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udemy-ML/23-PCA-Principal-Component-.../.ipynb_checkpoints/03-PCA-Exercise-Project-Sol...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
"___\n",
"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Principal Component Analysis - Project Exercise Solutions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----\n",
"-----\n",
"\n",
"## GOAL: Figure out which handwritten digits are most differentiated with PCA.\n",
"\n",
"**Imagine you are working on an image recognition service for a postal service. It would be very useful to be able to read in the digits automatically, even if they are handwritten. (Quick note, this is very much how modern postal services work for a long time now and its actually more accurate than a human). The manager of the postal service wants to know which handwritten numbers are the hardest to tell apart, so he can focus on getting more labeled examples of that data. You will have a dataset of hand written digits (a very famous data set) and you will perform PCA to get better insight into which numbers are easily separable from the rest.**\n",
"\n",
"-----\n",
"-----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
" Background:\n",
"\n",
" E. Alpaydin, Fevzi. Alimoglu\n",
" Department of Computer Engineering\n",
" Bogazici University, 80815 Istanbul Turkey\n",
" alpaydin '@' boun.edu.tr\n",
"\n",
"\n",
"#### Data Set Information from Original Authors:\n",
"\n",
"We create a digit database by collecting 250 samples from 44 writers. The samples written by 30 writers are used for training, cross-validation and writer dependent testing, and the digits written by the other 14 are used for writer independent testing. This database is also available in the UNIPEN format.\n",
"\n",
"We use a WACOM PL-100V pressure sensitive tablet with an integrated LCD display and a cordless stylus. The input and display areas are located in the same place. Attached to the serial port of an Intel 486 based PC, it allows us to collect handwriting samples. The tablet sends $x$ and $y$ tablet coordinates and pressure level values of the pen at fixed time intervals (sampling rate) of 100 miliseconds.\n",
"\n",
"These writers are asked to write 250 digits in random order inside boxes of 500 by 500 tablet pixel resolution. Subject are monitored only during the first entry screens. Each screen contains five boxes with the digits to be written displayed above. Subjects are told to write only inside these boxes. If they make a mistake or are unhappy with their writing, they are instructed to clear the content of a box by using an on-screen button. The first ten digits are ignored because most writers are not familiar with this type of input devices, but subjects are not aware of this.\n",
"\n",
"SOURCE: https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Complete the Tasks in bold below\n",
"\n",
"**TASK: Run the cells below to import the libraries and relevant data set.**"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"digits = pd.read_csv('../DATA/digits.csv')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
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"execution_count": 37,
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"source": [
"digits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Create a new DataFrame called *pixels* that consists only of the pixel feature values by dropping the number_label column.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"pixels = digits.drop('number_label',axis=1)"
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{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>16.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>13.0</td>\n",
" <td>13.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1792</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>13.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1793</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>16.0</td>\n",
" <td>13.0</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1794</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1795</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>10.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>16.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1796</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>14.0</td>\n",
" <td>8.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>12.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1797 rows × 64 columns</p>\n",
"</div>"
],
"text/plain": [
" pixel_0_0 pixel_0_1 pixel_0_2 pixel_0_3 pixel_0_4 pixel_0_5 \\\n",
"0 0.0 0.0 5.0 13.0 9.0 1.0 \n",
"1 0.0 0.0 0.0 12.0 13.0 5.0 \n",
"2 0.0 0.0 0.0 4.0 15.0 12.0 \n",
"3 0.0 0.0 7.0 15.0 13.0 1.0 \n",
"4 0.0 0.0 0.0 1.0 11.0 0.0 \n",
"... ... ... ... ... ... ... \n",
"1792 0.0 0.0 4.0 10.0 13.0 6.0 \n",
"1793 0.0 0.0 6.0 16.0 13.0 11.0 \n",
"1794 0.0 0.0 1.0 11.0 15.0 1.0 \n",
"1795 0.0 0.0 2.0 10.0 7.0 0.0 \n",
"1796 0.0 0.0 10.0 14.0 8.0 1.0 \n",
"\n",
" pixel_0_6 pixel_0_7 pixel_1_0 pixel_1_1 ... pixel_6_6 pixel_6_7 \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 ... 5.0 0.0 \n",
"3 0.0 0.0 0.0 8.0 ... 9.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"1792 0.0 0.0 0.0 1.0 ... 4.0 0.0 \n",
"1793 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n",
"1794 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n",
