Add Working Milestone with Initial Results and Model Inference (#82)
* wip: add function to create stratified train-test split from STFT data * feat(src): implement working function for dataset B to create ready data from STFT files stft_files and add setup.py for package configuration * feat(notebook): Update variable names for clarity, remove unused imports, and streamline data processing. Implement data concatenation using pandas concat for efficiency. Add validation steps for Dataset B and improve model training consistency across sensors. * fix(.gitignore): add rule to ignore egg-info directories and ensure proper formatting * docs(README): add instructions for running stft.ipynb notebook * feat(notebook): Add evaluation metrics and confusion matrix visualizations for model predictions on Dataset B. Remove commented-out code and integrate data preparation using create_ready_data function. --------- Co-authored-by: nuluh <dam.ar@outlook.com>
This commit was merged in pull request #82.
This commit is contained in:
Rifqi D. Panuluh
@@ -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": {},
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"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",
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"if ready_data2a: # 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_data2a, 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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"# Store the result in x1a for compatibility with subsequent code\n",
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"x2a = combined_data"
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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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"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",
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" 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",
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"X2a, y = create_ready_data('D:/thesis/data/converted/raw/sensor2')"
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]
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},
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{
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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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]
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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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"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",
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"rf_model.fit(x_train1, y_train)\n",
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"rf_pred1 = rf_model.predict(x_test1)\n",
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"rf_model1 = RandomForestClassifier()\n",
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"rf_model1.fit(x_train1, y_train)\n",
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"rf_pred1 = rf_model1.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, rf_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Random Forest Accuracy for sensor 1:\", acc1)\n",
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"rf_model.fit(x_train2, y_train)\n",
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"rf_pred2 = rf_model.predict(x_test2)\n",
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"rf_model2 = RandomForestClassifier()\n",
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"rf_model2.fit(x_train2, y_train)\n",
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"rf_pred2 = rf_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, rf_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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@@ -616,16 +644,17 @@
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"# print(y_test)\n",
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"\n",
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"# 2. Bagged Trees\n",
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"bagged_model = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
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"bagged_model.fit(x_train1, y_train)\n",
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"bagged_pred1 = bagged_model.predict(x_test1)\n",
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"bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
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"bagged_model1.fit(x_train1, y_train)\n",
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"bagged_pred1 = bagged_model1.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, bagged_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n",
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"bagged_model.fit(x_train2, y_train)\n",
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"bagged_pred2 = bagged_model.predict(x_test2)\n",
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"bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
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"bagged_model2.fit(x_train2, y_train)\n",
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"bagged_pred2 = bagged_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, bagged_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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@@ -641,8 +670,9 @@
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n",
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"dt_model.fit(x_train2, y_train)\n",
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"dt_pred2 = dt_model.predict(x_test2)\n",
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"dt_model2 = DecisionTreeClassifier()\n",
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"dt_model2.fit(x_train2, y_train)\n",
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"dt_pred2 = dt_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, dt_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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@@ -658,8 +688,9 @@
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n",
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"knn_model.fit(x_train2, y_train)\n",
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"knn_pred2 = knn_model.predict(x_test2)\n",
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"knn_model2 = KNeighborsClassifier()\n",
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"knn_model2.fit(x_train2, y_train)\n",
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"knn_pred2 = knn_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, knn_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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@@ -675,8 +706,9 @@
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n",
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"lda_model.fit(x_train2, y_train)\n",
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"lda_pred2 = lda_model.predict(x_test2)\n",
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"lda_model2 = LinearDiscriminantAnalysis()\n",
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"lda_model2.fit(x_train2, y_train)\n",
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"lda_pred2 = lda_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, lda_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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@@ -692,8 +724,9 @@
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n",
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"svm_model.fit(x_train2, y_train)\n",
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"svm_pred2 = svm_model.predict(x_test2)\n",
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"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in New Issue
Block a user