diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml new file mode 100644 index 00000000..cefff8d4 --- /dev/null +++ b/.github/workflows/docker.yaml @@ -0,0 +1,31 @@ +name: Construction image Docker + +on: + push: + branches: + - main + - dev + + +jobs: + docker: + runs-on: ubuntu-latest + steps: + - + name: Set up QEMU + uses: docker/setup-qemu-action@v3 + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Login to Docker Hub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + push: true + tags: remiguy/application:latest diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 00000000..86da2865 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,42 @@ +name: Python package + +on: + push: + branches: + - main + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + # latest python minor + python-version: '3.x' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install uv + uv sync + + - name: Test code quality + run: | + uv sync + uv run --with pylint pylint src --fail-under=5 + + - name: Test that code runs smoothly + env: + JETON_API: ${{ secrets.JETON_API }} + run: uv run train.py + + - name: Upload recording.log artifact + uses: actions/upload-artifact@v4 + with: + name: log-file + path: recording.log + if-no-files-found: warn diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..674626e1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +config.yaml +__pycache__/ +.env +*.log +data/ +model.skops \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..eab72f18 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,18 @@ +FROM ubuntu:22.04 + +# Install Python +RUN apt-get -y update && \ + apt-get install -y python3-pip curl + +# Install uv +RUN curl -LsSf https://astral.sh/uv/install.sh | sh +ENV PATH="/root/.local/bin/:$PATH" + +# Install project dependencies +COPY pyproject.toml . +RUN uv sync + +COPY app ./app +COPY train.py . +COPY src ./src +CMD ["bash", "-c", "./app/run.sh"] diff --git a/README.md b/README.md new file mode 100644 index 00000000..8f0a01bc --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +# Probabilité de survie sur le Titanic + +Pour pouvoir utiliser ce projet, il +est recommandé de créer un fichier `.env` +ayant la structure suivante: + +```yaml +JETON_API="####" #renseigner la valeur pertinente +``` \ No newline at end of file diff --git a/app/api.py b/app/api.py new file mode 100644 index 00000000..cf496f33 --- /dev/null +++ b/app/api.py @@ -0,0 +1,49 @@ +"""A simple API to expose our trained RandomForest model for Tutanic survival.""" + +from fastapi import FastAPI +import skops.io as sio +import pandas as pd + +unknown_types = sio.get_untrusted_types(file="model.skops") +model = sio.load("model.skops", trusted=unknown_types) + +app = FastAPI( + title="Prédiction de survie sur le Titanic", + description="Application de prédiction de survie sur le Titanic 🚢
Une version par API pour faciliter la réutilisation du modèle 🚀" + + '

', +) + + +@app.get("/", tags=["Welcome"]) +def show_welcome_page(): + """ + Show welcome page with model name and version. + """ + + return { + "Message": "API de prédiction de survie sur le Titanic", + "Model_name": "Titanic ML", + "Model_version": "0.1", + } + + +@app.get("/predict", tags=["Predict"]) +async def predict( + sex: str = "female", age: float = 29.0, fare: float = 16.5, embarked: str = "S" +) -> str: + """ """ + + df = pd.DataFrame( + { + "Sex": [sex], + "Age": [age], + "Fare": [fare], + "Embarked": [embarked], + } + ) + + prediction = int(model.predict(df)[0]) + + prediction = "Survived 🎉" if prediction == 1 else "Dead ⚰️" + + return prediction diff --git a/app/run.sh b/app/run.sh new file mode 100755 index 00000000..cbf799fc --- /dev/null +++ b/app/run.sh @@ -0,0 +1,2 @@ +uv run train.py +uv run uvicorn app.api:app --host "0.0.0.0" diff --git a/data.csv b/data.csv deleted file mode 100644 index 63b68ab0..00000000 --- a/data.csv +++ /dev/null @@ -1,892 +0,0 @@ -PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked -1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S -2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C -3,1,3,"Heikkinen, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S -890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C -891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/deployment/deployment.yaml b/deployment/deployment.yaml new file mode 100644 index 00000000..8fb3c100 --- /dev/null +++ b/deployment/deployment.yaml @@ -0,0 +1,28 @@ +#| filename: "deployment/deployment.yaml" +apiVersion: apps/v1 +kind: Deployment +metadata: + name: titanic-deployment + labels: + app: titanic +spec: + replicas: 1 + selector: + matchLabels: + app: titanic + template: + metadata: + labels: + app: titanic + spec: + containers: + - name: titanic + image: remiguy/application:latest + ports: + - containerPort: 8000 + env: + - name: JETON_API + valueFrom: + secretKeyRef: + name: api-jeton + key: JETON_API diff --git a/deployment/ingress.yaml b/deployment/ingress.yaml new file mode 100644 index 00000000..7b87e67a --- /dev/null +++ b/deployment/ingress.yaml @@ -0,0 +1,21 @@ +#| filename: "deployment/ingress.yaml" +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: titanic-ingress +spec: + ingressClassName: nginx + tls: + - hosts: + - remtita.lab.sspcloud.fr + rules: + - host: remtita.lab.sspcloud.fr + http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: titanic-service + port: + number: 80 \ No newline at end of file diff --git a/deployment/service.yaml b/deployment/service.yaml new file mode 100644 index 00000000..25f172d6 --- /dev/null +++ b/deployment/service.yaml @@ -0,0 +1,11 @@ +apiVersion: v1 +kind: Service +metadata: + name: titanic-service +spec: + selector: + app: titanic + ports: + - protocol: TCP + port: 80 + targetPort: 8000 \ No newline at end of file diff --git a/install.sh b/install.sh new file mode 100644 index 00000000..e4cde938 --- /dev/null +++ b/install.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash + +# Ensure everything is installed +sudo apt-get -y update +sudo apt-get install -y python3-pip python3-venv curl + +# Install uv +curl -LsSf https://astral.sh/uv/install.sh | sh + +# Restore environment +uv sync diff --git a/titanic.ipynb b/notebooks/titanic.ipynb similarity index 99% rename from titanic.ipynb rename to notebooks/titanic.ipynb index 5e6d98b5..3d4f0e81 100644 --- a/titanic.ipynb +++ b/notebooks/titanic.ipynb @@ -30,19 +30,16 @@ }, "outputs": [], "source": [ - "import pandas as pd ; import numpy as np\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import multiprocessing\n", - "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", + "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.model_selection import train_test_split\n", - "import pathlib\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.ensemble import RandomForestClassifier\n", - "import time\n", - "import os\n", + "\n", "%matplotlib inline\n", - "#import warnings\n", - "#warnings.filterwarnings('ignore')" + "# import warnings\n", + "# warnings.filterwarnings('ignore')" ] }, { @@ -213,9 +210,9 @@ } ], "source": [ - "#os.chdir('/home/coder/work/ensae-reproductibilite-application')\n", + "# os.chdir('/home/coder/work/ensae-reproductibilite-application')\n", "\n", - "TrainingData = pd.read_csv('train.csv')\n", + "TrainingData = pd.read_csv(\"train.csv\")\n", "TrainingData.head()" ] }, @@ -247,7 +244,7 @@ } ], "source": [ - "TrainingData['Ticket'].str.split(\"/\").str.len()" + "TrainingData[\"Ticket\"].str.split(\"/\").str.len()" ] }, { @@ -282,7 +279,7 @@ } ], "source": [ - "TrainingData['Name'].str.split(\",\").str.len()" + "TrainingData[\"Name\"].str.split(\",\").str.len()" ] }, { @@ -313,8 +310,8 @@ "outputs": [], "source": [ "n_trees = 20\n", - "max_depth =None\n", - "max_features='sqrt'" + "max_depth = None\n", + "max_features = \"sqrt\"" ] }, { @@ -433,9 +430,15 @@ } ], "source": [ - "fig, axes=plt.subplots(1,2, figsize=(12, 6)) #layout matplotlib 1 ligne 2 colonnes taile 16*8\n", - "fig1_pclass=sns.countplot(data=TrainingData, x =\"Pclass\", ax=axes[0]).set_title(\"fréquence des Pclass\")\n", - "fig2_pclass=sns.barplot(data=TrainingData, x= \"Pclass\",y= \"Survived\", ax=axes[1]).set_title(\"survie des Pclass\")" + "fig, axes = plt.subplots(\n", + " 1, 2, figsize=(12, 6)\n", + ") # layout matplotlib 1 ligne 2 colonnes taile 16*8\n", + "fig1_pclass = sns.countplot(data=TrainingData, x=\"Pclass\", ax=axes[0]).set_title(\n", + " \"fréquence des Pclass\"\n", + ")\n", + "fig2_pclass = sns.barplot(\n", + " data=TrainingData, x=\"Pclass\", y=\"Survived\", ax=axes[1]\n", + ").set_title(\"survie des Pclass\")" ] }, { @@ -469,7 +472,9 @@ } ], "source": [ - "sns.histplot(data= TrainingData, x='Age',bins=15, kde=False ) .set_title(\"Distribution de l'âge\")\n", + "sns.histplot(data=TrainingData, x=\"Age\", bins=15, kde=False).set_title(\n", + " \"Distribution de l'âge\"\n", + ")\n", "plt.show()" ] }, @@ -649,31 +654,38 @@ }, "outputs": [], "source": [ - "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n", - "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", "\n", - "numeric_features=[\"Age\", \"Fare\"]\n", - "categorical_features=[\"Embarked\", \"Sex\"]\n", + "numeric_features = [\"Age\", \"Fare\"]\n", + "categorical_features = [\"Embarked\", \"Sex\"]\n", "\n", - "numeric_transformer = Pipeline(steps=[(\"imputer\", SimpleImputer(strategy=\"median\")),\n", - "(\"scaler\", MinMaxScaler()),])\n", + "numeric_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", + " (\"scaler\", MinMaxScaler()),\n", + " ]\n", + ")\n", "\n", - "categorical_transformer = Pipeline(steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\")),(\"onehot\", OneHotEncoder()),])\n", + "categorical_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n", + " (\"onehot\", OneHotEncoder()),\n", + " ]\n", + ")\n", "\n", "\n", "preprocessor = ColumnTransformer(\n", - "transformers=[\n", - "(\"Preprocessing numerical\", numeric_transformer, numeric_features),\n", - "(\n", - "\"Preprocessing categorical\",\n", - "categorical_transformer,\n", - "categorical_features,\n", - "),\n", - " ]\n", - " )" + " transformers=[\n", + " (\"Preprocessing numerical\", numeric_transformer, numeric_features),\n", + " (\n", + " \"Preprocessing categorical\",\n", + " categorical_transformer,\n", + " categorical_features,\n", + " ),\n", + " ]\n", + ")" ] }, { @@ -1151,11 +1163,11 @@ ], "source": [ "pipe = Pipeline(\n", - " [\n", - " (\"preprocessor\", preprocessor),\n", - " (\"classifier\", RandomForestClassifier(n_estimators=20)),\n", - " ]\n", - " )\n", + " [\n", + " (\"preprocessor\", preprocessor),\n", + " (\"classifier\", RandomForestClassifier(n_estimators=20)),\n", + " ]\n", + ")\n", "pipe" ] }, @@ -1179,10 +1191,10 @@ "outputs": [], "source": [ "y = TrainingData[\"Survived\"]\n", - "X = TrainingData.drop(\"Survived\", axis = 'columns')\n", + "X = TrainingData.drop(\"Survived\", axis=\"columns\")\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n", - "pd.concat([X_train, y_train]).to_csv(\"train.csv\")\n", - "pd.concat([X_test, y_test]).to_csv(\"test.csv\")" + "pd.concat([X_train, y_train], axis=1).to_csv(\"train.csv\")\n", + "pd.concat([X_test, y_test], axis=1).to_csv(\"test.csv\")" ] }, { @@ -1231,9 +1243,8 @@ }, "outputs": [], "source": [ - "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", + "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.model_selection import train_test_split\n", - "import pathlib\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.ensemble import RandomForestClassifier" ] @@ -1748,13 +1759,14 @@ } ], "source": [ - "#calculons le score sur le dataset d'apprentissage et sur le dataset de test (10% du dataset d'apprentissage mis de côté)\n", + "# calculons le score sur le dataset d'apprentissage et sur le dataset de test (10% du dataset d'apprentissage mis de côté)\n", "# le score étant le nombre de bonne prédiction\n", "rdmf_score = pipe.score(X_test, y_test)\n", "rdmf_score_tr = pipe.score(X_train, y_train)\n", "print(f\"{rdmf_score:.1%} de bonnes réponses sur les données de test pour validation\")\n", "from sklearn.metrics import confusion_matrix\n", - "print(20*\"-\")\n", + "\n", + "print(20 * \"-\")\n", "print(\"matrice de confusion\")\n", "print(confusion_matrix(y_test, pipe.predict(X_test)))" ] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..6b33e144 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "application" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "duckdb>=1.4.4", + "fastapi>=0.128.7", + "pandas>=3.0.0", + "python-dotenv>=1.2.1", + "scikit-learn>=1.8.0", + "skops>=0.13.0", + "uvicorn>=0.40.0", +] diff --git a/src/validation/check.py b/src/validation/check.py new file mode 100644 index 00000000..9c377ab1 --- /dev/null +++ b/src/validation/check.py @@ -0,0 +1,57 @@ +""" +Fonctions de validation de données +""" + +import logging +import pandas as pd +import duckdb + + +def check_name_formatting( + connection: duckdb.DuckDBPyConnection, +): + + query = ( + "SELECT COUNT(*) AS n_bad " + "FROM titanic " + "WHERE list_count(string_split(Name, ',')) <> 2" + ) + + bad = connection.sql(query).fetchone()[0] + + if bad == 0: + logging.info( + "Test OK: colonne 'Name' se découpe toujours en 2 parties avec ','" + ) + else: + logging.warn( + f"Problème dans la colonne Name: {bad} ne se décomposent pas en 2 parties." + ) + + +def check_missing_values( + connection: duckdb.DuckDBPyConnection, + variable: str = "Survived", +): + + query = f"SELECT COUNT(*) AS n_missing FROM titanic WHERE {variable} IS NULL" + + n_missing = connection.sql(query).fetchone()[0] + + message_ok = f"Pas de valeur manquante pour la variable {variable}" + message_warn = f"{n_missing} valeurs manquantes pour la variable {variable}" + logging.info(message_ok) if n_missing == 0 else logging.warn(message_warn) + + +def check_data_leakage( + train_dataset: pd.DataFrame, + test_dataset: pd.DataFrame, + variable: str, +): + + if set(train_dataset[variable].dropna().unique()) - set( + test_dataset[variable].dropna().unique() + ): + logging.error(f"Problème de data leakage pour la variable {variable}") + else: + logging.info(f"Pas de problème de data leakage pour la variable {variable}") diff --git a/titanic.py b/titanic.py deleted file mode 100644 index 7ac8d593..00000000 --- a/titanic.py +++ /dev/null @@ -1,146 +0,0 @@ -import pandas as pd ; import numpy as np -import matplotlib.pyplot as plt -import multiprocessing -from sklearn.preprocessing import LabelEncoder, OneHotEncoder -from sklearn.model_selection import train_test_split -import pathlib -from sklearn.preprocessing import MinMaxScaler -from sklearn.ensemble import RandomForestClassifier -import time -import os - -os.chdir('/home/coder/work/ensae-reproductibilite-application') -titanic = pd.read_csv('data.csv') - - -con = duckdb.connect(database=":memory:") - -# Check la structure de Name "Nom, Prénom" -bad = con.sql(""" - SELECT COUNT(*) AS