{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pip install librosa\n", "%pip install xgboost\n", "%pip install pandas\n", "%pip install pycaret\n", "%pip install fastapi\n", "%pip install uvicorn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "import string\n", "import random\n", "from pycaret.classification import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id882
1Targetlabel
2Target TypeBinary
3Label Encoded0.0: 0, 1.0: 1
4Original Data(64800, 11)
5Missing ValuesFalse
6Numeric Features8
7Categorical Features2
8Ordinal FeaturesFalse
9High Cardinality FeaturesFalse
10High Cardinality MethodNone
11Transformed Train Set(45359, 19)
12Transformed Test Set(19441, 19)
13Shuffle Train-TestTrue
14Stratify Train-TestFalse
15Fold GeneratorStratifiedKFold
16Fold Number10
17CPU Jobs-1
18Use GPUFalse
19Log ExperimentFalse
20Experiment Nameclf-default-name
21USI17b7
22Imputation Typesimple
23Iterative Imputation IterationNone
24Numeric Imputermean
25Iterative Imputation Numeric ModelNone
26Categorical Imputerconstant
27Iterative Imputation Categorical ModelNone
28Unknown Categoricals Handlingleast_frequent
29NormalizeFalse
30Normalize MethodNone
31TransformationFalse
32Transformation MethodNone
33PCAFalse
34PCA MethodNone
35PCA ComponentsNone
36Ignore Low VarianceFalse
37Combine Rare LevelsFalse
38Rare Level ThresholdNone
39Numeric BinningFalse
40Remove OutliersFalse
41Outliers ThresholdNone
42Remove MulticollinearityFalse
43Multicollinearity ThresholdNone
44Remove Perfect CollinearityTrue
45ClusteringFalse
46Clustering IterationNone
47Polynomial FeaturesFalse
48Polynomial DegreeNone
49Trignometry FeaturesFalse
50Polynomial ThresholdNone
51Group FeaturesFalse
52Feature SelectionFalse
53Feature Selection Methodclassic
54Features Selection ThresholdNone
55Feature InteractionFalse
56Feature RatioFalse
57Interaction ThresholdNone
58Fix ImbalanceFalse
59Fix Imbalance MethodSMOTE
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# data = pd.read_csv('data/data_feature_eng.csv')\n", "data = pd.read_csv('data/feature_ori.csv')\n", "# print(data)\n", "m_setup = setup(data=data, target='label', normalize=False, \n", " feature_interaction=False,\n", " feature_ratio=False,\n", " trigonometry_features=False,\n", " use_gpu=False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transformation Pipeline and Model Successfully Loaded\n" ] } ], "source": [ "# plot_model(tuned_dt, plot='auc')\n", "# tuned_xgboost_0207, tuned_xgboost_orifeature_0323, tuned_xgboost_orifeature_0407, tuned_xgboost_239_0408\n", "mdl = load_model('tuned_xgboost_239_0408')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# plot_model(mdl, plot='auc')\n", "# evaluate_model(mdl)\n", "new_prediction = predict_model(mdl, data=data)\n", "from pycaret.utils import check_metric\n", "check_metric(new_prediction['label'], new_prediction['label'], metric = 'Accuracy')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "API sucessfully created. This function only creates a POST API, it doesn't run it automatically.\n", "\n", "To run your API, please run this command --> !python my_api.py\n", " \n" ] } ], "source": [ "create_api(mdl, 'my_api')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%python my_api.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "create_docker('my_api')\n", "# !docker image build -f \"Dockerfile\" -t my_api:latest ." ] } ], "metadata": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" }, "kernelspec": { "display_name": "Python 3.8.10 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }