diff --git a/Sensor Fault Detection/README.md b/Sensor Fault Detection/README.md new file mode 100644 index 000000000..253d334dd --- /dev/null +++ b/Sensor Fault Detection/README.md @@ -0,0 +1,150 @@ + +# Sensor Fault Detection + +This repository contains the source code for the Sensor Fault Detection system. + +## Table of Contents + +- [Introduction](#introduction) +- [Features](#features) +- [App View](#app) +- [Dataset](#Dataset) +- [Visualization](#visulazation) +- [Data Preprocessing](#dataPreprocessing) +- [Installation](#installation) +- [Usage](#usage) +- [API Endpoints](#api-endpoints) +- [Contributing](#contributing) +- [License](#license) + +## Introduction + +The Sensor Fault Detection system is designed to monitor sensors and detect any faults. It uses advanced algorithms to ensure the accuracy and reliability of sensor data. + +## Features + +- Real-time sensor fault detection +- RESTful API for integrating with other systems +- Easy to deploy with Docker + +## View the project + +### video Demonstration + + +### images +Your Image +Your Image +Your Image +Your Image + +## Dataset + - artifacts/data.csv + [link](https://github.com/daemonX10/Sensor-Fault-Detection/blob/main/artifacts/data.csv) + +## Visualization +Your Image +cluster + - more in NoteBooks + +## Data Preprocessing +Before training the machine learning models, the dataset undergoes preprocessing to prepare it for analysis. Here's an overview of the preprocessing steps applied: + +### Label Encoding and One-Hot Encoding +Categorical variables like 'Gender' are encoded using Label Encoding, converting them into numerical values. Additionally, to handle categorical variables with more than two categories, One-Hot Encoding is employed. This technique creates binary columns for each category, improving the model's ability to interpret categorical data. + +### Feature Scaling +Feature scaling is crucial for machine learning algorithms, especially those based on distance calculations or gradient descent optimization. Two common scaling techniques are applied: + +#### Min-Max Scaling +Min-Max scaling rescales features to a range between 0 and 1, preserving the distribution's shape while bringing all features to the same scale. This scaling is applied to features like 'Tenure' and 'NumOfProducts'. + +#### Standard Scaling (Z-score Normalization) +Standard scaling transforms features to have a mean of 0 and a standard deviation of 1. It's particularly useful when features have different units or scales. Features such as 'CreditScore', 'Age', and 'Balance' are standardized using this technique. + +### Final Preprocessed Dataset +After preprocessing, the dataset is ready for model training. Categorical variables are encoded, and numerical features are scaled to ensure all features contribute equally to model training. The preprocessed dataset is now suitable for feeding into machine learning algorithms for churn prediction. + +## Model +The model employs several algorithms commonly used in binary classification tasks. Here's a brief overview of the algorithms utilized and their rationale: + +### Logistic Regression +Logistic Regression is a simple yet powerful algorithm for binary classification. It's chosen for its interpretability, ease of implementation, and ability to provide probabilities for predictions. Despite its simplicity, Logistic Regression can capture linear relationships between features and the target variable. + +### Random Forest +Random Forest is an ensemble learning technique that constructs multiple decision trees during training and outputs the mode of the classes (classification) or the mean prediction (regression) of the individual trees. It's known for its robustness to overfitting, handling of high-dimensional datasets, and feature importance estimation. + +### Decision Tree +Decision Tree is a non-parametric supervised learning method used for classification and regression. It works by recursively partitioning the input space into regions based on feature values, with the goal of maximizing the homogeneity of the target variable within each region. Decision Trees are easy to interpret and visualize, making them valuable for understanding feature importance. + +### Gradient Boosting +Gradient Boosting is an ensemble learning technique that builds models sequentially, with each new model correcting errors made by the previous ones. It combines weak learners (usually decision trees) to create a strong learner. Gradient Boosting is effective in capturing complex relationships in the data and has been successful in various machine learning competitions. + +### AdaBoost +AdaBoost (Adaptive Boosting) is another ensemble learning method that combines multiple weak learners to form a strong classifier. It assigns higher weights to misclassified data points in each iteration, allowing subsequent weak learners to focus on the difficult examples. AdaBoost is particularly useful when dealing with imbalanced datasets and noisy data. + +These algorithms are chosen for their complementary strengths in handling different aspects of the dataset and their proven effectiveness in classification tasks. By employing a diverse set of algorithms, we aim to build a robust model capable of accurately predicting customer churn and providing valuable insights for retention strategies. +### XGBoost + +XGBoost (Extreme Gradient Boosting) is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way. + +## Installation + +1. **Clone the Repository:** + ```sh + git clone https://github.com/daemonX10/Sensor-Fault-Detection.git + cd Sensor-Fault-Detection + ``` + +2. **Install Dependencies:** + ```sh + pip install -r requirements.txt + ``` + +3. **Run the Application:** + ```sh + python app.py + ``` + +## Usage + +The application provides a RESTful API to interact with the sensor fault detection system. Once the application is running, you can access the API endpoints at `http://127.0.0.1:5000`. + +## API Endpoints + +### Get System Status + +- **URL:** `/status` +- **Method:** `GET` +- **Description:** Returns the status of the sensor fault detection system. +- **Response:** + ```json + { + "status": { + "system": "operational", + "faults_detected": false + } + } + ``` + +### Credit : Damodar Yadav (damodarryadav@gmail.com) @daemonX10 + +## Contributing + +Contributions are welcome! Please follow these steps to contribute: + +1. Fork the repository +2. Create a new branch (`git checkout -b feature-branch`) +3. Make your changes +4. Commit your changes (`git commit -am 'Add new feature'`) +5. Push to the branch (`git push origin feature-branch`) +6. Create a new Pull Request + +## License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + + diff --git a/Sensor Fault Detection/dataset/data.csv b/Sensor Fault Detection/dataset/data.csv new file mode 100644 index 000000000..0e260d8ce --- /dev/null +++ b/Sensor Fault Detection/dataset/data.csv @@ -0,0 +1,101 @@ +Unnamed: 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diff --git a/Sensor Fault Detection/dataset/test.csv b/Sensor Fault Detection/dataset/test.csv new file mode 100644 index 000000000..bea35e097 --- /dev/null +++ b/Sensor Fault Detection/dataset/test.csv @@ -0,0 +1,21 @@ +Unnamed: 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diff --git a/Sensor Fault Detection/dataset/train.csv b/Sensor Fault Detection/dataset/train.csv new file mode 100644 index 000000000..a9b849fc6 --- /dev/null +++ b/Sensor Fault Detection/dataset/train.csv @@ -0,0 +1,81 @@ +Unnamed: 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Binary files /dev/null and b/Sensor Fault Detection/model/preprocessor.pkl differ diff --git a/Sensor Fault Detection/notebooks/EDA and MODEL.ipynb b/Sensor Fault Detection/notebooks/EDA and MODEL.ipynb new file mode 100644 index 000000000..f8654fd3f --- /dev/null +++ b/Sensor Fault Detection/notebooks/EDA and MODEL.ipynb @@ -0,0 +1,3600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wafer fault Prediction\n", + "**Brief:** In electronics, a **wafer** (also called a slice or substrate) is a thin slice of semiconductor, such as a crystalline silicon (c-Si), used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The wafer serves as the substrate(serves as foundation for contruction of other components) for microelectronic devices built in and upon the wafer. \n", + "\n", + "It undergoes many microfabrication processes, such as doping, ion implantation, etching, thin-film deposition of various materials, and photolithographic patterning. Finally, the individual microcircuits are separated by wafer dicing and packaged as an integrated circuit.\n", + "## Problem Statement\n", + "\n", + "**Data:** Wafers data\n", + "\n", + "\n", + "**Problem Statement:** Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors. Wafers are fundamental of photovoltaic power generation, and production thereof requires high technology. Photovoltaic power generation system converts sunlight energy directly to electrical energy.\n", + "\n", + "The motto behind figuring out the faulty wafers is to obliterate the need of having manual man-power doing the same. And make no mistake when we're saying this, even when they suspect a certain wafer to be faulty, they had to open the wafer from the scratch and deal with the issue, and by doing so all the wafers in the vicinity had to be stopped disrupting the whole process and stuff anf this is when that certain wafer was indeed faulty, however, when their suspicion came outta be false negative, then we can only imagine the waste of time, man-power and ofcourse, cost incurred.\n", + "\n", + "**Solution:** Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of the features store dataset (100, 592)\n" + ] + }, + { + "data": { + "text/html": [ + "
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mean3019.0482282494.0588752202.7589881519.4670711.201382100.096.8811600.1221581.4600870.000946...63.7850930.4994930.0139050.0035912.7851030.0235430.0141550.00443972.116730-0.850000
std72.66537268.16689831.633772471.9621040.3658700.05.5201080.0020620.0721590.010865...30.7227900.0036830.0045770.0009080.9230640.0116680.0072630.00232450.7703940.530106
min2889.6700002254.9900002114.666700978.7832000.753100100.083.4233000.1160001.317900-0.026500...20.3091000.4925000.0076000.0021001.5152000.0099000.0048000.00170020.309100-1.000000
25%2975.4250002452.5175002189.9667001111.5436000.850075100.093.5472500.1208001.409950-0.005750...46.9614000.4971750.0115000.0031002.2983500.0138500.0090000.00265033.654025-1.000000
50%3004.3900002502.4450002200.9556001308.6479001.164250100.099.2178000.1221501.4519000.001500...54.4761000.4994500.0129500.0034002.5771500.0218000.0131000.00380059.082500-1.000000
75%3065.7300002532.7550002212.8667001997.6416001.383000100.0101.1333000.1233751.4986750.009000...80.7403000.5017000.0149500.0039252.9923000.0281000.0184000.00575093.190025-1.000000
max3221.2100002664.5200002315.2667002363.6412002.207300100.0103.0911000.1262001.6411000.025000...151.1930000.5087000.0437000.0089008.8160000.0545000.0334000.012000223.1018001.000000
\n", + "

8 rows × 591 columns

\n", + "
" + ], + "text/plain": [ + " Sensor-1 Sensor-2 Sensor-3 Sensor-4 Sensor-5 \\\n", + "count 79.000000 80.000000 78.000000 78.000000 78.000000 \n", + "mean 3019.048228 2494.058875 2202.758988 1519.467071 1.201382 \n", + "std 72.665372 68.166898 31.633772 471.962104 0.365870 \n", + "min 2889.670000 2254.990000 2114.666700 978.783200 0.753100 \n", + "25% 2975.425000 2452.517500 2189.966700 1111.543600 0.850075 \n", + "50% 3004.390000 2502.445000 2200.955600 1308.647900 1.164250 \n", + "75% 3065.730000 2532.755000 2212.866700 1997.641600 1.383000 \n", + "max 3221.210000 2664.520000 2315.266700 2363.641200 2.207300 \n", + "\n", + " Sensor-6 Sensor-7 Sensor-8 Sensor-9 Sensor-10 ... Sensor-582 \\\n", + "count 78.0 78.000000 78.000000 80.000000 80.000000 ... 27.000000 \n", + "mean 100.0 96.881160 0.122158 1.460087 0.000946 ... 63.785093 \n", + "std 0.0 5.520108 0.002062 0.072159 0.010865 ... 30.722790 \n", + "min 100.0 83.423300 0.116000 1.317900 -0.026500 ... 20.309100 \n", + "25% 100.0 93.547250 0.120800 1.409950 -0.005750 ... 46.961400 \n", + "50% 100.0 99.217800 0.122150 1.451900 0.001500 ... 54.476100 \n", + "75% 100.0 101.133300 0.123375 1.498675 0.009000 ... 80.740300 \n", + "max 100.0 103.091100 0.126200 1.641100 0.025000 ... 151.193000 \n", + "\n", + " Sensor-583 Sensor-584 Sensor-585 Sensor-586 Sensor-587 Sensor-588 \\\n", + "count 80.000000 80.000000 80.000000 80.000000 80.000000 80.000000 \n", + "mean 0.499493 0.013905 0.003591 2.785103 0.023543 0.014155 \n", + "std 0.003683 0.004577 0.000908 0.923064 0.011668 0.007263 \n", + "min 0.492500 0.007600 0.002100 1.515200 0.009900 0.004800 \n", + "25% 0.497175 0.011500 0.003100 2.298350 0.013850 0.009000 \n", + "50% 0.499450 0.012950 0.003400 2.577150 0.021800 0.013100 \n", + "75% 0.501700 0.014950 0.003925 2.992300 0.028100 0.018400 \n", + "max 0.508700 0.043700 0.008900 8.816000 0.054500 0.033400 \n", + "\n", + " Sensor-589 Sensor-590 Good/Bad \n", + "count 80.000000 80.000000 80.000000 \n", + "mean 0.004439 72.116730 -0.850000 \n", + "std 0.002324 50.770394 0.530106 \n", + "min 0.001700 20.309100 -1.000000 \n", + "25% 0.002650 33.654025 -1.000000 \n", + "50% 0.003800 59.082500 -1.000000 \n", + "75% 0.005750 93.190025 -1.000000 \n", + "max 0.012000 223.101800 1.000000 \n", + "\n", + "[8 rows x 591 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight:\n", + "\n", + "From the gist of only shown columns, it looks like some of features have pretty bad outliers. One thing is for sure, the data must be standardized." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Good/Bad\n", + "-1 74\n", + " 1 6\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers[\"Good/Bad\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight:\n", + "\n", + "Heavily imbalanced. Definitely gonna need `resampling`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## # Analyze Missing Data:\n", + "\n", + "Firstly, we'll check the missing data in the target feature and drop those records. **As if we already know a value of target feature then there's no need for a ML algorithm, damn right?** Therefore, the best way to deal with missing target entries is to delete them. For other missing features, we can definitely use impute strategies." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55 False\n", + "88 False\n", + "26 False\n", + "42 False\n", + "69 False\n", + " ... \n", + "60 False\n", + "71 False\n", + "14 False\n", + "92 False\n", + "51 False\n", + "Name: Good/Bad, Length: 80, dtype: bool" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers['Good/Bad'].isna()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1822" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.isna().sum().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.038536379018612524" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Check missing vals in dependent feature variables\n", + "\n", + "wafers.isna().sum().sum() / (wafers.shape[0] * (wafers.shape[1] - 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**=>** Almost 4% out of total cells we're having, are missing.\n", + "\n", + "We're gonna try all sort of imputation strategies and would choose the one with that's gonna give us least overall-error-val." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## # Visualization of Sensors' distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 1)\n", + "(1, 2)\n", + "(2, 3)\n", + "(3, 4)\n", + "(4, 5)\n" + ] + } + ], + "source": [ + "mylist = [1,2,3,4,5]\n", + "for i in enumerate(mylist):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,100))\n", + "\n", + "for i , cols in enumerate(wafers.columns[1:51]):\n", + " # plt.The `subplot` function in Matplotlib is used to create subplots within a figure. It allows you to divide the figure into a grid of rows and columns and specify which subplot you want to plot on.\n", + " plt.subplot(60,3,i+1)\n", + " sns.histplot(x=wafers[cols],color='indianred',kde=True)\n", + " plt.xlabel(cols,weight='bold')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "## random 50 sensor\n", + "\n", + "random_50_sensor_idx = []\n", + "for i in range(50):\n", + " random_50_sensor_idx.append(np.random.randint(1, 591))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# plt.figure(figsize=(15,100))\n", + "\n", + "# for i , col in enumerate(wafers.columns[random_50_sensor_idx,]):\n", + "# plt.subplot(60,3,i+1)\n", + "# sns.histplot(x=wafers[col],color='indianred',kde=True)\n", + "# plt.xlabel(col,weight='bold')\n", + "# plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight:\n", + "\n", + "Pretty good amount of them (either first 50 or random 50) either are constant (have 0 standard deviation) or have left skewness and right skewness. It ain't possible to analyze each feature and deal with its outliers individually, thus we oughta depend upon the scaling.\n", + "\n", + "In the context of data analysis or machine learning, a feature with a standard deviation of zero can often be safely excluded from the analysis or model, because it doesn't contain any useful information. If a feature has the same value in every sample, it doesn't contribute anything to the model's ability to predict the target variable.\n", + "\n", + "For the **features with 0 standard deviation**, we can straight away drop them and for others that do have outliers, we gotta go ahead with the `Robust Scaling`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### # Get Columns to Drop:\n", + "\n", + "Will drop columns with zero standard deviation as they are not influencing the target variable in any way." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80, 592)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 0\n", + "Sensor-1 1\n", + "Sensor-2 0\n", + "Sensor-3 2\n", + "Sensor-4 2\n", + " ..\n", + "Sensor-587 0\n", + "Sensor-588 0\n", + "Sensor-589 0\n", + "Sensor-590 0\n", + "Good/Bad 0\n", + "Length: 592, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 0.00\n", + "Sensor-1 1.25\n", + "Sensor-2 0.00\n", + "Sensor-3 2.50\n", + "Sensor-4 2.50\n", + " ... \n", + "Sensor-587 0.00\n", + "Sensor-588 0.00\n", + "Sensor-589 0.00\n", + "Sensor-590 0.00\n", + "Good/Bad 0.00\n", + "Length: 592, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This code snippet is calculating the percentage of missing values in each column of the `wafers` DataFrame.\n", + "wafers.isna().sum().div(wafers.shape[0]).mul(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 False\n", + "Sensor-1 False\n", + "Sensor-2 False\n", + "Sensor-3 False\n", + "Sensor-4 False\n", + " ... \n", + "Sensor-587 False\n", + "Sensor-588 False\n", + "Sensor-589 False\n", + "Sensor-590 False\n", + "Good/Bad False\n", + "Length: 592, dtype: bool" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers.isna().sum().div(wafers.shape[0])>0.7\n", + "#.div(wafers.shape[0]): This is dividing the count of missing values in each column by the total number of rows (wafers.shape[0]). The result is a Series where each value represents the proportion of missing values in each column." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 80 entries, 55 to 51\n", + "Columns: 592 entries, Unnamed: 0 to Good/Bad\n", + "dtypes: float64(494), int64(97), object(1)\n", + "memory usage: 370.6+ KB\n" + ] + } + ], + "source": [ + "wafers.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cols_with_zero_std_dev(df:pd.DataFrame):\n", + " cols_to_drop = []\n", + " num_cols = df.select_dtypes(include=np.number).columns\n", + " for col in num_cols:\n", + " if df[col].std() == 0:\n", + " cols_to_drop.append(col)\n", + " return cols_to_drop\n", + "\n", + "def get_redundant_cols(df:pd.DataFrame,missing_thresh=0.7):\n", + " cols_missing_ratios = df.isna().sum().div(df.shape[0])\n", + " col_to_drop = list(cols_missing_ratios[cols_missing_ratios > missing_thresh].index)\n", + " return col_to_drop\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns to drop due to missing values: ['Sensor-158', 'Sensor-159', 'Sensor-293', 'Sensor-294']\n", + "Columns to drop due to zero standard deviation: ['Sensor-6', 'Sensor-14', 'Sensor-43', 'Sensor-50', 'Sensor-53', 'Sensor-70', 'Sensor-75', 'Sensor-98', 'Sensor-142', 'Sensor-150', 'Sensor-179', 'Sensor-180', 'Sensor-187', 'Sensor-190', 'Sensor-191', 'Sensor-192', 'Sensor-193', 'Sensor-194', 'Sensor-195', 'Sensor-207', 'Sensor-210', 'Sensor-227', 'Sensor-230', 'Sensor-231', 'Sensor-232', 'Sensor-233', 'Sensor-234', 'Sensor-235', 'Sensor-236', 'Sensor-237', 'Sensor-238', 'Sensor-241', 'Sensor-242', 'Sensor-243', 'Sensor-244', 'Sensor-257', 'Sensor-258', 'Sensor-259', 'Sensor-260', 'Sensor-261', 'Sensor-262', 'Sensor-263', 'Sensor-264', 'Sensor-265', 'Sensor-266', 'Sensor-267', 'Sensor-277', 'Sensor-285', 'Sensor-314', 'Sensor-315', 'Sensor-316', 'Sensor-323', 'Sensor-326', 'Sensor-327', 'Sensor-328', 'Sensor-329', 'Sensor-330', 'Sensor-331', 'Sensor-343', 'Sensor-348', 'Sensor-365', 'Sensor-370', 'Sensor-371', 'Sensor-372', 'Sensor-373', 'Sensor-374', 'Sensor-375', 'Sensor-376', 'Sensor-379', 'Sensor-380', 'Sensor-381', 'Sensor-382', 'Sensor-395', 'Sensor-396', 'Sensor-397', 'Sensor-398', 'Sensor-399', 'Sensor-400', 'Sensor-401', 'Sensor-402', 'Sensor-403', 'Sensor-404', 'Sensor-405', 'Sensor-415', 'Sensor-423', 'Sensor-450', 'Sensor-451', 'Sensor-452', 'Sensor-459', 'Sensor-462', 'Sensor-463', 'Sensor-464', 'Sensor-465', 'Sensor-466', 'Sensor-467', 'Sensor-479', 'Sensor-482', 'Sensor-499', 'Sensor-502', 'Sensor-503', 'Sensor-504', 'Sensor-505', 'Sensor-506', 'Sensor-507', 'Sensor-508', 'Sensor-509', 'Sensor-510', 'Sensor-513', 'Sensor-514', 'Sensor-515', 'Sensor-516', 'Sensor-529', 'Sensor-530', 'Sensor-531', 'Sensor-532', 'Sensor-533', 'Sensor-534', 'Sensor-535', 'Sensor-536', 'Sensor-537', 'Sensor-538', 'Sensor-539']\n" + ] + } + ], + "source": [ + "cols_to_drop_1 = get_redundant_cols(wafers)\n", + "cols_to_drop_2 = get_cols_with_zero_std_dev(wafers)\n", + "print(\"Columns to drop due to missing values: \",cols_to_drop_1)\n", + "print(\"Columns to drop due to zero standard deviation: \",cols_to_drop_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total columns to drop: 126\n" + ] + } + ], + "source": [ + "cols_to_drop = list(set(cols_to_drop_1 + cols_to_drop_2))\n", + "print(\"Total columns to drop: \",len(cols_to_drop))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "X,y = wafers.drop(columns=cols_to_drop),wafers[['Good/Bad']]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..................................................................
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" + ], + "text/plain": [ + " Good/Bad\n", + "55 -1\n", + "88 -1\n", + "26 -1\n", + "42 -1\n", + "69 -1\n", + ".. ...\n", + "60 -1\n", + "71 -1\n", + "14 -1\n", + "92 -1\n", + "51 -1\n", + "\n", + "[80 rows x 1 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of the features store dataset (80, 466)\n", + "shape of the target store dataset (80, 1)\n" + ] + } + ], + "source": [ + "print(\"shape of the features store dataset\", X.shape)\n", + "print(\"shape of the target store dataset\", y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('imputer', KNNImputer(n_neighbors=3)),\n",
+       "                ('scaler', RobustScaler()),\n",
+       "                ('simple_imputer', SimpleImputer(strategy='median'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('imputer', KNNImputer(n_neighbors=3)),\n", + " ('scaler', RobustScaler()),\n", + " ('simple_imputer', SimpleImputer(strategy='median'))])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.impute import KNNImputer , SimpleImputer # KNNImputer is an imputer that replaces missing values using the k-Nearest Neighbors approach.\n", + "# SimpleImputer is an imputer that replaces missing values using the mean, median or most frequent value of the data.\n", + "from sklearn.preprocessing import RobustScaler # RobustScaler is used to scale the features to the median and quantiles.\n", + "\n", + "imputer = KNNImputer(n_neighbors=3)\n", + "preprocessing_pipeline = Pipeline(\n", + " steps=[\n", + " ('imputer',imputer),\n", + " ('scaler',RobustScaler()),\n", + " ('simple_imputer',SimpleImputer(strategy='median'))\n", + " ]\n", + ")\n", + "preprocessing_pipeline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NaN 0.4993 0.0151 \n", + "69 0.1226 1.4958 0.0004 ... 24.6547 0.4974 0.0171 \n", + "\n", + " Sensor-585 Sensor-586 Sensor-587 Sensor-588 Sensor-589 Sensor-590 \\\n", + "55 0.0033 2.2874 0.0133 0.0139 0.0038 104.3034 \n", + "88 0.0033 2.4860 0.0280 0.0078 0.0022 27.7601 \n", + "26 0.0045 3.5361 0.0286 0.0154 0.0056 53.8577 \n", + "42 0.0038 3.0214 0.0117 0.0262 0.0089 223.1018 \n", + "69 0.0040 3.4352 0.0218 0.0054 0.0020 24.6547 \n", + "\n", + " Good/Bad \n", + "55 -1 \n", + "88 -1 \n", + "26 -1 \n", + "42 -1 \n", + "69 -1 \n", + "\n", + "[5 rows x 466 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "X = X.drop(\"Unnamed: 0\",axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of transformed features set (80, 465)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-0.02781221, 0.37395233, -0.4289214 , ..., 0. ,\n", + " 0.75955556, 0. ],\n", + " [ 2.50431022, -1.38644649, -0.47986463, ..., -0.51612903,\n", + " -0.52610857, 0. ],\n", + " [-0.60204699, 0.28110298, -0.49685153, ..., 0.58064516,\n", + " -0.08775867, 0. ],\n", + " ...,\n", + " [-0.03223295, 0.21779093, 1.03590393, ..., 0.12903226,\n", + " -0.17805529, 0. ],\n", + " [ 0.03396281, 0.87459106, 0.53517467, ..., 0.29032258,\n", + " 0.28421459, 0. ],\n", + " [ 0.86164048, 0.3813055 , -0.59146288, ..., -0.16129032,\n", + " -0.20782888, 0. ]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Transform \"Wafers\" Feature\n", + "X_trans = preprocessing_pipeline.fit_transform(X)\n", + "print(\"shape of transformed features set\",X.shape)\n", + "X_trans" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## # Shall we cluster \"Wafers\" instances?\n", + "\n", + "Let's see whether clustering of data instances do us any good. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: kneed in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (0.8.5)\n", + "Requirement already satisfied: numpy>=1.14.2 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from kneed) (1.26.4)\n", + "Requirement already satisfied: scipy>=1.0.0 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from kneed) (1.13.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install kneed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Revision of K Means cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "### Revision the topic of elbow method , knee method and silhouette method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## # Shall we cluster \"Wafers\" instances?\n", + "\n", + "Let's see whether clustering of data instances do us any good. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.cluster import KMeans\n", + "from kneed import KneeLocator\n", + "from typing import Tuple\n", + "from dataclasses import dataclass\n", + "from sklearn.metrics import silhouette_score" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding ideal number of clusters for Wafers Features\n", + "computing WCSS for shortlisted number of clusters..\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " File \"d:\\DATA SCIENCE\\Data Science Project\\Senser Fault Detection\\.conda\\lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n", + " cpu_info = subprocess.run(\n", + " File \"d:\\DATA SCIENCE\\Data Science Project\\Senser Fault Detection\\.conda\\lib\\subprocess.py\", line 503, in run\n", + " with Popen(*popenargs, **kwargs) as process:\n", + " File \"d:\\DATA SCIENCE\\Data Science Project\\Senser Fault Detection\\.conda\\lib\\subprocess.py\", line 971, in __init__\n", + " self._execute_child(args, executable, preexec_fn, close_fds,\n", + " File \"d:\\DATA SCIENCE\\Data Science Project\\Senser Fault Detection\\.conda\\lib\\subprocess.py\", line 1456, in _execute_child\n", + " hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WCSS for 1 clusters :7157148.928067336\n", + "=====================================\n", + "Silhouette Score for 2 clusters :0.7796793447006414\n", + "WCSS for 2 clusters :2060306.1634277871\n", + "=====================================\n", + "Silhouette Score for 3 clusters :0.7946145493768249\n", + "WCSS for 3 clusters :1075227.4800905853\n", + "=====================================\n", + "Silhouette Score for 4 clusters :0.7751679339710064\n", + "WCSS for 4 clusters :530517.1820611967\n", + "=====================================\n", + "Silhouette Score for 5 clusters :0.7110065808632873\n", + "WCSS for 5 clusters :485669.8162062416\n", + "=====================================\n", + "Silhouette Score for 6 clusters :0.6239539439336065\n", + "WCSS for 6 clusters :437364.96477712144\n", + "=====================================\n", + "Silhouette Score for 7 clusters :0.6726377640712778\n", + "WCSS for 7 clusters :226364.9606360452\n", + "=====================================\n", + "Silhouette Score for 8 clusters :0.4104449470491113\n", + "WCSS for 8 clusters :190022.6930221619\n", + "=====================================\n", + "Silhouette Score for 9 clusters :0.42086155837552497\n", + "WCSS for 9 clusters :147066.9112426604\n", + "=====================================\n", + "Silhouette Score for 10 clusters :0.422585965147796\n", + "WCSS for 10 clusters :108703.08733901892\n", + "WCSS computed successfully for shortlisted number of clusters\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding the ideal number of clusters (by locating the dispersal point) via Elbow method..\n", + "Ideal number of clusters to be Formed : 3\n", + "Dividing the \"Wafers Features\" instances into 3 clusters via KMeans Clustering algorithm..\n", + "\"Wafers Features\" instances divided into 3 clusters successfully\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-0.02781221, 0.37395233, -0.4289214 , ..., 0.75955556,\n", + " 0. , 1. ],\n", + " [ 2.50431022, -1.38644649, -0.47986463, ..., -0.52610857,\n", + " 0. , 1. ],\n", + " [-0.60204699, 0.28110298, -0.49685153, ..., -0.08775867,\n", + " 0. , 1. ],\n", + " ...,\n", + " [-0.03223295, 0.21779093, 1.03590393, ..., -0.17805529,\n", + " 0. , 0. ],\n", + " [ 0.03396281, 0.87459106, 0.53517467, ..., 0.28421459,\n", + " 0. , 1. ],\n", + " [ 0.86164048, 0.3813055 , -0.59146288, ..., -0.20782888,\n", + " 0. , 1. ]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@dataclass\n", + "class ClusterDataInstances:\n", + " X : np.array\n", + " desc : str\n", + " \n", + " def _get_ideal_number_of_cluster(self):\n", + " \"\"\"Returns the ideal number of clusters the given data instances should be divided into by \n", + " locating the dispersal point in number of clusters vs WCSS plot.\n", + "\n", + " Raises:\n", + " e: Raises relevant exception should any kinda error pops up while determining the ideal\n", + " number of clusters.\n", + "\n", + " Returns:\n", + " int: Ideal number of clusters the given data instances should be divided into.\n", + " \"\"\"\n", + " try:\n", + " print(\"Finding ideal number of clusters for \",self.desc)\n", + " \n", + " ####################### Compute WCSS for shortlisted number of clusters ##########################\n", + " print(\"computing WCSS for shortlisted number of clusters..\")\n", + " silhouette_coeffients = []\n", + " wcss = []\n", + " for i in range(1, 11):\n", + " Kmeans=KMeans(n_clusters=i, init='k-means++', random_state=42)\n", + " Kmeans.fit(self.X)\n", + " if(i>1):\n", + " score = silhouette_score(self.X, Kmeans.labels_)\n", + " silhouette_coeffients.append(score)\n", + " print(\"=====================================\")\n", + " print(f\"Silhouette Score for {i} clusters :{score}\")\n", + " wcss.append(Kmeans.inertia_)\n", + " \n", + " print(f\"WCSS for {i} clusters :{Kmeans.inertia_}\")\n", + " \n", + " \n", + " print(\"WCSS computed successfully for shortlisted number of clusters\")\n", + " \n", + " plt.plot(range(1,11),wcss)\n", + " plt.xticks(range(1,11))\n", + " plt.xlabel(\"Number of Clusters\")\n", + " plt.ylabel(\"WCCS\")\n", + " plt.show()\n", + " \n", + " \n", + " ################### Finalize dispersal point as the ideal number of clusters #####################\n", + " print(\n", + " \"Finding the ideal number of clusters (by locating the dispersal point) via Elbow method..\")\n", + " \n", + " knee_finder = KneeLocator( range(1, 11), wcss, curve='convex', direction='decreasing')\n", + " print(f\"Ideal number of clusters to be Formed : {knee_finder.knee}\")\n", + " return knee_finder.knee\n", + " except Exception as e:\n", + " print(e)\n", + " raise e\n", + " \n", + " # The `create_cluster` method is expected to return a tuple\n", + " def create_cluster(self)->Tuple:\n", + " try:\n", + " ideal_cluster = self._get_ideal_number_of_cluster()\n", + " print(\n", + " f\"Dividing the \\\"{self.desc}\\\" instances into {ideal_cluster} clusters via KMeans Clustering algorithm..\")\n", + " kmeans = KMeans(n_clusters=ideal_cluster, init='k-means++', random_state=42)\n", + " y_kmeans = kmeans.fit_predict(self.X)\n", + " print(f\"\\\"{self.desc}\\\" instances divided into {ideal_cluster} clusters successfully\")\n", + " \n", + " return kmeans,np.c_[self.X,y_kmeans]\n", + " except Exception as e:\n", + " print(e)\n", + " raise e \n", + " \n", + "cluster_wafers = ClusterDataInstances(X=X_trans,desc=\"Wafers Features\")\n", + "cluster,X_clustered = cluster_wafers.create_cluster()\n", + "X_clustered\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 1., 2.])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(X_clustered[:,-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**=>** So the dataset was divided into 3 optimal clusters.\n", + "\n", + "Let's have a look at their shapes.." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(17, 467)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafers_clus = np.c_[X_clustered,y]\n", + "# This line of code is filtering the `wafers_clus` array to extract rows where the second-to-last column (index -2) has a value of 0. The result is stored in the variable `wafers_1`. This operation essentially selects a subset of data where the target feature (in this case, the second-to-last column) has a specific value (0 in this case).\n", + "wafers_1 = wafers_clus[wafers_clus[:,-2] == 0]\n", + "wafers_1.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(62, 467)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafer_2 = wafers_clus[wafers_clus[:,-2] == 1]\n", + "wafer_2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 467)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wafer_3 = wafers_clus[wafers_clus[:,-2] == 2]\n", + "wafer_3.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## # Resampling of Training Instances:\n", + "\n", + "Resampling is imperative in this case as the target variable is highly imbalanced." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: imbalanced-learn in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (0.12.2)\n", + "Requirement already satisfied: numpy>=1.17.3 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from imbalanced-learn) (1.26.4)\n", + "Requirement already satisfied: scipy>=1.5.0 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from imbalanced-learn) (1.13.0)\n", + "Requirement already satisfied: scikit-learn>=1.0.2 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from imbalanced-learn) (1.4.2)\n", + "Requirement already satisfied: joblib>=1.1.1 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from imbalanced-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from imbalanced-learn) (3.5.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install imbalanced-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80, 465)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_trans.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "from imblearn.combine import SMOTETomek\n", + "\n", + "X,y = X_trans,y\n", + "resampler = SMOTETomek(sampling_strategy=\"auto\")\n", + "X_res , y_res = resampler.fit_resample(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(148, 465)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_res.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(148, 1)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_res.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80, 1)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80, 465)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before resampling, Shape of training instances: (80, 466)\n", + "After resampling, Shape of training instances: (148, 466)\n" + ] + } + ], + "source": [ + "print(\"Before resampling, Shape of training instances: \", np.c_[X, y].shape)\n", + "print(\"After resampling, Shape of training instances: \", np.c_[X_res, y_res].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1 1]\n", + "Value Counts: \n", + "-1: 148, 1: 148\n" + ] + } + ], + "source": [ + "print(np.unique(y_res))\n", + "print(f\"Value Counts: \\n-1: {len(y_res[y_res == -1])}, 1: {len(y_res[y_res == 1])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train set: ((98, 465), (98, 1))\n", + "test set: ((50, 465), (50, 1))\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=1/3, random_state=42)\n", + "\n", + "print(f\"train set: {X_train.shape, y_train.shape}\")\n", + "print(f\"test set: {X_test.shape, y_test.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting xgboost==0.90\n", + " Downloading xgboost-0.90-py2.py3-none-win_amd64.whl.metadata (3.8 kB)\n", + "Requirement already satisfied: numpy in d:\\data science\\data science project\\senser fault detection\\.conda\\lib\\site-packages (from xgboost==0.90) (1.26.4)\n", + "Requirement already satisfied: scipy in d:\\data science\\data 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-------------------------------------- - 17.8/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.0/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.3/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.3/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.3/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.3/18.3 MB 4.7 MB/s eta 0:00:01\n", + " --------------------------------------- 18.3/18.3 MB 4.7 MB/s eta 0:00:01\n", + " ---------------------------------------- 18.3/18.3 MB 4.3 MB/s eta 0:00:00\n", + "Installing collected packages: xgboost\n", + " Attempting uninstall: xgboost\n", + " Found existing installation: xgboost 2.0.3\n", + " Uninstalling xgboost-2.0.3:\n", + " Successfully uninstalled xgboost-2.0.3\n", + "Successfully installed xgboost-0.90\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install xgboost==0.90" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(98, 465) (98, 1)\n", + "(50, 465) (50, 1)\n" + ] + } + ], + "source": [ + "X_prep = X_train\n", + "y_prep = y_train\n", + "X_test_prep = X_test\n", + "y_test_prep = y_test\n", + "\n", + "print(X_prep.shape, y_prep.shape)\n", + "print(X_test_prep.shape, y_test_prep.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.model_selection import cross_val_score, cross_val_predict\n", + "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "svc_clf = SVC(kernel='linear')\n", + "svc_rbf_clf = SVC(kernel='rbf')\n", + "random_clf = RandomForestClassifier(random_state=42)\n", + "xgb_clf = XGBClassifier(random_state=42,objective='binary:logistic')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def display_scores(scores):\n", + " print(\"Scores: \", scores)\n", + " print(\"Mean: \", scores.mean())\n", + " print(\"Standard deviation: \", scores.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "svc_scores = cross_val_score(svc_clf, X_prep, y_prep, cv=5, scoring='accuracy')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores: [0.95 1. 0.95 0.84210526 1. ]\n", + "Mean: 0.9484210526315788\n", + "Standard deviation: 0.05766941800342004\n" + ] + } + ], + "source": [ + "display_scores(svc_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9489795918367347" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score\n", + "svc_preds = cross_val_predict(svc_clf, X_prep, y_prep, cv=5)\n", + "svc_accuracy = roc_auc_score(y_prep, svc_preds)\n", + "svc_accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "svc_rbf_score = cross_val_score(svc_rbf_clf, X_prep, y_prep, cv=5, scoring='accuracy',verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores: [0.7 0.7 0.55 0.63157895 0.63157895]\n", + "Mean: 0.6426315789473683\n", + "Standard deviation: 0.0555107236776219\n" + ] + } + ], + "source": [ + "display_scores(svc_rbf_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6799999999999999" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Performance on test set using cross-validation\n", + "\n", + "# Predictions using cross-validation\n", + "svc_rbf_preds = cross_val_predict(svc_rbf_clf, X_test_prep, y_test_prep, cv=5)\n", + "\n", + "# AUC score\n", + "svc_rbf_auc = roc_auc_score(y_test_prep, svc_rbf_preds)\n", + "svc_rbf_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END .................................................... total time= 0.1s\n", + "[CV] END .................................................... total time= 0.1s\n", + "[CV] END .................................................... total time= 0.1s\n", + "[CV] END .................................................... total time= 0.1s\n", + "[CV] END .................................................... total time= 0.1s\n" + ] + } + ], + "source": [ + "## Random Forest Scores\n", + "\n", + "random_clf_scores = cross_val_score(random_clf, X_prep, y_prep, scoring='roc_auc', cv=5, verbose=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores: [1. 1. 1. 1. 1.]\n", + "Mean: 1.0\n", + "Standard deviation: 0.0\n" + ] + } + ], + "source": [ + "display_scores(random_clf_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Performance on test set using cross-validation\n", + "\n", + "# Predictions using cross-validation\n", + "random_clf_preds = cross_val_predict(random_clf, X_test_prep, y_test_prep, cv=5)\n", + "\n", + "# AUC score\n", + "random_clf_auc = roc_auc_score(y_test_prep, random_clf_preds)\n", + "random_clf_auc" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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 +} diff --git a/Sensor Fault Detection/web/.env b/Sensor Fault Detection/web/.env new file mode 100644 index 000000000..8cfc1bcbd --- /dev/null +++ b/Sensor Fault Detection/web/.env @@ -0,0 +1 @@ +MONGO_DB_URL = "" \ No newline at end of file diff --git a/Sensor Fault Detection/web/Readme.md b/Sensor Fault Detection/web/Readme.md new file mode 100644 index 000000000..0662bd5e8 --- /dev/null +++ b/Sensor Fault Detection/web/Readme.md @@ -0,0 +1,28 @@ +## Project Name/Title

