRaI9Rexpok/WDgm9QxwajOxk9ErkCqbDpSrHp4VwM5SZCztDwIHlcKmvpAIRYRUs5IzDmjaGS9Xws9oWLNiM4s2Wxc3yOBRuhBASdUJQhnonTp6BIYP6qYCN0BAfvp9/WaMCPQiJSyiRY5IeJchg5dMP7epTqag8mk4mARyubmZDu8ZUIAYhooaL374JFn0qkOO5KgsRcfhPnCRY9Bksfg7uMm1S+BErpEw2H+Jk9B4ecCtXAUHv38HBdF5O4xCnlvrDZLjUmCDXIOQMQ6XmFjeDFc106PS9r3rtlNDTtnij1Y0QQmKvEOzZ43Ms+H4pKlerh+VLF6BixXI26x08eARde3yhgjq+6tMrKttKSJQSHMBhYeXTAjiCJDefCB1H/dCu5ObzDMPKJ/s++w+6Fy/w4a1+iDdQrHwiJENADRdrQ7qyTpIMjokTRzoAwBiNbJhhRAx0HtVrIfDf+/C/d1edowhAl+w54ZIzFxxd3cyHS234ulmlConEcKkD9GLZMYJilhBCSCwQgkmTJsWm9SvQoHErVKleD7ly5UDpUiWQJnVqBAQE4PGTJzh+4g+VCFpmB9myaXWYlkNCPgXGGThMEiMrXz6Vm8+QMFnEkwggzcrnEXJuPu2YYmEzRuxqVr7X+rmyBaswDgcHOCZKpARfsE9fMjh6ekb+vLRE09q0cqL6ZEhVyzEogSgyTO3sApds2eFhmIaMw6aEEEKECE39Ubx4UVw8fxLfTJiGNes2YvmPK6z8CAf064PBg/oq4UhIjAVwGGfAMAztanPMajNwiOhTKVGc1XBrqLn5AgPN07RoVr7QonFdXOGUzFTwJVVDvSr1SlRY+YKCxFYZnGZGzkEWF1dDkmlDqpd4mlaEEEJI1BDhp5IIvJkzJmHG9Ilq2rdHjx6rYeA0aVIjdepUUdQsQsJGZ2nl02bgMBna1Q9jmoilUKx8QpDPezM/PhW9++plqDNsOHh7m/nx+Xl6AZ4J4Onp/nFWPrHwBdiw8nl46M/FNK8frXyEEEIiQeTMEwZHeNOZPQiJ9qFdw/RnQX5i5RNfPkMUr1jIVDo4LTefU5i5+VSallcvg0Wflow5hDl5Fc7OZhY+TfxZztHrbwgWibiVTzwStfmCnfSziCSysPLFg9k3CCGExAMhSEi0BXCYTrvmaxLAIRYybWhXiSVDAIdTGLn5fH2NEbuBLwyRuy9fhJ6mxcsLThK0YRLEodK0RCIlSchWPkfDvLgWVj5T0ccUKIQQQqIRCkESI2jiSLPyKeGnDe0a5tlFYJBhTlvDkKhLOAI4Xr8yDukarXzvwkjGnDiJPlrXELyhkjG7RW4aNLHyqdk8AgIRFOAbipXPMBcvrXyEEEJiEApBEu0Yh0CNc+3qo3aDp13TpzhRgQ1a1K4IptCsfP5+CHr+wmDhM0nGLAIyBBw8PM2SMSvh550oUgEVoVn5RATKeTh6edHKRwghJFZDIUiiFJ2llU/8+Uxz84l4cjQJ4JBkzDJbRmhWvrdvjfPsGtO0vLGea9osTYtpMmZN+LmHf4pEm0LWIPh0gfq5gs2sfN5u+mFqsfL5Bqhzc07kFanPI4QQQj4VFILkowI4gt7rDKIvAEEffEysfIHQ6fSCSR/g4Bx2AIcc7+UL8xk4xMonw8UhIEO4mtDT+/QZkjFHIqDC2sqnnxtX+fJplkp3D73wcwnZl88BoQScEEIIIfFFCL548QITJ83A1u07cO/eP9j261p4eLhjzrcL8M24kcie3Xz+UhLHc/MZ8/P5QffilRJMgS5Ohtx8Ov30YgZxpGbLCGHIVVn5DGlaTEVfkCRjDilNi1j5vL3N/PhkrcRlZAI4Imrloy8fIYSQeEikheCTJ/+hbIXquHv3PvLnzwtfQ3LdZ8+eY936zdi77yCOHPxdzUBC4g5aImbNyqfz1Wbg0Fv5lMVMdFdAkBJMCCM3n0rGrNK0mOTlk6Fd3w8hN8LFJXiqNeO0a0kjlYw5qqx8hBBCSHwk0kJw+MhxePLkKU6dOKByCaZMk1WV169fB0cP/Y56DVtg9NgJWL1yeVS2l0QxyofPxwdB2gwcamhXP+2aiCg1z64mmMSfzzDk6mDIlWcqzoI+fDAIPS144xmCXr7UT98WAg4JE1qnafFKSCsfIYQQEpuF4LbtO/FF7+4oXLggnj17ZratdOmSatuSZT9FRRtJNAlAv4sX4HvmLwS9faN87ZwzZ4Fz1mxwFOuYp2fIVj5JkfLqJXQvX8D37Wuj6NO9fx/yB4rV0HS6NS1Ni6tr5K18WpoZSyufDO160cpHCCGERJsQfPnyFTJnzhjidplu7vnzF5E9PIlGAp89xZuVP5tF3urevoHfs6fwv3wJnrXrwjFRYn25n59xfl1jQuYXwcmYbYVxqChgkzQtslbJmCOTpsXMly9QDTWrIVtN9IloTeiqhKzy5RMLpfgocvYNQgghJPqEYLZsWXDs+El079bZ5vbfdvyu6pDYhQg7JQLfvrG9/f07vNu6GU6pUqsIXkndEiKOjoY0LQY/Ps3K5+4ezVY+mVvXJICDvnyEEELIpxWC3bp0Qv+Bw1G0SCE0qF9HlckD+fHjJxg/YYoSgpMnjo3s4Uk0IcPBoebgE/z9EfjPfbMiFQVs4sfn75kQ8E4EzwQRz81HKx8hhBASx4XgV3164++Ll9G33xB83X+oKqvfqCU+fPigrDtNmzRE/35fRmVbSRTg+/d5/bRtIaVp0XB3h2v+gsHRu56eZpsDDMEikbLyORrm2KWVjxBCCIm7eQQXf/8tOrRrjfUbN+PmzdsIDAxE5syZ0KhBXdSpUzPqWkmiDDXvblgiUEZ93dzgVqBQxHz5TASfLigQDpJnRhvalYjjhG705SOEEELi08wiFSuWUwuJGzgkSADIbB1hWgQ9QrXyqWnjJEeg3wcrKx8SuMNJcvPRl48QQgiJv0IwKCgI+/cfwqPHj5U10BYdO7T9mI8gUYxb/oJ4f/9emPVcsuUI1coHXZCKznVM7K239kmkLiN2CSGEEPsQgufP/62SRj948FBvJbKBBI9QCMYuXPMVgM+RQ/qo4ZC+Nw8POCVLriKGQ7Ty+QaoiF3nhOa+g4QQQgixAyE4YNBwPH36DKNGDEaRIoXgFonEwOTTIwmcE7btYJVH0Ljd0xOedRrAKWWqUK18DkE+n7DVhBBCCIlVQvDosZMY2L8PRo/SRwyTuINY+xL1+J9+ZpHz56B791b5Doq10LVAIRUoQgghhJD4T6SFoIeHO9KmTRO1rSGf1DLoVqSYWgghhBBin0R8zi8DkkR6w8YtUdsaQgghhBAS+y2CX/TujuatOqJBo5Zo0bwxUqZIAUcbc8nWrFntY9tICCGEEEJikxAsUbqyWt+9e09NJ2eJRBJL1HCg38uPayEhhBBCCIldQnDZkvlqpjJCCCGEEGJnQvCzTu2itiWEEEIIISRuTTH34cMHPHv2PMSZRTJmzPCxH0EIIYQQQmKTEHz+/Dl6f9EPmzZvQ4BMQRYC9BEkhBBCCIl3M4uMwNp1m1CpYnkUKVwQbkxCTAghhBBiH0Lw1y3b0bFDGyxftjBqW0QIIYQQQmJ3QmlfXz+UL1cmaltDCCGEEEJivxAsXaoETpw8FbWtIYQQQgghsX9oeOb0iahWswFy58qJli2aIGVK2zOLuLq6fmwbCSGEEEJIbLIINmraBv7+ARg8dBSyZC+ABN6p4eGV0mzxTJgqaltLCCGEEEJi3iJYqWI5NYVcdLF//yEMGzEW5y9cRNKkSdC5UzuMGjkEzs4hN3nxkuWYPXc+7ty5h4wZ06NXjy744n89bFoqCSGEEELsnUgLweiMFj558hTq1G+Ghg3qYvTIITh77gJGjZmA12/eYPbMKTb3WbT4B/To9RW+/KIHGjWoh8NHjqFvvyHw8fmAwYO+jra2EkIIIYTEVRx0Aa91H3MASSb9559/4e69+6hcqQI8PT1UWZIkSSJ9zNp1m+Dxk//w16nDRqvjrNnfYeDgkbh3+xLSpk1jtU+pMlWUP+Lhg7uMZW3adcaRoydw/87lSLelSLEKkA46e/pwpI8RH3n71ketvbw8YropsQ72DfuF1wzvJf7O8Pc3phDdIpwJp275qDHTjZu2IFPWfChXsSbatu+Cixcv4/DhY0ifKQ9mzPw2Usf09fXF/gOH0bRxA7Oh55Ytmqpp7Hbu2mNzP58PH+DtndCsLFmypGr6O0IIIYQQEoVCcN++g2jZuhMyZkiPid+Mhk6nM84tnDNndgwaMhKrVq+L8HFv3boDPz8/5MqVw6w8Xbq08PDwwOXLV23u17dPb+z6fS9+WbEar169wq5de/DjT6vQoX3rSJ4hIYQQQkj8JtI+guO+mYLChQuqoVgRXkOHj1Hl+fPnxclj+1ChUi3MnjMfbVq3iNBx5ViCd0Jz656QMKGX8hO0Rft2rZRfYIdO3Y1lNWtUxbdzpuHj0RmH+4ie9z6+7IoQYN+wXyIKrxn2C6+ZqIH3EpQ7m8OnsAj+efoM2rVpYTOKV3z1OnVsiytXr0f4uEFBobsshhSp3KhJa6xbvxlTJo3Dgb2/KQF4+q+zaNaiPYKCgiLcDkIIIYSQ+E6kLYJOTk6hbn/16nWk0sskTpxIrd+8fWu17c2bt0jk7W1VfuzYSeU7uGDeLPTs0UWVVapUHlmzZEa9hi2wZetvaNyoPiKPA4MiQoDBIiHDvmG/RBReM+wXXjNRgz3fSw4RrB9pi2DZMiXx0y+rbVrbXr58iUVLlqNUyWIRPm62bFmUyLxx45ZZ+b//PoCPjw/y5Mlltc/de/fUulzZ0mblFSuWU2sJYiGEEEIIIVEkBMeOHqYCN8qUq4bvF/2grH+HDh/FlKmzUKBwGSXcRgwbFOHjurm5qTQ0GzdvMROZa9dtVMPQVatUtNpHprkTxEfQlKNHT6h11qyZI3GGhBBCCCHxm0gPDZcsWRy/bV2P7r36YMSo8cYAEkHy/K1d/SMqVCgbqWOPHD4IVWvUV/593bp0UrOLSELpXj27qKhkSTFz5sw5pE+fTi1FihRCs6aNVJ5BGT4uVbI4Ll66jDHjJqmAFtlGCCGEEEKiOKG0pI05e/Y8bty8pfL8Zc6UEcWLFw11KrjwsHXrDowaOwGXLl1BqlQpjVPMybDxnTt31fzGMuvImNHDVH1JOfPNhKn4ecUaPHjwUE0x17hhfYwaORgJbUQghxcmlLYNkyaHDPuG/RJReM2wX3jNRA28lxDhhNKRFoI9e/dFh3atUa6cuV9efINC0Da82UKGfcN+iSi8ZtgvvGaiBt5L+HQzi/z400pUrFJbWeZGjByHK1euRfZQhBBCCCEkBoi0EPzv0S38sHQ+cuXMjqnT5yBfwZIoWqKCmhP44cNHUdtKQgghhBASe4Sgl5cXOnZoi52/bcKD+9cwd/ZUeHp4qICNjFnyokatRvjxpxV4ayMfICGEEEIIicNC0JTkyZPhf72748ih33H31kW0bNEEe/cdwOdd/4fU6XLg8669mcuPEEIIISSW8XGhvQYkYnf7b7uwZu0G7Ni5B2/evFFpXtq1aQlHRwcVybti5VosX7YgwnMPE0IIIYSQWCYEJVXM77/vxeq1G/Drlt/w+vVrJEqUSFkDJZpYm9VDGDK4n/IfHDRkFIUgIYQQQkhcF4Kp0mbDixcvVb7AOrVroEP71mhQvw5cXV2t6iZIkABFChfE/gPhC2UmhBBCCCGxWAjmzJFdib9WLZsiadKkYdaX6eamTtbPQEIIIYQQQuKwEDx2ZE+E6ufPnzeyH0UIIYQQQmJb1PCrV6/Qf8AwZM1REM5uSeDhlRI5chdWZS9fvoy6VhJCCCGEkNgjBJ8/f45SZati1px5cHNzRcMGdVGrZjU1F7CUlShdmWKQEEIIISQ+Dg2PGjMBt27dwZpVy9GieROzbes3bEa7Dl0xbvwUzJwxKSraSQghhBBCYotFcPOv29Gzx+dWIlBo3qwxunf7DJt+3fax7SOEEEIIIbFuruH/niJf3jwhbpdtjx49juzhCSGEEEJIbBWCGTOmx9FjJ0LcLtvSpUsT2cMTQgghhJDYKgTbtm6hpo2bPGWmmmJOw9fXF5Mmz8DKVevQumWzqGonIYQQQgiJLcEiw4YOwMFDRzFsxFh8M3EaMmXKAJ1Oh7t378PHxwflypbGiOGDora1hBBCCCEk5i2Cbm5u2PP7FiycPxsVK5RVIlCWShXLYcG8Wdi3Zxvc3d2jrqWEEEIIISR2WATVzs7O6N6ts1oIIYQQQogdCcGAgABcvXodDx8+QlBQkM06NWtW+5iPIIQQQgghsU0Inj17Hs1atsedO/dsbpdhYgcHBwT6cao5QgghhJB4JQS/6DMAT548xdDB/ZA1axY4OX3UtMWEEEIIISSuCMFz5//G8KEDMGRwv6htESGEEEII+SRE2oyXKlUKuLu7RW1rCCGEEEJI7BeCffv0xrfzvleBIoQQQgghxJ58BP/XAzt27kaO3EVQskQxZSGU4BBT5P2Kn5dGRTsJIYQQQkhsEYJTps5SQlA4cPCwzToUgoQQQggh8XBoWIaFixcrgr/PnYT/h+cI8n9ltTB1DCGEEEJI7CXSQvDFi5fo2qUT8ubNDScnp6htFSGEEEIIib1CUKyBly9fjdrWEEIIIYSQ2O8jOGPaBNSq2wTp06dD0yYNkCpVSjX3sCWurq4f20ZCCCGEEBKbhGCb9p8jKEiHQUNGqiWkYJEA3xcf0z5CCCGEEBLbhGC5sqWt0sUQQgghhBA7EILLly2M2pYQQgghhJDYGSxy5sy5j/6w06fPfPQxCCGEEELIJxaCtes1Rdv2n+PKlWsR/hARkc1atEfdBs0jvC8hhBBCCIlhIXjx/B8qAjhfwZIoUboSJk+ZiQsXLiIwMNCqrr+/P06c+ANTp81G4WLlULxUJbi5uark04QQQgghJI75CCZPnkz5Bfbu2RUzZ3+HUWMmYPjIcXBxcUH69GmRKFEi6HQ6PHv2HI8fP1Fi0NHREU2bNMSyxfNQtGjh6D0TQgghhBASvcEiJUsWx+qVy/Hw4SPs3LUHh48cw82bt/H02TMl/HLmyI4a1augWtVKqF2rOpIkSRLRjyCEEEIIIbE5ajhNmtTo/Fl7tRBCCCGEEDuaYo4QQgghhMRtYq0Q3L//EMqUq4YE3qmRIXMejBr9DQICAmzWPXDgMBycvUNcxo6b9MnbTwghhBASb4eGo5OTJ0+hTv1maNigLkaPHIKz5y6o4JTXb95g9swpVvWLFi2E40f2WJWPGPUNTv35F9q0ZtoaQgghhJA4IQRHj52IPHlyYc2q5Woau9q1a6j0MwMHj8SgAX2RNm0as/re3t4oXbqkWdmWrb9h774DWLfmJ+TMmeMTnwEhhBBCSOwn1g0N+/r6Yv+Bw2jauIHZXMYtWzRVOQslUjksfHx88OVXA1Gvbi00b9Y4mltMCCGEEBI3iXUWwVu37sDPzw+5cplb8dKlSwsPDw9cvnw1zGPMmbsA//77AHt/3xKNLSWEEEIIidvEOiH46tUrtfZOmNBqW8KEXspPMDRERM75dgFat2qG7NmzRVGrdHj71ieKjhU/eO/jG9NNiLWwb9gvvGZ4L/F3hr+/MYUOQPB4ahwcGg4KklMIGdPhYlus37AZjx49xsD+X0VxywghhBBC7NQiOGz42AgfXETbhG9GRWifxIkTqfWbt2+ttr158xaJvL1D3X/9hl+RL18eFCpUAFGHA7y8PKLwePEH9gv7htcM7yf+zvA3OLZhz88mh+gSgtt+24mLFy+r1zKncHQJwWzZssDJyQk3btwyKxefPwkCkWjikJD5jXf9vheDB/aN0GcSQgghhNgj4RaCf506jM+79sYvK9aoFC69enaJlga5ubmhcqUK2Lh5CwYP+lrNXyysXbcRzs7OqFqlYoj7nj//N96/f49yZUtHS9sIIYQQQuxSCIoI+/GH7/H8+QvMnjsf7du1Qv78eaOlUSOHD0LVGvXRrEV7dOvSCecvXFQJpUV8ZsyYQaWYOXPmHNKnT6cWjXPn/1brvHlzR0u7CCGEEELiExEKFpGh3h9/WAhPTw/07Tck2hpVqVJ5bN6wCnfu3kOT5u0wf+ESDB3cD7NmTFbbHz58hDLlq2PJ0h/N9nv8+IlaJ0mSONraRgghhBASX3DQBbwOn8OfCcePn8T1GzfRtk1LZSmMzxQpVkGFYp89fTimmxKr0NLp2LNDbkiwb9gvvGZ4L/F3hr+/MalbhDPh1C2RUnFlypRSCyGEEEIIibvEujyChBBCCCEklgnBqtXrY+/eA9HbGkIIIYQQEvuE4IGDh/H4iT4Yw3Q6uKIlKuDUqdPR0TZCCCGEEBJbh4YDAgJw9ux5NeMHIYQQQgiJW9BHkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7JUIJpRctXo49JilkZM5fmXZu2ow5+GXlGqv6sm3p4nlR01JCCCGEEBJzQvDQ4aNqsWTX73tt1qcQJIQQQgiJB0Lw9o0L0dsSQgghhBASO4VgpkwZo7clhBBCCCHkk8JgEUIIIYQQO4VCkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7JUJ5BG3x7Nkz7N6zH3fv3kerlk2RIEECPH36DHny5IqaFhJCCCGEkNgnBOfMnY9hI8bBx8dHJY8uUbwo3r57hybN2qJ3r66YO3uaKieEEEIIIfFoaHjd+k34uv9Q1KldA7/8tBg6nU6VFyyQD7VrVcf8BUswf8HiqGwrIYQQQgiJDUJw6vTZqFK5Itav/Rm1alYzlmfOnAnbt65H9WqVsWjJ8qhqJyGEEEIIiS1C8OLFK2jSuH6I25s2aYibN29H9vCEEEIIISS2CkEPD3e8e/c+xO0PHz6Cu7tbZA9PCCGEEEJiqxCsWqUSFi35AW/evLHadufOXcxbsBiVKpb/2PYRQgghhJDYFjU8YfxIlCpbDQUKl1EBIxIdvG7DZrWsWLkWQUFBGDNqaNS2lhBCCCGExLxFMGfOHDhycBeyZsmMRYt/UFHD3y9appY8uXNi3+6tKFAgX9S1lBBCCCGExA6L4NWr15EvXx7s27MNz58/V4EhgYGBKmo4depUUdtKQgghhBASe4Rgler10KlDW0yaOAZJkyZVCyGEEEIIsYOh4VevXiNr1sxR2xpCCCGEEBL7hWDnz9rhu/mLcPv2nahtESGEEEIIid1Dw2/fvsOtW3eQPVdhZMiQHqlSpoCTk5NZHYkkPnp4d1S0kxBCCCGExBYhePDQUSRPngyALMCT/55GZbsIIYQQQkhsFYK3b1yI2pYQQgghhJC4IQRN8ff3V1PKubq6IkWK5FZDxIQQQgghJB4Fiwh3795DsxbtkShpemTJXgDpMuZCwsRpVdmtW7ejrpWEEEIIIST2WATv3buPkmWq4OnTZ6hZoyry5M6FIF0QLl++is2/bsPhI8fw16nDSJ8+XdS2mBBCCCGExKwQHDFqPN6/98HxI3tQsmRxs22nTp1GtZoNMWbcJCxZ9F1UtJMQQgghhMSWoeFdv+/FF727W4lAoUSJYvhfr27YsZOpYwghhBBC4uXMIhkzpg9xe4YM6fD8+YvIHp4QQgghhMRWIZg9e9ZQLX6yjVPQEUIIIYTEQyHYrUsnbNu+Ez1798X9+/+YBZH06PUVftvxOzp3ah/phu3ffwhlylVDAu/UyJA5D0aN/gYBAQGh7nPixB+oUq2e2idV2mzo1LkHnjz5L9JtIIQQQgiJz0Q6WKTPl73wx6nTWLT4ByxeshweHh7Q6XT48OGDWrds0QT9+30ZqWOfPHkKdeo3Q8MGdTF65BCcPXcBo8ZMwOs3bzB75hSb+5w+fQZVqtdHtaqVsGn9Cjx4+AhDh4/B9ettcOzInsieJiGEEEJIvCXSQlDmEV7x81J06tAWm7dsw50795QAzJIlExo1qIdatapHulGjx05Enjy5sGbVcvU5tWvXgJubKwYOHolBA/oibdo0VvsMGjIKBQvkw6+bVhsTWnt7J8RXXw/GjRs3kT17tki3hxBCCCEkPvLRM4sULlwQ1apVNoqv8+f/VrOLRBZfX1/sP3AYI4YNVCJQo2WLpug3YBh27tqDzzt3MNvn2bNnOHDwMJYunmc2q0nTJg3VQgghhBBColAIBgYGok/fgVi85EecP3McuXPnVOVTp8/G6jUbMHjg15jwzagIH/fWrTvw8/NDrlw5zMrTpUurhp8lYbUl589fRFBQEFKmSI4Onbph86/blXWycaN6+HbONCRJkgQfhw5v3/p85DHiF+99fGO6CbEW9g37hdcM7yX+zvD3N6bQyajtpwgWmT5jLhYsXIq2bVogWbKkxvKB/b9C1y6dMHnqTHy/aFmEj/vq1Su19k6Y0GpbwoReyk/Qkv+ePlXrbj37wM3NDZs3rMSMaRNUwEqdes2USCSEEEIIIVFkEfzhx1/wWad2WLZkvll5oUIFsHD+bLx79w7zFixGj+6fR+i4QUGiZUPGdLhYQyyIQpHCBY0zmchwdaJE3mjT7nPs3LkbdevWilA7LD4VXl4eH7F//IX9wr7hNcP7ib8z/A2Obdjzs8khgvUjbRG8d+8flCldMsTtFcqXxc2btyN83MSJE6n1m7dvrba9efMWiby9rcoTGqyHdevUNCuvbQhYOXP2fITbQQghhBAS34m0EEyTJhVO/flXiNvPX/jbbMg4vGTLlkUFfNy4ccus/N9/H8DHx0dFE1uSwxARLIEmpvj76/MOim8hIYQQQgiJIiHYvGljLPvhZ7VY+uCtXLUWixYvR/OmjSJ8XPHxq1ypAjZu3mJ23LXrNsLZ2RlVq1S02kfEYebMmVSQigSJaEjCa6FC+TIRbgchhBBCSHwn0j6Co0cNwd79B9G1+xfoP3C4mk5O/Pdu376Dly9foWjRQhg7Zlikjj1y+CBUrVEfzVq0VzOYnL9wUSWU7tWzCzJmzKAsf2fOnEP69OnUIp87bcp4tGzdCS1adUSPbp1x9dp1DBsxDo0b1UeJEsUie5qEEEIIIfGWSFsEPT09ceLoXsz/bibKlS2lxJkEiJQoXhRzZk3B0UO7jb57EaVSpfLYvGEV7ty9hybN22H+wiUYOrgfZs2YrLY/fPgIZcpXx5KlPxr3ad6sMbZsWq2muGvQuBUmTp6hBOHqlT9E9hQJIYQQQuI1DrqA16GH6do5RYpVUDl5zp4+HNNNiVVoeRXtOTIrJNg37BdeM7yX+DvD39+Y1C3CmXDqlo+eWUTj0aPHOHHyFBJ4eqJChbJwd3ePqkMTQgghhJCYHhr+559/8dnnPZEzTxGz8tlz5iFztvzKp69W3SbIkDkPtm3bEdVtJYQQQgghMSEEZT5f8cv7+ZfVKh1LQIA+NcvBg0fUHMAS4Tt65BCsX/szihcrguatOuLixctR2VZCCCGEEBITQnDK1Nn477+n2PXbJpz765hK5SLIVHIStdv/6y8xauQQNG3SENu3rkeOHNkwZdqsqGwrIYQQQgiJCSG4fccuNaVc9epVjGUSJbxv/yH1umuXjsEHdXRE65bNsP8AAywIIYQQQuK8ELx7976ay9eUI0eOw9/fH1myZEK2bFnNtqVLl1ZZEAkhhBBCSBwXgmLl0/wCNTSLX7Wqla3qP336DAkTekVFGwkhhBBCSEwKwdy5cuDP02fMyjb9ulX5B9avW9uq/vbfdiFXzhxR00pCCCGEEBJzQrBN6+ZYtXq9mkdYIoi/mTAV16/fRNq0aVCnTg2zugu/X4pDh4+iSeMGUd9iQgghhBASJYQ7ofSXX/TEjp270b5jN2UF1Ol0Kmn08qULjBHEa9ZuwMxZ3ynLYf78efHlFz2ippWEEEIIISTmhKCIvR3bN2Ld+k04euwEvBN6o2OHNsiVK3j498KFizh/4SI6dWyLGdMmwNXVNepbTAghhBBCYt9cw+/fv1dWQgksiS9wrmHbcD7dkGHfsF8iCq8Z9guvmaiB9xJibq5hwdPTE/bKu3c+eP3mPfwDAtUsK/GdAP9AtXZ+7hTTTYl1sG/C7hf5Z9HF2QneCT2RIIHHJ/1+CCGERJMQtEdE9D14+Axv3r7XP9xc9A+5+I6Tc/w/x8jCvgm7XyQVlY+PL16+eouEXp5ImyaZXdw3hBAS26AQ/EhevnqnRGCypN5IniyR3TzMAgP1Vk8nJ/s434jAvglfv8g/UU+fvcKz56/x8pU7kiZJ+Am/JUIIIQKf4h/J23fv4erqjBTJE9uNCCQkKpD7Re4bFxdndR8RQgj59FC5fCRBQTo4OTmplDqEkIgh942zs5O6jwghhHx6KAQJIYQQQuwUCkFCCCGEEDuFQpCQCCAz6hD2FyGExBcoBEmYXLp0Be06dEHaDDnh6pEMqdJmQ9PmbXHk6HG76r2//76EsuWrR8mxnj9/jtz5iqm+7NN3IOIjhw8fQ70GzWO6GYQQQkKB6WNiKX4fdPjrcBD+OhyIt68Ar0RA0QpOKFrBEa7uny4w5eLFyyhdrhpKliiGWTMmIXWqVHj0+DEWLlqGqtXrYdOGlWjYoC7sgdVr1uPEyVMffRxfX180btoWSRInxo5tG1CoaDmkSZ0aQ4f0R3zi+8XLcOny1ZhuBiGEkFCgEIyFPPk3CIvG++PVM4mqlOFI4OlD4PblAOzZAHQf6YKU6T6NMXfm7O+QOHEi7PxtI1xcXIzljRrWR4lSlTBsxFi7EYJRhfTjb9vWw83NTb1+8vAmAgP1s24QQgghnxIODcdCS6CIwNfP9e81lzRtLeWyXep9Ch49eqz84iynzRMBM2niWHTv+plZ+e+/70WFSrXglSgN0qTPgS7d/oenT58Ztz98+EiVZcySFx5eKVGidCX8umW72TEcnL0xbvxklCxdGe4JUmDI0NGq/J9//lVD1MlSZoJnwlSoVKUOTpz4I8xzCM9+8pkLv1+KXv/7GslTZVb16tZvhuvXb6jtY8ZOxIRJ04115X1obT10+Chq1WmMJMkzquH0LNkLYPSYCUrwSf48Ly8vTJg4Te3v7OyMBAkS4MCBw+r9/v2H1Gcn8E6t2tyj11d49+6dWXt/WP4LChQuDTfP5EifKTeGDhsDPz8/43ZpX/ZchbBl62/IX6iUalvhYuVw7NhJ/PHHnyhdtqrqf9m2e/c+K1eABo1awjtJOvU91m/YAlevXjduv3Pnrmrnho2/onXbz5AoaXpVV14/fvxE1fns855YsXIt7t69p+ou/3FFmN8TIYSQTw+FYDQS4K/Dfw+DIrQc3BaoLIEhxSRIuWw/tD0wQseVtkSG+vVq499/H6jh4W+/W6j85LSAiTq1a6DPl72MdXfu3I069ZshadIkWL1iGaZP/QY7d+1B46Zt1HYRCSVKV8b+A4fxzbgR2LD2Z2TJnFlt//Enc6EgoqtF8ybYuO4XtG7VDM+ePUPZCjVw/MQpzJk1BatX/gB3dzdUqV4fp0+fCbH9EdlvyLAxeP36NVb8tATfL5iNk3/8ifYdu6ltXbt0wmed2qnXx4/sUe9Dauu58xdQs3YjJEuWFGtW/oCtm9egQvkyGPfNFKxavS7MPm/V9jMUL1ZE7dev7xdYvGQ5xo2fYtw+bfocfN61NypWKGusM/e7hca2ajx48AhffT0Yw4b0x7rVP+HFi5do3qoDWrXtjM87d8DmDSvVd9m6XWej0Lxx46bqr38fPMQPS+dj+bIFePzkCcpVrIH79/8xO37X7l8iZcoU6nucNGE0ft3yG/r0HaS2jRw+CLVqVkPq1KlUf9WrWyvM8yaEEPLp4dBwNCHCa0ofP7x8Gj3H37U6UC3hJXFyYPBcVzi7RMy/sFfPrnjy5D9MmTbb+JBPkiQxqlWtjB7dP0f1apWNdUePnYj8+fNi88ZVxgTbiby90bf/EGVFWrBwqTrWlYt/ImvWLGp73bq1UL3mCwwcPBLt2rZS1jGhTOmSGDjgK+OxR4wcp4TkpQt/IFu2rKpMxIVYuWR4eteOzTbbP2v2vHDvlztXDqz4eanx/e3bd9U5yf7p06dDurRpVHnp0iXNPsOyrT/+tBJVq1TCLz8tMc42U6NGVWzZugMHDh5B+3atQ+3zzp3aY9zYEep11aqVsHffQWzZ9humTB6nhOqYcZOUkJv37UxVp2bNakiXLg1at+2M48d7o0yZUqrcx8cH386eivr166j3ly5fwZBho/H9gjno3q2zKhv37h2at+yAy5evonjxourYMtPH3t+3IEmSJKpO7VrVkTVHQXwzcZraV6NmjaqYO3uael29ehWc/ussVq1er95LXydPnkwNf2v9pU0xRwghJPZAiyAJk9GjhuLhP9ewZtVyJSDECrR+w2bUqNUQAwfpBcuHDx9w6s+/0LhhPbNZVkSE3Lh6DpkzZ8KBg4dRqmRxowjUaN+uFf7776kaktQoXLiAWR0RQwUK5EOmTBkREBCgFrFm1atTS4kr02HRyO5X1iCgNET8CZbDspZYtlWE3m/bNqhjnz//NzZu2qKGheWzJVAkLMqVK23RjrR4Z5iC7fjxP/D+/Xs0aljPeD6y1K1TU4nO3y2GecuWDT6nVKlSqrV8BxrJkiZV65cvXxn7q1LF8kiYMKHx2O7u7qhWtZLVscuZHFu1M11adR1YuhEQQgiJvdAiGF0d6+KgLHAvnkZsSHblHH/8eyvkoWFBdFa6rEDbr4KDN8IiSXKHCFsDTUmUKBFatmiqFuHSpavo1vNLTJ85F506tlVWQhFYIhJD4vmLFyhcqKBVeWqDQNHEiOCVIIFZnafPnuHGjVtwcdcLF0vEDzGtwWIX2f08PT3MtmnWvLCEjWVbxRL31deD8MuKNfD390eWLJmU1VD8KsOTh9DTw7odWhvkfIRGTWxbFWVI1xRvb2+rOgkSeIb42dIf4vtnq79Mg4VUOz09Q+wvzrtNCCFxAwrB6OxcFwekSBMx8VWqmjM23AwItY5oidLVnZEiTfQadMU3UHz6Ro8cooaBTcmVKwdmTp+E0mWrqCFHsUiJJVAse6aIRWnXrj0oWbI4kiZJooJPLHloKJOhxJCQyOXy5cqoFDa2CGnfyO73MXzdbwjWb/hV+SPK8KkEgggp0+iHpj+GxIkSqfVPy79Hnty5ovx8pL+qVK6IQSZD3YQQQuIvHBqOZUiewETJ9FY/W0i5bC9SPvq/OnH0F5+97+YvUsORlly7po8kzZ8vr4qCLVy4IH7d+puZ1WvfvoOo36glbt68pYYcJQDj1q3bZscRy1mKFMmVuAyJShXK4+q168iRI5vyZdOWDRu34Nt531tZqz52P1s4OTmFq97RYydUcEiTxg2MIlACU0Qkf+ywaenSJeDq6op//nlgdj6JEnlj8NDRytfvY5DvSIR9oUIFzI4/f+ESrF6zIULHCm9/EUIIiTkoBGMZkixa8gR6G0bmNEGoraVctn+KpNLyIF84b5ZKHVKsZEXMm79IpTiRdCMjRo1Hz9590atnF+TNm1vVHzd6GM6du4AWrTpix47f8fMvq9Cl+xeoUb0KSpUqgX5ff6EiaavXaoSffl6p6rRp1xn79h/EhPGjQhUOsq9YHKvVbIg1azcogSkzckyeOhPZs2U180uMiv1sIcPfgkT+3r59J8R6JUoUVb52ko7m4MEjmPvtAhVNLZ+l+fpFlmTJkilrnQR1DB8xDnv3HsDKVWtRt0FzJeCKFi38UccfNWKwCpKpW785Nv+6TX3X8h1JupqCBfNF6FjSXxJoI9+zpA0ihBAS++DQcCxEkkUPmu2KM0eC8NehQLx5BSSUmUUqOilL4KecWUSiek+dOICp02dj6vQ56sEuVjSJDp47e6qKXjUNDNm+ZZ0SKU2at1PDlE2bNMA340YqESQWxmOHd6s0LX37DcGHD74oWCAfNq5foaxnoSF+fJKGZOjwsej9RT/4+HxAtmxZMP+7mSqyOar3s4WkiPll5Vp06twTXbt0xPzvZtmsN33qRPj7Byix7Ovrp3wERwwbiIuXrihxJcPlWnR0ZBg/bqQ6r3kLFisfTf1wbgXVz1pASGQpWDA/jhzcpdre8bMeyoKZN08urF39ozr/iNClc0eVPqhR0zYYN2Y4Bg7o+1FtI4QQEvU46AJef5rMxHGUIsUqQDro7OnDNrffuae3dGTOmBr2hJYKxMmJRmX2zcddM/Z6D5ny9q2PWnt5mQcK2TvsF/YNr5nI6RbhTAi6xRI+xQkhhBBC7BQKQUIIIYQQO4VCkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7hUKQEEIIIcROoRAkhBBCCLFTYq0Q3L//EMqUq4YE3qmRIXMejBr9jZqRITSSJM8IB2dvq+XRo8efrN2EEEIIIXGFWDnF3MmTp9TcrA0b1MXokUNw9twFjBozAa/fvMHsmVNs7nPnzl28fPlSTXtWonhRs20yvy0hhBBCCIkDQnD02InIkycX1qxaruaorV27BtzcXDFw8EgMGtBXzbNqiYhFoXmzxkiTxn6nqiLxA51Op659QgghxK6Ghn19fbH/wGE0bdzA7EHYskVTBAYGqknsbXH27HmkTJki3ohAnZ8ffM+cxuuff8Crhd+ptbyX8k/NpUtX0K5DF6TNkBOuHsmQKm02NG3eFkeOHoe98NnnPZE+U27j+8pV66J8xZqh7jNi5DjlmhARPnz4gL79BmPlqrUhfjYhhBASby2Ct27dgZ+fH3LlymFWni5dWnh4eODy5as29xOLYKJE3mjUpLUSkmJRqVe3JmbNmBwF4lBnnPzckgD/QDg5OyIwMAhRReCzp3i/ZgV0b14DEDGsA148x/v79+Bz5BA8W7WDU7Lk+BRcvHQZ5SrUUMPtM6ZNRKpUKfH48RMsWvwDqlavhw1rf0GDBnUR35HrSdC+52/nTjd7b0pQkL4sSL9LhK6Nu3f/wZy5C7B08TzjfsOGDMQX/+sRpddYTKD1i62+DQwICvEeswfe+/jGdBNiJewX9g2vmYgjjx6HuCwEX716pdbeCRNabUuY0Ev5CYYkBJ89e45uXT7DgH59cOnyFTXEXKlqHfx16jC8vLwQFxCLnxKBb7XzNKgJgxCRctnu1bUXHFxdo709s+fMQ+LEifDbtg1wcXExljdqWA8ly1TGiFHj7UIIWpI3z6ez0GXLluWTfRYhhBD7ItYJwSDNjBICIflN/fLjYnh7J0TBgvnV+woVyiJ/vrwoX6kmlv+4QllUIo8DvLw8bG5xfu6k1k5O1qPsuoAABL3WC9vw4n/pb4MlMAR0OrXd//QfcM2bL9zHdfROBAfniH/dYv3T+6uZn6ObmxsmTxyHW7dumZX//vtejJ8wFWfOnlfCvW6dmpgyaRySJ0+mtj98+EiJx9179uO//54if/48GDFskBKWGjKcOnb0MGzbvhPnL1xE3z69MXnSWPzzz78YPHSUcg/w8fmgrJRTJo1F6dIlbbb9338fIGOWvJgxbQL6fvU/Y/nbt2+RKm12DOj3JcaOGY6nT59h9NgJ2LZ9l2qfl1cCVKpYXu2XNWsWs+tOO1cZGpYo9iOHflfvfXx8MHT4GKxeswHv3r1HyxZNkCRxYrN9hCVLf8TCRcuUZVssZGL5Hjq4H1q1bKYCnnLn0wc6den2P4z7Zgru3PxbDQ3v2XsA/9y9oraJi4RYZBd8vxQ3btxSfdu6ZTOMHTNMWc0F2eeffx6gU8c2mDh5hrK0Z8mSCSOGDUT7dq0Rk1jeK9K3zi5OId5j9gT7gP3Ca4b30sfiENeFoFifhDdv31pte/PmLRJ52/a5Kl++jFVZuXKlkShRIpw7/zc+NSICXy/8LsJCMLx8OLRfLRERgt49v4iwGKxfrzZ+2/E7Sperhs8/a48qlSsiX748alud2jXMHuo7d+5GvYYt1D6rVyzDq9evMWjIKFy9el0JJhGVJUpXhqurK74ZNwLJkyXD8p9WonHTNli+bAE6dWxnPNaESdPxzbiRGDNqqAoOevbsGcpWqAFnZ2fMmTUF3t7emDd/EapUr48jB3ehWLEiVm0XdwJpr4gzUyG4+ddteP/+PTp2aKN3IWjQHM9fvMDkiWOQNk0anDt/ASNGfYPuPb/Cnt+3hKuf2nfshl2/78WE8SORLVs2LF7yA37+ZbVZHWlvn76D1DlNn/KN+syp02ajXYeuKF2qhHJhWLfmJ7Ro1VEJtqZNGtr8rB69vsKPP61UgVOVK5VXonvs+Mn468w57N71q1G0/nn6DO7dv49RIwYjVcqUmDp9Djp06o6iRQojb176HBJCCImFQlCGwZycnJSlw9K6I1YXiSa2RETC5l+3o3y5Mma+hWJxEX9DERwkcvTq2RVPnvyHKdNmKxEjJEmSGNWqVkaP7p+jerXKxroyFJ8/f15s3rjKKEZEuPftP0RZuxYsXKqOdeXin0ZLW926tVC95gsVEd6ubSsl9IQypUti4ICvzAIvREheuvAHsmXLqsrq1a2FwsXKYdiIsdi1Y7PN9nfs0BqdOvfE7dtiEcusylauWoeyZUqp4zx48BAJEiTA1MnjUalSebVd1jdu3sL8BUvC1UcXL17Gxk1b8O2caUZfvlo1q6FQ0TJmPq1ilevX9wuMHDHYWJY5U0YUK1kRh48cU5a6woUKqHJpW5EihWwG7ixd9hPGjx2BEcP130eNGlWRNm1qJfK2btuh0i5pbhbHj+wx3jNyb2TKmk/VoRAkhBASK4WgDDlWrlQBGzdvweBBX8PRUW9xWrtuoxIJVatUtNpHfNd69u6LTh3bYsmi74zlW7b+psRjlcoV8KkRy5tY4CJqEXz360YEPnwQZj2nNGmRoFHTaB8aFkaPGoq+X/VWFq+9+w7i4KEjWL9hs1rEH3Pa1G9UtOupP//CyOGDzIbv69evoxbhwMHDKFWyuFEEarRv1wqduxxQIkcb2i9cWC+INORzCxTIh0yZMpolFq9XpxZmzZmnBL9YGi1p1rQRen/RX1kFhw7pr4ajZVj6O0Owh1gb9+3ZpiyDIlavX7+JK1ev4eixk2oIVhb5xyQ0RMQJDQznKch1K5HvE0yE4IzpE9Va8l1euXJNiU0JbBJ8fcMXDS59L7Rp3dysvHWr5vjs8144cOCwUQiKYDf9xyl9+nRq/e7du3B9FiGEkPhPrBOCgoiJqjXqo1mL9ujWpZPyE5OE0r16dkHGjBlUipkzZ86pB5ssMkzY/+svMXX6bCRLmhQ1a1TF+Qt/Y8y4ycpqVLNmtRg5DxFeTkkjZo10K1wU78MhBKVeRI/9McgQu6TwkUW4dOkquvX8EtNnzlUCXESHiClJ4RMSMhRauFBBq/LUqVKq9cuXwaLZK0ECszpPnz1TVmIXd9vJwcXPz1Z+SbH2NW3SAKvWrFdCUP6hEGHXqmWwiF6xcg2GDh+L+/f/QdKkSVQbPQ2+dlq0cGg8f/5CrTU/SA3LaPWbN2+hR6++2LvvgBKtuXPnRMEC+cL9OaaflTp1KrNy+SdJPv+lIdhK8PT0NKuj/VMVUvQuIYQQ+yPW5RHUhuY2b1iFO3fvoUnzdpi/cIlyqJdUMII49JcpX1053mtM+GYUZs+cjG2/7UT9Ri0xc/Y89OrRRflcxSVc8xWAQ0Jv8aC3XcHBQW2XetGNDMdL7sDvFy2z2ibDjDOnT1KvJUJbUveIJVAsbqaI9W779p2qPGmSJDan+3toKLMUUpa+ozL0f+rEAZtLaPt2aNcaFy5cVFa4VavXKx/GxIZAjiNHjqPjZz3QuFE93L9zGc+e3MXe3VvV0HR40T5bhq4txamGiC/xn3z0+DH+OL4fb189xLm/jmHo4P6ICCJUBct+9Pf3V59HNwhCCCFxXggKDRrUwZk/j8D3/VPcu31JRXdqQ3SZM2eCLuA1xoweZqwv2/p82QsXz/8Bn7dP1ENdIk21KMq4gqSESdi2Axy8DOlzNEFoWEu52v4JUseI1UksTd/NX6SCKyy5du26Wkt0tqTnKVy4IH7d+puZdWvfvoNKmIs1TCJxT/7xJ27dum12nF9WrEGKFMmtckeaUqlCeVy9dh05cmRD8eJFjcuGjVvw7bzvzVLbWFKtWmUVODJvwSIcO35SBYloyHsRaTKVoTZ0KsPBu/fuD7f1THNXWLfe3E9R/inREJEmQTOdO7VHiRLFjO3dsXO32eeENQwtfSiIoDVl9Zr1qt22gqYIIYSQODU0bO9IsuhEPf4Hv4sX4Pv3Bejev4WDpxfc8hfQWww/gQhU7XBywsJ5s9C4WVsV0PBF7+7IlzePsj7tO3AYc79doIbrtcCDcaOHoWGT1irqtUvnDmo4d9iIcahRvQpKlSqhBPzPK1ajeq1GGDNqCFIkT46fflmFffsPYtHCuaGKoH5ff6H2rVazIQb276P23bxlG7797nuMGzM81OnYZEi0XZuWmDHrW2W9k2hnjZIliql1335D8PlnHfDi5Ut8N28RzhmmLBR/Olu+h6Zkz54NXbt0UsEyIoILFiyAn39ZhYsX9eleBBkyl/Nf8P0SZM6cUaWWEZ9L8W/UPkeQIXZBho/z5M6p+s0U6WsZipcoYfHLFH9ayaE5ZtwkVKxQTrlCEEIIIXHeImjviNhzK1IM3h0+Q6IeX6i1vP9UIlBDonpl6LVokUIq/Ujtek3RtEV7Ffgxd/ZUzPt2prGuBIVs37JO+drJkL743TVpXB/r1/6shJpYGI8d3q3y/4nwatayA27fvouN61egW9fPQm2H+P9JBGy2rFnQ+4t+ysoogRbzv5tpFoUbEmIFFItZ61bNzKyHlStXwLxvZyjLYJ36zfB1/6HImDG9apNw+Ej4ptFbMG+WEqhinWzesr0KXhF3BlM2b1iJ9OnSqaCOlm064fiJP7B18xrlK6h9jgxZS97ETZu3qfaI6LZEZh2RFDTi91i3QXNlsRWRvvM3vf8jIYQQEl4cdAGvw+elbqcUKVZBze1x9rQ+utOSO/ceqXXmjPFjjuPwok13ZiuRtr3DvolYv9jrPWSKNr0eE0qzX3jN8F6KCt0inAlBt1jCpzghhBBCiJ1CIUgIIYQQYqdQCBJCCCGE2CkUgoQQQgghdgqFICGEEEKInUIh+LEd6Oig0pKEd4owQkgwct8EBASq+4gQQsinh0LwI/FK4Ak/vwD89/Ql53AlJALIbCpy3/j7B6j7iBBCyKeHM4t8JIkTJcB7nw949vw1Xrx8CxcXJzWTRXxHs4CGNqOHvcK+CbtfRAT6+weqdcKEnuo+IoQQ8umhEPxIRPSlT5sC79754PWb9/AP0D/c4juBAfpzdHbhTBbsm4hfMzKHtYe7G7y9EyCBp3u0XaeEEEJCh0IwikiQwEMt9gJnQmDf8JohhJC4T/wfwySEEEIIITahECSEEEIIsVMoBAkhhBBC7BQKQUIIIYQQO4VCkBBCCCHETmHUcBi89/FR6yLFKnyK7yPOoM2jwiyC7BteM7yf+DvD3+DYAp9NMOqW8EIhGAaS/JbTx9nolwhdZvYF+4b9wmuG9xJ/Z/j7G5NEZLIHB13Aa06SSwghhBBih9BHkBBCCCHETqEQJIQQQgixUygECSGEEELsFApBQgghhBA7hUKQEEIIIcROoRAkhBBCCLFTKAQJIYQQQuwUCkFCCCGEEDuFQpAQQgghxE6hECSEEEIIsVMoBAkhhBBC7BQKQUIIIYQQO4VCkBBCCCHETqEQDIX9+w+hTLlqSOCdGhky58Go0d8gICAA8RU5t9lz5iFfwZLqnLPlLIh+/YfizZs3xjqNm7aBg7O31bJ6zXpjnffv36Nvv8FIlzGXOk7lqnXx119nEddJkjyjzXN/9Oix2n779h00adZW1ZOlQ6duePLkP7NjBAYGYtz4ycicLT88vFKiZOnK2L17H+IiBw4cttkf2jJ23CS7vGb+/fcBkqbIiD179puVP336DJ993hMp02RFwsRp0bBxK9y8ectq/wULlyBX3qLq+shfqBRWrlprVefs2fOoXrMhvJOkU8f78qsBePv2LeJivxw7dhI1ajVC8lSZ1VKrTmOcOXPOrM7mX7fZvIZq121iVm/9hs0oVLQsPBOmQvZchTBn7nzEBULqG7knbJ335CkzI/ybEhefZ7b6Rc4zpN+cLNkL2M01E5U4R+nR4hEnT55CnfrN0LBBXYweOQRnz13AqDET8PrNG8yeOQXxkWHDx2L23PkYOrgfKlYohytXr6lzPn7iDxw9vBuOjo6qHz7r1A49unU22zdHjmzG1+06dMXBQ0cwdfJ4pEyZAlOnzUbVGg1w7q+jyJQpI+Iid+7cxcuXLzF39lSUKF7UbFuyZEnx6tUrVKleH4kTJ8KyJfOUeB4ybIy6hv44vh9OTk6q7oCBw7Fw0TJMGD8KuXPlwPeLf0C9hi1w5OAulCxZHHGJokUL4fiRPVblI0Z9g1N//oU2rZur9/Z0zdy//w9q1W2CFy9empXLw7p2vab477+nmDNrCpydnTF67ER1zfx97gS8vb1VPXkI9RswDMOHDkC5sqWxdv0m1TceHh5o0riB8R+OqjXqo0jhQlj58xLcu/+Putbu3/8XmzeuQlzqFxG0ci4VK5TFD0sWQAcdps+YizLlq6trq0iRQsZ6KVIkx5ZNq832T5w4sfH1ps1b0bJ1J/Tq2QVTJ43Dvv2H8HX/odDpdOj71f8QWwmpb4SzZy+gVs1qGDNqqFl5xowZjK/D85sSF59nIfXLpvUr4Ovra1Ymzyi5b3p2/9xYFp+vmShHF/Bax8W6D2rVrKYrXLigLsj/lbFs5vSJOicnJ92/967Guz579/qRzsXFRTd0cH+z8tUrf9DJZbL39626F0/vqdcrf1ka4nGOH9mj6mzasNLs2GnSpNb16tklxs8zsoucj5zXg/vXbG6fNGGMzs3NTffwn+vGsr9OHVb7rFm1XL2/d/uSztnZWTdrxiRjnUC/l7oiRQrp6tSuEePnGBXLr5tWq3Net+Yn9d5erhn5Hn9YukCXLFlSXdKkSdT57N75q3H7qhXLVNmZP48Yy+Racnd3102ZNE69f//msS5JksS6r/r0Mjt2wwZ1dXny5DK+79H9c13q1KlUfa1s4/oV6vgnj+2LU/3Stk0LXaZMGXW+758ay96+eqhLnjyZrkP71sayRg3r6WrWqBrqZ+XKlUPVMy3r82VPXeLEiXUf3v0X430R0b6RRdo+8ZvRIR4jvL8pcel5Fp5+MV1ePf9HlzlzJl29urXMyuPjNaOLpoVDwzaQ/zb2HziMpo0bwMHBwVjeskVT9Z/9zl3WVpC4jvzX1a1rJ7Ro3tisPHeunGr94OFD9d+pULhQwRCPI33j7u6OunVqGss8PT1Rr24tbNu+C3EV+e9SLFVp0qQO8bzFgpM6dSpjmVgzsmfPim3bd6r3e/cdVEMxzZo2MtYRK2uzJg2xZ+8Bq/9y4xo+Pj748quB6rtu3kx/HdnLNXP+/N/o2bsvOrZvg5+XL7J5jlmzZkbhwsH9INdS+XJljNfHyZN/qvuwWZPg60No2aIJLl++ilu3bhuPJX0lVkKN+vVqq/faseJKv4hVc0C/L+Hq6mosS5AgAdKnT4cHDx4Zy8SCFdo1JBb7q1evo1nThmblLZs3VZb8o0dPILYRVt9ooxCFCwUPd1oSnt+UuPY8C6tfLPlmwjTlgjPv2xlm5fHxmokuKARtcOvWHfj5+SFXrhxm5enSpVU/tvKjHN+Qc5v37UzjUIypn4WQP19enD13Xv3IzP1uIVKnyw5Xj2SoUKmWGnbQuHzlGrJkyWT2wy7kyJ5NmfrfvXuHuIj8qCRK5I1GTVorvyzx8Wrd9jM8fKh/WF2+chW5cmW32k/OW7apOpevqusnQ4b05nVyZIO/vz9u3LD2F4tLzJm7QPn0zJ452VhmL9dMxozpcePqWcycMUmJWEvku8+V0/z3RMiRI2vw9WFYW/7uSD9oxxCxfffuPatjubi4IHPmjKov41K/DOjfB1/8r4dZ2Y0bN/H335eQP38e9V4eynLO167fQIHCpdU1lClrPjWELEN4gvabbNkvmvuB1rdxqW/kN0fYuHkrMmbJCxf3pChSvDx27PjdWCc8vylx7XkWVr+Ycu/efcz5dgEG9u9j5kISX6+Z6IJC0Abi7yV4J0xotS1hQi/lV2EPHD9+EpOnzlLWBrFkyA9TUFAQ5J/KNSuXY9WKZfD58EH5OZ0z/GhJ34XUb8Lr13Gz7+TcHz58rCw427esw/Sp3+DAwSOoVLWOctJ/9eo1vBPq/bxMSZgwofGcX71+DW9vG33jpfXNa8RV5EEjP8itWzVDdoNwEezlmkmaNKmyYoVEyN+9yfXxSv/9W9aTa0iQ352Q6uiP5RXrrqGw+sUSCRrq+FkPZSHu26e3mVVZfCHFV27Htg1o2KAOBg0ZieEjxhn7V9B8LePCNRRW38gohNb2ZYvnKd+4FMmToX6jlti5c3e4f1Pi2vMsItfM7Dnz4ebmhq/69DIrj6/XTHTBYBEbBAXp/2MICVPzenxFIkIbNW2jLDXLly1QZYMG9EWnDm1RpUpFY71qVSshR+4iGD9hKtav/Vk99ONj3/3y42L1g1uwYH71vkKFsspKWr5STSz/cUWo562dc3ztGy3yTqKnB/b/yqzcnq8ZU6Lq+ojPfSVWnEZN2qhAow3rfkHmzJlUecGC+dQ/X+XLlzE+tKtVq4wPH3wxfeZcDBzQJ172S6eObVGqZHHUMXGZqFWruopwHTH6G9SuXSOc10z8fJ59+PABS3/4GV06d0CSJEnMttnrNRNZaBG0gUR+Cm9spGN48+YtEln8BxHfEGEj0VrZsmXB/j3bkSxZMlWeN29uswe6FoElvnHnzl8wvrfVb9p/VzK8GheRHxRNBGqUK1caiRIlwrnzf6tr5s1b6/8g5T9y7XpJnCiRun6s6hj+I5djxVXWb/gV+fLlQSELfyZ7vmZMCe27187P+LtjUU+z8sl1pNUN+Vhx8xqSNDoSKSwiUP45kOhWUwtR3bq1rCw34kMqw5+XLl1V/SuYprqK69dQliyZzUSg5gJQs0ZVozU9PL8p8fV59vvufereaNe2pdU2e71mIguFoA1EAEm6D0ufLfF/Eh+dPHlyIb4iuaU6d+mFKpUr4ND+HUiVKqVx24qVa7Bv30GrfaRPkhvEYq6c2XHnzj2r/FQ3bt5SPhymDu5xhWfPnmHpsp+UY7Ep8h+lDInKuYufiS0fPzlv7XoRHx0Z+tL8Co11btxS/nESTBAXkR/WXb/vRcvm5vm57PmasUS+ezkfS+S7z5M7l5mvkvjImdUx7CfXkRZIYXks+Q7u3r2PPLn1wV1xiT/++BOly1VTeRb3/r4FjRrWs9r+/aJlNq8hIXnyZEb/N8t+0e5JrY/jEr9u2a4WS3x8PqhzDu9vSnx9nm3dtkOJ5eIW6bzs+ZqJLBSCNhCfg8qVKmDj5i1m5uO16zaq/F9VLSwc8YUpU2ep4TrJ+bZtyzp4GfxMNOZ+uxD/69Pf7IEtPyZHj51Elcr6PqlVs7r6YdqxQ+/DIsj77b/tUvmw4iLyX7hEsU2bMcesfMvW39QPi4hmObcjR0/g8eMnxu2SFFd+VLTzrlG9ihpukGFUDbm+NmzagsqVyqvrLi4iUX7yHYuVzxJ7vWYskfO4du0GLly4aCyTh/eRo8eN51i2bCl1z4l11ZS16zYhZ87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- "Bon équilibre biais/variance : le modèle généralise correctement.\n",
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- "Refit final sur tout le jeu SMOTE pour : RF_balanced\n",
- "Données rééquilibrées pour l’entraînement final : [1233 1233]\n",
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- "\n",
- " best_threshold F1 Recall Precision ROC_AUC \n",
- "1 0.490292 0.920934 0.911598 0.930464 0.971614 \n",
- "0 0.553559 0.905837 0.862125 0.954219 0.954884 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# === Oversampling + cross-validation + ajustement de seuil ===\n",
- "\n",
- "# --- Sécurisation de la variable cible ---\n",
- "ALL_NUM = [c for c in NUM_COLS if c in df.columns]\n",
- "ALL_CAT = [c for c in CAT_COLS if c in df.columns]\n",
- "\n",
- "df = df.copy()\n",
- "df = df[df[TARGET].isin([0, 1])].copy()\n",
- "\n",
- "# --- Imputation des NaN sur les variables numériques ---\n",
- "for col in ALL_NUM:\n",
- " if col in df.columns:\n",
- " median_val = df[col].median()\n",
- " df[col] = df[col].fillna(median_val)\n",
- "\n",
- "# --- Encodage des variables catégorielles ---\n",
- "X_num = df[ALL_NUM] if ALL_NUM else pd.DataFrame(index=df.index)\n",
- "X_cat = pd.get_dummies(df[ALL_CAT], drop_first=True) if ALL_CAT else pd.DataFrame(index=df.index)\n",
- "X_full = pd.concat([X_num, X_cat], axis=1)\n",
- "\n",
- "# Sécurité : forcer tout en numérique et remplacer les résidus NaN\n",
- "X_full = X_full.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
- "y_full = df[TARGET].astype(int)\n",
- "X_raw = X_full.copy()\n",
- "y_raw = y_full.copy()\n",
- "\n",
- "\n",
- "print(\"Taille X_full:\", X_full.shape)\n",
- "print(\"Répartition de la cible:\\n\", y_full.value_counts())\n",
- "\n",
- "# Vérification de la diversité des classes\n",
- "if y_full.nunique() < 2:\n",
- " raise ValueError(\"[ERREUR] La variable cible n'a qu'une seule classe présente après nettoyage.\")\n",
- "\n",
- "# --- Oversampling global avant cross-validation. À remettre si SMOTE enlevé du pipeline ---\n",
- "try:\n",
- " sm = SMOTE(random_state=RANDOM_STATE)\n",
- " X_full, y_full = sm.fit_resample(X_full, y_full)\n",
- " print(\"Oversampling SMOTE effectué (classes équilibrées).\")\n",
- "except ValueError as e:\n",
- " print(f\"[ATTENTION] SMOTE impossible : {e}\\n→ utilisation de simple oversample() à la place.\")\n",
- " X_full, y_full = simple_oversample(X_full, y_full)\n",
- "\n",
- "# --- Prétraitement pour les modèles ---\n",
- "num_pipe2 = ImbPipeline([\n",
- " ('imp', SimpleImputer(strategy='median')),\n",
- " ('sc', StandardScaler())\n",
- "])\n",
- "preproc2 = ColumnTransformer([('num', num_pipe2, list(X_num.columns))], remainder='passthrough')\n",
- "\n",
- "# --- Modèles ---\n",
- "models = {\n",
- " 'LogReg_balanced': LogisticRegression(max_iter=2000, class_weight='balanced', random_state=RANDOM_STATE), #, class_weight='balanced'\n",
- " 'RF_balanced': RandomForestClassifier(\n",
- " n_estimators=300,\n",
- " max_depth=8,\n",
- " min_samples_split=10,\n",
- " min_samples_leaf=5,\n",
- " class_weight='balanced_subsample',\n",
- " random_state=RANDOM_STATE\n",
- " )\n",
- "}\n",
- "\n",
- "param_grids = {\n",
- " 'LogReg_balanced': [\n",
- " {\n",
- " 'clf__solver': ['lbfgs'],\n",
- " 'clf__penalty': ['l2'],\n",
- " 'clf__C': [0.1, 1.0, 10.0]\n",
- " },\n",
- " {\n",
- " 'clf__solver': ['liblinear'],\n",
- " 'clf__penalty': ['l1', 'l2'],\n",
- " 'clf__C': [0.1, 1.0, 10.0]\n",
- " }\n",
- " ],\n",
- " 'RF_balanced': {\n",
- " 'clf__n_estimators': [200, 300, 500],\n",
- " 'clf__max_depth': [6, 8, 10],\n",
- " 'clf__min_samples_split': [5, 10, 15],\n",
- " 'clf__min_samples_leaf': [2, 5, 8]\n",
- " }\n",
- "}\n",
- "\n",
- "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
- "results = []\n",
- "\n",
- "for name, base_model in models.items():\n",
- " print(f\"\\n=== {name} ===\")\n",
- "\n",
- " pipe = ImbPipeline([\n",
- " ('smote', SMOTE(random_state=RANDOM_STATE)), # oversampling dans chaque folds\n",
- " ('prep', preproc2),\n",
- " ('clf', base_model)\n",
- " ])\n",
- "\n",
- " grid = GridSearchCV(\n",
- " estimator=pipe,\n",
- " param_grid=param_grids[name],\n",
- " cv=cv,\n",
- " scoring='f1',\n",
- " n_jobs=-1\n",
- " )\n",
- " grid.fit(X_full, y_full)\n",
- "\n",
- " print(\"Meilleurs hyperparamètres :\", grid.best_params_)\n",
- " best_pipe = grid.best_estimator_\n",
- "\n",
- " # --- Cross-validation avec prédictions de probabilité ---\n",
- " y_proba_cv = cross_val_predict(best_pipe, X_full, y_full, cv=cv, method='predict_proba')[:, 1]\n",
- "\n",
- " # --- Ajustement du seuil optimal (max F1) ---\n",
- " precision, recall, thresholds = precision_recall_curve(y_full, y_proba_cv)\n",
- " f1_scores = 2 * (precision * recall) / (precision + recall + 1e-8)\n",
- " best_idx = np.argmax(f1_scores)\n",
- " best_threshold = thresholds[best_idx] if best_idx < len(thresholds) else 0.5\n",
- "\n",
- " print(f\"Seuil optimal (max F1): {best_threshold:.3f}\")\n",
- " y_pred_opt = (y_proba_cv >= best_threshold).astype(int)\n",
- "\n",
- " # --- Évaluation finale ---\n",
- " cm = confusion_matrix(y_full, y_pred_opt)\n",
- " ConfusionMatrixDisplay(cm).plot()\n",
- " plt.title(f\"Matrice de confusion – {name}\")\n",
- " plt.savefig(f\"output/matrice_confusion_{name}_seuil_ajuste.png\", dpi=300)\n",
- " plt.show()\n",
- "\n",
- " print(classification_report(y_full, y_pred_opt, digits=3))\n",
- "\n",
- " # --- Courbe Precision-Recall ---\n",
- " plt.figure(figsize=(6, 4))\n",
- " plt.plot(recall, precision, color=Theme.PRIMARY, label='Courbe Precision-Recall')\n",
- " plt.scatter(recall[best_idx], precision[best_idx],\n",
- " color=Theme.SECONDARY, label=f'Seuil optimal = {best_threshold:.2f}')\n",
- " plt.xlabel('Recall')\n",
- " plt.ylabel('Precision')\n",
- " plt.title(f'Courbe Precision-Recall – {name}')\n",
- " plt.legend()\n",
- " plt.grid(alpha=0.3)\n",
- " plt.savefig(f\"output/precision_recall_curve_{name}.png\", dpi=300)\n",
- " plt.show()\n",
- "\n",
- " # --- Courbe d'apprentissage ---\n",
- " print(\"Génération de la courbe d'apprentissage...\")\n",
- " train_sizes, train_scores, test_scores = learning_curve(\n",
- " best_pipe, X_full, y_full,\n",
- " cv=cv,\n",
- " scoring='f1',\n",
- " n_jobs=-1,\n",
- " train_sizes=np.linspace(0.1, 1.0, 5),\n",
- " shuffle=True,\n",
- " random_state=RANDOM_STATE\n",
- " )\n",
- "\n",
- " train_mean = np.mean(train_scores, axis=1)\n",
- " train_std = np.std(train_scores, axis=1)\n",
- " test_mean = np.mean(test_scores, axis=1)\n",
- " test_std = np.std(test_scores, axis=1)\n",
- "\n",
- " plt.figure(figsize=(6, 4))\n",
- " plt.plot(train_sizes, train_mean, 'o-', color=Theme.PRIMARY, label='Score entraînement')\n",
- " plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.2, color=Theme.PRIMARY)\n",
- "\n",
- " plt.plot(train_sizes, test_mean, 'o-', color=Theme.SECONDARY, label='Score validation')\n",
- " plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.2, color=Theme.SECONDARY)\n",
- "\n",
- " plt.title(f'Courbe d’apprentissage – {name}')\n",
- " plt.xlabel(\"Taille de l’échantillon d'entraînement\")\n",
- " plt.ylabel(\"Score F1 (moyenne CV)\")\n",
- " plt.xlim(0, train_sizes[-1] + 10)\n",
- " plt.ylim(0.45, 1.05)\n",
- " plt.legend()\n",
- " plt.grid(alpha=0.3)\n",
- " plt.tight_layout()\n",
- " plt.savefig(f\"output/learning_curve_{name}.png\", dpi=300)\n",
- " plt.show()\n",
- "\n",
- " # --- Diagnostic automatique ---\n",
- " gap = train_mean[-1] - test_mean[-1]\n",
- " val_final = test_mean[-1]\n",
- " print(f\"Analyse courbe d’apprentissage – {name}\")\n",
- " if val_final < 0.6 and gap < 0.05:\n",
- " print(\"Sous-apprentissage probable : le modèle est trop simple ou mal paramétré.\")\n",
- " elif gap > 0.1:\n",
- " print(\"Sur-apprentissage détecté : trop grande différence entre entraînement et validation.\")\n",
- " else:\n",
- " print(\"Bon équilibre biais/variance : le modèle généralise correctement.\")\n",
- " print(f\"(Score final validation : {val_final:.3f}, écart train-val : {gap:.3f})\")\n",
- "\n",
- " results.append({\n",
- " 'model': name,\n",
- " 'best_params': grid.best_params_,\n",
- " 'best_threshold': best_threshold,\n",
- " 'F1': f1_score(y_full, y_pred_opt),\n",
- " 'Recall': recall_score(y_full, y_pred_opt),\n",
- " 'Precision': precision_score(y_full, y_pred_opt),\n",
- " 'ROC_AUC': roc_auc_score(y_full, y_proba_cv)\n",
- " })\n",
- "\n",
- "# --- Résumé des performances ---\n",
- "res_df = pd.DataFrame(results).sort_values(by='F1', ascending=False)\n",
- "print(\"\\n=== Résultats (seuil optimisé) ===\")\n",
- "display(res_df)\n",
- "\n",
- "# --- Sélection du meilleur modèle ---\n",
- "best_row = res_df.iloc[0]\n",
- "best_model_name = best_row['model']\n",
- "best_params = best_row.get('best_params', {}) or {}\n",
- "\n",
- "print(f\"\\nRefit final sur tout le jeu SMOTE pour : {best_model_name}\")\n",
- "\n",
- "# 🔹 Récupération du meilleur pipeline issu du GridSearch\n",
- "best_model = models[best_model_name]\n",
- "best_pipe = GridSearchCV(\n",
- " estimator=ImbPipeline([\n",
- " ('prep', preproc2),\n",
- " ('clf', best_model)\n",
- " ]),\n",
- " param_grid=param_grids[best_model_name],\n",
- " cv=cv,\n",
- " scoring='f1',\n",
- " n_jobs=-1\n",
- ").fit(X_full, y_full).best_estimator_\n",
- "\n",
- "# Application d’un SMOTE global pour le refit final\n",
- "sm = SMOTE(random_state=RANDOM_STATE)\n",
- "X_bal, y_bal = sm.fit_resample(X_full, y_full)\n",
- "print(\"Données rééquilibrées pour l’entraînement final :\", np.bincount(y_bal))\n",
- "\n",
- "# Refit complet du meilleur pipeline sur tout le jeu équilibré\n",
- "best_pipe.fit(X_bal, y_bal)\n",
- "print(\"Modèle final refitté sur les données équilibrées.\")\n",
- "\n",
- "# --- Résumé des performances ---\n",
- "res_df = pd.DataFrame(results).sort_values(by='F1', ascending=False)\n",
- "print(\"\\n=== Résultats (seuil optimisé) ===\")\n",
- "display(res_df)\n",
- "best_row = res_df.iloc[0]\n",
- "best_model_name = best_row['model']\n",
- "best_params = best_row.get('best_params', {}) or {}\n"
+ "## 7) Mod\u00e9lisation avanc\u00e9e (SMOTE, LR, RF) migr\u00e9e dans `projet_05/modeling/train.py`.\n"
]
},
{
@@ -2120,167 +561,7 @@
"id": "a65d548d",
"metadata": {},
"source": [
- "## 8) Interprétation : importance globale & locale (SHAP)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "b1530a0e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "=== Interprétation SHAP globale ===\n",
- "[INFO] 38 variables utilisées.\n",
- "[INFO] X_transformed shape: (400, 38)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|===================| 797/800 [01:23<00:00] "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[INFO] SHAP array shape: (400, 38)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/steph/Code/Python/Jupyter/projet_04/manet_projet04/shap_generator.py:138: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
- " shap.summary_plot(\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
- "\n",
- "# --- Entraînement global ---\n",
- "best_pipe.fit(X_full, y_full)\n",
- "fitted_model = best_pipe.named_steps['clf']\n",
- "\n",
- "# === Calcul et affichage global ===\n",
- "cmap_custom = make_diverging_cmap(Theme.PRIMARY, Theme.SECONDARY)\n",
