Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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d151062115 | ||
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a32415cebf | ||
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12669ed24c |
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.gitignore
vendored
1
.gitignore
vendored
@@ -2,3 +2,4 @@
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data/**/*.csv
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.venv/
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*.pyc
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*.egg-info/
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@@ -21,6 +21,7 @@
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#
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# Scope:
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# latex (changes to thesis LaTeX)
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# documentclass (LaTeX in-house document class changes)
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# src (changes to Python source code)
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# nb (changes to notebooks)
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# ml (ML model specific changes)
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3
.vscode/settings.json
vendored
3
.vscode/settings.json
vendored
@@ -1,3 +1,4 @@
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{
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"python.analysis.extraPaths": ["./code/src/features"]
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"python.analysis.extraPaths": ["./code/src/features"],
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"jupyter.notebookFileRoot": "${workspaceFolder}/code"
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}
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@@ -16,3 +16,8 @@ The repository is private and access is restricted only to those who have been g
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All contents of this repository, including the thesis idea, code, and associated data, are copyrighted © 2024 by Rifqi Panuluh. Unauthorized use or duplication is prohibited.
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[LICENSE](https://github.com/nuluh/thesis?tab=License-1-ov-file#readme)
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## How to Run `stft.ipynb`
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1. run `pip install -e .` in root project first
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2. run the notebook
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@@ -155,7 +155,7 @@
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"import pandas as pd\n",
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"import numpy as np\n",
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"from scipy.signal import stft, hann\n",
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"from multiprocessing import Pool\n",
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"# from multiprocessing import Pool\n",
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"\n",
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"# Function to compute and append STFT data\n",
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"def process_stft(args):\n",
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@@ -321,9 +321,9 @@
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"ready_data1 = []\n",
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"ready_data1a = []\n",
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"for file in os.listdir('D:/thesis/data/converted/raw/sensor1'):\n",
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" ready_data1.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
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" ready_data1a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
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"# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
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"# Plotting the STFT Data"
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]
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@@ -334,8 +334,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ready_data1[0]\n",
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"plt.pcolormesh(ready_data1[0])"
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"len(ready_data1a)\n",
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"# plt.pcolormesh(ready_data1[0])"
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]
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},
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{
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@@ -345,7 +345,7 @@
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"outputs": [],
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"source": [
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"for i in range(6):\n",
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" plt.pcolormesh(ready_data1[i])\n",
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" plt.pcolormesh(ready_data1a[i])\n",
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" plt.title(f'STFT Magnitude for case {i} sensor 1')\n",
