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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'c:\\\\Users\\\\nikhil\\\\OneDrive\\\\Desktop\\\\ML Projects\\\\ipp\\\\research'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"os.chdir(\"../\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'c:\\\\Users\\\\nikhil\\\\OneDrive\\\\Desktop\\\\ML Projects\\\\ipp'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"@dataclass(frozen=True)\n",
"class ModelPredictionConfig:\n",
" root_dir : Path\n",
" preprocessor_obj_file_path : Path\n",
" model_path : Path"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from insurancePP.logging import logger\n",
"from insurancePP.utils.common import read_yaml, create_directories, load_object\n",
"from insurancePP.constants import *"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class ConfigurationManager:\n",
" def __init__(self, config_filepath = CONFIG_FILE_PATH, params_filepath = PARAMS_FILE_PATH):\n",
" self.config = read_yaml(config_filepath)\n",
" self.params = read_yaml(params_filepath)\n",
" create_directories([self.config.artifacts_root])\n",
"\n",
" def get_model_prediction_config(self) -> ModelPredictionConfig:\n",
" config = self.config.model_prediction\n",
" create_directories([config.root_dir])\n",
"\n",
" return ModelPredictionConfig(\n",
" root_dir = config.root_dir,\n",
" preprocessor_obj_file_path = config.preprocessor_obj_file_path,\n",
" model_path = config.model_path\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class ModelPrediction:\n",
" def __init__(self, config : ModelPredictionConfig):\n",
" self.config = config\n",
" \n",
" def predict_sample_data(self, data):\n",
" preprocessor = load_object(self.config.preprocessor_obj_file_path)\n",
" model = load_object(self.config.model_path)\n",
"\n",
" logger.info(\"Both preprocessor and model are loaded successfully from disk\")\n",
" return model.predict(preprocessor.transform(data))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"sample = pd.DataFrame({'age':[19], 'sex':['female'], 'bmi':[27.9], 'children':[0], 'smoker':['yes'], 'region':['southwest']})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age int64\n",
"sex object\n",
"bmi float64\n",
"children int64\n",
"smoker object\n",
"region object\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-02-27 20:16:02,081 : INFO : common : yaml file: config\\config.yaml loaded successfully]\n",
"[2024-02-27 20:16:02,086 : INFO : common : yaml file: params.yaml loaded successfully]\n",
"[2024-02-27 20:16:02,089 : INFO : common : directory artifacts created]\n",
"[2024-02-27 20:16:02,092 : INFO : common : directory artifacts/model_prediction created]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-02-27 20:16:03,269 : INFO : 82938115 : Both preprocessor and model are loaded successfully from disk]\n",
"[18381.42742799]\n"
]
}
],
"source": [
"try:\n",
" config = ConfigurationManager()\n",
" model_prediction_config = config.get_model_prediction_config()\n",
" model_prediction = ModelPrediction(config = model_prediction_config)\n",
" result = model_prediction.predict_sample_data(sample)\n",
" print(result)\n",
" \n",
"except Exception as e:\n",
" raise e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# temp comment"
]
}
],
"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",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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