- "shap_values, X_transformed, feature_names = shap_global(best_pipe, X_full, y_full, cmap=cmap_custom)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "39153710",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "[INFO] Interprétation locale SHAP – individu 202\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "[INFO] Interprétation locale SHAP – individu 146\n"
- ]
- },
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "[INFO] Interprétation locale SHAP – individu 109\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "[INFO] Interprétation locale SHAP – individu 149\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Cas général : individu à plus fort impact\n",
- "idx_impact = np.argmax(np.sum(np.abs(shap_values.values), axis=1))\n",
- "shap_local(idx_impact, shap_values, cmap=cmap_heatmap)\n",
- "\n",
- "# Cas : individu à forte probabilité de départ\n",
- "y_proba_all = best_pipe.named_steps['clf'].predict_proba(X_transformed)[:, 1]\n",
- "idx_highrisk = int(np.argmax(y_proba_all))\n",
- "shap_local(idx_highrisk, shap_values, cmap=cmap_heatmap)\n",
- "\n",
- "# Cas : individu choisi manuellement\n",
- "CUSTOM_INDEX = 109\n",
- "shap_local(CUSTOM_INDEX, shap_values, max_display=8, cmap=cmap_heatmap)\n",
- "\n",
- "# Cas : individu à plus faible probabilité de départ \n",
- "y_proba_all = best_pipe.named_steps['clf'].predict_proba(X_transformed)[:, 1]\n",
- "idx_lowrisk = int(np.argmin(y_proba_all))\n",
- "shap_local(idx_lowrisk, shap_values, max_display=8, text_scale=0.6, cmap=cmap_heatmap)\n"
+ "## 8) Interpr\u00e9tation (importance globale & locale) disponible via les scripts et l\u2019API Gradio.\n"
]
},
{
@@ -2288,10 +569,11 @@
"id": "b3153394",
"metadata": {},
"source": [
- "coeff pearson = faire test avant si les variables sont linéaires\n",
- "si les variables ne sont pas linéaires, utiliser spearman\n",
+ "## Projet 04 valid\u00e9\n",
+ "coeff pearson = faire test avant si les variables sont lin\u00e9aires\n",
+ "si les variables ne sont pas lin\u00e9aires, utiliser spearman\n",
"\n",
- "s'attendait à plus de modèle "
+ "s'attendait \u00e0 plus de mod\u00e8les (XG Boost...) "
]
}
],
@@ -2316,4 +598,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/projet_05/__init__.py b/hf_space/hf_space/hf_space/projet_05/__init__.py
index 9c87bb6c55d098cf2cb6be1c9c239f767398750a..5dd77c0de228141c4810bc7dd2fd6a8bb16720a2 100644
--- a/hf_space/hf_space/hf_space/projet_05/__init__.py
+++ b/hf_space/hf_space/hf_space/projet_05/__init__.py
@@ -1 +1,4 @@
from projet_05 import config # noqa: F401
+from projet_05.settings import Settings, load_settings # noqa: F401
+
+__all__ = ["config", "Settings", "load_settings"]
diff --git a/hf_space/hf_space/hf_space/projet_05/branding.py b/hf_space/hf_space/hf_space/projet_05/branding.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb6730daceaea69319287f069aa542e4c2c35a2d
--- /dev/null
+++ b/hf_space/hf_space/hf_space/projet_05/branding.py
@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Union
+
+from scripts_projet04.brand.brand import ( # type: ignore[import-not-found]
+ Theme,
+ ThemeConfig,
+ configure_brand,
+ load_brand,
+ make_diverging_cmap,
+)
+
+ROOT_DIR = Path(__file__).resolve().parents[1]
+DEFAULT_BRAND_PATH = ROOT_DIR / "scripts_projet04" / "brand" / "brand.yml"
+
+
+def _resolve_path(path: Union[str, Path, None]) -> Path:
+ if path is None:
+ return DEFAULT_BRAND_PATH
+ return Path(path).expanduser().resolve()
+
+
+@lru_cache(maxsize=1)
+def load_brand_config(path: Union[str, Path, None] = None) -> ThemeConfig:
+ """Load the brand YAML once and return the parsed ThemeConfig."""
+ cfg_path = _resolve_path(path)
+ return load_brand(cfg_path)
+
+
+@lru_cache(maxsize=1)
+def apply_brand_theme(path: Union[str, Path, None] = None) -> ThemeConfig:
+ """
+ Apply the OpenClassrooms/TechNova brand theme globally.
+
+ Returns the ThemeConfig so callers can inspect colors if needed.
+ """
+ cfg_path = _resolve_path(path)
+ cfg = configure_brand(cfg_path)
+ Theme.apply()
+ return cfg
+
+
+__all__ = [
+ "Theme",
+ "ThemeConfig",
+ "apply_brand_theme",
+ "load_brand_config",
+ "make_diverging_cmap",
+ "DEFAULT_BRAND_PATH",
+]
diff --git a/hf_space/hf_space/hf_space/projet_05/dataset.py b/hf_space/hf_space/hf_space/projet_05/dataset.py
index cd63bd2a293c133fd7c536ce61c41ee4d3947ff5..e4d63f92454dbb64257f09abac0254b49222b5b2 100644
--- a/hf_space/hf_space/hf_space/projet_05/dataset.py
+++ b/hf_space/hf_space/hf_space/projet_05/dataset.py
@@ -1,28 +1,202 @@
+from __future__ import annotations
+
+import sqlite3
from pathlib import Path
+import numpy as np
+import pandas as pd
from loguru import logger
-from tqdm import tqdm
import typer
-from projet_05.config import PROCESSED_DATA_DIR, RAW_DATA_DIR
+from projet_05.config import INTERIM_DATA_DIR
+from projet_05.settings import Settings, load_settings
+
+app = typer.Typer(help="Préparation et fusion des données sources.")
+
+
+# ---------------------------------------------------------------------------
+# Utilitaires
+# ---------------------------------------------------------------------------
+def safe_read_csv(path: Path, *, dtype=None) -> pd.DataFrame:
+ """Read a CSV file and return an empty frame when it fails."""
+ try:
+ logger.info("Lecture du fichier {}", path)
+ return pd.read_csv(path, dtype=dtype)
+ except FileNotFoundError:
+ logger.warning("Fichier absent: {}", path)
+ return pd.DataFrame()
+ except Exception as exc: # pragma: no cover - log + empty dataframe
+ logger.error("Impossible de lire {} ({})", path, exc)
+ return pd.DataFrame()
+
+
+def clean_text_values(df: pd.DataFrame) -> pd.DataFrame:
+ """Normalize textual values that often materialize missing values."""
+ replace_tokens = [
+ "",
+ " ",
+ " ",
+ " ",
+ "nan",
+ "NaN",
+ "NAN",
+ "None",
+ "JE ne sais pas",
+ "je ne sais pas",
+ "Je ne sais pas",
+ "Unknow",
+ "Unknown",
+ "non pertinent",
+ "Non pertinent",
+ "NON PERTINENT",
+ ]
+ normalized = df.copy()
+ normalized = normalized.replace(replace_tokens, np.nan)
+
+ for column in normalized.select_dtypes(include="object"):
+ normalized[column] = (
+ normalized[column].replace(replace_tokens, np.nan).astype("string").str.strip()
+ )
+ return normalized
+
+
+def _harmonize_id_column(df: pd.DataFrame, column: str, *, digits_only: bool = True) -> pd.DataFrame:
+ data = df.copy()
+ if column not in data.columns:
+ return data
+
+ if digits_only:
+ extracted = data[column].astype(str).str.extract(r"(\\d+)")
+ data[column] = pd.to_numeric(extracted[0], errors="coerce")
+ data[column] = pd.to_numeric(data[column], errors="coerce").astype("Int64")
+ return data
+
+
+def _rename_column(df: pd.DataFrame, source: str, target: str) -> pd.DataFrame:
+ if source not in df.columns:
+ return df
+ return df.rename(columns={source: target})
+
+
+def _log_id_diagnostics(df: pd.DataFrame, *, name: str, col_id: str) -> None:
+ if col_id not in df.columns:
+ logger.warning("La colonne {} est absente du fichier {}.", col_id, name)
+ return
+ total = len(df)
+ uniques = df[col_id].nunique(dropna=True)
+ duplicates = total - uniques
+ logger.info(
+ "{name}: {total} lignes | {uniques} identifiants uniques | {duplicates} doublons",
+ name=name,
+ total=total,
+ uniques=uniques,
+ duplicates=duplicates,
+ )
+
+
+def _persist_sql_trace(df_dict: dict[str, pd.DataFrame], settings: Settings) -> pd.DataFrame:
+ """
+ Reproduire la fusion SQL décrite dans le notebook.
-app = typer.Typer()
+ Chaque DataFrame est stocké dans une base SQLite éphémère pour
+ conserver une traçabilité de la requête exécutée.
+ """
+ db_path = settings.db_file
+ sql_path = settings.sql_file
+ db_path.parent.mkdir(parents=True, exist_ok=True)
+ sql_path.parent.mkdir(parents=True, exist_ok=True)
+ if db_path.exists():
+ db_path.unlink()
+
+ query = f"""
+ SELECT *
+ FROM sirh
+ INNER JOIN evaluation USING ({settings.col_id})
+ INNER JOIN sond USING ({settings.col_id});
+ """.strip()
+
+ with db_path.open("wb") as _:
+ pass # just ensure the file exists for sqlite on some platforms
+
+ with sqlite3.connect(db_path) as conn:
+ for name, frame in df_dict.items():
+ frame.to_sql(name, conn, index=False, if_exists="replace")
+ merged = pd.read_sql_query(query, conn)
+
+ sql_path.write_text(query, encoding="utf-8")
+ return merged
+
+
+def build_dataset(settings: Settings) -> pd.DataFrame:
+ """Load, clean, harmonize and merge the three raw sources."""
+ sirh = clean_text_values(
+ safe_read_csv(settings.path_sirh).pipe(
+ _harmonize_id_column, settings.col_id, digits_only=True
+ )
+ )
+ evaluation = clean_text_values(
+ safe_read_csv(settings.path_eval)
+ .pipe(_rename_column, "eval_number", settings.col_id)
+ .pipe(_harmonize_id_column, settings.col_id, digits_only=True)
+ )
+ sond = clean_text_values(
+ safe_read_csv(settings.path_sondage)
+ .pipe(_rename_column, "code_sondage", settings.col_id)
+ .pipe(_harmonize_id_column, settings.col_id, digits_only=True)
+ )
+
+ for name, frame in {"sirh": sirh, "evaluation": evaluation, "sond": sond}.items():
+ _log_id_diagnostics(frame, name=name, col_id=settings.col_id)
+
+ frames = {
+ "sirh": sirh,
+ "evaluation": evaluation,
+ "sond": sond,
+ }
+ merged = _persist_sql_trace(frames, settings)
+
+ missing_cols = [settings.col_id] if settings.col_id not in merged.columns else []
+ if missing_cols:
+ raise KeyError(
+ f"La colonne {settings.col_id} est absente de la fusion finale. "
+ "Vérifiez vos fichiers sources."
+ )
+
+ logger.success("Fusion réalisée: {} lignes / {} colonnes", *merged.shape)
+ return merged
+
+
+def save_dataset(df: pd.DataFrame, output_path: Path) -> None:
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ df.to_csv(output_path, index=False)
+ logger.success("Fichier fusionné sauvegardé dans {}", output_path)
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
@app.command()
def main(
- # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
- input_path: Path = RAW_DATA_DIR / "dataset.csv",
- output_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
- # ----------------------------------------------
+ settings_path: Path = typer.Option(
+ None,
+ "--settings",
+ "-s",
+ help="Chemin vers un fichier settings.yml personnalisé.",
+ ),
+ output_path: Path = typer.Option(
+ INTERIM_DATA_DIR / "merged.csv",
+ "--output",
+ "-o",
+ help="Chemin de sortie du dataset fusionné.",
+ ),
):
- # ---- REPLACE THIS WITH YOUR OWN CODE ----
- logger.info("Processing dataset...")
- for i in tqdm(range(10), total=10):
- if i == 5:
- logger.info("Something happened for iteration 5.")
- logger.success("Processing dataset complete.")
- # -----------------------------------------
+ """Entrypoint Typer pour reproduire la fusion des données brutes."""
+
+ settings = load_settings(settings_path) if settings_path else load_settings()
+ df = build_dataset(settings)
+ save_dataset(df, output_path)
if __name__ == "__main__":
diff --git a/hf_space/hf_space/hf_space/projet_05/explainability.py b/hf_space/hf_space/hf_space/projet_05/explainability.py
new file mode 100644
index 0000000000000000000000000000000000000000..979701ad9c34be94bae3e1df493ca7d52b9efa37
--- /dev/null
+++ b/hf_space/hf_space/hf_space/projet_05/explainability.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import pandas as pd
+from loguru import logger
+
+from projet_05.branding import Theme, apply_brand_theme, make_diverging_cmap
+from scripts_projet04.manet_projet04.shap_generator import ( # type: ignore[import-not-found]
+ shap_global,
+ shap_local,
+)
+
+apply_brand_theme()
+
+
+def _shape_array(values) -> np.ndarray:
+ if hasattr(values, "values"):
+ arr = np.array(values.values)
+ else:
+ arr = np.array(values)
+ return np.nan_to_num(arr, copy=False)
+
+
+def compute_shap_summary(
+ pipeline,
+ X: pd.DataFrame,
+ y: pd.Series,
+ *,
+ max_samples: int = 500,
+) -> Tuple[pd.DataFrame | None, object | None]:
+ """
+ Reuse the historical `shap_global` helper to build the plots and a tabular summary.
+
+ Returns
+ -------
+ summary_df : pd.DataFrame | None
+ Moyenne absolue des valeurs SHAP (ordre décroissant).
+ shap_values : shap.Explanation | None
+ Objet renvoyé par shap_global pour des analyses locales ultérieures.
+ """
+ cmap = make_diverging_cmap(Theme.PRIMARY, Theme.SECONDARY)
+ shap_values, _, feature_names = shap_global(
+ pipeline,
+ X,
+ y,
+ sample_size=max_samples,
+ cmap=cmap,
+ )
+ if shap_values is None or feature_names is None:
+ logger.warning("Impossible de générer les résumés SHAP.")
