diff --git a/titanic/titanic_models.ipynb b/titanic/titanic_models.ipynb index 11e3538..ea8aee0 100644 --- a/titanic/titanic_models.ipynb +++ b/titanic/titanic_models.ipynb @@ -13,13 +13,14 @@ "from sklearn import datasets\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix,roc_auc_score\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n", "\n", "from xgboost import XGBClassifier\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", + "import numpy as np\n", "import pandas as pd\n", "import os" ] @@ -159,13 +160,14 @@ " # recall\n", " recall = recall_score(actual, preds)\n", "\n", - " # AUC\n", - " auc = roc_auc_score(actual, preds)\n", + " # precision\n", + " precision = precision_score(actual, preds)\n", "\n", " # Confusion matrix\n", " cnf_matr = confusion_matrix(actual,preds)\n", + " cnf_matr_nm = cnf_matr.astype('float') / cnf_matr.sum(axis=1)[:, np.newaxis]\n", "\n", - " return accuracy, recall, auc, cnf_matr" + " return accuracy, recall, precision, cnf_matr_nm" ] }, { @@ -184,16 +186,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024/06/27 15:44:02 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of sklearn. If you encounter errors during autologging, try upgrading / downgrading sklearn to a supported version, or try upgrading MLflow.\n", - "2024/06/27 15:44:02 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", + "2024/06/28 10:12:36 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of sklearn. If you encounter errors during autologging, try upgrading / downgrading sklearn to a supported version, or try upgrading MLflow.\n", + "2024/06/28 10:12:36 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", "c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", - "2024/06/27 15:44:02 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", - "2024/06/27 15:44:05 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of sklearn. If you encounter errors during autologging, try upgrading / downgrading sklearn to a supported version, or try upgrading MLflow.\n", - "2024/06/27 15:44:05 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", - "2024/06/27 15:44:07 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", - "2024/06/27 15:44:07 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [15:44:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0750514818a16474a-1\\xgboost\\xgboost-ci-windows\\src\\c_api\\c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\"\n", - "2024/06/27 15:44:10 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n" + "2024/06/28 10:12:36 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", + "2024/06/28 10:12:39 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of sklearn. If you encounter errors during autologging, try upgrading / downgrading sklearn to a supported version, or try upgrading MLflow.\n", + "2024/06/28 10:12:39 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", + "2024/06/28 10:12:40 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", + "2024/06/28 10:12:40 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [10:12:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0750514818a16474a-1\\xgboost\\xgboost-ci-windows\\src\\c_api\\c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\"\n", + "2024/06/28 10:12:43 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\mlflow\\types\\utils.py:406: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n" ] } ], @@ -223,69 +225,2658 @@ " preds_train = model.predict(X_train)\n", "\n", " # Log the train metric\n", - " accuracy_train, recall_train, auc_train, cnf_matr_train = eval_metrics(y_train,preds_train)\n", + " accuracy_train, recall_train, precision_train, cnf_matr_train = eval_metrics(y_train,preds_train)\n", " mlflow.log_metric(\"accuracy_train\", accuracy_train)\n", " mlflow.log_metric(\"recall_train\", recall_train)\n", - " mlflow.log_metric(\"auc_train\", auc_train)\n", + " mlflow.log_metric(\"precision_train\", precision_train)\n", "\n", " fig, ax = plt.subplots()\n", "\n", " sns.heatmap(cnf_matr_train, annot=True)\n", - " ax.set_title(\"Feature confusion Matrix Test Set\", fontsize=14)\n", + " ax.set_title(\"Normalized confusion Matrix Train Set\", fontsize=14)\n", + " plt.xlabel('Predicted Label')\n", + " plt.ylabel('True label')\n", " plt.tight_layout()\n", " plt.close(fig)\n", "\n", - " mlflow.log_figure(fig, \"confusion_matrix_train.png\")\n", + " mlflow.log_figure(fig, \"normalized_confusion_matrix_train.png\")\n", " \n", " log_model(disable=False)\n", " # Make