nuluh/thesis
1
0
Fork 0
Code Issues 47 Pull Requests Actions Packages Projects Releases 1 Wiki Activity

refactor(notebooks): update STFT notebook to improve clarity and structure of sensor evaluation sections

Browse Source
This commit is contained in:
nuluh
2025-07-24 17:00:31 +07:00
parent 2fbdeac1eb
commit 9b018efc15
1 changed files with 61 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

135
code/notebooks/stft.ipynb
View File

@@ -803,6 +803,13 @@
"Following code is load the exported model for specific sensor side that was trained on Dataset A and then used to predict the class labels for the unseen Dataset B."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sensor A"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -828,84 +835,11 @@
"print(classification_report(y, y_pred_svm_1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
"# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
"y_pred_svm_2 = svm_model.predict(X2b)\n",
"\n",
"# 5. Evaluate\n",
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
"print(classification_report(y, y_pred_svm_2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model sensor 1 to predict sensor 2 data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, classification_report\n",
"# 4. Validate on Dataset B\n",
"from joblib import load\n",
"svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
"y_pred_svm = svm_model.predict(X2b)\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"
"#### Confusion Matrix Sensor A"
]
},
{
@@ -930,6 +864,35 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sensor B"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
"# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
"y_pred_svm_2 = svm_model.predict(X2b)\n",
"\n",
"# 5. Evaluate\n",
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
"print(classification_report(y, y_pred_svm_2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Confusion Matrix Sensor B"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -951,6 +914,30 @@
"plt.title(\"Confusion Matrix of Sensor B Test on Dataset B\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model sensor 1 to predict sensor 2 data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, classification_report\n",
"# 4. Validate on Dataset B\n",
"from joblib import load\n",
"svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
"y_pred_svm = svm_model.predict(X2b)\n",
"\n",
"# 5. Evaluate\n",
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
"print(classification_report(y, y_pred_svm))"
]
}
],
"metadata": {