Sensor Fault Detection

+ +### Goal 🎯 +The Sensor Fault Detection system is designed to monitor sensors and detect any faults. + +### Model(s) used for the Web App 🧮 + Best model : xgboost on this dataset + used: + "Random Forest": RandomForestClassifier(), + "Decision Tree": DecisionTreeClassifier(), + "AdaBoost": AdaBoostClassifier(algorithm='SAMME'), + "Gradient Boosting": GradientBoostingClassifier(), + `XGBoost: XGBClassifier()`, + "KNN": KNeighborsClassifier() + +### Video Demonstration 🎥 + + + +[Video](https://github.com/abhisheks008/ML-Crate/assets/115637298/7712a38e-94b3-4463-ba65-c34f3e3a484d) + + +### Signature ✒️ +## [Github](https://www.github.com/daemonX10) +## [linkedin](https://www.linkedin.com/in/damodar-yadav-690425177/) diff --git a/Sensor Fault Detection/web/app.py b/Sensor Fault Detection/web/app.py new file mode 100644 index 000000000..97e7b4e2b --- /dev/null +++ b/Sensor Fault Detection/web/app.py @@ -0,0 +1,54 @@ +from flask import Flask, render_template, jsonify, request, send_file, session +from src.exception import CustomException +from src.logger import logging as lg +import os,sys +from src.pipeline.train_pipeline import TrainPipeline +from src.pipeline.predict_pipeline import PredicationPipeline + +app = Flask(__name__) +app.secret_key = 'damodar' # replace 'your secret key' with your actual secret key + +@app.route("/") +def home(): + return render_template('home.html') + +@app.route("/train") +def train_route(): + try: + train_pipeline = TrainPipeline() + train_pipeline.run_pipeline() + + return "Training Completed" + except Exception as e: + raise CustomException(e,sys) + +@app.route('/upload',methods=['POST','GET']) +def upload(): + try: + if request.method == "POST": + predication_pipeline = PredicationPipeline(request) + predication_file_details = predication_pipeline.runpipeline() + + lg.info("predication completed . Downloading Predication File .") + + # Store the file path in the session + session['file_path'] = predication_file_details.predication_file_path + + return render_template('upload_file.html', prediction_text = 'Predicted Sensor : {}'.format(predication_file_details.predication_file_text)) + + else: + return render_template('upload_file.html') + except Exception as e: + raise CustomException(e,sys) + +@app.route('/get_file') +def get_file(): + # Retrieve the file path from the session + file_path = session.get('file_path', None) + if file_path is not None: + return send_file(file_path, as_attachment=True) + else: + return "No file available" + +if __name__ =="__main__": + app.run(host='0.0.0.0',port=5000,debug=True) \ No newline at end of file diff --git a/Sensor Fault Detection/web/artifacts/data.csv b/Sensor Fault Detection/web/artifacts/data.csv new file mode 100644 index 000000000..0e260d8ce --- /dev/null +++ b/Sensor Fault Detection/web/artifacts/data.csv @@ -0,0 +1,101 @@ 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diff --git a/Sensor Fault Detection/web/artifacts/model.pkl b/Sensor Fault Detection/web/artifacts/model.pkl new file mode 100644 index 000000000..2331fd93b Binary files /dev/null and b/Sensor Fault Detection/web/artifacts/model.pkl differ diff --git a/Sensor Fault Detection/web/artifacts/preprocessor.pkl b/Sensor Fault Detection/web/artifacts/preprocessor.pkl new file mode 100644 index 000000000..50fd41d50 Binary files /dev/null and b/Sensor Fault Detection/web/artifacts/preprocessor.pkl differ diff --git a/Sensor Fault Detection/web/artifacts/test.csv b/Sensor Fault Detection/web/artifacts/test.csv new file mode 100644 index 000000000..bea35e097 --- /dev/null +++ b/Sensor Fault Detection/web/artifacts/test.csv @@ -0,0 +1,21 @@ +Unnamed: 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diff --git a/Sensor Fault Detection/web/artifacts/train.csv b/Sensor Fault Detection/web/artifacts/train.csv new file mode 100644 index 000000000..a9b849fc6 --- /dev/null +++ b/Sensor Fault Detection/web/artifacts/train.csv @@ -0,0 +1,81 @@ +Unnamed: 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diff --git a/Sensor Fault Detection/web/database/__init__.py b/Sensor Fault Detection/web/database/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Sensor Fault Detection/web/database/upload.ipynb b/Sensor Fault Detection/web/database/upload.ipynb new file mode 100644 index 000000000..c3bf46b73 --- /dev/null +++ b/Sensor Fault Detection/web/database/upload.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "all files in the directory are: wafer_23012020_041211.csv\n", + "Uploading wafer_23012020_041211 to MongoDB\n", + "Uploaded wafer_23012020_041211 to MongoDB\n" + ] + } + ], + "source": [ + "from pymongo import MongoClient\n", + "import os , sys ,json\n", + "import pandas as pd \n", + "\n", + "mongo_client_conn_str= \"mongodb+srv://legend:yadav152530@mongo-practice-js.cfemuts.mongodb.net/?retryWrites=true&w=majority&appName=mongo-practice-js\"\n", + "database_name = \"sensor\"\n", + "\n", + "def upload_files_to_mongodb(\n", + " mongo_client_conn_str,\n", + " database_name,\n", + " datasets_dir_name ):\n", + " \n", + " client = MongoClient(mongo_client_conn_str)\n", + " \n", + " for file in os.listdir(datasets_dir_name):\n", + " print('all files in the directory are:',file)\n", + " if file.endswith('.csv'):\n", + " file_name = file.split('.')[0]\n", + " \n", + " print(f\"Uploading {file_name} to MongoDB\")\n", + " \n", + " db = client[database_name]\n", + " collection = db[file_name]\n", + " file_path = os.path.join(datasets_dir_name,file_name)\n", + " \n", + " data = pd.read_csv('../data/wafer_23012020_041211.csv')\n", + " json_data = data.to_json(orient='records')\n", + " dict_data = json.loads(json_data)\n", + " collection.insert_many(dict_data)\n", + " print(f\"Uploaded {file_name} to MongoDB\")\n", + " \n", + "upload_files_to_mongodb(\n", + " mongo_client_conn_str=mongo_client_conn_str,\n", + " database_name=database_name,\n", + " datasets_dir_name=\"./../data\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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 +} diff --git a/Sensor Fault Detection/web/database/upload.py b/Sensor Fault Detection/web/database/upload.py new file mode 100644 index 000000000..e69de29bb diff --git a/Sensor Fault Detection/web/predication_artifacts/testing sensor data.csv b/Sensor Fault Detection/web/predication_artifacts/testing sensor data.csv new file mode 100644 index 000000000..50131ecc0 --- /dev/null +++ b/Sensor Fault Detection/web/predication_artifacts/testing sensor data.csv @@ -0,0 +1,15 @@ 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diff --git a/Sensor Fault Detection/web/predications/predication_file.csv b/Sensor Fault Detection/web/predications/predication_file.csv new file mode 100644 index 000000000..ece024096 --- /dev/null +++ b/Sensor Fault Detection/web/predications/predication_file.csv @@ -0,0 +1,15 @@ 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sor-465,Sensor-466,Sensor-467,Sensor-468,Sensor-469,Sensor-470,Sensor-471,Sensor-472,Sensor-473,Sensor-474,Sensor-475,Sensor-476,Sensor-477,Sensor-478,Sensor-479,Sensor-480,Sensor-481,Sensor-482,Sensor-483,Sensor-484,Sensor-485,Sensor-486,Sensor-487,Sensor-488,Sensor-489,Sensor-490,Sensor-491,Sensor-492,Sensor-493,Sensor-494,Sensor-495,Sensor-496,Sensor-497,Sensor-498,Sensor-499,Sensor-500,Sensor-501,Sensor-502,Sensor-503,Sensor-504,Sensor-505,Sensor-506,Sensor-507,Sensor-508,Sensor-509,Sensor-510,Sensor-511,Sensor-512,Sensor-513,Sensor-514,Sensor-515,Sensor-516,Sensor-517,Sensor-518,Sensor-519,Sensor-520,Sensor-521,Sensor-522,Sensor-523,Sensor-524,Sensor-525,Sensor-526,Sensor-527,Sensor-528,Sensor-529,Sensor-530,Sensor-531,Sensor-532,Sensor-533,Sensor-534,Sensor-535,Sensor-536,Sensor-537,Sensor-538,Sensor-539,Sensor-540,Sensor-541,Sensor-542,Sensor-543,Sensor-544,Sensor-545,Sensor-546,Sensor-547,Sensor-548,Sensor-549,Sensor-550,Sensor-551,Sensor-552,Sensor-553,Sensor-554,Sensor-555,Sensor-556,Sensor-557,Sensor-558,Sensor-559,Sensor-560,Sensor-561,Sensor-562,Sensor-563,Sensor-564,Sensor-565,Sensor-566,Sensor-567,Sensor-568,Sensor-569,Sensor-570,Sensor-571,Sensor-572,Sensor-573,Sensor-574,Sensor-575,Sensor-576,Sensor-577,Sensor-578,Sensor-579,Sensor-580,Sensor-581,Sensor-582,Sensor-583,Sensor-584,Sensor-585,Sensor-586,Sensor-587,Sensor-588,Sensor-589,Sensor-590,Good/Bad +2968.33,2476.58,2216.7333,1748.0885,1.1127,100,97.5822,0.1242,1.53,-0.0279,-0.004,0.9468,198.1219,0,6.0959,416.595,9.5431,0.9686,188.5788,12.4349,1.4188,-5349.75,2663.5,-3855.5,10126.6667,1.265,1.986,7.3152,75.4333,1.4,0.1018,3.3376,83.6583,8.5596,50.3882,64.4017,49.6118,66.0405,86.7827,119.4242,79.71,2.849,70,358.1082,10.2528,128.6364,727.6867,1.3467,142.3127,1,629.0573,203.8858,0,4.549,4.786,2887.0,0.9215,0.9506,4.6004,2.4182,358.8636,10.5336,111.0545,14.0237,17.5818,24.3727,703.314,1.1051,145.1155,1,615.0336,129.6649,146.7674,468.2663,0,-0.0158,-0.0631,0.0448,-0.0053,-0.012,-0.0122,-0.0453,0.1055,8.3051,0.1448,0.1083,2.4044,0.9906,1816.1957,0.1569,8966.2599,-0.0468,0.0,-0.0002,0.0,0.0,-0.047,0,-0.0493,-0.0037,0.0002,0.0,-0.0156,-0.0105,0.0001,0.0,0.0002,-0.0164,-0.0644,0.9793,101.3766,232.4647,0.4601,0.9609,0,773.9219,0.9909,58.6677,0.6019,0.98,6.2643,15.79,4.817,15.84,15.8,1.475,2.76,0.7743,3.272,-0.4258,0.7217,0.9968,2.2989,998.6109,37.8933,112,173.7,187.2,66.5,1062.2041,0.2942,0,6.31,0.0042,0.1481,0.076,0.0329,0.0266,7.6634,0,4.901,7.409,0.3048,0.0134,7.4377,0.22,0.0397,,,461,375,1960,1711,0.119,0.089,0.22,1.7,2.6,0.164,0.344,0.5693,0.0988,0.2275,0.4025,0.2275,0.6599,0.3532,0.117,0,0,19.31,0.496,5.58,25.466,0.1801,2.29,0,18.48,55.026,0,0.0,0.0,0.0,0.0,0.0,0.267,4.48,18.99,0.377,5.05,22.17,4.48,7.592,31.051,0.0809,7.39,0,22.17,99.106,0,0.0797,0.1163,0.0592,0.0296,0.0916,0.0135,0.0374,0.0508,3.5914,0.0015,0.0104,0.0563,0.0008,156.333,0.0283,983.7998,0,0.0096,0.014,0,0,0,0,0,0,0,0,0,0.0045,0.0044,0,0,0,0,0.0028,1.0247,3.6149,0.0193,0.0192,0,100.1752,0.0008,2.3929,0.0362,0.0042,0.2092,0,0,0,0,0,0,0,0,0,0,0,0.0911,12.117,3.628,30.9697,50.1268,58.1409,21.3926,308.498,0.0985,0,1.9866,0.0013,0.0488,0.0228,0.0087,0.0089,2.5665,0,1.5831,2.2461,0.094,0.0043,2.5168,0.0584,0.0139,,,199.8506,176.9717,886.6192,880.8657,0.0519,0.0432,0.0926,0.7089,0.7842,0.047,0.1018,0.2215,0.0388,0.0922,0.1542,0.0921,0.2756,0.1422,0.0482,0,0,0,5.96,0.1322,1.6683,8.222,0.0561,0.9501,0,5.6734,15.8699,0,0.0,0.0,0.0,0.0,0.0,0.0871,1.3214,5.9317,0.1372,1.5617,8.6863,1.3214,2.1729,9.8719,0.0211,2.3748,0,7.4814,36.104,6.2091,2.7828,0,0.0236,0.0479,0.029,0.0146,0.0228,0.0056,0.0173,0.0214,1.1627,0.0005,0.0029,0.0174,0.0003,47.2458,0.008,328.5346,0,0.0028,0.0041,0.0018,0.0029,0,0,0,0,0,0,0,0.0014,0.0016,0,0,0,0,0.0009,0.3616,0.9081,0.0062,0.0067,0,30.6917,0.0002,0.6171,0.0103,0.0016,0.0744,0,0,0,0,0,0,0,0,0,0,0,0.0336,3.684,1.4148,5.1519,5.8518,7.5588,2.9999,60.7637,26.436,0,6.4663,3.3812,9.68,272.0358,821.2168,2.809,3.868,0,80.3982,1.7785,3.1943,1.384,3.9441,1.7692,2.7954,8.6172,14.0792,50.8365,16.896,9.4071,4.4814,3.0074,2.2536,185.7143,161.1354,10.3069,0.6805,1.1541,0.4515,0.625,0.4586,0.9992,0.407,0.098,0,0,0,5.3922,4.8377,4.3378,3.4996,13.3755,1.6091,0,2.9377,26.9886,0,0.0,0.0,0.0,0.0,0.0,5.8039,185.2632,5.2917,3.579,4.5473,158.09,25.4809,31.1496,4.415,7.3233,5.0925,0,3.6047,76.4324,0,503.6335,184.3837,132.1429,561.1374,764.9269,110.4294,82.5152,48.1517,43.2437,1.0362,9.6238,2.34,0.0769,8.6077,18.009,10.9722,0,0.0,0.0,0,0,0,0,0,0,0,0,0,43.0108,0.0,0,0,0,0,0.2829,1.0108,1.555,4.2022,1.995,0,12.9438,0.0825,4.0788,6.0161,0.4296,3.3392,0,0,0,0,0,0,0,0,0,0,0,3.9638,1.2134,9.5742,0.1083,0.0104,0.0029,9.6238,,,,,,,,,,,,,0.9815,0.5123,0.0909,52.1953,265.37,0.717,5.18,0.2259,2.0235,0.0886,1.952,31.5069,534.1482,1.6633,10.92,0.2937,3.7983,0.0853,2.0444,17.6552,,,,,0.5004,0.012,0.0033,2.4069,0.0545,0.0184,0.0055,33.7876,positive +2961.04,2506.43,2170.0666,1364.5157,1.5447,100,96.77,0.123,1.3953,0.0084,0.0062,0.9461,204.6134,0,5.1756,406.329,10.7168,0.972,193.8966,12.5531,1.4372,-5714.25,2573.5,-2819.5,-1472.75,1.3163,2.011,7.4093,71.3222,1.9889,0.1632,3.479,86.1155,8.5801,50.294,64.0748,49.7061,65.8602,86.9331,119.2266,79.31,3.331,70,357.69,10.4125,120.9055,717.3122,1.4512,150.5109,1,629.1064,227.0841,0,,,,,,,4.6209,353.1564,10.9223,105.5264,17.8428,15.3791,22.4441,694.8681,1.0804,152.5809,1,611.2636,95.4405,141.4404,469.8232,0,0.0263,-0.0319,0.0373,0.0095,-0.0092,0.0147,-0.068,0.0176,7.2257,0.1284,,2.3741,0.9906,1816.1957,0.1583,8772.49,0.0,0.0003,-0.0013,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,-0.0093,-0.0004,0.0,0.0,0.0,0.0,,,,0.4634,0.9624,0,757.1382,0.9906,58.6659,0.6016,0.9658,6.4478,15.59,4.44,15.61,15.54,1.239,2.511,0.7635,3.232,-0.1419,0.6847,0.9988,2.3499,986.0991,39.537,139,86.3,96.0,77.2,542.931,0.1541,0,6.86,0.0053,0.0882,0.087,0.0647,0.0257,7.2291,0,8.41,22.042,0.4588,0.0122,6.7734,0.3,0.0392,,,1049,478,3399,3075,0.082,0.155,0.216,1.5,1.3,0.111,0.503,0.7151,0.0325,0.2842,0.6629,0.2843,0.9018,0.2087,0.242,0,0,19.1,0.461,8.37,31.159,0.1381,6.12,0,20.83,53.404,0,,,,,,,4.84,20.95,0.269,7.62,19.29,4.84,3.343,31.736,0.0843,9.11,0,19.29,58.565,0,0.0616,0.0512,0.0573,0.0352,0.0727,0.1037,0.0364,0.0352,4.6083,0.0032,,0.0288,0.0008,156.333,0.0546,827.7998,0,0.0122,0.0108,0,0,0,0,0,0,0,0,0,0.0046,0.0059,0,0,0,0,,,,0.0394,0.0089,0,82.1998,0.001,3.1193,0.0297,0.0243,0.5065,0,0,0,0,0,0,0,0,0,0,0,0.0917,16.524,4.2578,30.7316,30.8476,33.1309,28.0614,152.7782,0.0573,0,2.514,0.0016,0.029,0.0227,0.0147,0.0086,2.5899,0,2.5765,6.7675,0.1332,0.004,2.5013,0.0825,0.013,,,435.2251,196.8849,1478.1093,1425.8147,0.0419,0.0703,0.0909,0.4711,0.454,0.038,0.1327,0.2643,0.0133,0.1036,0.235,0.1036,0.3668,0.0898,0.1041,0,0,0,5.9229,0.1263,2.4195,9.7021,0.0451,2.076,0,6.2158,15.0289,0,,,,,,,1.4625,6.6325,0.0769,2.2503,9.7813,1.4625,1.1516,10.0226,0.0302,2.8256,0,5.7373,19.8463,4.1085,3.4842,0,0.0186,0.0262,0.0277,0.0166,0.0222,0.0502,0.0168,0.0148,1.7182,0.001,,0.0116,0.0003,47.2458,0.0182,279.8021,0,0.0036,0.0033,0.0,0.0,0,0,0,0,0,0,0,0.0017,0.0019,0,0,0,0,,,,0.0095,0.0026,0,28.3864,0.0003,0.9856,0.0093,0.0074,0.166,0,0,0,0,0,0,0,0,0,0,0,0.0323,5.545,1.3004,6.3654,2.9145,3.8301,3.5575,39.7893,9.9768,0,7.089,4.3082,6.3213,0.0,0.0,2.7155,3.533,0,162.4921,5.4247,4.2815,1.2535,3.4933,2.3899,2.7241,18.3576,18.5739,120.5533,208.7931,6.2298,7.7076,2.9153,2.1031,65.3631,68.0054,14.4582,0.8304,0.379,0.5651,1.0346,0.572,1.3693,0.2401,0.203,0,0,0,5.3398,4.4273,6.9228,4.3439,9.5135,4.0661,0,3.311,23.5173,0,,,,,,,104.7413,5.9322,2.4629,7.2209,108.111,31.4713,14.8947,4.5672,7.8031,5.9706,0,3.1558,61.3628,0,233.8316,160.6275,153.5164,369.5538,785.9459,706.644,53.4901,200.2845,63.7762,2.4924,,1.2143,0.0769,8.6077,34.4718,9.4363,0,0.0,851.2315,0,0,0,0,0,0,0,0,0,49.5289,0.0,0,0,0,0,,,,8.4933,0.9223,0,10.8566,0.1008,5.3171,4.9297,2.516,7.8553,0,0,0,0,0,0,0,0,0,0,0,3.9038,1.6757,10.7691,0.1161,0.0075,0.0021,6.491,1.4404,412.458,75.006,0.5099,18.56,2.32,0.2269,7.8484,0.8557,35.4007,4.4999,3.0931,0.9811,0.4201,0.0755,42.8218,264.272,0.5671,4.98,0.0877,2.0902,0.0382,1.8844,15.4662,535.9527,2.2638,9.62,0.2673,3.2966,0.075,1.7949,11.8075,,,,,0.4994,0.0115,0.0031,2.302,0.0545,0.0184,0.0055,33.7876,negative +3072.03,2500.68,2205.7445,1363.1048,1.0518,100,101.8644,0.122,1.3896,0.0138,0.0,0.9656,199.5093,0,4.8205,414.1385,10.0666,0.9746,189.4428,12.6257,1.4231,-5538.0,2971.75,-4168.5,4729.5,1.2942,1.9743,7.2903,73.4778,0.8778,0.0341,3.4573,83.7568,8.2981,50.628,64.638,49.372,65.9373,86.6174,119.9514,80.57,3.675,70,358.8255,10.2234,129.1882,729.5739,1.3843,143.5227,1,631.5364,245.7086,0,4.61,4.871,2863.0,0.9252,0.9463,4.5938,2.2336,359.1782,10.6156,111.4218,12.5904,17.7664,20.5025,709.0714,1.5049,149.6727,1,620.2727,227.2577,148.7429,471.5298,0,0.0038,-0.007,-0.0494,0.0588,-0.0092,-0.0023,-0.0168,0.0196,7.2528,0.1356,0.1081,2.4079,0.9898,1925.1642,0.2018,9250.51,0.0,-0.0032,0.0008,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,-0.0098,-0.0006,0.0,0.0,0.0,0.0,0.9799,101.6138,232.4957,,0.9535,0,803.5868,0.9908,59.3162,0.598,0.9592,6.4202,15.9,3.816,15.85,15.92,0.9129,2.911,0.6956,3.394,0.0,0.8536,1.002,2.3817,1010.6921,37.117,91,214.7,182.0,50.0,484.67,0.2233,0,10.53,0.0028,0.0979,0.0531,0.0626,0.0163,7.7448,0,7.679,10.643,0.419,0.009,7.6173,0.33,0.0635,,,526,687,1335,1943,0.096,0.162,0.058,3.4,3.1,0.221,0.483,0.5941,0.1229,0.2147,0.497,0.2148,0.7974,0.2244,0.142,0,0,16.47,0.544,5.06,27.896,0.0776,4.23,0,17.76,69.151,0,0.0,0.0,0.0,0.0,0.0,0.255,7.85,19.45,0.133,7.58,19.07,7.85,14.001,37.846,0.0928,5.98,0,25.16,48.815,0,0.0805,0.042,0.0646,0.0474,0.0686,0.1451,0.0759,0.0495,1.7607,0.0012,0.0086,0.0344,0.0021,146.4335,0.0333,1563.0996,0,0.0161,0.0099,0,0,0,0,0,0,0,0,0,0.0049,0.0047,0,0,0,0,0.0028,0.6529,2.2691,,0.0229,0,180.8325,0.0003,2.6597,0.0429,0.0189,0.303,0,0,0,0,0,0,0,0,0,0,0,0.1102,17.561,5.2328,28.3169,71.5407,61.8228,18.8084,186.6624,0.0743,0,3.4613,0.0008,0.0363,0.0191,0.0161,0.0053,2.6779,0,2.4152,3.7285,0.1271,0.003,2.5907,0.0982,0.0172,,,221.6017,313.9739,625.5326,834.0921,0.0409,0.0704,0.0255,1.058,1.1222,0.0821,0.1468,0.2274,0.0499,0.0861,0.1825,0.0862,0.3233,0.0927,0.0723,0,0,0,5.5864,0.1818,1.6543,8.5462,0.0274,1.2854,0,5.5808,23.58,0,0.0,0.0,0.0,0.0,0.0,0.0834,2.2496,6.3306,0.0383,2.2407,8.9128,2.2496,3.6552,11.2994,0.0312,1.7875,0,7.3274,15.5505,6.0441,2.1054,0,0.0216,0.0203,0.0327,0.0199,0.0198,0.0654,0.0404,0.0277,0.5773,0.0004,0.0021,0.0128,0.0006,47.3238,0.0124,423.9136,0,0.0043,0.0032,0.0,0.0,0,0,0,0,0,0,0,0.0017,0.0016,0,0,0,0,0.0011,0.2322,0.6953,,0.0078,0,48.4822,0.0001,0.6887,0.0135,0.0066,0.1082,0,0,0,0,0,0,0,0,0,0,0,0.0415,6.3801,1.6082,3.9934,6.9889,7.278,2.2668,35.5563,21.2293,0,10.3373,2.2947,7.0453,386.0064,0.0,1.6928,3.8819,0,159.3003,2.5699,4.162,0.9215,4.0209,2.6137,4.4591,9.498,23.1177,32.0259,41.0826,7.4174,8.2056,0.7956,4.6272,353.1646,647.8827,13.9703,0.7093,1.4805,0.4241,0.7689,0.4351,1.2093,0.2591,0.1184,0,0,0,4.59,5.3211,3.9168,3.8236,5.6021,2.9473,0,2.8122,28.1435,0,0.0,0.0,0.0,0.0,0.0,5.551,351.4449,5.4151,1.2529,6.803,151.4644,44.1846,68.2893,5.3374,6.1643,3.9954,0,4.0563,21.48,0,0.0,597.8648,130.8354,80.6122,748.704,0.0,453.1343,252.2293,24.276,0.8852,7.9861,1.4291,0.2132,7.6063,16.4919,16.8974,0,502.1442,0.0,0,0,0,0,0,0,0,0,0,49.8094,800.0,0,0,0,0,0.2907,0.6425,0.976,,2.4069,0,22.5032,0.0304,4.4839,7.1817,1.9702,4.7201,0,0,0,0,0,0,0,0,0,0,0,4.6287,1.7375,14.0981,0.1081,0.0086,0.0021,7.9861,1.3531,395.638,74.17,0.8341,19.59,1.33,0.3352,8.826,0.5484,61.6419,4.9515,1.7932,0.9929,0.2454,0.0525,24.7203,266.004,0.8756,11.18,0.1832,4.2535,0.0714,4.2029,20.9204,534.1482,1.6633,10.92,0.2937,3.7983,0.0853,2.0444,17.6552,,,,,0.4987,0.0118,0.0036,2.3719,0.0545,0.0184,0.0055,33.7876,positive 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+3008.28,2504.21,2202.2556,1914.0689,1.6013,100,94.6922,0.1242,1.4055,-0.0134,-0.0135,0.9691,198.8975,0,9.2803,400.4087,9.7648,0.9717,189.1327,12.6257,1.4166,-4764.0,2903.75,-4312.25,-409.0,1.2818,1.9905,7.2882,75.0889,2.0111,0.1494,3.3056,84.8949,8.3588,50.3833,64.4228,49.6168,66.2366,86.5828,120.3726,81.27,2.955,70,357.1827,10.1735,133.0409,732.2075,0.9834,144.7009,1,634.9245,115.9665,0,4.571,4.831,2865.0,0.9242,0.946,4.5663,5.6155,358.0509,10.4219,118.6564,6.4102,14.3845,14.8409,717.3666,1.5244,155.7355,1,632.4427,238.4775,161.9905,470.4522,0,-0.0049,-0.0506,-0.0288,0.0564,-0.0339,-0.0164,-0.0258,0.0051,7.8021,0.1308,,2.2663,0.97,1804.9217,0.2105,8091.77,-0.0132,-0.0023,-0.0005,0.0001,0.0,-0.1073,0,0.0892,-0.0065,0.0002,0.0,0.0195,-0.0101,0.0001,0.0023,0.0005,0.0778,0.0012,,,,0.4585,0.9522,0,676.803,0.9908,58.0213,0.5986,0.9645,6.3864,15.75,3.235,15.8,15.73,0.9838,2.783,0.5653,3.153,0.7096,0.8543,0.9962,2.2802,998.0122,37.2972,84,65.3,96.2,44.0,318.9119,0.3104,0,4.38,0.0063,0.116,0.062,0.0444,0.0175,9.6082,0,10.078,9.226,0.4312,0.0112,9.1982,0.33,0.0906,,,1985,613,998,2236,0.079,0.077,0.044,1.8,1.4,0.13,0.286,0.6053,0.1165,0.2068,0.2538,0.2068,0.7726,0.1828,0.205,0,0,26.92,0.556,4.16,27.636,0.0745,7.18,0,22.15,38.47,0,0.0,0.0,0.0,0.0,0.0,0.264,9.49,19.19,0.185,8.81,13.13,9.49,11.552,38.104,0.0788,7.32,0,22.34,53.66,0,0.0803,0.0535,0.0246,0.057,0.0521,0.0268,0.0978,0.0329,3.0655,0.0039,,0.0881,0.0029,59.1167,0.067,1058.9995,0,0.011,0.0088,0,0,0,0,0,0,0,0,0,0.0052,0.0065,0,0,0,0,,,,0.0155,0.0213,0,100.8655,0.0006,1.5621,0.0365,0.0239,0.5215,0,0,0,0,0,0,0,0,0,0,0,0.044,31.404,4.1345,24.466,19.7684,30.2213,17.9486,102.1894,0.0995,0,1.521,0.0018,0.0378,0.0185,0.0111,0.0059,2.9223,0,2.6513,2.61,0.1319,0.0036,2.8063,0.0982,0.026,,,812.0891,306.5272,502.565,1034.0255,0.0379,0.032,0.0197,0.6412,0.4226,0.0345,0.1044,0.2687,0.0448,0.0787,0.0913,0.0787,0.315,0.0726,0.0849,0,0,0,7.4824,0.1531,1.3491,8.4021,0.0241,2.1532,0,6.1284,11.4295,0,0.0,0.0,0.0,0.0,0.0,0.086,3.0368,6.17,0.0613,2.5426,9.3375,3.0368,3.5801,11.5349,0.0222,2.258,0,6.7556,16.8098,5.9207,2.6834,0,0.0204,0.0244,0.0126,0.0274,0.0185,0.0112,0.0503,0.0171,1.0401,0.0011,,0.0301,0.0009,19.5296,0.0195,331.6717,0,0.0035,0.0026,0.0024,0.0018,0,0,0,0,0,0,0,0.0019,0.0023,0,0,0,0,,,,0.0049,0.0077,0,35.4195,0.0003,0.4361,0.0102,0.0076,0.1752,0,0,0,0,0,0,0,0,0,0,0,0.0131,11.7622,1.2469,3.8155,2.1707,3.8415,1.998,16.6615,19.3863,0,4.6255,5.072,8.2532,463.985,329.3463,1.807,4.8307,0,108.5959,2.3041,4.416,1.1482,4.8633,2.6137,6.3978,41.6667,21.1106,23.1434,546.6993,6.1634,3.8684,0.6037,2.3972,69.6133,86.9888,8.6521,0.713,1.3942,0.4105,0.394,0.4168,1.1664,0.2111,0.1703,0,0,0,7.5368,5.4652,3.1268,3.7743,7.5764,4.962,0,3.4886,33.1734,0,0.0,0.0,0.0,0.0,0.0,5.7814,168.9979,5.3596,1.7751,7.4248,204.8292,65.9736,77.8391,5.3116,5.1662,4.7003,0,3.5323,22.5011,0,0.0,105.7835,85.3426,101.1086,153.7157,162.9179,378.3366,641.9512,39.2905,2.9827,,3.8852,0.3007,3.2753,31.843,13.0874,0,480.8743,0.0,0,0,0,0,0,0,0,0,0,51.358,0.0,0,0,0,0,,,,3.3801,2.2352,0,14.9032,0.0643,2.6923,6.0995,2.478,8.1664,0,0,0,0,0,0,0,0,0,0,0,1.9293,3.1467,11.0853,0.1088,0.0091,0.0025,8.3227,,,,,,,,,,,,,1.0072,0.8354,0.1699,82.9503,264.958,0.9733,7.55,0.2216,3.0647,0.0909,2.8495,22.7632,534.1482,1.6633,10.92,0.2937,3.7983,0.0853,2.0444,17.6552,,,,,0.4989,0.0125,0.0032,2.5022,0.0128,0.0193,0.006,151.193,positive +3084.81,2445.2,2224.6778,1308.6479,1.3907,100,101.1333,0.1208,1.4997,0.0027,0.0056,0.9591,199.8681,0,9.5711,400.4493,10.3944,0.9771,189.4737,12.4078,1.4258,-5195.75,2541.0,-3663.75,3444.3333,1.3043,2.0032,7.336,68.4889,2.5889,0.21,3.3603,84.9173,9.131,50.0942,64.3185,49.9058,65.9561,86.7486,118.0202,76.72,3.312,70,357.4273,10.2907,126.62,724.1972,1.0486,143.66,1,627.7073,120.5192,0,4.639,4.899,2856.0,0.9293,0.9468,4.5807,2.8136,358.2,10.6726,109.4336,14.4382,17.1864,21.9929,702.2043,1.0774,145.6355,1,613.2691,121.8235,,,0,0.0039,-0.0739,0.0558,0.0463,-0.0059,0.0061,0.001,0.0276,7.4217,0.1364,0.1139,2.4073,0.9861,1742.9685,0.1696,8366.46,0.0196,0.0009,0.0009,0.0,0.0,0.0114,0,-0.0199,0.0121,0.0,0.0,-0.0487,-0.0101,-0.0002,-0.0009,-0.0009,-0.0375,-0.0189,0.9764,101.7706,233.0776,0.468,0.9408,0,723.0611,0.9911,57.8977,0.6141,0.9654,6.207,15.75,3.235,15.8,15.73,0.9838,2.783,0.5653,3.153,0.7096,0.8543,0.9962,2.2802,998.0122,37.2972,132,148.9,178.5,51.5,184.64,0.33,0,5.65,0.0033,0.0819,0.0547,0.0416,0.0185,6.4908,0,9.536,13.748,0.2446,0.0094,6.2565,0.36,0.0267,,,598,438,3959,13134,0.087,0.082,0.11,3.1,0.9,0.094,0.361,0.6062,0.1798,0.3413,0.5555,0.3413,0.6778,0.2857,1.039,0,0,19.2,0.093,3.75,28.193,0.102,5.04,0,17.54,18.036,0,0.0,0.0,0.0,0.0,0.0,0.271,3.05,21.44,0.393,5.56,22.23,3.05,3.19,29.662,0.2452,8.45,0,22.23,119.535,0,0.0543,0.0666,0.0452,0.0206,0.0391,0.0059,0.0917,0.0512,6.3036,0.0026,0.0089,0.0326,0.0061,104.5221,0.0685,633.9004,0,0.0118,0.0134,0,0,0,0,0,0,0,0,0,0.0031,0.0049,0,0,0,0,0.0034,1.0004,2.1707,0.0125,0.0221,0,68.4254,0.0009,1.456,0.0315,0.0243,0.5565,0,0,0,0,0,0,0,0,0,0,0,0.044,31.404,4.1345,30.3144,48.0535,56.9028,19.3278,59.5384,0.1099,0,1.9283,0.0009,0.028,0.0155,0.01,0.006,2.1618,0,2.6266,3.7589,0.0744,0.0031,2.0979,0.1077,0.0112,,,313.1596,181.5764,1723.7851,6792.1642,0.0358,0.037,0.0556,0.9804,0.306,0.0305,0.1113,0.225,0.0696,0.1366,0.21,0.1366,0.257,0.1153,0.438,0,0,0,6.2503,0.0283,1.1789,8.5624,0.0362,1.4862,0,5.1373,5.1216,0,0.0,0.0,0.0,0.0,0.0,0.0897,1.0154,6.713,0.1191,1.6266,8.9504,1.0154,1.1164,9.2495,0.0621,2.4509,0,6.1844,31.4937,,,0,0.0164,0.0314,0.0206,0.0088,0.0127,0.0026,0.0485,0.0221,2.5951,0.0008,0.0026,0.013,0.0018,43.2642,0.0193,176.8093,0,0.0028,0.0036,0.0022,0.0025,0,0,0,0,0,0,0,0.001,0.0017,0,0,0,0,0.0013,0.3191,0.6561,0.0032,0.0079,0,20.6738,0.0003,0.4176,0.0127,0.0076,0.185,0,0,0,0,0,0,0,0,0,0,0,0.0131,11.7622,1.2469,6.0509,4.8269,7.3,2.3149,14.1092,23.7274,0,5.5867,2.7308,5.461,0.0,744.519,1.9253,3.2475,0,99.6334,3.4331,2.3535,0.9597,3.3021,2.9014,1.8762,11.5094,17.2373,108.0587,381.322,6.6705,4.0933,1.4995,4.5263,34.7639,44.7619,10.743,0.7139,1.969,0.6813,0.8637,0.6839,1.0277,0.3293,0.8804,0,0,0,5.3717,0.9037,2.9616,3.893,9.7295,3.5083,0,2.7943,14.9653,0,0.0,0.0,0.0,0.0,0.0,5.9161,108.4006,5.9855,3.6823,5.0807,153.9664,17.7466,14.5046,4.2241,22.7578,5.8022,0,3.6248,98.1214,0,0.0,90.1218,81.0036,44.5165,660.6125,96.3265,0.0,185.6754,84.9345,1.9065,7.8124,1.3532,0.6178,5.9968,40.3784,7.5767,0,0.0,0.0,0,0,0,0,0,0,0,0,0,30.7311,0.0,0,0,0,0,0.3469,0.983,0.9313,2.6619,2.3489,0,9.4633,0.0921,2.5148,5.1226,2.5213,8.966,0,0,0,0,0,0,0,0,0,0,0,1.9293,3.1467,11.0853,0.1139,0.0089,0.0026,7.8124,1.506,411.18,73.588,1.7593,21.51,1.08,0.7306,10.7162,0.4347,116.816,5.2313,1.4676,0.9825,0.3629,0.063,36.9328,267.726,0.9214,4.62,0.154,1.7453,0.0586,1.7256,16.7109,532.0164,2.3658,13.49,0.2662,4.6475,0.0914,2.5356,11.251,,,,,0.5035,0.0142,0.0036,2.8109,0.0128,0.0193,0.006,151.193,positive +3034.5,2431.35,2220.0445,2253.2847,1.7112,100,88.0444,0.1222,1.4175,-0.0144,-0.0119,0.9661,197.9151,0,5.604,408.6007,10.7348,0.9737,187.1803,12.6184,1.4096,-5569.75,2958.0,-3395.5,-105.0,1.2732,1.9875,7.2383,74.0,2.0222,0.1526,3.4412,84.8949,8.3588,50.3833,64.4228,49.6168,66.2366,86.5828,120.3726,81.27,2.955,70,356.9836,10.2531,130.3991,729.0202,0.9827,141.51,1,628.8927,124.7381,0,4.61,4.871,2863.0,0.9252,0.9463,4.5938,3.9527,357.5873,10.5269,114.3518,8.8717,16.0473,18.7066,710.3135,1.0944,149.4427,1,621.3818,119.3609,151.1373,470.2445,0,0.0267,-0.0533,-0.0342,0.0587,-0.0152,-0.0424,-0.0207,-0.0015,7.593,0.1282,0.11,2.3782,0.9871,1891.431,0.2011,9020.8602,0.0,-0.0014,0.0008,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,-0.0115,-0.0014,0.0,0.0,0.0,0.0,0.9796,101.9161,231.6601,0.4552,0.9484,0,788.4125,0.9905,59.0297,0.6019,0.9544,6.5115,15.75,3.235,15.8,15.73,0.9838,2.783,0.5653,3.153,0.7096,0.8543,0.9962,2.2802,998.0122,37.2972,108,153.5,105.4,45.0,288.3828,0.4508,0,3.39,0.0034,0.0796,0.0686,0.0607,0.0156,7.0283,0,7.533,12.641,0.367,0.0089,6.8867,0.32,0.053,,,959,695,1341,163,0.067,0.105,0.138,2.8,1.6,0.215,0.557,0.6053,0.1165,0.2068,0.2538,0.2068,0.7726,0.1828,0.205,0,0,16.71,0.418,5.12,26.347,0.0677,8.15,0,18.17,14.218,0,0.0,0.0,0.0,0.0,0.0,0.255,7.57,16.9,0.26,8.05,16.9,7.57,9.896,35.308,0.0652,2.99,0,23.89,16.582,0,0.0821,0.1039,0.0271,0.0574,0.0645,0.0332,0.0855,0.0372,3.6095,0.0038,0.0084,0.0957,0.0027,135.9242,0.0504,550.7002,0,0.0224,0.0296,0,0,0,0,0,0,0,0,0,0.0041,0.005,0,0,0,0,0.0028,0.5402,4.8896,0.0307,0.0064,0,40.9911,0.0008,1.7789,0.0356,0.0285,0.3132,0,0,0,0,0,0,0,0,0,0,0,0.044,31.404,4.1345,28.7203,53.5463,42.3476,16.6597,80.0542,0.1445,0,1.1262,0.001,0.0255,0.0193,0.0144,0.0051,2.3938,0,2.3133,4.2138,0.1159,0.0029,2.3158,0.0877,0.0144,,,407.189,326.9077,550.7453,69.5749,0.0314,0.0493,0.0651,0.7874,0.4994,0.0648,0.1581,0.2687,0.0448,0.0787,0.0913,0.0787,0.315,0.0726,0.0849,0,0,0,5.7861,0.1198,1.7254,8.3231,0.0181,2.7076,0,5.7911,3.9713,0,0.0,0.0,0.0,0.0,0.0,0.0834,2.2974,5.3638,0.0825,2.3428,8.2877,2.2974,3.0439,10.9307,0.0208,0.9431,0,7.1618,5.1936,5.8602,4.6196,0,0.0228,0.0457,0.0121,0.0288,0.0202,0.0143,0.0445,0.0164,1.3466,0.0011,0.0024,0.0315,0.001,46.7243,0.0146,168.1055,0,0.0055,0.0063,0.0,0.0,0,0,0,0,0,0,0,0.0016,0.0018,0,0,0,0,0.001,0.1615,1.2993,0.0065,0.0022,0,14.1613,0.0003,0.4973,0.0129,0.0091,0.1141,0,0,0,0,0,0,0,0,0,0,0,0.0131,11.7622,1.2469,4.8921,5.0585,4.335,2.027,12.7983,26.3461,0,3.8503,2.782,5.6153,477.6327,511.1579,1.6111,3.5511,0,134.4218,3.0937,3.4192,0.9103,3.6792,2.536,3.7605,17.218,23.4956,39.4934,155.2381,5.2621,5.283,1.9065,3.7838,79.1209,140.9323,16.1861,0.713,1.3942,0.4105,0.394,0.4168,1.1664,0.2111,0.1703,0,0,0,4.6809,4.0768,3.9264,3.614,6.8875,5.7593,0,2.8892,11.3983,0,0.0,0.0,0.0,0.0,0.0,5.551,191.5133,4.7261,2.4699,7.0397,190.4935,47.1731,52.9011,4.9708,5.9574,2.0008,0,3.8447,13.8923,0,307.5626,194.8429,79.1819,97.7853,425.567,78.3481,414.0436,0.0,47.5376,2.9631,7.6035,4.0233,0.2725,7.1863,25.0512,6.1047,0,0.0,0.0,0,0,0,0,0,0,0,0,0,35.5363,363.6364,0,0,0,0,0.2847,0.5301,2.1107,6.7342,0.6776,0,5.1992,0.0847,3.0136,5.9107,2.9862,4.8104,0,0,0,0,0,0,0,0,0,0,0,1.9293,3.1467,11.0853,0.11,0.0084,0.0024,7.6035,0.53,396.412,72.68,0.15,22.31,1.05,0.0629,9.9929,0.4145,28.2962,5.628,1.4447,0.9794,0.1459,0.0398,14.896,269.176,0.7193,4.96,0.1894,2.2603,0.0701,1.8427,26.3287,536.4418,2.3416,6.66,0.3509,2.3039,0.1026,1.2415,14.985,0.02,0.0095,0.0037,47.7832,0.4944,0.0123,0.0035,2.4854,0.02,0.0095,0.0037,47.7832,positive diff --git a/Sensor Fault Detection/web/requirements.txt b/Sensor Fault Detection/web/requirements.txt new file mode 100644 index 000000000..f7698edc3 --- /dev/null +++ b/Sensor Fault Detection/web/requirements.txt @@ -0,0 +1,16 @@ + +Flask # for web server +jinja2 # for Flask templates +python-dotenv # for environment variables +seaborn # for data visualization +xgboost # for gradient boosting +scikit-learn # for machine learning +pandas # for data manipulation +waitress +numpy +ipykernel +matplotlib +kneed # for knee point detection +imbalanced-learn +csv + diff --git a/Sensor Fault Detection/web/setup.py b/Sensor Fault Detection/web/setup.py new file mode 100644 index 000000000..8f0ce46db --- /dev/null +++ b/Sensor Fault Detection/web/setup.py @@ -0,0 +1,23 @@ +from setuptools import setup, find_packages +from typing import List + +HYPEN_E_DASH = '-e .' + +def get_requirements(file_path: str) -> List[str]: + requirements = [] + with open(file_path) as file_obj: + requirements = file_obj.readlines() + requirements = [req.replace('\n', '') for req in requirements] + if HYPEN_E_DASH in requirements: + requirements.remove(HYPEN_E_DASH) + return requirements + +setup( + name="src", + version='0.0.1', + author="Damodar", + author_email='damodarryadav@gmail.com', + description=('Sensor fault predication using machine learning.'), + packages=find_packages(), + install_requires=[], +) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/__init__.py b/Sensor Fault Detection/web/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Sensor Fault Detection/web/src/__pycache__/__init__.cpython-310.pyc b/Sensor Fault Detection/web/src/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..168876a87 Binary files /dev/null and b/Sensor Fault Detection/web/src/__pycache__/__init__.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/__pycache__/exception.cpython-310.pyc b/Sensor Fault 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Detection/web/src/components/__pycache__/data_transformation.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/components/__pycache__/model_trainer.cpython-310.pyc b/Sensor Fault Detection/web/src/components/__pycache__/model_trainer.cpython-310.pyc new file mode 100644 index 000000000..e2661e9cc Binary files /dev/null and b/Sensor Fault Detection/web/src/components/__pycache__/model_trainer.