some prediction on the test set\n", " preds_test = model.predict(X_test)\n", "\n", " # Log the tests metric\n", - " accuracy_test, recall_test, auc_test, cnf_matr_test = eval_metrics(y_test,preds_test)\n", + " accuracy_test, recall_test, precision_test, cnf_matr_test = eval_metrics(y_test,preds_test)\n", " mlflow.log_metric(\"accuracy_test\", accuracy_test)\n", " mlflow.log_metric(\"recall_test\", recall_test)\n", - " mlflow.log_metric(\"auc_test\", auc_test)\n", - "\n", + " mlflow.log_metric(\"precision_test\", precision_test)\n", " fig, ax = plt.subplots()\n", "\n", " sns.heatmap(cnf_matr_test, annot=True)\n", - " ax.set_title(\"Feature confusion Matrix Test Set\", fontsize=14)\n", + " ax.set_title(\"Normalized confusion Matrix Test Set\", fontsize=14)\n", + " plt.xlabel('Predicted Label')\n", + " plt.ylabel('True label')\n", " plt.tight_layout()\n", " plt.close(fig)\n", "\n", - " mlflow.log_figure(fig, \"confusion_matrix_test.png\")\n", + " mlflow.log_figure(fig, \"normalized_confusion_matrix_test.png\")\n", "\n", " # Set a tag that we can use to remind ourselves what this run was for\n", " mlflow.set_tag(\"Training Info\", f\"{name} model training for {type_of_dataset} titanic dataset\")\n", "\n", - " # mlflow.set_tag(\"mlflow.runName\", f\"{name}\")" + " mlflow.set_tag(\"mlflow.runName\", f\"{name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference with the chosen model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the model" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"XGBoost\" # To be determined\n", + "model_version = \"1\" # Also to be determined\n", + "\n", + "# Load saved model and make predictions\n", + "model_uri = f\"models:/{model_name}/{model_version}\"\n", + "loaded_model = mlflow.pyfunc.load_model(model_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Your survival test" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 0-1: truncated \\uXXXX escape (822299344.py, line 7)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m Cell \u001b[1;32mIn[10], line 7\u001b[1;36m\u001b[0m\n\u001b[1;33m inference_dataset = os.path.join(gen_dirname,f\"data\\{type_of_dataset}\\unlabelled.csv\")\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 0-1: truncated \\uXXXX escape\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "You died\n" ] } ], "source": [ - "model_name = \"XGBoost\"\n", - "model_version = \"1\"\n", - "# Load saved model and make predictions\n", - "model_uri = f\"models:/{model_name}/{model_version}\"\n", - "loaded_model = mlflow.pyfunc.load_model(model_uri)\n", + "pclass = 3\n", + "sex = 0 # Reminder 0 for Male 1 for Female\n", + "age = 10\n", + "sibsp = 2\n", + "parch = 2\n", + "fare = 25.5467\n", "\n", + "one_person_data = pd.DataFrame([[pclass,sex,float(age),sibsp,parch,fare]],columns=[\"Pclass\",\"Sex\",\"Age\",\"SibSp\",\"Parch\",\"Fare\"])\n", + "predict_one_person = loaded_model.predict(one_person_data)\n", + "\n", + "if predict_one_person == 0:\n", + " print(\"You died\")\n", + "else:\n", + " print(\"Still standing\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing inference on the whole dataset that was unlabelled " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ "inference_dataset = os.path.join(gen_dirname,f\"data\\\\{type_of_dataset}\\\\unlabelled.csv\")\n", "\n", - "unllabeled_data = pd.read_csv(inference_dataset)" + "unllabeled_data = pd.read_csv(inference_dataset)\n", + "predictions = loaded_model.predict(unllabeled_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Pclass': {0: 3,\n", + " 1: 3,\n", + " 2: 2,\n", + " 3: 3,\n", + " 4: 3,\n", + " 5: 3,\n", + " 6: 3,\n", + " 7: 2,\n", + " 8: 3,\n", + " 9: 3,\n", + " 10: 3,\n", + " 11: 1,\n", + " 12: 1,\n", + " 13: 2,\n", + " 14: 1,\n", + " 15: 2,\n", + " 16: 2,\n", + " 17: 3,\n", + " 18: 3,\n", + " 19: 3,\n", + " 20: 1,\n", + " 21: 3,\n", + " 22: 1,\n", + " 23: 1,\n", + " 24: 1,\n", + " 25: 3,\n", + " 26: 1,\n", + " 27: 3,\n", + " 28: 1,\n", + " 29: 3,\n", + " 30: 2,\n", + " 31: 2,\n", + " 32: 3,\n", + " 33: 3,\n", + " 34: 1,\n", + " 35: 3,\n", + " 36: 3,\n", + " 37: 3,\n", + " 38: 3,\n", + " 39: 3,\n", + " 40: 3,\n", + " 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415: 7.25,\n", + " 416: 8.05,\n", + " 417: 22.3583}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_data = unllabeled_data\n", + "predicted_data[\"Survived\"] = predictions" ] } ],