"1795 0.0 0.0 0.0 0.0 ... 2.0 0.0 \n",
"1796 0.0 0.0 0.0 2.0 ... 8.0 0.0 \n",
"\n",
" pixel_7_0 pixel_7_1 pixel_7_2 pixel_7_3 pixel_7_4 pixel_7_5 \\\n",
"0 0.0 0.0 6.0 13.0 10.0 0.0 \n",
"1 0.0 0.0 0.0 11.0 16.0 10.0 \n",
"2 0.0 0.0 0.0 3.0 11.0 16.0 \n",
"3 0.0 0.0 7.0 13.0 13.0 9.0 \n",
"4 0.0 0.0 0.0 2.0 16.0 4.0 \n",
"... ... ... ... ... ... ... \n",
"1792 0.0 0.0 2.0 14.0 15.0 9.0 \n",
"1793 0.0 0.0 6.0 16.0 14.0 6.0 \n",
"1794 0.0 0.0 2.0 9.0 13.0 6.0 \n",
"1795 0.0 0.0 5.0 12.0 16.0 12.0 \n",
"1796 0.0 1.0 8.0 12.0 14.0 12.0 \n",
"\n",
" pixel_7_6 pixel_7_7 \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 9.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"1792 0.0 0.0 \n",
"1793 0.0 0.0 \n",
"1794 0.0 0.0 \n",
"1795 0.0 0.0 \n",
"1796 1.0 0.0 \n",
"\n",
"[1797 rows x 64 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pixels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Displaying an Image\n",
"\n",
"**TASK: Grab a single image row representation by getting the first row of the pixels DataFrame.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"single_image = pixels.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pixel_0_0 0.0\n",
"pixel_0_1 0.0\n",
"pixel_0_2 5.0\n",
"pixel_0_3 13.0\n",
"pixel_0_4 9.0\n",
" ... \n",
"pixel_7_3 13.0\n",
"pixel_7_4 10.0\n",
"pixel_7_5 0.0\n",
"pixel_7_6 0.0\n",
"pixel_7_7 0.0\n",
"Name: 0, Length: 64, dtype: float64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Convert this single row Series into a numpy array.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13., 15., 10.,\n",
" 15., 5., 0., 0., 3., 15., 2., 0., 11., 8., 0., 0., 4.,\n",
" 12., 0., 0., 8., 8., 0., 0., 5., 8., 0., 0., 9., 8.,\n",
" 0., 0., 4., 11., 0., 1., 12., 7., 0., 0., 2., 14., 5.,\n",
" 10., 12., 0., 0., 0., 0., 6., 13., 10., 0., 0., 0.])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image.to_numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Reshape this numpy array into an (8,8) array.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(64,)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image.to_numpy().shape"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 5., 13., 9., 1., 0., 0.],\n",
" [ 0., 0., 13., 15., 10., 15., 5., 0.],\n",
" [ 0., 3., 15., 2., 0., 11., 8., 0.],\n",
" [ 0., 4., 12., 0., 0., 8., 8., 0.],\n",
" [ 0., 5., 8., 0., 0., 9., 8., 0.],\n",
" [ 0., 4., 11., 0., 1., 12., 7., 0.],\n",
" [ 0., 2., 14., 5., 10., 12., 0., 0.],\n",
" [ 0., 0., 6., 13., 10., 0., 0., 0.]])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image.to_numpy().reshape(8,8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Use Matplotlib or Seaborn to display the array as an image representation of the number drawn. Remember your palette or cmap choice would change the colors, but not the actual pixel values.**"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1d45ca0e608>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(single_image.to_numpy().reshape(8,8))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1d45c508f88>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(single_image.to_numpy().reshape(8,8),cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(single_image.to_numpy().reshape(8,8),annot=True,cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------\n",
"\n",
"Now let's move on to PCA."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scaling Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Use Scikit-Learn to scale the pixel feature dataframe.**"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"scaled_pixels = scaler.fit_transform(pixels)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , -0.33501649, -0.04308102, ..., -1.14664746,\n",
" -0.5056698 , -0.19600752],\n",
" [ 0. , -0.33501649, -1.09493684, ..., 0.54856067,\n",
" -0.5056698 , -0.19600752],\n",
" [ 0. , -0.33501649, -1.09493684, ..., 1.56568555,\n",
" 1.6951369 , -0.19600752],\n",
" ...,\n",
" [ 0. , -0.33501649, -0.88456568, ..., -0.12952258,\n",
" -0.5056698 , -0.19600752],\n",
" [ 0. , -0.33501649, -0.67419451, ..., 0.8876023 ,\n",
" -0.5056698 , -0.19600752],\n",
" [ 0. , -0.33501649, 1.00877481, ..., 0.8876023 ,\n",
" -0.26113572, -0.19600752]])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scaled_pixels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PCA\n",
"\n",
"**TASK: Perform PCA on the scaled pixel data set with 2 components.**"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"pca_model = PCA(n_components=2)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"pca_pixels = pca_model.fit_transform(scaled_pixels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: How much variance is explained by 2 principal components.**"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.21594970492246052"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(pca_model.explained_variance_ratio_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Create a scatterplot of the digits in the 2 dimensional PCA space, color/label based on the original number_label column in the original dataset.**"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1d45c6c33c8>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x900 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6),dpi=150)\n",
"labels = digits['number_label'].values\n",
"sns.scatterplot(pca_pixels[:,0],pca_pixels[:,1],hue=labels,palette='Set1')\n",