n_bad - FROM titanic - WHERE list_count(string_split(Name, ',')) <> 2 -""").fetchone()[0] - -if bad == 0: - print("Test 'Name' OK se découpe toujours en 2 parties avec ','") -else: - print(f"Problème dans la colonne Name: {bad} ne se décomposent pas en 2 parties.") - - -n_trees = 20 -max_depth =None -max_features='sqrt' - - -## Encoder les données imputées ou transformées. -from sklearn.preprocessing import MinMaxScaler, OneHotEncoder -from sklearn.ensemble import RandomForestClassifier -from sklearn.impute import SimpleImputer -from sklearn.pipeline import Pipeline -from sklearn.compose import ColumnTransformer - -numeric_features=["Age", "Fare"] -categorical_features=["Embarked", "Sex"] - -numeric_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="median")), -("scaler", MinMaxScaler()),]) - -categorical_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="most_frequent")),("onehot", OneHotEncoder()),]) - - -preprocessor = ColumnTransformer( -transformers=[ -("Preprocessing numerical", numeric_transformer, numeric_features), -( -"Preprocessing categorical", -categorical_transformer, -categorical_features, -), - ] - ) - -pipe = Pipeline( - [ - ("preprocessor", preprocessor), - ("classifier", RandomForestClassifier(n_estimators=20)), - ] - ) - - - -# splitting samples -y = titanic["Survived"] -X = titanic.drop("Survived", axis = 'columns') - -# On _split_ notre _dataset_ d'apprentisage pour faire de la validation croisée une partie pour apprendre une partie pour regarder le score. -# Prenons arbitrairement 10% du dataset en test et 90% pour l'apprentissage. -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) - -# check que pas de problème de data leakage -if set(X_train["Embarked"].dropna().unique()) - set(X_test["Embarked"].dropna().unique()): - message = "Problème de data leakage pour la variable Embarked" -else: - message = "Pas de problème de data leakage pour la variable Embarked" - -print(message) - -if set(X_train["Sex"].dropna().unique()) - set(X_test["Sex"].dropna().unique()): - message = "Problème de data leakage pour la variable Sex" -else: - message = "Pas de problème de data leakage pour la variable Embarked" - -print(message) - - - -jetonapi = "$trotskitueleski1917" - -# Vérifie les valeurs manquantes -# TODO: généraliser à toutes les variables -n_missing = con.sql(""" - SELECT COUNT(*) AS n_missing - FROM titanic - WHERE Survived IS NULL -""").fetchone()[0] - -message_ok = "Pas de valeur manquante pour la variable Survived" -message_warn = f"{n_missing} valeurs manquantes pour la variable Survived" -message = message_ok if n_missing == 0 else message_warn -print(message) - -n_missing = con.sql(""" - SELECT COUNT(*) AS n_missing - FROM titanic - WHERE Age IS NULL -""").fetchone()[0] - -message_ok = "Pas de valeur manquante pour la variable Age" -message_warn = f"{n_missing} valeurs manquantes pour la variable Age" -message = message_ok if n_missing == 0 else message_warn -print(message) - - -# Random Forest - -from sklearn.preprocessing import LabelEncoder, OneHotEncoder -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import MinMaxScaler -from sklearn.ensemble import RandomForestClassifier - - -#Ici demandons d'avoir 20 arbres -pipe.fit(X_train, y_train) - - -#calculons le score sur le dataset d'apprentissage et sur le dataset de test (10% du dataset d'apprentissage mis de côté) -# le score étant le nombre de bonne prédiction -rdmf_score = pipe.score(X_test, y_test) -rdmf_score_tr = pipe.score(X_train, y_train) -print(f"{rdmf_score:.1%} de bonnes réponses sur les données de test pour validation") -from sklearn.metrics import confusion_matrix -print(20*"-") -print("matrice de confusion") -print(confusion_matrix(y_test, pipe.predict(X_test))) - - diff --git a/train.py b/train.py new file mode 100644 index 00000000..ee48d435 --- /dev/null +++ b/train.py @@ -0,0 +1,158 @@ +""" +Prediction de la survie d'un individu sur le Titanic +""" + +import os +import argparse +import logging +from dotenv import load_dotenv + +from sklearn.preprocessing import OneHotEncoder +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import MinMaxScaler +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline +from sklearn.compose import ColumnTransformer +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import confusion_matrix +import skops.io as sio + +import duckdb + +from src.validation.check import ( + check_name_formatting, + check_missing_values, + check_data_leakage, +) + + +load_dotenv() +con = duckdb.connect(database=":memory:") + +logging.basicConfig( + format="{asctime} - {levelname} - {message}", + style="{", + datefmt="%Y-%m-%d %H:%M", + level=logging.DEBUG, + handlers=[logging.FileHandler("recording.log"), logging.StreamHandler()], +) + + +# PARAMETERS --------------------------------------- + + +N_TREES = 20 +MAX_DEPTH = None +MAX_FEATURES = "sqrt" +NUMERIC_FEATURES = ["Age", "Fare"] +CATEGORICAL_FEATURES = ["Embarked", "Sex"] +URL_RAW = "https://minio.lab.sspcloud.fr/lgaliana/ensae-reproductibilite/data/raw/data.parquet" + +jeton_api = os.environ["JETON_API"] + + +# ENVIRONMENT CONFIGURATION --------------------------- + +parser = argparse.ArgumentParser(description="Paramètres du random forest") +parser.add_argument("--n_trees", type=int, default=20, help="Nombre d'arbres") +args = parser.parse_args() + +n_trees = args.n_trees + +logging.debug(f"Valeur de l'argument n_trees: {n_trees}") + + +# QUALITY DIAGNOSTICS --------------------------------------- + +logging.debug(f"\n{80 * '-'}\nStarting data validation step\n{80 * '-'}") + +query_definition = ( + f"CREATE TEMP TABLE titanic AS (SELECT * FROM read_parquet('{URL_RAW}'))" +) +con.sql(query_definition) + +column_names = con.sql("SELECT column_name FROM (DESCRIBE titanic)").to_df()[ + "column_name" +] # DuckDB ici, sinon titanic.columns serait OK + +check_name_formatting(connection=con) + +for var in column_names: + check_missing_values(connection=con, variable=var) + + +# FEATURE ENGINEERING ----------------------------------------- + +logging.debug(f"\n{80 * '-'}\nStarting feature engineering phase\n{80 * '-'}") + +titanic = con.sql( + f"SELECT Survived, {', '.join(CATEGORICAL_FEATURES + NUMERIC_FEATURES)} FROM titanic" +).to_df() + +y = titanic["Survived"] +X = titanic.drop("Survived", axis="columns") + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) + +for string_var in CATEGORICAL_FEATURES: + check_data_leakage(X_train, X_test, string_var) + + +# MODEL DEFINITION ----------------------------------------- + +numeric_transformer = Pipeline( + steps=[ + ("imputer", SimpleImputer(strategy="median")), + ("scaler", MinMaxScaler()), + ] +) + +categorical_transformer = Pipeline( + steps=[ + ("imputer", SimpleImputer(strategy="most_frequent")), + ("onehot", OneHotEncoder()), + ] +) + + +preprocessor = ColumnTransformer( + transformers=[ + ("Preprocessing numerical", numeric_transformer, NUMERIC_FEATURES), + ( + "Preprocessing categorical", + categorical_transformer, + CATEGORICAL_FEATURES, + ), + ] +) + +pipe = Pipeline( + [ + ("preprocessor", preprocessor), + ( + "classifier", + RandomForestClassifier( + n_estimators=N_TREES, max_depth=MAX_DEPTH, max_features=MAX_FEATURES + ), + ), + ] +) + +# TRAINING AND EVALUATION -------------------------------------------- + +logging.debug(f"\n{80 * '-'}\nStarting model fitting phase\n{80 * '-'}") + +pipe.fit(X_train, y_train) +obj = 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