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" plt.xlabel(f'Frequency [Hz]')\n",
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" plt.ylabel(f'Time [sec]')\n",
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@@ -358,9 +358,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ready_data2 = []\n",
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"ready_data2a = []\n",
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"for file in os.listdir('D:/thesis/data/converted/raw/sensor2'):\n",
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" ready_data2.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
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" ready_data2a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
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]
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},
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{
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@@ -369,8 +369,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(len(ready_data1))\n",
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"print(len(ready_data2))"
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"print(len(ready_data1a))\n",
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"print(len(ready_data2a))"
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]
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},
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{
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@@ -379,10 +379,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x1 = 0\n",
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"print(type(ready_data1[0]))\n",
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"ready_data1[0].iloc[:,0]\n",
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"# x1 = x1 + ready_data1[0].shape[0]"
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"x1a = 0\n",
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"print(type(ready_data1a[0]))\n",
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"ready_data1a[0].iloc[:,0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Checking length of the total array"
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]
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},
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{
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@@ -391,16 +397,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x1 = 0\n",
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"print(type(x1))\n",
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"for i in range(len(ready_data1)):\n",
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" # print(ready_data1[i].shape)\n",
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" # print(ready_data1[i].)\n",
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" print(type(ready_data1[i].shape[0]))\n",
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" x1 = x1 + ready_data1[i].shape[0]\n",
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" print(type(x1))\n",
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"x1a = 0\n",
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"print(type(x1a))\n",
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"for i in range(len(ready_data1a)):\n",
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" print(type(ready_data1a[i].shape[0]))\n",
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" x1a = x1a + ready_data1a[i].shape[0]\n",
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" print(type(x1a))\n",
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"\n",
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"print(x1)"
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"print(x1a)"
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]
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},
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{
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@@ -409,13 +413,20 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x2 = 0\n",
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"x2a = 0\n",
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"\n",
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"for i in range(len(ready_data2)):\n",
|
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" print(ready_data2[i].shape)\n",
|
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" x2 = x2 + ready_data2[i].shape[0]\n",
|
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"for i in range(len(ready_data2a)):\n",
|
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" print(ready_data2a[i].shape)\n",
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" x2a = x2a + ready_data2a[i].shape[0]\n",
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"\n",
|
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"print(x2)"
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"print(x2a)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Flatten 6 array into one array"
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]
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},
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{