"plt.legend(loc=(1.05,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TASK: Which numbers are the most \"distinct\"?**"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"# You should see label #4 as being the most separated group, \n",
"# implying its the most distinct, similar situation for #2, #6 and #9."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----------\n",
"---------\n",
"\n",
"## Bonus Challenge \n",
"\n",
"**TASK: Create an \"interactive\" 3D plot of the result of PCA with 3 principal components. Lot's of ways to do this, including different libraries like plotly or bokeh, but you can actually do this just with Matplotlib and Jupyter Notebook. Search Google and StackOverflow if you get stuck, lots of solutions are posted online.**"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"pca_model = PCA(n_components=3)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"pca_pixels = pca_model.fit_transform(scaled_pixels)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"from mpl_toolkits import mplot3d"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,8),dpi=150)\n",
"ax = plt.axes(projection='3d')\n",
"ax.scatter3D(pca_pixels[:,0],pca_pixels[:,1],pca_pixels[:,2],c=df['number_label']);"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"/* global mpl */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function () {\n",
" if (typeof WebSocket !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert(\n",
" 'Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.'\n",
" );\n",
" }\n",
"};\n",
"\n",
"mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = this.ws.binaryType !== undefined;\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById('mpl-warnings');\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent =\n",
" 'This browser does not support binary websocket messages. ' +\n",
" 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = document.createElement('div');\n",
" this.root.setAttribute('style', 'display: inline-block');\n",
" this._root_extra_style(this.root);\n",
"\n",
" parent_element.appendChild(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message('supports_binary', { value: fig.supports_binary });\n",
" fig.send_message('send_image_mode', {});\n",
" if (mpl.ratio !== 1) {\n",
" fig.send_message('set_dpi_ratio', { dpi_ratio: mpl.ratio });\n",
" }\n",
" fig.send_message('refresh', {});\n",
" };\n",
"\n",
" this.imageObj.onload = function () {\n",
" if (fig.image_mode === 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function () {\n",
" fig.ws.close();\n",
" };\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"};\n",
"\n",
"mpl.figure.prototype._init_header = function () {\n",
" var titlebar = document.createElement('div');\n",
" titlebar.classList =\n",
" 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
" var titletext = document.createElement('div');\n",
" titletext.classList = 'ui-dialog-title';\n",
" titletext.setAttribute(\n",
" 'style',\n",
" 'width: 100%; text-align: center; padding: 3px;'\n",
" );\n",
" titlebar.appendChild(titletext);\n",
" this.root.appendChild(titlebar);\n",
" this.header = titletext;\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
"mpl.figure.prototype._init_canvas = function () {\n",
" var fig = this;\n",
"\n",
" var canvas_div = (this.canvas_div = document.createElement('div'));\n",
" canvas_div.setAttribute(\n",
" 'style',\n",
" 'border: 1px solid #ddd;' +\n",
" 'box-sizing: content-box;' +\n",
" 'clear: both;' +\n",
" 'min-height: 1px;' +\n",
" 'min-width: 1px;' +\n",
" 'outline: 0;' +\n",
" 'overflow: hidden;' +\n",
" 'position: relative;' +\n",
" 'resize: both;'\n",
" );\n",
"\n",
" function on_keyboard_event_closure(name) {\n",
" return function (event) {\n",
" return fig.key_event(event, name);\n",
" };\n",
" }\n",
"\n",
" canvas_div.addEventListener(\n",
" 'keydown',\n",
" on_keyboard_event_closure('key_press')\n",
" );\n",
" canvas_div.addEventListener(\n",
" 'keyup',\n",
" on_keyboard_event_closure('key_release')\n",
" );\n",
"\n",
" this._canvas_extra_style(canvas_div);\n",
" this.root.appendChild(canvas_div);\n",
"\n",
" var canvas = (this.canvas = document.createElement('canvas'));\n",
" canvas.classList.add('mpl-canvas');\n",
" canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
" this.context = canvas.getContext('2d');\n",
"\n",
" var backingStore =\n",
" this.context.backingStorePixelRatio ||\n",
" this.context.webkitBackingStorePixelRatio ||\n",
" this.context.mozBackingStorePixelRatio ||\n",
" this.context.msBackingStorePixelRatio ||\n",
" this.context.oBackingStorePixelRatio ||\n",
" this.context.backingStorePixelRatio ||\n",
" 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
" 'canvas'\n",
" ));\n",
" rubberband_canvas.setAttribute(\n",
" 'style',\n",
" 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
" );\n",
"\n",
" var resizeObserver = new ResizeObserver(function (entries) {\n",
" var nentries = entries.length;\n",
" for (var i = 0; i < nentries; i++) {\n",
" var entry = entries[i];\n",
" var width, height;\n",
" if (entry.contentBoxSize) {\n",
" if (entry.contentBoxSize instanceof Array) {\n",
" // Chrome 84 implements new version of spec.\n",
" width = entry.contentBoxSize[0].inlineSize;\n",
" height = entry.contentBoxSize[0].blockSize;\n",
" } else {\n",