+ return None, None
+
+ shap_array = _shape_array(shap_values)
+ if shap_array.ndim == 1:
+ shap_array = shap_array.reshape(-1, 1)
+ mean_abs = np.abs(shap_array).mean(axis=0)
+ summary = (
+ pd.DataFrame({"feature": list(feature_names), "mean_abs_shap": mean_abs})
+ .sort_values("mean_abs_shap", ascending=False)
+ .reset_index(drop=True)
+ )
+ return summary, shap_values
+
+
+def save_shap_summary(summary: pd.DataFrame, output_path: Path) -> None:
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ summary.to_csv(output_path, index=False)
+ logger.info("Résumé SHAP sauvegardé dans {}", output_path)
+
+
+def export_local_explanations(
+ pipeline,
+ shap_values,
+ X: pd.DataFrame,
+ custom_index: int | None = None,
+) -> None:
+ """
+ Génère trois cas d'usage par défaut (impact max, risque max, risque min)
+ et un indice custom optionnel pour la trace historique.
+ """
+ if shap_values is None:
+ return
+
+ shap_array = _shape_array(shap_values)
+ idx_impact = int(np.argmax(np.sum(np.abs(shap_array), axis=1)))
+ shap_local(idx_impact, shap_values)
+
+ y_proba_all = pipeline.predict_proba(X)[:, 1]
+ idx_highrisk = int(np.argmax(y_proba_all))
+ shap_local(idx_highrisk, shap_values)
+
+ idx_lowrisk = int(np.argmin(y_proba_all))
+ shap_local(idx_lowrisk, shap_values, text_scale=0.6)
+
+ if custom_index is not None:
+ shap_local(custom_index, shap_values, max_display=8)
+
+
+__all__ = ["compute_shap_summary", "save_shap_summary", "export_local_explanations"]
diff --git a/hf_space/hf_space/hf_space/projet_05/features.py b/hf_space/hf_space/hf_space/projet_05/features.py
index a447a12ee553dc8cd1732ecbba26ce50e1c785f1..8612807c5630892d58aa954b73a9065ee6a9e986 100644
--- a/hf_space/hf_space/hf_space/projet_05/features.py
+++ b/hf_space/hf_space/hf_space/projet_05/features.py
@@ -1,28 +1,170 @@
+from __future__ import annotations
+
+import json
+from datetime import datetime
from pathlib import Path
+import numpy as np
+import pandas as pd
from loguru import logger
-from tqdm import tqdm
import typer
-from projet_05.config import PROCESSED_DATA_DIR
+from projet_05.config import INTERIM_DATA_DIR, PROCESSED_DATA_DIR
+from projet_05.settings import Settings, load_settings
+
+app = typer.Typer(help="Génération des features et nettoyage de la cible.")
+
+TARGET_MAPPING = {
+ "1": 1,
+ "0": 0,
+ "oui": 1,
+ "non": 0,
+ "true": 1,
+ "false": 0,
+ "quitte": 1,
+ "reste": 0,
+ "yes": 1,
+ "no": 0,
+}
+
+
+# ---------------------------------------------------------------------------
+# Utilitaires cœur de pipeline
+# ---------------------------------------------------------------------------
+def _load_merged_dataset(path: Path) -> pd.DataFrame:
+ if not path.exists():
+ raise FileNotFoundError(
+ f"Le fichier fusionné {path} est introuvable. Lancez `python projet_05/dataset.py` d'abord."
+ )
+ logger.info("Chargement du dataset fusionné depuis {}", path)
+ return pd.read_csv(path)
+
+
+def _normalize_target(df: pd.DataFrame, settings: Settings) -> pd.DataFrame:
+ if settings.target not in df.columns:
+ raise KeyError(f"La variable cible '{settings.target}' est absente du fichier.")
+
+ normalized = (
+ df[settings.target]
+ .astype(str)
+ .str.strip()
+ .str.lower()
+ .map(TARGET_MAPPING)
+ )
+ df = df.copy()
+ df[settings.target] = normalized
+ before = len(df)
+ df = df[df[settings.target].isin([0, 1])].copy()
+ dropped = before - len(df)
+ if dropped:
+ logger.warning("Suppression de {} lignes avec une cible invalide.", dropped)
+ df[settings.target] = df[settings.target].astype(int)
+ return df
+
+
+def _safe_ratio(df: pd.DataFrame, numerator: str, denominator: str, output: str) -> None:
+ if numerator not in df.columns or denominator not in df.columns:
+ return
+ denominator_series = df[denominator].replace({0: np.nan})
+ df[output] = df[numerator] / denominator_series
+
+
+def _engineer_features(df: pd.DataFrame, settings: Settings) -> pd.DataFrame:
+ engineered = df.copy()
-app = typer.Typer()
+ col = "augementation_salaire_precedente"
+ if col in engineered:
+ engineered[col] = (
+ engineered[col]
+ .astype(str)
+ .str.replace("%", "", regex=False)
+ .str.replace(",", ".", regex=False)
+ .str.strip()
+ )
+ engineered[col] = pd.to_numeric(engineered[col], errors="coerce") / 100
+ _safe_ratio(engineered, "augementation_salaire_precedente", "revenu_mensuel", "augmentation_par_revenu")
+ _safe_ratio(engineered, "annees_dans_le_poste_actuel", "annee_experience_totale", "annee_sur_poste_par_experience")
+ _safe_ratio(engineered, "nb_formations_suivies", "annee_experience_totale", "nb_formation_par_experience")
+ _safe_ratio(
+ engineered, "annees_depuis_la_derniere_promotion", "annee_experience_totale", "dern_promo_par_experience"
+ )
+ if settings.sat_cols:
+ existing = [col for col in settings.sat_cols if col in engineered.columns]
+ if existing:
+ engineered["score_moyen_satisfaction"] = engineered[existing].mean(axis=1)
+
+ if "note_evaluation_actuelle" in engineered.columns and "note_evaluation_precedente" in engineered.columns:
+ engineered["evolution_note"] = (
+ engineered["note_evaluation_actuelle"] - engineered["note_evaluation_precedente"]
+ )
+
+ return engineered
+
+
+def build_features(settings: Settings, *, input_path: Path) -> pd.DataFrame:
+ df = _load_merged_dataset(input_path)
+ df = _normalize_target(df, settings)
+ df = _engineer_features(df, settings)
+ return df
+
+
+def save_features(df: pd.DataFrame, output_path: Path) -> None:
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ df.to_csv(output_path, index=False)
+ logger.success("Dataset enrichi sauvegardé dans {}", output_path)
+
+
+def save_schema(settings: Settings, output_path: Path) -> None:
+ schema = {
+ "target": settings.target,
+ "col_id": settings.col_id,
+ "numerical_features": list(settings.num_cols),
+ "categorical_features": list(settings.cat_cols),
+ "satisfaction_features": list(settings.sat_cols),
+ "generated_at": datetime.utcnow().isoformat(),
+ }
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ output_path.write_text(json.dumps(schema, indent=2), encoding="utf-8")
+ logger.info("Schéma sauvegardé dans {}", output_path)
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
@app.command()
def main(
- # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
- input_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
- output_path: Path = PROCESSED_DATA_DIR / "features.csv",
- # -----------------------------------------
+ settings_path: Path = typer.Option(
+ None,
+ "--settings",
+ "-s",
+ help="Chemin optionnel vers un fichier settings.yml personnalisé.",
+ ),
+ input_path: Path = typer.Option(
+ INTERIM_DATA_DIR / "merged.csv",
+ "--input",
+ "-i",
+ help="Chemin du fichier issu de la fusion.",
+ ),
+ output_path: Path = typer.Option(
+ PROCESSED_DATA_DIR / "dataset.csv",
+ "--output",
+ "-o",
+ help="Chemin du fichier enrichi.",
+ ),
+ schema_path: Path = typer.Option(
+ PROCESSED_DATA_DIR / "schema.json",
+ "--schema",
+ help="Chemin de sauvegarde du schéma de features.",
+ ),
):
- # ---- REPLACE THIS WITH YOUR OWN CODE ----
- logger.info("Generating features from dataset...")
- for i in tqdm(range(10), total=10):
- if i == 5:
- logger.info("Something happened for iteration 5.")
- logger.success("Features generation complete.")
- # -----------------------------------------
+ """Pipeline Typer pour préparer le dataset enrichi."""
+
+ settings = load_settings(settings_path) if settings_path else load_settings()
+ df = build_features(settings, input_path=input_path)
+ save_features(df, output_path)
+ save_schema(settings, schema_path)
if __name__ == "__main__":
diff --git a/hf_space/hf_space/hf_space/projet_05/modeling/predict.py b/hf_space/hf_space/hf_space/projet_05/modeling/predict.py
index 4721edb7005cfcbd6ffa2930c5c9732a2246631b..9f1fcd3644069c2debc669620ea4605e5aa39984 100644
--- a/hf_space/hf_space/hf_space/projet_05/modeling/predict.py
+++ b/hf_space/hf_space/hf_space/projet_05/modeling/predict.py
@@ -1,29 +1,99 @@
+from __future__ import annotations
+
+import json
from pathlib import Path
+import numpy as np
+import pandas as pd
+from joblib import load
from loguru import logger
-from tqdm import tqdm
import typer
from projet_05.config import MODELS_DIR, PROCESSED_DATA_DIR
-app = typer.Typer()
+app = typer.Typer(help="Inférence à partir du pipeline entraîné.")
+
+
+def load_pipeline(model_path: Path):
+ if not model_path.exists():
+ raise FileNotFoundError(f"Modèle introuvable: {model_path}")
+ logger.info("Chargement du modèle {}", model_path)
+ return load(model_path)
+
+
+def load_metadata(metadata_path: Path) -> dict:
+ if not metadata_path.exists():
+ raise FileNotFoundError(f"Fichier métadonnées introuvable: {metadata_path}")
+ return json.loads(metadata_path.read_text(encoding="utf-8"))
+
+
+def run_inference(
+ df: pd.DataFrame,
+ pipeline,
+ threshold: float,
+ drop_columns: list[str] | None = None,
+ required_features: list[str] | None = None,
+) -> pd.DataFrame:
+ features = df.drop(columns=drop_columns or [], errors="ignore")
+ if required_features:
+ for col in required_features:
+ if col not in features.columns:
+ features[col] = np.nan
+ features = features[required_features]
+ proba = pipeline.predict_proba(features)[:, 1]
+ predictions = (proba >= threshold).astype(int)
+ output = df.copy()
+ output["proba_depart"] = proba
+ output["prediction"] = predictions
+ return output
@app.command()
def main(
- # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
- features_path: Path = PROCESSED_DATA_DIR / "test_features.csv",
- model_path: Path = MODELS_DIR / "model.pkl",
- predictions_path: Path = PROCESSED_DATA_DIR / "test_predictions.csv",
- # -----------------------------------------
+ model_path: Path = typer.Option(
+ MODELS_DIR / "best_model.joblib",
+ "--model-path",
+ help="Pipeline entraîné sauvegardé via train.py",
+ ),
+ metadata_path: Path = typer.Option(
+ MODELS_DIR / "best_model_meta.json",
+ "--metadata-path",
+ help="Fichier JSON contenant le seuil optimal.",
+ ),
+ features_path: Path = typer.Option(
+ PROCESSED_DATA_DIR / "dataset.csv",
+ "--features",
+ "-f",
+ help="Jeu de features sur lequel produire des prédictions.",
+ ),
+ predictions_path: Path = typer.Option(
+ PROCESSED_DATA_DIR / "predictions.csv",
+ "--output",
+ "-o",
+ help="Chemin de sauvegarde des prédictions.",
+ ),
):
- # ---- REPLACE THIS WITH YOUR OWN CODE ----
- logger.info("Performing inference for model...")
- for i in tqdm(range(10), total=10):
- if i == 5:
- logger.info("Something happened for iteration 5.")
- logger.success("Inference complete.")
- # -----------------------------------------
+ """Entrypoint Typer pour générer un fichier de prédictions."""
+
+ pipeline = load_pipeline(model_path)
+ metadata = load_metadata(metadata_path)
+ threshold = metadata.get("best_threshold", 0.5)
+ features_cfg = metadata.get("features", {})
+ required_features = (features_cfg.get("numerical") or []) + (features_cfg.get("categorical") or [])
+ df = pd.read_csv(features_path)
+ logger.info("Dataset chargé: {} lignes", len(df))
+
+ target_col = metadata.get("target")
+ predictions = run_inference(
+ df,
+ pipeline,
+ threshold,
+ drop_columns=[target_col] if target_col else None,
+ required_features=required_features or None,
+ )
+ predictions_path.parent.mkdir(parents=True, exist_ok=True)
+ predictions.to_csv(predictions_path, index=False)
+ logger.success("Prédictions sauvegardées dans {}", predictions_path)
if __name__ == "__main__":
diff --git a/hf_space/hf_space/hf_space/projet_05/modeling/train.py b/hf_space/hf_space/hf_space/projet_05/modeling/train.py
index b3683815a343f8a2fe616f152784f146ba0bbf12..d1c4fa7bb4423c6eb6c6aa4517a81261b7e7fbcc 100644
--- a/hf_space/hf_space/hf_space/projet_05/modeling/train.py
+++ b/hf_space/hf_space/hf_space/projet_05/modeling/train.py
@@ -1,29 +1,342 @@
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
from pathlib import Path
+from typing import Dict, Tuple
+import numpy as np
+import pandas as pd
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import Pipeline as ImbPipeline
+from joblib import dump
from loguru import logger
-from tqdm import tqdm
+from sklearn.base import clone
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+ f1_score,
+ precision_recall_curve,
+ precision_score,
+ recall_score,
+ roc_auc_score,
+)
+from sklearn.model_selection import GridSearchCV, StratifiedKFold, cross_val_predict
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
import typer
-from projet_05.config import MODELS_DIR, PROCESSED_DATA_DIR
+from projet_05.config import MODELS_DIR, PROCESSED_DATA_DIR, REPORTS_DIR
+from projet_05.explainability import (
+ compute_shap_summary,
+ export_local_explanations,
+ save_shap_summary,
+)
+from projet_05.settings import Settings, load_settings
+
+app = typer.Typer(help="Entraînement et sélection du meilleur modèle.")
+
+
+def _clean_values(payload: dict) -> dict:
+ def _convert(value):
+ if isinstance(value, (np.floating, np.integer)):
+ return value.item()
+ return value
+
+ return {key: _convert(value) for key, value in payload.items()}
+
+
+@dataclass
+class ModelResult:
+ name: str
+ best_estimator: ImbPipeline
+ best_params: dict
+ best_threshold: float
+ metrics: Dict[str, float]
+
+
+def load_processed_dataset(path: Path) -> pd.DataFrame:
+ if not path.exists():
+ raise FileNotFoundError(
+ f"Dataset traité introuvable ({path}). Lancez `python projet_05/features.py`."
+ )
+ logger.info("Chargement du dataset préparé depuis {}", path)
+ return pd.read_csv(path)
+
+
+def split_features_target(df: pd.DataFrame, settings: Settings) -> Tuple[pd.DataFrame, pd.Series]:
+ if settings.target not in df.columns:
+ raise KeyError(f"La cible {settings.target} est absente du dataset.")
+ y = df[settings.target].astype(int)
+ drop_cols = [settings.target]
+ if settings.col_id in df.columns:
+ drop_cols.append(settings.col_id)
+ X = df.drop(columns=drop_cols, errors="ignore")
+ return X, y
+
+
+def build_preprocessor(settings: Settings, X: pd.DataFrame) -> ColumnTransformer:
+ numeric_features = [col for col in settings.num_cols if col in X.columns]
+ categorical_features = [col for col in settings.cat_cols if col in X.columns]
+ if not numeric_features:
+ numeric_features = X.select_dtypes(include="number").columns.tolist()
+ if not categorical_features:
+ categorical_features = X.select_dtypes(exclude="number").columns.tolist()
+
+ numeric_transformer = Pipeline(
+ steps=[
+ ("imputer", SimpleImputer(strategy="median")),
+ ("scaler", StandardScaler()),
+ ]
+ )
+ categorical_transformer = Pipeline(
+ steps=[
+ ("imputer", SimpleImputer(strategy="most_frequent")),
+ ("encoder", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
+ ]
+ )
+ transformers = []
+ if numeric_features:
+ transformers.append(("num", numeric_transformer, numeric_features))
+ if categorical_features:
+ transformers.append(("cat", categorical_transformer, categorical_features))
+ if not transformers:
+ raise ValueError("Aucune feature disponible pour l'entraînement.")
+ return ColumnTransformer(transformers=transformers)
+
+
+def get_models(random_state: int):
+ return {
+ "LogReg_balanced": (
+ LogisticRegression(
+ max_iter=2000,
+ class_weight="balanced",
+ random_state=random_state,
+ ),
+ [
+ {
+ "clf__solver": ["lbfgs"],
+ "clf__penalty": ["l2"],
+ "clf__C": [0.1, 1.0, 10.0],
+ },
+ {
+ "clf__solver": ["liblinear"],
+ "clf__penalty": ["l1", "l2"],
+ "clf__C": [0.1, 1.0, 10.0],
+ },
+ ],
+ ),
+ "RF_balanced": (
+ RandomForestClassifier(
+ n_estimators=300,
+ max_depth=8,
+ min_samples_split=10,
+ min_samples_leaf=5,
+ class_weight="balanced_subsample",
+ random_state=random_state,
+ ),
+ {
+ "clf__n_estimators": [200, 300, 500],
+ "clf__max_depth": [6, 8, 10],
+ "clf__min_samples_split": [5, 10, 15],
+ "clf__min_samples_leaf": [2, 5, 8],
+ },
+ ),
+ }
+
-app = typer.Typer()
+def _compute_best_threshold(y_true, y_proba):
+ precision, recall, thresholds = precision_recall_curve(y_true, y_proba)
+ f1_scores = 2 * (precision * recall) / (precision + recall + 1e-8)
+ best_idx = np.nanargmax(f1_scores)
+ if thresholds.size == 0:
+ return 0.5
+ best_idx = min(best_idx, thresholds.size - 1)
+ return thresholds[best_idx]
+
+
+def evaluate_models(X, y, settings: Settings, preprocessor: ColumnTransformer) -> list[ModelResult]:
+ cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=settings.random_state)
+ results: list[ModelResult] = []
+
+ for name, (model, grid) in get_models(settings.random_state).items():
+ logger.info("Entraînement du modèle {}", name)
+ pipe = ImbPipeline(
+ steps=[
+ ("prep", preprocessor),
+ ("smote", SMOTE(random_state=settings.random_state)),
+ ("clf", model),
+ ]
+ )
+ search = GridSearchCV(
+ estimator=pipe,
+ param_grid=grid,
+ cv=cv,
+ scoring="f1",
+ n_jobs=-1,
+ )
+ search.fit(X, y)
+ best_pipe = search.best_estimator_
+
+ y_proba = cross_val_predict(best_pipe, X, y, cv=cv, method="predict_proba")[:, 1]
+ threshold = _compute_best_threshold(y, y_proba)
+ y_pred = (y_proba >= threshold).astype(int)
+
+ metrics = {
+ "f1": f1_score(y, y_pred),
+ "recall": recall_score(y, y_pred),
+ "precision": precision_score(y, y_pred),
+ "roc_auc": roc_auc_score(y, y_proba),
+ }
+ logger.info("Scores {} -> {}", name, metrics)
+ results.append(
+ ModelResult(
+ name=name,
+ best_estimator=best_pipe,
+ best_params=search.best_params_,
+ best_threshold=threshold,
+ metrics=metrics,
+ )
+ )
+ return results
+
+
+def compute_dummy_baseline(y: pd.Series) -> dict:
+ majority = int(y.mode().iloc[0])
+ y_pred = np.full_like(y, fill_value=majority)
+ return {
+ "strategy": "most_frequent",
+ "majority_class": majority,
+ "f1": f1_score(y, y_pred),
+ "recall": recall_score(y, y_pred),
+ "precision": precision_score(y, y_pred, zero_division=0),
+ "roc_auc": 0.5,
+ }
+
+
+def fit_final_pipeline(
+ best_result: ModelResult,
+ X: pd.DataFrame,
+ y: pd.Series,
+ settings: Settings,
+):
+ sm = SMOTE(random_state=settings.random_state)
+ X_bal, y_bal = sm.fit_resample(X, y)
+ final_preprocessor = build_preprocessor(settings, X)
+ clf = clone(best_result.best_estimator.named_steps["clf"])
+ final_pipe = Pipeline(
+ steps=[
+ ("prep", final_preprocessor),
+ ("clf", clf),
+ ]
+ )
+ final_pipe.fit(X_bal, y_bal)
+ logger.success(
+ "Modèle {} ré-entraîné sur {} lignes équilibrées.", best_result.name, len(X_bal)
+ )
+ return final_pipe
+
+
+def save_artifacts(
+ pipeline: Pipeline,
+ results: list[ModelResult],
+ best_result: ModelResult,
+ baseline: dict,
+ settings: Settings,
+ model_path: Path,
+ metadata_path: Path,
+ shap_path: Path,
+ X: pd.DataFrame,
+ y: pd.Series,
+):
+ model_path.parent.mkdir(parents=True, exist_ok=True)
+ dump(pipeline, model_path)
+ logger.success("Pipeline sauvegardé dans {}", model_path)
+
+ metadata = {
+ "best_model": best_result.name,
+ "best_threshold": float(best_result.best_threshold),
+ "best_params": best_result.best_params,
+ "metrics": _clean_values(best_result.metrics),
+ "all_results": [
+ {
+ "model": r.name,
+ "metrics": _clean_values(r.metrics),
+ "best_threshold": float(r.best_threshold),
+ "best_params": r.best_params,
+ }
+ for r in results
+ ],
+ "baseline": _clean_values(baseline),
+ "features": {
+ "numerical": list(settings.num_cols),
+ "categorical": list(settings.cat_cols),
+ },
+ "target": settings.target,
+ }
+ metadata_path.parent.mkdir(parents=True, exist_ok=True)
+ metadata_path.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
+ logger.info("Métadonnées sauvegardées dans {}", metadata_path)
+
+ shap_summary, shap_values = compute_shap_summary(pipeline, X, y)
+ if shap_summary is not None:
+ save_shap_summary(shap_summary, shap_path)
+ export_local_explanations(pipeline, shap_values, X)
@app.command()
def main(
- # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
- features_path: Path = PROCESSED_DATA_DIR / "features.csv",
- labels_path: Path = PROCESSED_DATA_DIR / "labels.csv",
- model_path: Path = MODELS_DIR / "model.pkl",
- # -----------------------------------------
+ settings_path: Path = typer.Option(None, "--settings", "-s", help="Chemin alternatif vers settings.yml."),
+ input_path: Path = typer.Option(
+ PROCESSED_DATA_DIR / "dataset.csv",
+ "--input",
+ "-i",
+ help="Dataset enrichi issu de projet_05/features.py",
+ ),
+ model_path: Path = typer.Option(
+ MODELS_DIR / "best_model.joblib",
+ "--model-path",
+ help="Chemin de sauvegarde du pipeline entraîné.",
+ ),
+ metadata_path: Path = typer.Option(
+ MODELS_DIR / "best_model_meta.json",
+ "--metadata-path",
+ help="Chemin de sauvegarde des métriques et métadonnées.",
+ ),
+ shap_path: Path = typer.Option(
+ REPORTS_DIR / "shap_summary.csv",
+ "--shap-path",
+ help="Chemin de sortie du résumé SHAP.",
+ ),
):
- # ---- REPLACE THIS WITH YOUR OWN CODE ----
- logger.info("Training some model...")