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@@ -424,28 +435,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x1 = ready_data1[0]\n",
|
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"# print(x1)\n",
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"print(type(x1))\n",
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"for i in range(len(ready_data1) - 1):\n",
|
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" #print(i)\n",
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" x1 = np.concatenate((x1, ready_data1[i + 1]), axis=0)\n",
|
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"# print(x1)\n",
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"pd.DataFrame(x1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x2 = ready_data2[0]\n",
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"# Combine all dataframes in ready_data1a into a single dataframe\n",
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"if ready_data1a: # Check if the list is not empty\n",
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" # Use pandas concat function instead of iterative concatenation\n",
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" combined_data = pd.concat(ready_data1a, axis=0, ignore_index=True)\n",
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" \n",
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" print(f\"Type of combined data: {type(combined_data)}\")\n",
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" print(f\"Shape of combined data: {combined_data.shape}\")\n",
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" \n",
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" # Display the combined dataframe\n",
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" combined_data\n",
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"else:\n",
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" print(\"No data available in ready_data1a list\")\n",
|
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" combined_data = pd.DataFrame()\n",
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"\n",
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"for i in range(len(ready_data2) - 1):\n",
|
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" #print(i)\n",
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" x2 = np.concatenate((x2, ready_data2[i + 1]), axis=0)\n",
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"pd.DataFrame(x2)"
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"# Store the result in x1a for compatibility with subsequent code\n",
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"x1a = combined_data"
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]
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},
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{
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||||
@@ -454,20 +459,29 @@
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||||
"metadata": {},
|
||||
"outputs": [],
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"source": [
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"print(x1.shape)\n",
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"print(x2.shape)"
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||||
"# Combine all dataframes in ready_data1a into a single dataframe\n",
|
||||
"if ready_data2a: # Check if the list is not empty\n",
|
||||
" # Use pandas concat function instead of iterative concatenation\n",
|
||||
" combined_data = pd.concat(ready_data2a, axis=0, ignore_index=True)\n",
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||||
" \n",
|
||||
" print(f\"Type of combined data: {type(combined_data)}\")\n",
|
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" print(f\"Shape of combined data: {combined_data.shape}\")\n",
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" \n",
|
||||
" # Display the combined dataframe\n",
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" combined_data\n",
|
||||
"else:\n",
|
||||
" print(\"No data available in ready_data1a list\")\n",
|
||||
" combined_data = pd.DataFrame()\n",
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"\n",
|
||||
"# Store the result in x1a for compatibility with subsequent code\n",
|
||||
"x2a = combined_data"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"y_1 = [1,1,1,1]\n",
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"y_2 = [0,1,1,1]\n",
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"y_3 = [1,0,1,1]\n",
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"y_4 = [1,1,0,0]"
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"### Creating the label"
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]
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||||
},
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{
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@@ -490,7 +504,8 @@
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"y_data = [y_1, y_2, y_3, y_4, y_5, y_6]"
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"y_data = [y_1, y_2, y_3, y_4, y_5, y_6]\n",
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"y_data"
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]
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||||
},
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{