" // Firefox implements old version of spec.\n",
" width = entry.contentBoxSize.inlineSize;\n",
" height = entry.contentBoxSize.blockSize;\n",
" }\n",
" } else {\n",
" // Chrome <84 implements even older version of spec.\n",
" width = entry.contentRect.width;\n",
" height = entry.contentRect.height;\n",
" }\n",
"\n",
" // Keep the size of the canvas and rubber band canvas in sync with\n",
" // the canvas container.\n",
" if (entry.devicePixelContentBoxSize) {\n",
" // Chrome 84 implements new version of spec.\n",
" canvas.setAttribute(\n",
" 'width',\n",
" entry.devicePixelContentBoxSize[0].inlineSize\n",
" );\n",
" canvas.setAttribute(\n",
" 'height',\n",
" entry.devicePixelContentBoxSize[0].blockSize\n",
" );\n",
" } else {\n",
" canvas.setAttribute('width', width * mpl.ratio);\n",
" canvas.setAttribute('height', height * mpl.ratio);\n",
" }\n",
" canvas.setAttribute(\n",
" 'style',\n",
" 'width: ' + width + 'px; height: ' + height + 'px;'\n",
" );\n",
"\n",
" rubberband_canvas.setAttribute('width', width);\n",
" rubberband_canvas.setAttribute('height', height);\n",
"\n",
" // And update the size in Python. We ignore the initial 0/0 size\n",
" // that occurs as the element is placed into the DOM, which should\n",
" // otherwise not happen due to the minimum size styling.\n",
" if (width != 0 && height != 0) {\n",
" fig.request_resize(width, height);\n",
" }\n",
" }\n",
" });\n",
" resizeObserver.observe(canvas_div);\n",
"\n",
" function on_mouse_event_closure(name) {\n",
" return function (event) {\n",
" return fig.mouse_event(event, name);\n",
" };\n",
" }\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mousedown',\n",
" on_mouse_event_closure('button_press')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseup',\n",
" on_mouse_event_closure('button_release')\n",
" );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband_canvas.addEventListener(\n",
" 'mousemove',\n",
" on_mouse_event_closure('motion_notify')\n",
" );\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseenter',\n",
" on_mouse_event_closure('figure_enter')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseleave',\n",
" on_mouse_event_closure('figure_leave')\n",
" );\n",
"\n",
" canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
" canvas_div.appendChild(canvas);\n",
" canvas_div.appendChild(rubberband_canvas);\n",
"\n",
" this.rubberband_context = rubberband_canvas.getContext('2d');\n",
" this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
" this._resize_canvas = function (width, height, forward) {\n",
" if (forward) {\n",
" canvas_div.style.width = width + 'px';\n",
" canvas_div.style.height = height + 'px';\n",
" }\n",
" };\n",
"\n",
" // Disable right mouse context menu.\n",
" this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
" event.preventDefault();\n",
" return false;\n",
" });\n",
"\n",
" function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'mpl-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"};\n",
"\n",
"mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
"};\n",
"\n",
"mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"};\n",
"\n",
"mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0,\n",
" 0,\n",
" fig.canvas.width / mpl.ratio,\n",
" fig.canvas.height / mpl.ratio\n",
" );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch (cursor) {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message('ack', {});\n",
"};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = 'image/png';\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src\n",
" );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
" console.log(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\n",
" }\n",
" }\n",
" };\n",
"};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e) {\n",
" e = window.event;\n",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
" return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys(original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object') {\n",
" obj[key] = original[key];\n",
" }\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function (event, name) {\n",
" var canvas_pos = mpl.findpos(event);\n",
"\n",
" if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"};\n",
"\n",
"mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
" if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / mpl.ratio;\n",
" fig.root.removeEventListener('remove', this._remove_fig_handler);\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable();\n",
" fig.parent_element.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
"};\n",
"\n",
"mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"};\n",
"\n",
"mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width / mpl.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function () {\n",
" this.close_ws(this, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
" el.addEventListener('remove', this._remove_fig_handler);\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
" }\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code') {\n",
" for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel !== null) {\n",
" IPython.notebook.kernel.comm_manager.register_target(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.axes(projection='3d')\n",
"ax.scatter3D(pca_pixels[:,0],pca_pixels[:,1],pca_pixels[:,2],c=df['number_label']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}