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/components/data_ingestion.py b/Sensor Fault Detection/web/src/components/data_ingestion.py new file mode 100644 index 000000000..81ff4f9dd --- /dev/null +++ b/Sensor Fault Detection/web/src/components/data_ingestion.py @@ -0,0 +1,46 @@ +import os +import sys +from dataclasses import dataclass + +import pandas as pd +from sklearn.model_selection import train_test_split + +from src.constant import * +from src.exception import CustomException +from src.logger import logging +from src.utils import export_collection_as_dataframe + +@dataclass +class DataIngestionConfig: + train_data_path:str = os.path.join("artifacts","train.csv") + raw_data_path:str = os.path.join("artifacts","data.csv") + test_data_path:str = os.path.join("artifacts","test.csv") + +class DataIngestion: + def __init__(self): + self.ingestion_config = DataIngestionConfig() + + def initiate_data_ingestion(self): + logging.info("Entered initiate_data_ingestion method of DataIngestion class") + print(os.getcwd()) + try: + df:pd.DataFrame = export_collection_as_dataframe(db_name=MONGO_DATABASE_NAME,collection_name=MONGO_COLLECTION_NAME) + + logging.info("Exported collection as dataframe") + + os.makedirs(os.path.dirname(self.ingestion_config.train_data_path),exist_ok=True) + + df.to_csv(self.ingestion_config.raw_data_path,index=False,header=True) + + train_set,test_set = train_test_split(df,test_size=0.2,random_state=42) + + train_set.to_csv(self.ingestion_config.train_data_path,index=False,header=True) + + test_set.to_csv(self.ingestion_config.test_data_path,index=False,header=True) + + logging.info("Exited initiate_data_ingestion method of DataIngestion class") + + return (self.ingestion_config.train_data_path,self.ingestion_config.test_data_path) + + except Exception as e: + raise CustomException(e,sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/components/data_transformation.py b/Sensor Fault Detection/web/src/components/data_transformation.py new file mode 100644 index 000000000..146a970c8 --- /dev/null +++ b/Sensor Fault Detection/web/src/components/data_transformation.py @@ -0,0 +1,101 @@ +import csv +import sys , os +from dataclasses import dataclass + +import numpy as np +import pandas as pd +from src.exception import CustomException +from src.logger import logging +from src.utils import save_object + +from imblearn.combine import SMOTETomek +from imblearn.over_sampling import SMOTE +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import RobustScaler, FunctionTransformer +from sklearn.pipeline import Pipeline + + +@dataclass +class DataTransformationConfig: + preprocessor_obj_file_path = os.path.join("artifacts", "preprocessor.pkl") + +class DataTransformation: + def __init__(self): + self.data_transformation_config = DataTransformationConfig() + + def get_data_transformer_object(self): + try: + logging.info("Entered get_data_transformer_object method of DataTransformation class") + # define custom function to replace 'NA' with np.nan + replace_na_with_nan = lambda X : np.where(X.isin(['na', 'NaN','NaN.']), np.nan, X) + + # define the steps for the preprocessor pipeline + nan_replacement_step = ('nan_replacement',FunctionTransformer(replace_na_with_nan)) + + imputer_step = ('imputer',SimpleImputer(strategy='constant',fill_value=0)) + scaler_step = ('scaler',RobustScaler()) + + preprocessor = Pipeline( + steps = [ + nan_replacement_step, + imputer_step, + scaler_step + ] + ) + return preprocessor + + except Exception as e: + raise CustomException(e, sys) + + def initiate_data_transformation (self,train_path,test_path): + try: + logging.info("Entered initiate_data_transformation method of DataTransformation class") + train_df = pd.read_csv(train_path).drop(columns='Unnamed: 0') + test_df = pd.read_csv(test_path).drop(columns='Unnamed: 0') + + ## Drop rows where "Good/Bad" is NaN + train_df = train_df.dropna(subset=['Good/Bad']) + test_df = test_df.dropna(subset=['Good/Bad']) + + preprocessor = self.get_data_transformer_object() + + target_column_name = "Good/Bad" + target_column_mapping = {1:0,-1:1} + + target_feature_train_df = train_df[target_column_name].map(target_column_mapping) + + input_feature_train_df = train_df.drop(columns=[target_column_name],axis=1) + + target_feature_test_df = test_df[target_column_name].map(target_column_mapping) + + input_feature_test_df = test_df.drop(columns=[target_column_name],axis=1) + + # Save feature names to a CSV file + feature_names = input_feature_train_df.columns.tolist() + with open('feature_names.csv', 'w', newline='') as file: + writer = csv.writer(file) + writer.writerow(feature_names) + + transformed_input_train_feature = preprocessor.fit_transform(input_feature_train_df) + + transformed_input_test_feature = preprocessor.transform(input_feature_test_df) + + smt = SMOTETomek(sampling_strategy='minority', smote=SMOTE(k_neighbors=1)) # reduce the number of neighbors + + input_feature_train_final , target_feature_train_final = smt.fit_resample(transformed_input_train_feature,target_feature_train_df) + + input_feature_test_final,target_feature_test_final = smt.fit_resample(transformed_input_test_feature,target_feature_test_df) + + train_arr = np.c_[input_feature_train_final,np.array(target_feature_train_final)] + test_arr = np.c_[input_feature_test_final,np.array(target_feature_test_final)] + + save_object(self.data_transformation_config.preprocessor_obj_file_path,obj=preprocessor) + + return( + train_arr, + test_arr, + self.data_transformation_config.preprocessor_obj_file_path, + ) + + except Exception as e: + raise CustomException(e,sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/components/model_trainer.py b/Sensor Fault Detection/web/src/components/model_trainer.py new file mode 100644 index 000000000..6868a55bd --- /dev/null +++ b/Sensor Fault Detection/web/src/components/model_trainer.py @@ -0,0 +1,93 @@ +import os ,sys +from dataclasses import dataclass +from sklearn.ensemble import ( + AdaBoostClassifier, + GradientBoostingClassifier, + RandomForestClassifier +) + +from sklearn.metrics import r2_score +from sklearn.neighbors import KNeighborsClassifier +from sklearn.tree import DecisionTreeClassifier +from xgboost import XGBClassifier + +from src.constant import * +from src.exception import CustomException +from src.logger import logging +from src.utils import evaluate_models,load_object,save_object + +@dataclass +class ModelTrainerConfig: + trained_model_file_path = os.path.join("artifacts","model.pkl") + +class CustomModel: + def __init__(self,preprocessing_object , trained_model_object): + self.preprocessing_object = preprocessing_object + self.trained_model_object = trained_model_object + + def predict(self,X): + transformed_feature = self.preprocessing_object.transform(X) + return self.trained_model_object.predict(transformed_feature) + + def __repr__(self): + return f"{type(self.trained_model_object).__name__}()" + + def __str__(self): + return f"{type(self.trained_model_object).__name__}()" + + +class ModelTrainer: + def __init__(self): + self.model_trainer_config = ModelTrainerConfig() + + def initiate_model_trainer(self,train_array,test_array,preprocessor_path): + try: + logging.info(f"Splitting training and testing input and target Feature") + + X_train,y_train,X_test,y_test = ( + train_array[:,:-1], + train_array[:,-1], + test_array[:,:-1], + test_array[:,-1] + ) + + models = { + "Random Forest": RandomForestClassifier(), + "Decision Tree": DecisionTreeClassifier(), + "AdaBoost": AdaBoostClassifier(algorithm='SAMME'), + "Gradient Boosting": GradientBoostingClassifier(), + "XGBoost": XGBClassifier(), + "KNN": KNeighborsClassifier() + } + + logging.info(f"Extraction Model config file path") + + model_report : dict = evaluate_models(X = X_train,y=y_train,models=models) + best_model_score =max((model_report.values())) + best_model_name = list(model_report.keys())[list(model_report.values()).index(best_model_score)] + + best_model = models[best_model_name] + + + logging.info(f"Best found model on both training and testing data is {best_model_name}") + + preprocessing_object = load_object(file_path=preprocessor_path) + + custom_model = CustomModel( + preprocessing_object = preprocessing_object, + trained_model_object = best_model + ) + + logging.info(f"Saving the best model {self.model_trainer_config.trained_model_file_path}") + + save_object( + file_path=self.model_trainer_config.trained_model_file_path, + obj=custom_model + ) + + predicated = best_model.predict(X_test) + r2 = r2_score(y_test,predicated) + logging.info(f"R2 Score for the model is {r2}") + + except Exception as e: + raise CustomException(e,sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/constant/__init__.py b/Sensor Fault Detection/web/src/constant/__init__.py new file mode 100644 index 000000000..b44e38265 --- /dev/null +++ b/Sensor Fault Detection/web/src/constant/__init__.py @@ -0,0 +1,2 @@ +MONGO_DATABASE_NAME = "sensor" +MONGO_COLLECTION_NAME = "wafer_23012020_041211" \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/constant/__pycache__/__init__.cpython-310.pyc b/Sensor Fault Detection/web/src/constant/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..285c1cb15 Binary files /dev/null and b/Sensor Fault Detection/web/src/constant/__pycache__/__init__.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/exception.py b/Sensor Fault Detection/web/src/exception.py new file mode 100644 index 000000000..1278462ec --- /dev/null +++ b/Sensor Fault Detection/web/src/exception.py @@ -0,0 +1,30 @@ +import sys + +def error_message_details(error,error_details:sys): + _,_,exc_tb = error_details.exc_info() + + file_name = exc_tb.tb_frame.f_code.co_filename + + error_message = "Error occurred python script name [{0}] line number [{1}] error message [{2}]".format( + file_name,exc_tb.tb_lineno,str(error) + ) + + return error_message + +class CustomException(Exception): + def __init__(self,error_message,error_details:sys): + # `super().__init__(error_message)` is calling the constructor of the parent class of + # `CustomException`, which is `Exception`. By calling `super().__init__(error_message)`, the + # `error_message` is passed to the constructor of the `Exception` class, which sets the error + # message for the custom exception. This allows the custom exception `CustomException` to + # inherit behavior from the `Exception` class and set the error message when an instance of + # `CustomException` is created. + super().