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||||
@@ -500,7 +515,7 @@
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||||
"outputs": [],
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||||
"source": [
|
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"for i in range(len(y_data)):\n",
|
||||
" print(ready_data1[i].shape[0])"
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" print(ready_data1a[i].shape[0])"
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]
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},
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{
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@@ -509,9 +524,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"for i in range(len(y_data)):\n",
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" y_data[i] = [y_data[i]]*ready_data1[i].shape[0]\n",
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" y_data[i] = np.array(y_data[i])"
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" y_data[i] = [y_data[i]]*ready_data1a[i].shape[0]"
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]
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},
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{
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@@ -520,6 +535,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# len(y_data[0])\n",
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"y_data"
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]
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},
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@@ -552,10 +568,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from src.ml.model_selection import create_ready_data\n",
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"\n",
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"x_train1, x_test1, y_train, y_test = train_test_split(x1, y, test_size=0.2, random_state=2)\n",
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"x_train2, x_test2, y_train, y_test = train_test_split(x2, y, test_size=0.2, random_state=2)"
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"X1a, y = create_ready_data('D:/thesis/data/converted/raw/sensor1')\n",
|
||||
"X2a, y = create_ready_data('D:/thesis/data/converted/raw/sensor2')"
|
||||
]
|
||||
},
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||||
{
|
||||
@@ -565,6 +581,17 @@
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"x_train1, x_test1, y_train, y_test = train_test_split(X1a, y, test_size=0.2, random_state=2)\n",
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"x_train2, x_test2, y_train, y_test = train_test_split(X2a, y, test_size=0.2, random_state=2)"
|
||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score\n",
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"from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
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||||
"from sklearn.tree import DecisionTreeClassifier\n",
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@@ -597,16 +624,17 @@
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"\n",
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"\n",
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"# 1. Random Forest\n",
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"rf_model = RandomForestClassifier()\n",
|
||||
"rf_model.fit(x_train1, y_train)\n",
|
||||
"rf_pred1 = rf_model.predict(x_test1)\n",
|
||||
"rf_model1 = RandomForestClassifier()\n",
|
||||
"rf_model1.fit(x_train1, y_train)\n",
|
||||
"rf_pred1 = rf_model1.predict(x_test1)\n",
|
||||
"acc1 = accuracy_score(y_test, rf_pred1) * 100\n",
|
||||
"accuracies1.append(acc1)\n",
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"Random Forest Accuracy for sensor 1:\", acc1)\n",
|
||||
"rf_model.fit(x_train2, y_train)\n",
|
||||
"rf_pred2 = rf_model.predict(x_test2)\n",
|
||||
"rf_model2 = RandomForestClassifier()\n",
|
||||
"rf_model2.fit(x_train2, y_train)\n",
|
||||
"rf_pred2 = rf_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, rf_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -616,16 +644,17 @@
|
||||
"# print(y_test)\n",
|
||||
"\n",
|
||||
"# 2. Bagged Trees\n",
|
||||
"bagged_model = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
|
||||
"bagged_model.fit(x_train1, y_train)\n",
|
||||
"bagged_pred1 = bagged_model.predict(x_test1)\n",
|
||||
"bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
|
||||
"bagged_model1.fit(x_train1, y_train)\n",
|
||||
"bagged_pred1 = bagged_model1.predict(x_test1)\n",
|
||||
"acc1 = accuracy_score(y_test, bagged_pred1) * 100\n",
|
||||
"accuracies1.append(acc1)\n",
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n",
|
||||
"bagged_model.fit(x_train2, y_train)\n",
|
||||
"bagged_pred2 = bagged_model.predict(x_test2)\n",
|
||||
"bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
|
||||
"bagged_model2.fit(x_train2, y_train)\n",
|
||||
"bagged_pred2 = bagged_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, bagged_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -641,8 +670,9 @@
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n",
|
||||
"dt_model.fit(x_train2, y_train)\n",
|
||||
"dt_pred2 = dt_model.predict(x_test2)\n",
|
||||
"dt_model2 = DecisionTreeClassifier()\n",
|
||||
"dt_model2.fit(x_train2, y_train)\n",
|
||||
"dt_pred2 = dt_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, dt_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -658,8 +688,9 @@
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n",
|
||||
"knn_model.fit(x_train2, y_train)\n",
|
||||
"knn_pred2 = knn_model.predict(x_test2)\n",
|
||||
"knn_model2 = KNeighborsClassifier()\n",
|
||||
"knn_model2.fit(x_train2, y_train)\n",
|
||||
"knn_pred2 = knn_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, knn_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -675,8 +706,9 @@
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n",
|
||||
"lda_model.fit(x_train2, y_train)\n",
|
||||
"lda_pred2 = lda_model.predict(x_test2)\n",
|
||||
"lda_model2 = LinearDiscriminantAnalysis()\n",
|
||||
"lda_model2.fit(x_train2, y_train)\n",
|
||||
"lda_pred2 = lda_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, lda_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -692,8 +724,9 @@
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n",
|
||||
"svm_model.fit(x_train2, y_train)\n",
|
||||
"svm_pred2 = svm_model.predict(x_test2)\n",
|
||||
"svm_model2 = SVC()\n",
|
||||
"svm_model2.fit(x_train2, y_train)\n",
|
||||
"svm_pred2 = svm_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, svm_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -709,8 +742,9 @@
|
||||
"# format with color coded if acc1 > 90\n",
|
||||
"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
|
||||
"print(\"XGBoost Accuracy:\", acc1)\n",
|
||||
"xgboost_model.fit(x_train2, y_train)\n",
|
||||
"xgboost_pred2 = xgboost_model.predict(x_test2)\n",
|
||||
"xgboost_model2 = XGBClassifier()\n",
|
||||
"xgboost_model2.fit(x_train2, y_train)\n",
|
||||
"xgboost_pred2 = xgboost_model2.predict(x_test2)\n",
|
||||
"acc2 = accuracy_score(y_test, xgboost_pred2) * 100\n",
|
||||
"accuracies2.append(acc2)\n",
|
||||
"# format with color coded if acc2 > 90\n",
|
||||
@@ -787,51 +821,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def spectograph(data_dir: str):\n",
|
||||
" # print(os.listdir(data_dir))\n",
|
||||
" for damage in os.listdir(data_dir):\n",
|
||||
" # print(damage)\n",
|
||||
" d = os.path.join(data_dir, damage)\n",
|
||||
" # print(d)\n",
|
||||
" for file in os.listdir(d):\n",
|
||||
" # print(file)\n",
|
||||
" f = os.path.join(d, file)\n",
|
||||
" print(f)\n",
|
||||
" # sensor1 = pd.read_csv(f, skiprows=1, sep=';')\n",
|
||||
" # sensor2 = pd.read_csv(f, skiprows=1, sep=';')\n",
|
||||
"from src.ml.model_selection import create_ready_data\n",
|
||||
"\n",
|
||||
" # df1 = pd.DataFrame()\n",
|
||||
"\n",
|
||||
" # df1['s1'] = sensor1[sensor1.columns[-1]]\n",
|
||||
" # df1['s2'] = sensor2[sensor2.columns[-1]]\n",
|
||||
"ed\n",
|
||||
" # # Combined Plot for sensor 1 and sensor 2 from data1 file in which motor is operated at 800 rpm\n",
|
||||
"\n",
|
||||
" # plt.plot(df1['s2'], label='sensor 2')\n",
|
||||
" # plt.plot(df1['s1'], label='sensor 1')\n",
|
||||
" # plt.xlabel(\"Number of samples\")\n",
|
||||
" # plt.ylabel(\"Amplitude\")\n",
|
||||
" # plt.title(\"Raw vibration signal\")\n",
|
||||
" # plt.legend()\n",
|
||||
" # plt.show()\n",
|
||||
"\n",
|
||||
" # from scipy import signal\n",
|
||||
" # from scipy.signal.windows import hann\n",
|
||||
"\n",
|
||||
" # vibration_data = df1['s1']\n",
|
||||
"\n",
|
||||
" # # Applying STFT\n",
|
||||
" # window_size = 1024\n",
|
||||
" # hop_size = 512\n",
|
||||
" # window = hann(window_size) # Creating a Hanning window\n",
|
||||
" # frequencies, times, Zxx = signal.stft(vibration_data, window=window, nperseg=window_size, noverlap=window_size - hop_size)\n",
|
||||
"\n",
|
||||
" # # Plotting the STFT Data\n",
|
||||
" # plt.pcolormesh(times, frequencies, np.abs(Zxx), shading='gouraud')\n",
|
||||
" # plt.title(f'STFT Magnitude for case 1 signal sensor 1 ')\n",
|
||||
" # plt.ylabel('Frequency [Hz]')\n",
|
||||
" # plt.xlabel('Time [sec]')\n",
|
||||
" # plt.show()"
|
||||
"X1b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor1')\n",
|
||||
"X2b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor2')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -840,7 +833,115 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"spectograph('D:/thesis/data/converted/raw')"
|
||||
"y.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||||
"# 4. Validate on Dataset B\n",
|
||||
"y_pred_svm = svm_model.predict(X1b)\n",
|
||||
"\n",
|
||||
"# 5. Evaluate\n",
|
||||
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
|
||||
"print(classification_report(y, y_pred_svm))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||||
"# 4. Validate on Dataset B\n",
|
||||
"y_pred = rf_model2.predict(X2b)\n",
|
||||
"\n",
|
||||
"# 5. Evaluate\n",
|
||||
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred))\n",
|
||||
"print(classification_report(y, y_pred))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_predict = svm_model2.predict(X2b.iloc[[5312],:])\n",
|
||||
"print(y_predict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y[5312]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Confusion Matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"cm = confusion_matrix(y, y_pred_svm) # -> ndarray\n",
|
||||
"\n",
|
||||
"# get the class labels\n",
|
||||