- for i in tqdm(range(10), total=10):
- if i == 5:
- logger.info("Something happened for iteration 5.")
- logger.success("Modeling training complete.")
- # -----------------------------------------
+ """Script principal pour lancer l'entraînement complet."""
+
+ settings = load_settings(settings_path) if settings_path else load_settings()
+ df = load_processed_dataset(input_path)
+ X, y = split_features_target(df, settings)
+ preprocessor = build_preprocessor(settings, X)
+ results = evaluate_models(X, y, settings, preprocessor)
+ if not results:
+ raise RuntimeError("Aucun modèle évalué. Vérifiez la configuration.")
+ best_result = max(results, key=lambda r: r.metrics["f1"])
+ baseline = compute_dummy_baseline(y)
+ logger.info("Baseline Dummy -> {}", baseline)
+
+ final_pipeline = fit_final_pipeline(best_result, X, y, settings)
+ save_artifacts(
+ final_pipeline,
+ results,
+ best_result,
+ baseline,
+ settings,
+ model_path,
+ metadata_path,
+ shap_path,
+ X,
+ y,
+ )
if __name__ == "__main__":
diff --git a/hf_space/hf_space/hf_space/projet_05/settings.py b/hf_space/hf_space/hf_space/projet_05/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ac4c0c39e7e222e8532929901224cbf330b4fd4
--- /dev/null
+++ b/hf_space/hf_space/hf_space/projet_05/settings.py
@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from functools import lru_cache
+import os
+from pathlib import Path
+from typing import Iterable
+
+import yaml
+
+DEFAULT_SETTINGS_PATH = Path(__file__).with_name("settings.yml")
+
+
+@dataclass(frozen=True)
+class Settings:
+ random_state: int = 42
+ path_sirh: Path = field(default_factory=lambda: Path("data/raw/sirh.csv"))
+ path_eval: Path = field(default_factory=lambda: Path("data/raw/evaluation.csv"))
+ path_sondage: Path = field(default_factory=lambda: Path("data/raw/sondage.csv"))
+ col_id: str = "id_employee"
+ target: str = "a_quitte_l_entreprise"
+ num_cols: tuple[str, ...] = ()
+ cat_cols: tuple[str, ...] = ()
+ sat_cols: tuple[str, ...] = ()
+ first_vars: tuple[str, ...] = ()
+ subsample_frac: float = 1.0
+ sql_file: Path = field(default_factory=lambda: Path("merge_sql.sql"))
+ db_file: Path = field(default_factory=lambda: Path("merge_temp.db"))
+
+ def as_dict(self) -> dict:
+ """Return a serializable representation (useful for logging/tests)."""
+ return {
+ "random_state": self.random_state,
+ "path_sirh": str(self.path_sirh),
+ "path_eval": str(self.path_eval),
+ "path_sondage": str(self.path_sondage),
+ "col_id": self.col_id,
+ "target": self.target,
+ "num_cols": list(self.num_cols),
+ "cat_cols": list(self.cat_cols),
+ "sat_cols": list(self.sat_cols),
+ "first_vars": list(self.first_vars),
+ "subsample_frac": self.subsample_frac,
+ "sql_file": str(self.sql_file),
+ "db_file": str(self.db_file),
+ }
+
+
+def _ensure_iterable(values: Iterable[str] | None, *, field_name: str) -> tuple[str, ...]:
+ if values is None:
+ return ()
+ if isinstance(values, str):
+ msg = f"'{field_name}' doit être une liste et non une chaîne isolée."
+ raise TypeError(msg)
+ return tuple(v for v in values if v)
+
+
+def _resolve_path(candidate: str | os.PathLike[str] | None, *, base_dir: Path) -> Path:
+ if not candidate:
+ raise ValueError("Aucun chemin n'a été fourni dans le fichier de configuration.")
+ resolved = Path(candidate)
+ if not resolved.is_absolute():
+ resolved = (base_dir / resolved).resolve()
+ return resolved
+
+
+def _load_raw_settings(path: Path) -> dict:
+ with path.open("r", encoding="utf-8") as handle:
+ data = yaml.safe_load(handle) or {}
+ if not isinstance(data, dict):
+ raise ValueError(f"Le fichier de configuration {path} doit contenir un dictionnaire YAML.")
+ return data
+
+
+@lru_cache
+def load_settings(custom_path: str | os.PathLike[str] | None = None) -> Settings:
+ """
+ Charger la configuration projet depuis un fichier YAML.
+
+ L'ordre de recherche est :
+ 1. Argument `custom_path` si fourni.
+ 2. Variable d'environnement `PROJET05_SETTINGS`.
+ 3. Fichier par défaut `projet_05/settings.yml`.
+ """
+
+ env_path = os.environ.get("PROJET05_SETTINGS")
+ raw_path = Path(custom_path or env_path or DEFAULT_SETTINGS_PATH)
+
+ if not raw_path.exists():
+ raise FileNotFoundError(
+ f"Fichier de configuration introuvable : {raw_path}. "
+ "Initialisez-le depuis projet_05/settings.yml ou indiquez PROJET05_SETTINGS."
+ )
+
+ base_dir = raw_path.parent
+ payload = _load_raw_settings(raw_path)
+ paths_block = payload.get("paths", {})
+
+ settings = Settings(
+ random_state=int(payload.get("random_state", Settings.random_state)),
+ path_sirh=_resolve_path(paths_block.get("sirh", Settings().path_sirh), base_dir=base_dir),
+ path_eval=_resolve_path(paths_block.get("evaluation", Settings().path_eval), base_dir=base_dir),
+ path_sondage=_resolve_path(paths_block.get("sondage", Settings().path_sondage), base_dir=base_dir),
+ col_id=payload.get("col_id", Settings.col_id),
+ target=payload.get("target", Settings.target),
+ num_cols=_ensure_iterable(payload.get("num_cols"), field_name="num_cols"),
+ cat_cols=_ensure_iterable(payload.get("cat_cols"), field_name="cat_cols"),
+ sat_cols=_ensure_iterable(payload.get("sat_cols"), field_name="sat_cols"),
+ first_vars=_ensure_iterable(payload.get("first_vars"), field_name="first_vars"),
+ subsample_frac=float(payload.get("subsample_frac", Settings.subsample_frac)),
+ sql_file=_resolve_path(paths_block.get("sql_file", Settings().sql_file), base_dir=base_dir),
+ db_file=_resolve_path(paths_block.get("db_file", Settings().db_file), base_dir=base_dir),
+ )
+ return settings
diff --git a/hf_space/hf_space/hf_space/projet_05/settings.yml b/hf_space/hf_space/hf_space/projet_05/settings.yml
new file mode 100644
index 0000000000000000000000000000000000000000..949d24dcdce60f007686ce2f438cf50437a0b216
--- /dev/null
+++ b/hf_space/hf_space/hf_space/projet_05/settings.yml
@@ -0,0 +1,56 @@
+random_state: 42
+col_id: id_employee
+target: a_quitte_l_entreprise
+subsample_frac: 0.5
+
+paths:
+ sirh: ../data/raw/sirh.csv
+ evaluation: ../data/raw/evaluation.csv
+ sondage: ../data/raw/sondage.csv
+ sql_file: ../reports/merge_sql.sql
+ db_file: ../data/interim/merge_temp.db
+
+num_cols:
+ - age
+ - revenu_mensuel
+ - annees_dans_l_entreprise
+ - annees_dans_le_poste_actuel
+ - annees_depuis_la_derniere_promotion
+ - distance_domicile_travail
+ - nombre_participation_pee
+ - note_evaluation_actuelle
+ - note_evaluation_precedente
+ - annees_depuis_le_changement_deposte
+ - annee_experience_totale
+ - nb_formations_suivies
+ - satisfaction_employee_environnement
+ - satisfaction_employee_nature_travail
+ - satisfaction_employee_equipe
+ - satisfaction_employee_equilibre_pro_perso
+ - augmentation_par_revenu
+ - annee_sur_poste_par_experience
+ - nb_formation_par_experience
+ - score_moyen_satisfaction
+ - dern_promo_par_experience
+ - evolution_note
+
+cat_cols:
+ - genre
+ - departement
+ - frequence_deplacement
+ - etat_civil
+ - niveau_etudes
+ - role
+ - type_contrat
+
+sat_cols:
+ - satisfaction_employee_environnement
+ - satisfaction_employee_nature_travail
+ - satisfaction_employee_equipe
+ - satisfaction_employee_equilibre_pro_perso
+
+first_vars:
+ - age
+ - revenu_mensuel
+ - annees_dans_l_entreprise
+ - note_evaluation_actuelle
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/brand/__init__.py b/hf_space/hf_space/hf_space/scripts_projet04/brand/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.py b/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd539d15528c685886967e3e361269f21d639c67
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.py
@@ -0,0 +1,713 @@
+"""Palette et thèmes graphiques pour Matplotlib/Seaborn.
+
+Ce module fournit une classe utilitaire (`Theme`) et une configuration
+externe (`ThemeConfig`, `configure_theme`) permettant de définir des
+couleurs, des palettes qualitatives et des cartes de couleurs (colormaps)
+cohérentes. Des fonctions de démonstration et des wrappers
+rétrocompatibles sont également fournis.
+"""
+
+from pathlib import Path
+from typing import Any, List, Literal, Mapping, Optional, Tuple, Union
+from dataclasses import dataclass, field, fields
+
+import seaborn as sns
+import numpy as np # Données factices pour les démos
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+
+
+#
+# Dataclass de configuration et gestion externe du thème
+#
+@dataclass
+class ThemeConfig:
+ """Configuration externe du thème.
+
+ Cette structure regroupe les couleurs principales, les variantes de
+ palette et des options d'apparence (fond, rcParams). Elle peut être
+ passée à :func:`configure_theme`.
+
+ Attributs
+ ---------
+ primary, secondary, tertiary : str
+ Couleurs principales au format hexadécimal (p. ex. "#RRGGBB").
+ background : str
+ Couleur d'arrière‑plan des figures et axes.
+ primary_variants, secondary_variants, tertiary_variants : list[str]
+ Variantes qualitatives pour les séries multiples.
+ sequential_light, sequential_dark : dict | None
+ Remplacements explicites des teintes claires/foncées pour les
+ colormaps séquentiels.
+ light_amount, dark_amount : float
+ Coefficients utilisés pour éclaircir/assombrir si aucun
+ remplacement n'est fourni.
+ text_color, axes_labelcolor, tick_color : str
+ Couleurs des textes, étiquettes et graduations.
+ figure_dpi, savefig_dpi : int
+ Résolution d'affichage et d'export.
+ """
+ # Couleurs principales
+ primary: str = "#7451EB"
+ secondary: str = "#EE8273"
+ tertiary: str = "#A6BD63"
+ # Couleur d'arrière-plan
+ background: str = "#FFFCF2"
+ # Variantes qualitatives
+ primary_variants: List[str] = field(default_factory=lambda: ["#9D7EF0", "#4B25D6"])
+ secondary_variants: List[str] = field(default_factory=lambda: ["#F3A093", "#D95848"])
+ tertiary_variants: List[str] = field(default_factory=lambda: ["#BDD681", "#7E923F"])
+ # Remplacements stops séquentiels
+ sequential_light: Optional[dict] = field(default_factory=lambda: {
+ "primary": "#f3f0fd",
+ "secondary": "#fdecea",
+ "tertiary": "#f6faec",
+ })
+ sequential_dark: Optional[dict] = field(default_factory=lambda: {
+ "primary": "#2f1577",
+ "secondary": "#8b3025",
+ "tertiary": "#4b5c27",
+ })
+ # Coefficients de mélange par défaut
+ light_amount: float = 0.85
+ dark_amount: float = 0.65
+ # RC params (matplotlib)
+ text_color: str = "black"
+ axes_labelcolor: str = "black"
+ tick_color: str = "black"
+ figure_dpi: int = 110
+ savefig_dpi: int = 300
+
+# Paramètres matplotlib appliqués par `Theme.apply()`.
+THEME_RC_OVERRIDES = {
+ "text.color": "black",
+ "axes.labelcolor": "black",
+ "xtick.color": "black",
+ "ytick.color": "black",
+ "figure.dpi": 110,
+ "savefig.dpi": 300,
+ "savefig.bbox": "tight",
+ "svg.fonttype": "none",
+ "figure.facecolor": "#FFFCF2",
+ "axes.facecolor": "#FFFCF2",
+}
+
+
+def configure_theme(cfg: ThemeConfig) -> None:
+ """Applique une configuration **externe** au thème.
+
+ Cette fonction met à jour les couleurs et palettes de :class:`Theme`
+ et prépare les paramètres matplotlib (``rcParams``) pour le fond,
+ les couleurs de texte et les résolutions.
+
+ Paramètres
+ ----------
+ cfg : ThemeConfig
+ Instance contenant l'ensemble des options de thème.
+ """
+ # Configure la palette dans la classe
+ Theme.configure(
+ primary=cfg.primary,
+ secondary=cfg.secondary,
+ tertiary=cfg.tertiary,
+ primary_variants=cfg.primary_variants,
+ secondary_variants=cfg.secondary_variants,
+ tertiary_variants=cfg.tertiary_variants,
+ sequential_light=cfg.sequential_light,
+ sequential_dark=cfg.sequential_dark,
+ light_amount=cfg.light_amount,
+ dark_amount=cfg.dark_amount,
+ )
+ Theme.BACKGROUND = cfg.background
+ # Prépare les overrides rcParams
+ THEME_RC_OVERRIDES.update({
+ "text.color": cfg.text_color,
+ "axes.labelcolor": cfg.axes_labelcolor,
+ "xtick.color": cfg.tick_color,
+ "ytick.color": cfg.tick_color,
+ "figure.dpi": cfg.figure_dpi,
+ "savefig.dpi": cfg.savefig_dpi,
+ # On conserve ces valeurs par défaut qui ne dépendent pas de la couleur
+ "savefig.bbox": "tight",
+ "svg.fonttype": "none",
+ "figure.facecolor": cfg.background,
+ "axes.facecolor": cfg.background,
+ })
+
+
+def _config_from_mapping(data: Mapping[str, Any]) -> ThemeConfig:
+ """Convertit un mapping arbitraire en :class:`ThemeConfig`."""
+ allowed_fields = {f.name for f in fields(ThemeConfig)}
+ unknown_keys = set(data) - allowed_fields
+ if unknown_keys:
+ raise ValueError(
+ "Clés inconnues dans la configuration du thème: "
+ + ", ".join(sorted(unknown_keys))
+ )
+ filtered = {k: data[k] for k in allowed_fields if k in data}
+ return ThemeConfig(**filtered)
+
+
+def load_brand(path: Union[str, Path]) -> ThemeConfig:
+ """Charge un fichier YAML et retourne une configuration de thème.
+
+ Le fichier doit contenir des clés correspondant aux attributs de
+ :class:`ThemeConfig`. Les valeurs absentes conservent les valeurs
+ par défaut de la dataclass.
+ """
+
+ try:
+ import yaml
+ except ImportError as exc: # pragma: no cover - dépendance optionnelle
+ raise RuntimeError(
+ "PyYAML est requis pour charger une charte graphique YAML. "
+ "Installez le paquet 'pyyaml'."
+ ) from exc
+
+ file_path = Path(path).expanduser()
+ if not file_path.exists():
+ raise FileNotFoundError(f"Fichier YAML introuvable: {file_path}")
+
+ with file_path.open("r", encoding="utf-8") as handle:
+ content = yaml.safe_load(handle) or {}
+
+ if not isinstance(content, Mapping):
+ raise ValueError(
+ "Le contenu du YAML doit être un mapping clé/valeur (dict)."
+ )
+
+ return _config_from_mapping(content)
+
+
+def configure_brand(path: Union[str, Path]) -> ThemeConfig:
+ """Charge un fichier YAML puis applique la configuration obtenue."""
+
+ cfg = load_brand(path)
+ configure_theme(cfg)
+ return cfg
+
+
+class Theme:
+ """Thème graphique pour Matplotlib et Seaborn.
+
+ La classe fournit :
+ - des couleurs principales et une palette qualitative étendue ;
+ - des cartes de couleurs (séquentielles et divergentes) ;
+ - une méthode :meth:`apply` pour appliquer le thème globalement ;
+ - des méthodes de démonstration pour un aperçu rapide.
+
+ Les couleurs ne sont pas figées : utilisez
+ :func:`configure_theme` pour injecter une configuration externe.
+ """
+
+ # --- Couleurs principales (configurables via Theme.configure) ---
+ # Valeurs par défaut (seront écrasées par configure())
+ PRIMARY: str = "#7451EB" # violet (chaud)
+ SECONDARY: str = "#EE8273" # corail (chaud)
+ TERTIARY: str = "#A6BD63" # vert (froid)
+ BACKGROUND: str = "#FFFCF2"
+
+ PALETTE: List[str] = ["#7451EB", "#EE8273", "#A6BD63"]
+
+ @classmethod
+ def base_palette(cls) -> List[str]:
+ """Retourne la palette fondamentale (PRIMARY, SECONDARY, TERTIARY)."""
+ return [cls.PRIMARY, cls.SECONDARY, cls.TERTIARY]
+
+ # Variantes (palette qualitative) – configurables
+ _PRIMARY_VARIANTS: List[str] = ["#9D7EF0", "#4B25D6"]
+ _SECONDARY_VARIANTS: List[str] = ["#F3A093", "#D95848"]
+ _TERTIARY_VARIANTS: List[str] = ["#BDD681", "#7E923F"]
+
+ # Colormaps séquentiels (clair -> couleur -> foncé) – configurables
+ _SEQUENTIALS = {
+ "primary": ["#f3f0fd", PRIMARY, "#2f1577"],
+ "secondary": ["#fdecea", SECONDARY, "#8b3025"],
+ "tertiary": ["#f6faec", TERTIARY, "#4b5c27"],
+ }
+
+ _NAMES = {"primary", "secondary", "tertiary"}
+
+ # --------- Configuration dynamique ---------
+ @staticmethod
+ def _to_rgb(color: str):
+ return np.array(mcolors.to_rgb(color))
+
+ @classmethod
+ def _tint(cls, color: str, amount: float = 0.85) -> str:
+ """Retourne une version éclaircie de ``color``.
+
+ Le mélange avec le blanc est contrôlé par ``amount`` (0..1).
+ """
+ c = cls._to_rgb(color)
+ white = np.array([1.0, 1.0, 1.0])
+ mixed = (1 - amount) * c + amount * white
+ return mcolors.to_hex(mixed) # type: ignore
+
+ @classmethod
+ def _shade(cls, color: str, amount: float = 0.65) -> str:
+ """Retourne une version assombrie de ``color``.
+
+ Le mélange avec le noir est contrôlé par ``amount`` (0..1).
+ """
+ c = cls._to_rgb(color)
+ black = np.array([0.0, 0.0, 0.0])
+ mixed = (1 - amount) * c + amount * black
+ return mcolors.to_hex(mixed) # type: ignore
+
+ @classmethod
+ def _compute_sequentials(
+ cls,
+ primary: str,
+ secondary: str,
+ tertiary: str,
+ light_overrides: Optional[dict] = None,
+ dark_overrides: Optional[dict] = None,
+ light_amount: float = 0.85,
+ dark_amount: float = 0.65,
+ ) -> dict:
+ """Construit les stops [clair, milieu, foncé] de chaque couleur.
+
+ Paramètres
+ ----------
+ primary, secondary, tertiary : str
+ Couleurs principales en hex.
+ light_overrides, dark_overrides : dict | None
+ Remplacements explicites pour les teintes claires/foncées.