__init__(error_message) + self.error_message = error_message_details(error_message,error_details=error_details) + + def __str__(self): + """ + This function is used to return a string representation of an object when the `str()` function + is called on it. + """ + return self.error_message \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/logger.py b/Sensor Fault Detection/web/src/logger.py new file mode 100644 index 000000000..381bf4e6e --- /dev/null +++ b/Sensor Fault Detection/web/src/logger.py @@ -0,0 +1,17 @@ +import logging +import os +from datetime import datetime + +LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log" + +logs_path = os.path.join(os.getcwd(), "logs", LOG_FILE) + +os.makedirs(logs_path, exist_ok=True) + +LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE) + +logging.basicConfig( + filename=LOG_FILE_PATH, + format = "[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s", + level=logging.INFO +) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/pipeline/__init__.py b/Sensor Fault Detection/web/src/pipeline/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Sensor Fault Detection/web/src/pipeline/__pycache__/__init__.cpython-310.pyc b/Sensor Fault Detection/web/src/pipeline/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..33f5c4b80 Binary files /dev/null and b/Sensor Fault Detection/web/src/pipeline/__pycache__/__init__.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/pipeline/__pycache__/predict_pipeline.cpython-310.pyc b/Sensor Fault Detection/web/src/pipeline/__pycache__/predict_pipeline.cpython-310.pyc new file mode 100644 index 000000000..117528168 Binary files /dev/null and b/Sensor Fault Detection/web/src/pipeline/__pycache__/predict_pipeline.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/pipeline/__pycache__/train_pipeline.cpython-310.pyc b/Sensor Fault Detection/web/src/pipeline/__pycache__/train_pipeline.cpython-310.pyc new file mode 100644 index 000000000..903c4b816 Binary files /dev/null and b/Sensor Fault Detection/web/src/pipeline/__pycache__/train_pipeline.cpython-310.pyc differ diff --git a/Sensor Fault Detection/web/src/pipeline/predict_pipeline.py b/Sensor Fault Detection/web/src/pipeline/predict_pipeline.py new file mode 100644 index 000000000..5fb1c8fe9 --- /dev/null +++ b/Sensor Fault Detection/web/src/pipeline/predict_pipeline.py @@ -0,0 +1,99 @@ +import os,sys +import pandas as pd +from src.logger import logging + +from src.exception import CustomException +import sys +from flask import request +from src.constant import * +from src.utils import download_model , load_object + +from dataclasses import dataclass + +@dataclass +class PredicationFileDetails: + predication_output_dirname : str= "predications" + predication_file_name:str = "predication_file.csv" + predication_file_path:str = os.path.join(predication_output_dirname,predication_file_name) + predication_file_text:str = "" + +class PredicationPipeline: + def __init__(self,request): + self.request = request + self.predication_file_detail = PredicationFileDetails() + + def save_input_files(self) ->str: + try: + pred_file_input_dir = 'predication_artifacts' + os.makedirs(pred_file_input_dir,exist_ok=True) + + input_csv_file = self.request.files['file'] + pred_file_path = os.path.join(pred_file_input_dir,input_csv_file.filename) + input_csv_file.save(pred_file_path) + return pred_file_path + except Exception as e: + raise CustomException(e,sys) + + def select_and_fill_features(self,features, df_required_feature): + # Convert features to DataFrame for easier manipulation + df_features = pd.DataFrame(features, columns=df_required_feature) + + # Remove extra columns + df_features = df_features[df_required_feature] + + # Fill missing values with median + for column in df_features.columns: + if df_features[column].isnull().any(): + df_features[column].fillna(df_features[column].median(), inplace=True) + + return df_features + + def predict(self,features): + try: + # model_path = download_model( + # bucket_name="something", + # bucket_file_name="model.pkl", + # dest_file_name = 'model.pkl' + # ) + + df_required_feature = ["Sensor-" + str(i) for i in range(1, 591)] + features = self.select_and_fill_features(features, df_required_feature) + + model_path = os.path.join(os.path.dirname(__file__), '../../artifacts/model.pkl') + model = load_object(file_path=model_path) + + preds = model.predict(features) + return preds + except Exception as e: + raise CustomException (e,sys) + + def get_predicated_dataframe(self,input_dataframe_path:pd.DataFrame): + try: + predication_column_name : str = "Good/Bad" + input_dataframe:pd.DataFrame = pd.read_csv(input_dataframe_path) + predications = self.predict(input_dataframe) + input_dataframe[predication_column_name] = [pred for pred in predications] + target_column_mapping = {0:'negative',1:'positive'} + + input_dataframe[predication_column_name] = input_dataframe[predication_column_name].map(target_column_mapping) + + positive = input_dataframe[predication_column_name] == 'positive' + negative = input_dataframe[predication_column_name] == 'negative' + + self.predication_file_detail.predication_file_text = f"Good : {positive.sum()} , Bad : {negative.sum()}" + + os.makedirs(self.predication_file_detail.predication_output_dirname,exist_ok=True) + + input_dataframe.to_csv(self.predication_file_detail.predication_file_path,index=False) + + logging.info("Predication complete . ") + except Exception as e: + raise CustomException (e,sys) + + def runpipeline(self): + try: + input_csv_path = self.save_input_files() + self.get_predicated_dataframe(input_csv_path) + return self.predication_file_detail + except Exception as e: + raise CustomException (e,sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/pipeline/train_pipeline.py b/Sensor Fault Detection/web/src/pipeline/train_pipeline.py new file mode 100644 index 000000000..a32cba0b8 --- /dev/null +++ b/Sensor Fault Detection/web/src/pipeline/train_pipeline.py @@ -0,0 +1,27 @@ +import sys + +from src.components.data_ingestion import DataIngestion +from src.components.data_transformation import DataTransformation +from src.components.model_trainer import ModelTrainer +from src.exception import CustomException +from src.logger import logging + +class TrainPipeline: + def __init__(self) -> None: + self.data_ingestion = DataIngestion() + self.data_transformation = DataTransformation() + self.model_trainer = ModelTrainer() + + def run_pipeline(self): + try: + logging.info("Training pipeline initiated.") + train_path,test_path = self.data_ingestion.initiate_data_ingestion() + print(train_path,'train_path') + print(test_path,'test_path') + + (train_arr,test_arr,preprocessor_file_path) = self.data_transformation.initiate_data_transformation(train_path=train_path,test_path=test_path) + + r2_square = self.model_trainer.initiate_model_trainer(train_array=train_arr,test_array=test_arr,preprocessor_path=preprocessor_file_path) + logging.info("training completed. Trained model score : "+str(r2_square)) + except Exception as e: + raise CustomException(e,sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/src/utils.py b/Sensor Fault Detection/web/src/utils.py new file mode 100644 index 000000000..66357b3e3 --- /dev/null +++ b/Sensor Fault Detection/web/src/utils.py @@ -0,0 +1,74 @@ +import os , sys +import dill +# Set the LOKY_MAX_CPU_COUNT environment variable +LOKY_MAX_CPU_COUNT = 4 +import numpy as np +import boto3 +from src.exception import CustomException +from sklearn.metrics import r2_score +import pandas as pd +from sklearn.model_selection import train_test_split +from pymongo import MongoClient +from src.logger import logging +from dotenv import load_dotenv + +def export_collection_as_dataframe(collection_name, db_name): + try: + logging.info("Entered export_collection_as_dataframe method of utils module") + load_dotenv() + mongo_client = MongoClient(os.getenv("MONGO_DB_URL")) + collection = mongo_client[db_name][collection_name] + df = pd.DataFrame(list(collection.find())) + if "_id" in df.columns.to_list(): + df = df.drop(columns=["_id"],axis=1) + df.replace({"na":np.nan},inplace=True) + logging.info("Exited export_collection_as_dataframe method of utils module") + return df + + except Exception as e: + raise CustomException(e, sys) + +def save_object(file_path,obj): + try: + with open(file_path,"wb") as file_obj: + dill.dump(obj,file_obj) + except Exception as e: + raise CustomException(e, sys) + +def load_object(file_path): + try: + with open(file_path,"rb") as file_obj: + return dill.load(file_obj) + except Exception as e: + raise CustomException(e, sys) + +def upload_file(from_filename , to_filename , bucket_name): + try: + s3_resource = boto3.resource("s3") + s3_resource.meta.client.upload_file(from_filename,bucket_name,to_filename) + except Exception as e: + raise CustomException(e, sys) + +def download_model(bucket_name,bucket_file_name,dest_file_name): + try: + s3_client = boto3.client("s3") + s3_client.download_file(bucket_name,bucket_file_name,dest_file_name) + return dest_file_name + except Exception as e: + raise CustomException(e, sys) + +def evaluate_models(models, X,y): + try: + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + model_report = {} + for i in range(len(list(models))): + model = list(models.values())[i] + model.fit(X_train, y_train) + y_train_pred = model.predict(X_train) + y_test_pred = model.predict(X_test) + test_model_score = r2_score(y_test, y_test_pred) + model_report[list(models.keys())[i]] = [test_model_score] + print(model_report) + return model_report + except Exception as e: + raise CustomException(e, sys) \ No newline at end of file diff --git a/Sensor Fault Detection/web/templates/home.html b/Sensor Fault Detection/web/templates/home.html new file mode 100644 index 000000000..ba6fec6b8 --- /dev/null +++ b/Sensor Fault Detection/web/templates/home.html @@ -0,0 +1,32 @@ + + + + + + + Welcome + + + + + +
+

Welcome to Sensor Working Predication Model

+

Click the button below to start predicting.

+ Start + Predicting +
+ + + \ No newline at end of file diff --git a/Sensor Fault Detection/web/templates/upload_file.html b/Sensor Fault Detection/web/templates/upload_file.html new file mode 100644 index 000000000..35b003718 --- /dev/null +++ b/Sensor Fault Detection/web/templates/upload_file.html @@ -0,0 +1,73 @@ + + + + + + + Diamond Price Prediction + + + + + +
+
+

Sensor Health Prediction Model

+

Predict Working Condition of Sensor with machine learning

+
+
+
+

Upload CSV for the prediction

+ +
+
+

Drop file to upload

+
+ +
+
+ +
+ +
+
+
+
+

About the Model

+

Our model uses machine learning algorithms to predict . For this + model we have used multiple Base Model and ensembling technique in which RandomForest and AdaBoost + Perform best . This is + end to end project where we have trained the model and deployed , pipeline is for ingestion of data + and prediction + of price. +

+

Model Accuracy 99%

+

+ {{prediction_text}} +

+ + {% if prediction_text %} + + Download File + + {% endif %} +
+
+
+ + + \ No newline at end of file