"labels = svm_model.classes_\n",
|
||||
"\n",
|
||||
"# Plot\n",
|
||||
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
|
||||
"disp.plot(cmap=plt.cm.Blues) # You can change colormap\n",
|
||||
"plt.title(\"SVM Sensor1 CM Train w/ Dataset A Val w/ Dataset B\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Self-test CM"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 1. Predict sensor 1 on Dataset A\n",
|
||||
"y_train_pred = svm_model.predict(x_train1)\n",
|
||||
"\n",
|
||||
"# 2. Import confusion matrix tools\n",
|
||||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# 3. Create and plot confusion matrix\n",
|
||||
"cm_train = confusion_matrix(y_train, y_train_pred)\n",
|
||||
"labels = svm_model.classes_\n",
|
||||
"\n",
|
||||
"disp = ConfusionMatrixDisplay(confusion_matrix=cm_train, display_labels=labels)\n",
|
||||
"disp.plot(cmap=plt.cm.Blues)\n",
|
||||
"plt.title(\"Confusion Matrix: Train & Test on Dataset A\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
0
code/src/ml/__init__.py
Normal file
0
code/src/ml/__init__.py
Normal file
57
code/src/ml/model_selection.py
Normal file
57
code/src/ml/model_selection.py
Normal file
@@ -0,0 +1,57 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
from sklearn.model_selection import train_test_split as sklearn_split
|
||||
|
||||
|
||||
def create_ready_data(
|
||||
stft_data_path: str,
|
||||
stratify: np.ndarray = None,
|
||||
) -> tuple:
|
||||
"""
|
||||
Create a stratified train-test split from STFT data.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
stft_data_path : str
|
||||
Path to the directory containing STFT data files (e.g. 'data/converted/raw/sensor1')
|
||||
stratify : np.ndarray, optional
|
||||
Labels to use for stratified sampling
|
||||
|
||||
Returns:
|
||||
--------
|
||||
tuple
|
||||
(X_train, X_test, y_train, y_test) - Split datasets
|
||||
"""
|
||||
ready_data = []
|
||||
for file in os.listdir(stft_data_path):
|
||||
ready_data.append(pd.read_csv(os.path.join(stft_data_path, file)))
|
||||
|
||||
y_data = [i for i in range(len(ready_data))]
|
||||
|
||||
# Combine all dataframes in ready_data into a single dataframe
|
||||
if ready_data: # Check if the list is not empty
|
||||
# Use pandas concat function instead of iterative concatenation
|
||||
combined_data = pd.concat(ready_data, axis=0, ignore_index=True)
|
||||
|
||||
print(f"Type of combined data: {type(combined_data)}")
|
||||
print(f"Shape of combined data: {combined_data.shape}")
|
||||
else:
|
||||
print("No data available in ready_data list")
|
||||
combined_data = pd.DataFrame()
|
||||
|
||||
# Store the result in x1a for compatibility with subsequent code
|
||||
X = combined_data
|
||||
|
||||
for i in range(len(y_data)):
|
||||
y_data[i] = [y_data[i]] * ready_data[i].shape[0]
|
||||
y_data[i] = np.array(y_data[i])
|
||||
|
||||
if y_data:
|
||||
# Use numpy concatenate function instead of iterative concatenation
|
||||
y = np.concatenate(y_data, axis=0)
|
||||
else:
|
||||
print("No labels available in y_data list")
|
||||
y = np.array([])
|
||||
|
||||
return X, y
|
||||
@@ -1,31 +1,30 @@
|
||||
\begin{titlepage}
|
||||
\centering
|
||||
\vspace*{1cm}
|
||||
\centering
|
||||
\vspace*{1cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thesistitle}}\par}
|
||||
\vspace{1.5cm}
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thetitle}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
|
||||
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentname}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\theauthor}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
|
||||
|
||||
\vfill
|
||||
\vfill
|
||||
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
|
||||
29
latex/frontmatter/maketitle_secondary.tex
Normal file
29
latex/frontmatter/maketitle_secondary.tex
Normal file
@@ -0,0 +1,29 @@
|
||||
\begin{titlepage}
|
||||
\centering
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thetitle}}\par}
|
||||
\vspace{1cm}
|
||||
{\normalsize\selectfont Diajukan guna melengkapi persyaratan untuk memenuhi gelar Sarjana Teknik di Program Studi Teknik Sipil, Fakultas Teknik, Universitas Muhammadiyah Yogyakarta\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
|
||||
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\theauthor}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
|
||||
|
||||
\vfill
|
||||
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
@@ -21,17 +21,25 @@
|
||||
\RequirePackage{tocloft}
|
||||
\RequirePackage{tocbibind}
|
||||
\RequirePackage{amsmath,amsfonts,amssymb}
|
||||
\RequirePackage{svg} % Allows including SVG images directly
|
||||
\RequirePackage{indentfirst} % Makes first paragraph after headings indented
|
||||
\RequirePackage{float} % Provides [H] option to force figure/table placement
|
||||
|
||||
% Polyglossia set language
|
||||
\setmainlanguage{bahasai}
|
||||
% \setotherlanguage{english}
|
||||
+ \setdefaultlanguage[variant=indonesian]{malay} % Proper Indonesian language setup
|
||||
+ \setotherlanguage{english} % Enables English as secondary language
|
||||
|
||||
+ \DefineBibliographyStrings{english}{% % Customizes bibliography text
|
||||
+ andothers={dkk\adddot}, % Changes "et al." to "dkk."
|
||||
+ pages={hlm\adddot}, % Changes "pp." to "hlm."