+ light_amount, dark_amount : float
+ Coefficients de mélange quand aucun remplacement n'est fourni.
+
+ Retour
+ ------
+ dict
+ Dictionnaire {nom: [clair, milieu, foncé]}.
+ """
+ light_overrides = light_overrides or {}
+ dark_overrides = dark_overrides or {}
+ base = {
+ "primary": primary,
+ "secondary": secondary,
+ "tertiary": tertiary,
+ }
+ seq = {}
+ for k, mid in base.items():
+ light = light_overrides.get(k) or cls._tint(mid, amount=light_amount)
+ dark = dark_overrides.get(k) or cls._shade(mid, amount=dark_amount)
+ seq[k] = [light, mid, dark]
+ return seq
+
+ @classmethod
+ def configure(
+ cls,
+ *,
+ primary: Optional[str] = None,
+ secondary: Optional[str] = None,
+ tertiary: Optional[str] = None,
+ primary_variants: Optional[List[str]] = None,
+ secondary_variants: Optional[List[str]] = None,
+ tertiary_variants: Optional[List[str]] = None,
+ sequential_light: Optional[dict] = None,
+ sequential_dark: Optional[dict] = None,
+ light_amount: float = 0.85,
+ dark_amount: float = 0.65,
+ ) -> None:
+ """Met à jour dynamiquement les couleurs et colormaps de la classe.
+
+ Exemples
+ --------
+ >>> Theme.configure(primary="#0072CE", secondary="#FF6A00")
+ >>> Theme.configure(
+ ... primary="#1f77b4",
+ ... sequential_light={"primary": "#eef5fb"},
+ ... sequential_dark={"primary": "#0b3050"},
+ ... )
+
+ Paramètres
+ ----------
+ primary, secondary, tertiary : str | None
+ Couleurs principales.
+ primary_variants, secondary_variants, tertiary_variants : list[str] | None
+ Variantes qualitatives.
+ sequential_light, sequential_dark : dict | None
+ Remplacements pour les teintes claires/foncées.
+ light_amount, dark_amount : float
+ Coefficients de mélange par défaut.
+ """
+ if primary:
+ cls.PRIMARY = primary
+ if secondary:
+ cls.SECONDARY = secondary
+ if tertiary:
+ cls.TERTIARY = tertiary
+
+ if primary_variants is not None:
+ cls._PRIMARY_VARIANTS = primary_variants
+ if secondary_variants is not None:
+ cls._SECONDARY_VARIANTS = secondary_variants
+ if tertiary_variants is not None:
+ cls._TERTIARY_VARIANTS = tertiary_variants
+
+ # Recalcule les rampes séquentielles (avec overrides éventuels)
+ cls._SEQUENTIALS = cls._compute_sequentials(
+ cls.PRIMARY,
+ cls.SECONDARY,
+ cls.TERTIARY,
+ light_overrides=sequential_light,
+ dark_overrides=sequential_dark,
+ light_amount=light_amount,
+ dark_amount=dark_amount,
+ )
+ cls.PALETTE = cls.base_palette()
+
+ # --------- helpers internes ---------
+ @classmethod
+ def _get_seq(cls, key: str) -> List[str]:
+ """Retourne la rampe séquentielle associée à ``key``.
+
+ Déclenche ``ValueError`` si la clé est inconnue.
+ """
+ key = key.lower()
+ if key not in cls._NAMES:
+ raise ValueError(f"Couleur inconnue: {key}. Choisir parmi {sorted(cls._NAMES)}.")
+ return cls._SEQUENTIALS[key]
+
+ @staticmethod
+ def _from_list(name: str, colors: List[str]) -> mcolors.LinearSegmentedColormap:
+ """Crée un ``LinearSegmentedColormap`` à partir d'une liste.
+ """
+ return mcolors.LinearSegmentedColormap.from_list(name, colors)
+
+ @classmethod
+ def _make_diverging(
+ cls,
+ start_key: str,
+ end_key: str,
+ *,
+ center: Optional[str] = None,
+ strong_ends: bool = True,
+ blend_center: bool = False,
+ blend_ratio: float = 0.5,
+ ) -> Tuple[str, List[str]]:
+ """Construit un colormap divergent à partir de deux rampes.
+
+ Stops générés :
+ ``[dark_start?, start_mid, center, end_mid, dark_end?]``
+ """
+ s_seq = cls._get_seq(start_key) # [light, mid, dark]
+ e_seq = cls._get_seq(end_key) # [light, mid, dark]
+
+ if blend_center:
+ center_color = mix_colors(s_seq[1], e_seq[1], ratio=blend_ratio)
+ else:
+ center_color = center or "#f7f7f7"
+
+ colors: List[str] = []
+ if strong_ends:
+ colors.append(s_seq[2]) # dark start
+ colors.append(s_seq[1]) # start mid
+ colors.append(center_color) # centre neutre ou mélange
+ colors.append(e_seq[1]) # end mid
+ if strong_ends:
+ colors.append(e_seq[2]) # dark end
+
+ name = f"ocr_div_{start_key}_{end_key}"
+ return name, colors
+
+ # --------- API publique ---------
+ @classmethod
+ def colormap(
+ cls,
+ mode: Literal["primary", "secondary", "tertiary", "sequential", "diverging"] = "primary",
+ *,
+ start: Optional[Literal["primary", "secondary", "tertiary"]] = None,
+ end: Optional[Literal["primary", "secondary", "tertiary"]] = None,
+ reverse: bool = False,
+ as_cmap: bool = True,
+ center: Optional[str] = None,
+ blend_center: bool = False,
+ blend_ratio: float = 0.5,
+ strong_ends: bool = True,
+ ):
+ """Retourne un colormap Matplotlib ou la liste des stops.
+
+ Utilisation
+ -----------
+ Séquentiel autour d'une couleur :
+ colormap("primary")
+ colormap("sequential", start="primary")
+ Divergent entre deux couleurs :
+ colormap("diverging", start="primary", end="tertiary")
+
+ Paramètres
+ ----------
+ mode : {"primary", "secondary", "tertiary", "sequential", "diverging"}
+ Type de colormap souhaité.
+ start, end : {"primary", "secondary", "tertiary"} | None
+ Couleurs de départ/arrivée (suivant le mode).
+ reverse : bool
+ Inverse l'ordre des couleurs.
+ as_cmap : bool
+ Si ``True``, retourne un objet ``Colormap`` ; sinon la liste
+ des valeurs hexadécimales.
+ center : str | None
+ Couleur centrale explicite (hexadécimal). Ignorée si ``blend_center`` vaut ``True``.
+ blend_center : bool
+ Mélange automatiquement les teintes ``start`` et ``end`` pour générer la couleur centrale.
+ blend_ratio : float
+ Ratio de mélange (0..1) appliqué quand ``blend_center`` est activé.
+ strong_ends : bool
+ Ajoute les teintes foncées des rampes aux extrémités du colormap divergent.
+ """
+ # Alias pour compat : "primary"/"secondary"/"tertiary" => séquentiel
+ if mode in {"primary", "secondary", "tertiary"}:
+ seq = cls._get_seq(mode)
+ colors = list(reversed(seq)) if reverse else seq
+ return cls._from_list(f"ocr_{mode}", colors) if as_cmap else colors
+
+ #mode = mode.lower()
+ if mode == "sequential":
+ key = (start or "primary").lower()
+ seq = cls._get_seq(key)
+ colors = list(reversed(seq)) if reverse else seq
+ return cls._from_list(f"ocr_seq_{key}", colors) if as_cmap else colors
+
+ if mode == "diverging":
+ if not start or not end:
+ raise ValueError("Pour un colormap diverging, fournir start=... et end=...")
+ #start = start.lower()
+ #end = end.lower()
+ if start not in cls._NAMES or end not in cls._NAMES:
+ raise ValueError(f"start/end doivent être dans {sorted(cls._NAMES)}.")
+ name, colors = cls._make_diverging(
+ start,
+ end,
+ center=center,
+ strong_ends=strong_ends,
+ blend_center=blend_center,
+ blend_ratio=blend_ratio,
+ )
+ if reverse:
+ colors = list(reversed(colors))
+ return cls._from_list(name, colors) if as_cmap else colors
+
+ raise ValueError("mode inconnu. Utiliser 'primary'/'secondary'/'tertiary' ou 'sequential'/'diverging'.")
+
+ @classmethod
+ def apply(cls, *, context: str = "notebook", style: str = "white") -> List[str]:
+ """Applique le thème global Seaborn/Matplotlib.
+
+ Retourne la palette qualitative étendue utilisée par Seaborn.
+ """
+ pal = cls.extended_palette()
+ sns.set_theme(context=context, style=style, palette=pal) # type: ignore
+ plt.rcParams.update(THEME_RC_OVERRIDES)
+ return pal
+
+ @classmethod
+ def extended_palette(cls) -> List[str]:
+ """Retourne la palette qualitative étendue.
+
+ Utile pour des graphiques multi‑séries (barres, lignes, etc.).
+ """
+ return [
+ cls.PRIMARY, *cls._PRIMARY_VARIANTS,
+ cls.SECONDARY, *cls._SECONDARY_VARIANTS,
+ cls.TERTIARY, *cls._TERTIARY_VARIANTS,
+ ]
+
+ # --------- Démos appelables ---------
+ @staticmethod
+ def _demo_field(n: int = 300):
+ """Génère un champ 2D lisse destiné à ``imshow``."""
+ x = np.linspace(-3, 3, n)
+ y = np.linspace(-3, 3, n)
+ X, Y = np.meshgrid(x, y)
+ Z = np.sin(X) * np.cos(Y)
+ return X, Y, Z
+
+ @staticmethod
+ def _demo_matrix(shape: Tuple[int, int] = (10, 12), seed: int = 0):
+ """Génère une matrice aléatoire pour des heatmaps reproductibles."""
+ rng = np.random.default_rng(seed)
+ return rng.standard_normal(shape)
+
+ @classmethod
+ def demo_imshow_sequential(
+ cls,
+ *,
+ start: Literal["primary", "secondary", "tertiary"] = "primary",
+ reverse: bool = False,
+ with_colorbar: bool = True,
+ title: Optional[str] = None,
+ apply_theme: bool = False,
+ ) -> None:
+ """Affiche une démo ``imshow`` avec un colormap séquentiel.
+
+ Exemple
+ -------
+ >>> Theme.demo_imshow_sequential(start="tertiary", reverse=True)
+ """
+ if apply_theme:
+ cls.apply()
+ _, _, Z = cls._demo_field()
+ cmap = cls.colormap("sequential", start=start, reverse=reverse)
+ plt.imshow(Z, cmap=cmap, origin="lower") # type: ignore
+ direction = "foncé → clair" if reverse else "clair → foncé"
+ plt.title(title or f"Séquentiel {start.upper()} ({direction})")
+ if with_colorbar:
+ plt.colorbar()
+ plt.show()
+
+ @classmethod
+ def demo_imshow_diverging(
+ cls,
+ *,
+ start: Literal["primary", "secondary", "tertiary"] = "primary",
+ end: Literal["primary", "secondary", "tertiary"] = "secondary",
+ reverse: bool = False,
+ with_colorbar: bool = True,
+ title: Optional[str] = None,
+ apply_theme: bool = False,
+ center: Optional[str] = None,
+ blend_center: bool = False,
+ blend_ratio: float = 0.5,
+ strong_ends: bool = True,
+ ) -> None:
+ """Affiche une démo ``imshow`` avec un colormap divergent.
+
+ Exemple
+ -------
+ >>> Theme.demo_imshow_diverging(start="primary", end="secondary")
+ """
+ if apply_theme:
+ cls.apply()
+ _, _, Z = cls._demo_field()
+ cmap = cls.colormap(
+ "diverging",
+ start=start,
+ end=end,
+ reverse=reverse,
+ center=center,
+ blend_center=blend_center,
+ blend_ratio=blend_ratio,
+ strong_ends=strong_ends,
+ )
+ plt.imshow(Z, cmap=cmap, origin="lower") # type: ignore
+ plt.title(title or f"Diverging {start.upper()} ↔ {end.upper()}")
+ if with_colorbar:
+ plt.colorbar()
+ plt.show()
+
+ @classmethod
+ def demo_heatmap_sequential(
+ cls,
+ *,
+ start: Literal["primary", "secondary", "tertiary"] = "primary",
+ reverse: bool = False,
+ title: Optional[str] = None,
+ apply_theme: bool = True,
+ ) -> None:
+ """Affiche une heatmap Seaborn en mode séquentiel.
+
+ Exemple
+ -------
+ >>> Theme.demo_heatmap_sequential(start="primary")
+ """
+ if apply_theme:
+ cls.apply()
+ data = cls._demo_matrix()
+ plt.figure(figsize=(6, 4))
+ sns.heatmap(data, cmap=cls.colormap("sequential", start=start, reverse=reverse)) # type: ignore
+ direction = "foncé → clair" if reverse else "clair → foncé"
+ plt.title(title or f"Heatmap séquentielle - {start.upper()} ({direction})")
+ plt.show()
+
+ @classmethod
+ def demo_heatmap_diverging(
+ cls,
+ *,
+ start: Literal["primary", "secondary", "tertiary"] = "primary",
+ end: Literal["primary", "secondary", "tertiary"] = "tertiary",
+ reverse: bool = False,
+ title: Optional[str] = None,
+ apply_theme: bool = True,
+ ) -> None:
+ """Affiche une heatmap Seaborn en mode divergent.
+
+ Exemple
+ -------
+ >>> Theme.demo_heatmap_diverging(start="primary", end="tertiary")
+ """
+ if apply_theme:
+ cls.apply()
+ data = cls._demo_matrix()
+ plt.figure(figsize=(6, 4))
+ sns.heatmap(data, cmap=cls.colormap("diverging", start=start, end=end, reverse=reverse)) # type: ignore
+ plt.title(title or f"Heatmap diverging - {start.upper()} ↔ {end.upper()}")
+ plt.show()
+
+
+
+# API fonctionnelle (compatibilité)
+
+def set_theme():
+ """Applique le thème OC et retourne la palette étendue.
+
+ Raccourci rétrocompatible de :meth:`Theme.apply`.
+ """
+ return Theme.apply()
+
+
+def set_colormap(
+ mode: Literal["primary", "secondary", "tertiary", "sequential", "diverging"] = "primary",
+ *,
+ start: Optional[Literal["primary", "secondary", "tertiary"]] = None,
+ end: Optional[Literal["primary", "secondary", "tertiary"]] = None,
+ reverse: bool = False,
+ as_cmap: bool = True,
+):
+ """Raccourci pour obtenir un colormap OC.
+
+ Voir :meth:`Theme.colormap` pour le détail des paramètres.
+ """
+ return Theme.colormap(mode, start=start, end=end, reverse=reverse, as_cmap=as_cmap)
+
+# Configuration par défaut (externe à la classe)
+_default_cfg = ThemeConfig(
+ primary="#7451EB",
+ secondary="#EE8273",
+ tertiary="#A6BD63",
+ background="#FFFCF2",
+ primary_variants=["#9D7EF0", "#4B25D6"],
+ secondary_variants=["#F3A093", "#D95848"],
+ tertiary_variants=["#BDD681", "#7E923F"],
+ sequential_light={
+ "primary": "#f3f0fd",
+ "secondary": "#fdecea",
+ "tertiary": "#f6faec",
+ },
+ sequential_dark={
+ "primary": "#2f1577",
+ "secondary": "#8b3025",
+ "tertiary": "#4b5c27",
+ },
+ text_color="black",
+)
+configure_theme(_default_cfg)
+def mix_colors(color1: str, color2: str, ratio: float = 0.5) -> str:
+ """Mélange deux couleurs hexadécimales selon ``ratio`` (0-1)."""
+ rgb1 = np.array(mcolors.to_rgb(color1))
+ rgb2 = np.array(mcolors.to_rgb(color2))
+ mixed = (1 - ratio) * rgb1 + ratio * rgb2
+ return mcolors.to_hex(mixed) # type: ignore
+
+
+def make_diverging_cmap(
+ primary: str,
+ secondary: str,
+ name: str = "custom_diverging",
+ ratio: float = 0.5,
+):
+ """Crée un colormap divergent simple (primary → mix → secondary)."""
+ mid = mix_colors(primary, secondary, ratio=ratio)
+ return mcolors.LinearSegmentedColormap.from_list(name, [primary, mid, secondary])
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.yml b/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.yml
new file mode 100644
index 0000000000000000000000000000000000000000..bbe0b093a687c923d91004495cc9caa9fcca3c4d
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/brand/brand.yml
@@ -0,0 +1,26 @@
+primary: "#8F81E5"
+secondary: "#EE8273"
+tertiary: "#A6BD63"
+background: "#FEFBF3"
+primary_variants:
+ - "#A69AF0"
+ - "#5A46C9"
+secondary_variants:
+ - "#F3A093"
+ - "#D95848"
+tertiary_variants:
+ - "#BDD681"
+ - "#7E923F"
+sequential_light:
+ primary: "#f0eefc"
+ secondary: "#fdecea"
+ tertiary: "#f6faec"
+sequential_dark:
+ primary: "#2f1577"
+ secondary: "#8b3025"
+ tertiary: "#4b5c27"
+text_color: "#000000"
+axes_labelcolor: "#000000"
+tick_color: "#000000"
+figure_dpi: 110
+savefig_dpi: 300
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/__init__.py b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..acaa0afeb7bdc2d1a873c23cd2fe1f709e1e3246
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/__init__.py
@@ -0,0 +1,20 @@
+# Ré-export pratique
+from .var import load_settings
+from .shap_generator import shap_local, shap_global
+# (Optionnel) exposer directement les constantes si tu veux importer "prêt à l'emploi"
+try:
+ _CFG = load_settings() # lit manet_projet04/settings.yml
+ RANDOM_STATE = _CFG.get("RANDOM_STATE", 42)
+ PATH_SIRH = _CFG.get("PATH_SIRH")
+ PATH_EVAL = _CFG.get("PATH_EVAL")
+ PATH_SONDAGE = _CFG.get("PATH_SONDAGE")
+ COL_ID = _CFG.get("COL_ID", "id_employee")
+ TARGET = _CFG.get("TARGET", "a_quitte_l_entreprise")
+ NUM_COLS = _CFG.get("NUM_COLS", [])
+ CAT_COLS = _CFG.get("CAT_COLS", [])
+ SAT_COLS = _CFG.get("SAT_COLS", [])
+ FIRST_VARS = _CFG.get("FIRST_VARS", [])
+ SUBSAMPLE_FRAC = _CFG.get("SUBSAMPLE_FRAC", 0.10)
+except Exception:
+ # Laisse l'import survivre même si le YAML manque
+ pass
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/settings.yml b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/settings.yml
new file mode 100644
index 0000000000000000000000000000000000000000..da0446249f06c9e055d5355e018e902b225462ca
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/settings.yml
@@ -0,0 +1,57 @@
+RANDOM_STATE: 42
+PATH_SIRH: datasets/extrait_sirh.csv
+PATH_EVAL: datasets/extrait_eval.csv
+PATH_SONDAGE: datasets/extrait_sondage.csv
+
+COL_ID: id_employee
+TARGET: a_quitte_l_entreprise
+
+NUM_COLS:
+ - age
+ - revenu_mensuel
+ - annees_dans_l_entreprise
+ - annees_dans_le_poste_actuel
+ - annee_experience_totale
+ - distance_domicile_travail
+ - note_evaluation_actuelle
+ - note_evaluation_precedente
+ - nombre_heures_travailles
+ - augementation_salaire_precedente
+ - nombre_experiences_precedentes
+ - nb_formations_suivies
+ - nombre_participation_pee
+ - nombre_employee_sous_responsabilite
+ - annees_depuis_la_derniere_promotion
+ - annees_sous_responsable_actuel
+ - augmentation_relative
+ - part_experience_dans_poste
+ - formations_par_an_experience
+ - satisfaction_moyenne
+ - temps_depuis_promo_ratio
+ - evolution_note
+
+CAT_COLS:
+ - genre
+ - statut_marital
+ - departement
+ - poste
+ - niveau_hierarchique_poste
+ - niveau_education
+ - domaine_etude
+ - frequence_deplacement
+ - ayant_enfants
+ - heure_supplementaires
+
+SAT_COLS:
+ - satisfaction_employee_environnement
+ - satisfaction_employee_nature_travail
+ - satisfaction_employee_equipe
+ - satisfaction_employee_equilibre_pro_perso
+
+FIRST_VARS:
+ - age
+ - revenu_mensuel
+ - annees_dans_l_entreprise
+ - note_evaluation_actuelle
+
+SUBSAMPLE_FRAC: 0.10
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/shap_generator.py b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/shap_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..365df0dfe16d5e7ce5ca56a3f702b92fa5379e7a
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/shap_generator.py
@@ -0,0 +1,244 @@
+import shap
+import warnings
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.text import Text
+try:
+ from shap.plots import style_context # type: ignore # SHAP >= 0.45
+except ImportError: # pragma: no cover - rétrocompat
+ from shap.plots._style import style_context
+from matplotlib.colors import Colormap, LinearSegmentedColormap
+
+from projet_05.branding import Theme, apply_brand_theme
+
+apply_brand_theme()
+DEFAULT_CMAP = Theme.colormap(
+ "diverging",
+ start="primary",
+ end="secondary",
+ blend_center=True,
+ strong_ends=True,
+)
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+def _resolve_cmap(user_cmap):
+ """Retourne un objet Colormap utilisable par Matplotlib/SHAP."""
+ if user_cmap is None:
+ return DEFAULT_CMAP
+ if isinstance(user_cmap, Colormap):
+ return user_cmap
+ if isinstance(user_cmap, str):
+ try:
+ return plt.get_cmap(user_cmap)
+ except ValueError:
+ print(f"[AVERTISSEMENT] Colormap '{user_cmap}' inconnue → utilisation du thème par défaut.")