|
||||
+ }
|
||||
|
||||
% Conditionally load the watermark package and settings
|
||||
\if@draftmark
|
||||
\RequirePackage{draftwatermark}
|
||||
\SetWatermarkText{Draft: \today [wip]}
|
||||
\SetWatermarkColor[gray]{0.7}
|
||||
\SetWatermarkFontSize{2cm}
|
||||
\SetWatermarkText{nuluh/thesis (wip) draft: \today}
|
||||
\SetWatermarkColor[gray]{0.8} % Opacity: 0.8 = 20% transparent
|
||||
\SetWatermarkFontSize{1.5cm}
|
||||
\SetWatermarkAngle{90}
|
||||
\SetWatermarkHorCenter{1.5cm}
|
||||
\fi
|
||||
@@ -79,7 +87,10 @@
|
||||
}
|
||||
|
||||
% Chapter formatting
|
||||
\titlespacing{\chapter}{0pt}{0pt}{*1.5}
|
||||
\titlespacing{\chapter}{0pt}{0cm}{*1.5} % 0pt→0cm: same value, different unit
|
||||
% 0pt = no space above chapter title
|
||||
% *1.5 = 1.5× line spacing after title
|
||||
|
||||
\titleformat{\chapter}[display]
|
||||
{\normalsize\bfseries\centering}
|
||||
{BAB~\Roman{chapter}} % << display format
|
||||
@@ -91,6 +102,10 @@
|
||||
\titleformat{\subsection}
|
||||
{\normalsize\bfseries}{\thesubsection}{1em}{}
|
||||
|
||||
% Section numbering depth
|
||||
\setcounter{secnumdepth}{3} % Enables numbering for:
|
||||
% 1 = chapters, 2 = sections, 3 = subsections
|
||||
|
||||
% Ensure chapter reference in TOC matches
|
||||
\renewcommand{\cftchappresnum}{BAB~}
|
||||
\renewcommand{\cftchapaftersnum}{\quad}
|
||||
@@ -108,16 +123,22 @@
|
||||
|
||||
|
||||
% Dot leaders, spacing, indentation
|
||||
\setlength{\cftbeforetoctitleskip}{0cm} % Space above "DAFTAR ISI" title
|
||||
\setlength{\cftbeforeloftitleskip}{0cm} % Space above "DAFTAR GAMBAR" title
|
||||
\setlength{\cftbeforelottitleskip}{0cm} % Space above "DAFTAR TABEL" title
|
||||
|
||||
\setlength{\cftbeforechapskip}{0em}
|
||||
\setlength{\cftchapindent}{0pt}
|
||||
\setlength{\cftsecindent}{0em}
|
||||
\setlength{\cftsubsecindent}{2.5em}
|
||||
\setlength{\cftsubsecindent}{2em}
|
||||
\setlength{\cftchapnumwidth}{3.5em}
|
||||
\setlength{\cftsecnumwidth}{3.5em}
|
||||
\setlength{\cftsecnumwidth}{2em}
|
||||
\setlength{\cftsubsecnumwidth}{2.5em}
|
||||
\setlength{\cftfignumwidth}{5em}
|
||||
\setlength{\cfttabnumwidth}{4em}
|
||||
\renewcommand \cftchapdotsep{4.5} % https://tex.stackexchange.com/a/273764
|
||||
\renewcommand \cftchapdotsep{1} % Denser dots (closer together) https://tex.stackexchange.com/a/273764
|
||||
\renewcommand \cftsecdotsep{1} % Apply to sections too
|
||||
\renewcommand \cftsubsecdotsep{1} % Apply to subsections too
|
||||
\renewcommand{\cftchapleader}{\normalfont\cftdotfill{\cftsecdotsep}}
|
||||
\renewcommand{\cftchappagefont}{\normalfont}
|
||||
\renewcommand{\cftfigpresnum}{\figurename~}
|
||||
|
||||
Reference in New Issue
Block a user