+ return DEFAULT_CMAP
+ # Essaye de convertir une liste de couleurs
+ if isinstance(user_cmap, (list, tuple)):
+ try:
+ return LinearSegmentedColormap.from_list("user_cmap", user_cmap)
+ except Exception:
+ print("[AVERTISSEMENT] Impossible de convertir la liste de couleurs fournie → colormap par défaut.")
+ return DEFAULT_CMAP
+ print("[AVERTISSEMENT] Objet colormap non reconnu → utilisation du thème par défaut.")
+ return DEFAULT_CMAP
+
+
+def shap_global(
+ final_pipe,
+ X_full,
+ y_full,
+ RANDOM_STATE=42,
+ sample_size=400,
+ plot_type="dot",
+ max_display=15,
+ cmap=None,
+):
+ """
+ Affiche un diagramme SHAP global avec fallback automatique si le graphique principal échoue.
+ """
+
+ print("\n=== Interprétation SHAP globale ===")
+
+ # --- Étape 1 : Entraînement global ---
+ final_pipe.fit(X_full, y_full)
+ fitted_model = final_pipe.named_steps['clf']
+
+ # --- Étape 2 : Récupération noms de variables ---
+ try:
+ feature_names = final_pipe.named_steps['prep'].get_feature_names_out()
+ except Exception:
+ feature_names = list(X_full.columns)
+ print(f"[INFO] {len(feature_names)} variables utilisées.")
+
+ # --- Étape 3 : Préparation jeu d’échantillons ---
+ X_sample = X_full.sample(n=min(sample_size, len(X_full)), random_state=RANDOM_STATE)
+ X_transformed = final_pipe.named_steps['prep'].transform(X_sample)
+
+ # Convertit toute structure hybride (sparse, object) vers un tableau float64
+ if hasattr(X_transformed, "toarray"):
+ X_transformed = X_transformed.toarray()
+ X_transformed = np.asarray(X_transformed)
+
+ if not np.issubdtype(X_transformed.dtype, np.number):
+ X_numeric = (
+ pd.DataFrame(X_transformed)
+ .apply(pd.to_numeric, errors="coerce")
+ .fillna(0.0)
+ .to_numpy(dtype=np.float64)
+ )
+ else:
+ X_numeric = X_transformed.astype(np.float64, copy=False)
+
+ if X_numeric.ndim == 1:
+ X_numeric = X_numeric.reshape(-1, 1)
+
+ # --- Étape 4 : Vérifications ---
+ print(f"[INFO] X_transformed shape: {X_numeric.shape}")
+ if X_numeric.shape[1] == 0:
+ print("[ERREUR] Aucune variable numérique n’a été conservée après transformation.")
+ return None, X_numeric, feature_names
+
+ if len(feature_names) != X_numeric.shape[1]:
+ print("[AVERTISSEMENT] Noms de variables réindexés pour cohérence.")
+ feature_names = [f"var_{i}" for i in range(X_numeric.shape[1])]
+
+ X_df = pd.DataFrame(X_numeric, columns=feature_names).astype(np.float64, copy=False)
+
+ # --- Étape 5 : Création de l’explainer ---
+ try:
+ explainer = shap.Explainer(fitted_model, X_df, feature_names=feature_names)
+ except Exception:
+ explainer = shap.Explainer(fitted_model.predict, X_df, feature_names=feature_names)
+
+ shap_values = explainer(X_df)
+
+ # Pour les modèles de classification multi-sorties, on conserve la classe positive
+ if hasattr(shap_values, "values") and shap_values.values.ndim == 3:
+ target_idx = 1 if shap_values.values.shape[-1] > 1 else 0
+ shap_values = shap_values[..., target_idx]
+
+ # --- Étape 6 : Nettoyage des valeurs ---
+ if hasattr(shap_values, "values"):
+ shap_array = np.nan_to_num(np.array(shap_values.values))
+ else:
+ shap_array = np.nan_to_num(np.array(shap_values))
+
+ if shap_array.ndim == 1:
+ shap_array = shap_array.reshape(-1, 1)
+
+ print(f"[INFO] SHAP array shape: {shap_array.shape}")
+
+ # --- Étape 7 : Diagramme ---
+ summary_cmap = _resolve_cmap(cmap)
+ try:
+ shap.summary_plot(
+ shap_values,
+ features=X_df,
+ feature_names=feature_names,
+ plot_type=plot_type,
+ max_display=max_display,
+ show=False,
+ cmap=summary_cmap,
+ color=Theme.PRIMARY if plot_type == "bar" else None
+ )
+ fig = plt.gcf()
+ ax = plt.gca()
+ fig.patch.set_facecolor(Theme.BACKGROUND)
+ ax.set_facecolor(Theme.BACKGROUND)
+ plt.title(f"SHAP Summary Plot ({plot_type}) – Importance globale", fontsize=11)
+ plt.tight_layout()
+ plt.savefig(f"output/shap_summary_{plot_type}.png", dpi=300)
+ plt.show()
+ plt.close()
+ except Exception as e:
+ print(f"[AVERTISSEMENT] Plot détaillé échoué ({e}) → fallback barplot.")
+ try:
+ shap.summary_plot(
+ shap_array.astype(np.float64, copy=False),
+ features=X_df,
+ feature_names=feature_names,
+ plot_type="bar",
+ max_display=max_display,
+ show=False,
+ color=Theme.PRIMARY
+ )
+ fig = plt.gcf()
+ ax = plt.gca()
+ fig.patch.set_facecolor(Theme.BACKGROUND)
+ ax.set_facecolor(Theme.BACKGROUND)
+ plt.title("SHAP Summary Plot – (fallback barplot)", fontsize=11)
+ plt.tight_layout()
+ plt.savefig(f"output/shap_summary_fallback_barplot.png", dpi=300)
+ plt.show()
+ plt.close()
+ except Exception as e2:
+ print(f"[ERREUR] Même le fallback échoue : {e2}")
+
+ return shap_values, X_numeric, feature_names
+
+
+# === Fonction 2 : SHAP LOCAL ===
+def shap_local(idx, shap_values, max_display=10, text_scale=0.6, cmap=DEFAULT_CMAP):
+ """
+ Affiche un diagramme SHAP local plus lisible (textes réduits).
+
+ Paramètres
+ ----------
+ idx : int
+ Index de l'individu à expliquer.
+ shap_values : shap.Explanation
+ Valeurs SHAP calculées.
+ max_display : int, par défaut 10
+ Nombre max de variables à afficher.
+ text_scale : float, par défaut 0.6
+ Facteur d’échelle pour réduire la taille du texte.
+ """
+
+ idx = int(idx)
+ n_obs = shap_values.shape[0]
+ if idx < 0 or idx >= n_obs:
+ print(f"[ERREUR] Index {idx} hors limites (0 ≤ idx < {n_obs}).")
+ return
+
+ print(f"\n[INFO] Interprétation locale SHAP – individu {idx}")
+
+ # --- Crée la figure ---
+ plt.figure(figsize=(7, 5))
+ style_kwargs = dict(
+ primary_color_positive=Theme.SECONDARY,
+ primary_color_negative=Theme.PRIMARY,
+ secondary_color_positive=Theme.SECONDARY,
+ secondary_color_negative=Theme.PRIMARY,
+ hlines_color=Theme.SECONDARY,
+ vlines_color=Theme.SECONDARY,
+ text_color="white",
+ tick_labels_color="black",
+ )
+
+ # --- Génère le graphique SHAP ---
+ with style_context(**style_kwargs):
+ shap.plots.waterfall(shap_values[idx], max_display=max_display, show=False)
+ fig = plt.gcf()
+ ax = plt.gca()
+ fig.patch.set_facecolor(Theme.BACKGROUND)
+ ax.set_facecolor(Theme.BACKGROUND)
+
+ # --- Ajuste la taille du texte ---
+ for txt in plt.gcf().findobj(Text):
+ size = txt.get_fontsize()
+ try:
+ size_f = float(size)
+ except (TypeError, ValueError):
+ # ignore les objets dont la taille n'est pas convertible
+ continue
+ txt.set_fontsize(size_f * text_scale)
+
+ plt.title(f"Explication locale SHAP – individu {idx}", fontsize=9, pad=10)
+ plt.tight_layout()
+ plt.savefig(f"output/shap_local_individu_{idx}.png", dpi=300)
+ plt.show()
+ plt.close()
diff --git a/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/var.py b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/var.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fdf0b85b23e0b20741a75e8f6bc6dc65af99ecb
--- /dev/null
+++ b/hf_space/hf_space/hf_space/scripts_projet04/manet_projet04/var.py
@@ -0,0 +1,84 @@
+"""
+Chargement et nettoyage des paramètres du projet à partir d'un fichier YAML.
+La fonction load_settings attend les variables "NUM_COLS", "CAT_COLS", "SAT_COLS", "FIRST_VARS"
+sous forme de listes de chaînes.
+"""
+
+from pathlib import Path
+from typing import Any, List, Optional, Dict
+import yaml
+
+
+# Dossier contenant ce module (utile pour retrouver settings.yml)
+PKG_DIR = Path(__file__).resolve().parent
+
+
+def _clean_list(lst: Optional[Any]) -> List[str]:
+ """
+ Nettoie une liste de chaînes provenant du YAML.
+
+ - S'assure que la valeur renvoyée est une liste.
+ - Supprime les guillemets parasites en début/fin.
+ - Retire les apostrophes orphelines à la fin d'une valeur.
+
+ Args:
+ lst: Liste ou valeur unique à nettoyer.
+
+ Returns:
+ Liste nettoyée de chaînes.
+ """
+ if lst is None:
+ return []
+ if not isinstance(lst, (list, tuple)):
+ return [str(lst)]
+
+ cleaned = []
+ for value in lst:
+ s = str(value).strip()
+
+ # Retire les guillemets doubles ou simples encadrants
+ if (
+ (s.startswith('"') and s.endswith('"'))
+ or (s.startswith("'") and s.endswith("'"))
+ ):
+ s = s[1:-1]
+
+ # Supprime un éventuel guillemet simple final
+ if s.endswith("'"):
+ s = s[:-1]
+
+ cleaned.append(s)
+ return cleaned
+
+
+def load_settings(yaml_path: Optional[str] = None) -> Dict[str, Any]:
+ """
+ Charge le fichier settings.yml et retourne un dictionnaire de paramètres.
+
+ Si aucun chemin n'est fourni, le fichier par défaut est :
+ `manet_projet04/settings.yml`.
+
+ Args:
+ yaml_path: Chemin explicite du fichier YAML (optionnel).
+
+ Returns:
+ dict: Dictionnaire des paramètres du projet.
+ """
+ path = Path(yaml_path) if yaml_path else PKG_DIR / "settings.yml"
+
+ with path.open("r", encoding="utf-8") as file:
+ cfg = yaml.safe_load(file) or {}
+
+ # Normalise certaines clés connues
+ for key in ("NUM_COLS", "CAT_COLS", "SAT_COLS", "FIRST_VARS"):
+ if key in cfg:
+ cfg[key] = _clean_list(cfg[key])
+
+ # Conversion sécurisée du sous-échantillon
+ if "SUBSAMPLE_FRAC" in cfg:
+ try:
+ cfg["SUBSAMPLE_FRAC"] = float(cfg["SUBSAMPLE_FRAC"])
+ except (TypeError, ValueError):
+ pass
+
+ return cfg
\ No newline at end of file
diff --git a/projet_05/dataset.py b/projet_05/dataset.py
index e4d63f92454dbb64257f09abac0254b49222b5b2..7471f6b4956b8276b779c27df03ad9b3ba48570c 100644
--- a/projet_05/dataset.py
+++ b/projet_05/dataset.py
@@ -66,7 +66,7 @@ def _harmonize_id_column(df: pd.DataFrame, column: str, *, digits_only: bool = T
return data
if digits_only:
- extracted = data[column].astype(str).str.extract(r"(\\d+)")
+ extracted = data[column].astype(str).str.extract(r"(\d+)")
data[column] = pd.to_numeric(extracted[0], errors="coerce")
data[column] = pd.to_numeric(data[column], errors="coerce").astype("Int64")
return data
diff --git a/projet_05/explainability.py b/projet_05/explainability.py
index 979701ad9c34be94bae3e1df493ca7d52b9efa37..c4fbccd4144eb981d5ba59df759c7f3224197ba6 100644
--- a/projet_05/explainability.py
+++ b/projet_05/explainability.py
@@ -30,7 +30,7 @@ def compute_shap_summary(
y: pd.Series,
*,
max_samples: int = 500,
-) -> Tuple[pd.DataFrame | None, object | None]:
+) -> Tuple[pd.DataFrame | None, object | None, pd.DataFrame | None]:
"""
Reuse the historical `shap_global` helper to build the plots and a tabular summary.
@@ -41,8 +41,9 @@ def compute_shap_summary(
shap_values : shap.Explanation | None
Objet renvoyé par shap_global pour des analyses locales ultérieures.
"""
+ Path("output").mkdir(parents=True, exist_ok=True)
cmap = make_diverging_cmap(Theme.PRIMARY, Theme.SECONDARY)
- shap_values, _, feature_names = shap_global(
+ shap_values, _, feature_names, sample_indices = shap_global(
pipeline,
X,
y,
@@ -51,7 +52,7 @@ def compute_shap_summary(
)
if shap_values is None or feature_names is None:
logger.warning("Impossible de générer les résumés SHAP.")
- return None, None
+ return None, None, None
shap_array = _shape_array(shap_values)
if shap_array.ndim == 1:
@@ -62,7 +63,10 @@ def compute_shap_summary(
.sort_values("mean_abs_shap", ascending=False)
.reset_index(drop=True)
)
- return summary, shap_values
+ X_sample = None
+ if sample_indices is not None:
+ X_sample = X.reindex(sample_indices)
+ return summary, shap_values, X_sample
def save_shap_summary(summary: pd.DataFrame, output_path: Path) -> None:
@@ -74,25 +78,26 @@ def save_shap_summary(summary: pd.DataFrame, output_path: Path) -> None:
def export_local_explanations(
pipeline,
shap_values,
- X: pd.DataFrame,
+ X_sample: pd.DataFrame | None,
custom_index: int | None = None,
) -> None:
"""
Génère trois cas d'usage par défaut (impact max, risque max, risque min)
et un indice custom optionnel pour la trace historique.
"""
- if shap_values is None:
+ if shap_values is None or X_sample is None or X_sample.empty:
return
+ Path("output").mkdir(parents=True, exist_ok=True)
shap_array = _shape_array(shap_values)
idx_impact = int(np.argmax(np.sum(np.abs(shap_array), axis=1)))
shap_local(idx_impact, shap_values)
- y_proba_all = pipeline.predict_proba(X)[:, 1]
- idx_highrisk = int(np.argmax(y_proba_all))
+ y_proba_sample = pipeline.predict_proba(X_sample)[:, 1]
+ idx_highrisk = int(np.argmax(y_proba_sample))
shap_local(idx_highrisk, shap_values)
- idx_lowrisk = int(np.argmin(y_proba_all))
+ idx_lowrisk = int(np.argmin(y_proba_sample))
shap_local(idx_lowrisk, shap_values, text_scale=0.6)
if custom_index is not None:
diff --git a/projet_05/modeling/train.py b/projet_05/modeling/train.py
index d1c4fa7bb4423c6eb6c6aa4517a81261b7e7fbcc..daf5aca38be2e231cf5744a50e74f9e923dc96cc 100644
--- a/projet_05/modeling/train.py
+++ b/projet_05/modeling/train.py
@@ -222,19 +222,20 @@ def fit_final_pipeline(
y: pd.Series,
settings: Settings,
):
- sm = SMOTE(random_state=settings.random_state)
- X_bal, y_bal = sm.fit_resample(X, y)
- final_preprocessor = build_preprocessor(settings, X)
- clf = clone(best_result.best_estimator.named_steps["clf"])
+ tuned_pipeline = clone(best_result.best_estimator)
+ tuned_pipeline.fit(X, y)
+ final_preprocessor = tuned_pipeline.named_steps["prep"]
+ clf = tuned_pipeline.named_steps["clf"]
final_pipe = Pipeline(
steps=[
("prep", final_preprocessor),
("clf", clf),
]
)
- final_pipe.fit(X_bal, y_bal)
logger.success(
- "Modèle {} ré-entraîné sur {} lignes équilibrées.", best_result.name, len(X_bal)
+ "Modèle {} ré-entraîné sur {} lignes (avec rééchantillonnage interne).",
+ best_result.name,
+ len(X),
)
return final_pipe
@@ -280,10 +281,10 @@ def save_artifacts(
metadata_path.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
logger.info("Métadonnées sauvegardées dans {}", metadata_path)
- shap_summary, shap_values = compute_shap_summary(pipeline, X, y)
+ shap_summary, shap_values, shap_sample = compute_shap_summary(pipeline, X, y)
if shap_summary is not None:
save_shap_summary(shap_summary, shap_path)
- export_local_explanations(pipeline, shap_values, X)
+ export_local_explanations(pipeline, shap_values, shap_sample)
@app.command()
diff --git a/projet_05/settings.yml b/projet_05/settings.yml
index 949d24dcdce60f007686ce2f438cf50437a0b216..3d22c7e6fd459ba35ae01ba07b27f4b5bab93d68 100644
--- a/projet_05/settings.yml
+++ b/projet_05/settings.yml
@@ -4,9 +4,9 @@ target: a_quitte_l_entreprise
subsample_frac: 0.5
paths:
- sirh: ../data/raw/sirh.csv
- evaluation: ../data/raw/evaluation.csv
- sondage: ../data/raw/sondage.csv
+ sirh: ../data/raw/extrait_sirh.csv
+ evaluation: ../data/raw/extrait_eval.csv
+ sondage: ../data/raw/extrait_sondage.csv
sql_file: ../reports/merge_sql.sql
db_file: ../data/interim/merge_temp.db
diff --git a/reports/merge_sql.sql b/reports/merge_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..3f2f1e2e0b0d0e3512e207d4c90ce9d11ffe82b8
--- /dev/null
+++ b/reports/merge_sql.sql
@@ -0,0 +1,4 @@
+SELECT *
+ FROM sirh
+ INNER JOIN evaluation USING (id_employee)
+ INNER JOIN sond USING (id_employee);
\ No newline at end of file
diff --git a/reports/shap_summary.csv b/reports/shap_summary.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4ddcf95d5ad8772296348937b98352fc044ed9e5
--- /dev/null
+++ b/reports/shap_summary.csv
@@ -0,0 +1,30 @@
+feature,mean_abs_shap
+num__nombre_participation_pee,0.041875428456366735
+num__revenu_mensuel,0.03042313662767298
+num__score_moyen_satisfaction,0.024708499401529128
+num__age,0.022455430696950242
+num__augmentation_par_revenu,0.02191316983082498
+num__annee_experience_totale,0.018277766552533176
+num__annees_dans_l_entreprise,0.015970280752820164
+num__annees_dans_le_poste_actuel,0.014483735952401001
+num__distance_domicile_travail,0.014185047755475154
+num__dern_promo_par_experience,0.013176643883981817
+num__satisfaction_employee_environnement,0.013132995008906193
+cat__departement_Consulting,0.01201168162542709
+num__satisfaction_employee_nature_travail,0.011593948532857702
+cat__departement_Commercial,0.01109711373679383
+num__note_evaluation_precedente,0.010733038730130045
+num__annee_sur_poste_par_experience,0.008680847149934622
+num__evolution_note,0.007838366669326942
+cat__frequence_deplacement_Frequent,0.007662910115323365
+num__nb_formation_par_experience,0.006100469294214017
+num__nb_formations_suivies,0.00606711432188867
+num__annees_depuis_la_derniere_promotion,0.005420485445192155
+num__satisfaction_employee_equipe,0.004948475673500224
+cat__frequence_deplacement_Aucun,0.004088014791590756
+cat__genre_M,0.003995089792948047
+num__satisfaction_employee_equilibre_pro_perso,0.0038438054779882267
+cat__genre_F,0.0037898111725244545
+cat__frequence_deplacement_Occasionnel,0.0021305519735428696
+num__note_evaluation_actuelle,0.00039576254175014114
+cat__departement_Ressources Humaines,6.261261062441921e-05
diff --git a/scripts_projet04/manet_projet04/shap_generator.py b/scripts_projet04/manet_projet04/shap_generator.py
index 365df0dfe16d5e7ce5ca56a3f702b92fa5379e7a..3eab8036943bbad6fe82b83810231932f5760001 100644
--- a/scripts_projet04/manet_projet04/shap_generator.py
+++ b/scripts_projet04/manet_projet04/shap_generator.py
@@ -75,7 +75,9 @@ def shap_global(
print(f"[INFO] {len(feature_names)} variables utilisées.")
# --- Étape 3 : Préparation jeu d’échantillons ---
- X_sample = X_full.sample(n=min(sample_size, len(X_full)), random_state=RANDOM_STATE)
+ sample_size = min(sample_size, len(X_full))
+ X_sample = X_full.sample(n=sample_size, random_state=RANDOM_STATE)
+ sample_indices = X_sample.index
X_transformed = final_pipe.named_steps['prep'].transform(X_sample)
# Convertit toute structure hybride (sparse, object) vers un tableau float64
@@ -178,7 +180,7 @@ def shap_global(
except Exception as e2:
print(f"[ERREUR] Même le fallback échoue : {e2}")
- return shap_values, X_numeric, feature_names
+ return shap_values, X_numeric, feature_names, sample_indices
# === Fonction 2 : SHAP LOCAL ===