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Created November 5, 2024 19:12
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 95-865: More on PCA, Argsort\n",
"Author: Erick Rodriguez (erickger [at symbol] cmu.edu), former TA for 95-865 <br>\n",
"Modified by George H. Chen (georgechen [at symbol] cmu.edu), Nov 5, 2024\n",
"\n",
"This demo is based on Mark Richardson's 2009 \"Principle Component Analysis\" notes and uses data he pulled from DEFRA on 1997 UK food consumption (grams/person/week). This dataset is also used as a nice illustrated example of PCA here:\n",
"http://setosa.io/ev/principal-component-analysis/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating the dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('seaborn-v0_8') # prettier plots\n",
"import numpy as np\n",
"\n",
"# grams per person per week\n",
"food_data = np.array([[105, 103, 103, 66],\n",
" [245, 227, 242, 267],\n",
" [685, 803, 750, 586],\n",
" [147, 160, 122, 93],\n",
" [193, 235, 184, 209], \n",
" [156, 175, 147, 139],\n",
" [720, 874, 566, 1033],\n",
" [253, 265, 171, 143],\n",
" [488, 570, 418, 355],\n",
" [198, 203, 220, 187],\n",
" [360, 365, 337, 334],\n",
" [1102, 1137, 957, 674],\n",
" [1472, 1582, 1462, 1494],\n",
" [57, 73, 53, 47],\n",
" [1374, 1256, 1572, 1506],\n",
" [375, 475, 458, 135],\n",
" [54, 64, 62, 41]])\n",
"row_labels = ['Cheese',\n",
" 'Carcass meat',\n",
" 'Other meat',\n",
" 'Fish',\n",
" 'Fats and oils',\n",
" 'Sugars',\n",
" 'Fresh potatoes',\n",
" 'Fresh Veg',\n",
" 'Other Veg',\n",
" 'Processed potatoes',\n",
" 'Processed Veg',\n",
" 'Fresh fruit',\n",
" 'Cereals',\n",
" 'Beverages',\n",
" 'Soft drinks',\n",
" 'Alcoholic drinks',\n",
" 'Confectionary']\n",
"column_labels = ['England', 'Wales', 'Scotland', 'N. Ireland']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Looking at the table with a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"A module that was compiled using NumPy 1.x cannot be run in\n",
"NumPy 2.0.2 as it may crash. To support both 1.x and 2.x\n",
"versions of NumPy, modules must be compiled with NumPy 2.0.\n",
"Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n",
"\n",
"If you are a user of the module, the easiest solution will be to\n",
"downgrade to 'numpy<2' or try to upgrade the affected module.\n",
"We expect that some modules will need time to support NumPy 2.\n",
"\n",
"Traceback (most recent call last): File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
" File \"<frozen runpy>\", line 88, in _run_code\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel_launcher.py\", line 17, in <module>\n",
" app.launch_new_instance()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/traitlets/config/application.py\", line 992, in launch_instance\n",
" app.start()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 736, in start\n",
" self.io_loop.start()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 195, in start\n",
" self.asyncio_loop.run_forever()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/base_events.py\", line 607, in run_forever\n",
" self._run_once()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/base_events.py\", line 1922, in _run_once\n",
" handle._run()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/events.py\", line 80, in _run\n",
" self._context.run(self._callback, *self._args)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n",
" await self.process_one()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n",
" await dispatch(*args)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n",
" await result\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n",
" reply_content = await reply_content\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n",
" res = shell.run_cell(\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n",
" return super().run_cell(*args, **kwargs)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n",
" result = self._run_cell(\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n",
" result = runner(coro)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n",
" coro.send(None)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n",
" has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n",
" if await self.run_code(code, result, async_=asy):\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
" File \"/var/folders/ms/yzwxs7r54nx0q978svcpsnbh0000gn/T/ipykernel_84926/3546127722.py\", line 1, in <module>\n",
" import pandas as pd\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/__init__.py\", line 26, in <module>\n",
" from pandas.compat import (\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/compat/__init__.py\", line 27, in <module>\n",
" from pandas.compat.pyarrow import (\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/compat/pyarrow.py\", line 8, in <module>\n",
" import pyarrow as pa\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pyarrow/__init__.py\", line 65, in <module>\n",
" import pyarrow.lib as _lib\n"
]
},
{
"ename": "AttributeError",
"evalue": "_ARRAY_API not found",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;31mAttributeError\u001b[0m: _ARRAY_API not found"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"A module that was compiled using NumPy 1.x cannot be run in\n",
"NumPy 2.0.2 as it may crash. To support both 1.x and 2.x\n",
"versions of NumPy, modules must be compiled with NumPy 2.0.\n",
"Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n",
"\n",
"If you are a user of the module, the easiest solution will be to\n",
"downgrade to 'numpy<2' or try to upgrade the affected module.\n",
"We expect that some modules will need time to support NumPy 2.\n",
"\n",
"Traceback (most recent call last): File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
" File \"<frozen runpy>\", line 88, in _run_code\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel_launcher.py\", line 17, in <module>\n",
" app.launch_new_instance()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/traitlets/config/application.py\", line 992, in launch_instance\n",
" app.start()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 736, in start\n",
" self.io_loop.start()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 195, in start\n",
" self.asyncio_loop.run_forever()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/base_events.py\", line 607, in run_forever\n",
" self._run_once()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/base_events.py\", line 1922, in _run_once\n",
" handle._run()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/asyncio/events.py\", line 80, in _run\n",
" self._context.run(self._callback, *self._args)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n",
" await self.process_one()\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n",
" await dispatch(*args)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n",
" await result\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n",
" reply_content = await reply_content\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n",
" res = shell.run_cell(\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n",
" return super().run_cell(*args, **kwargs)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n",
" result = self._run_cell(\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n",
" result = runner(coro)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n",
" coro.send(None)\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n",
" has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n",
" if await self.run_code(code, result, async_=asy):\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
" File \"/var/folders/ms/yzwxs7r54nx0q978svcpsnbh0000gn/T/ipykernel_84926/3546127722.py\", line 1, in <module>\n",
" import pandas as pd\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/__init__.py\", line 49, in <module>\n",
" from pandas.core.api import (\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/core/api.py\", line 9, in <module>\n",
" from pandas.core.dtypes.dtypes import (\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pandas/core/dtypes/dtypes.py\", line 24, in <module>\n",
" from pandas._libs import (\n",
" File \"/Users/georgehc/anaconda3/lib/python3.11/site-packages/pyarrow/__init__.py\", line 65, in <module>\n",
" import pyarrow.lib as _lib\n"
]
},
{
"ename": "AttributeError",
"evalue": "_ARRAY_API not found",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;31mAttributeError\u001b[0m: _ARRAY_API not found"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>England</th>\n",
" <th>Wales</th>\n",
" <th>Scotland</th>\n",
" <th>N. Ireland</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Cheese</th>\n",
" <td>105</td>\n",
" <td>103</td>\n",
" <td>103</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Carcass meat</th>\n",
" <td>245</td>\n",
" <td>227</td>\n",
" <td>242</td>\n",
" <td>267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other meat</th>\n",
" <td>685</td>\n",
" <td>803</td>\n",
" <td>750</td>\n",
" <td>586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fish</th>\n",
" <td>147</td>\n",
" <td>160</td>\n",
" <td>122</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fats and oils</th>\n",
" <td>193</td>\n",
" <td>235</td>\n",
" <td>184</td>\n",
" <td>209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sugars</th>\n",
" <td>156</td>\n",
" <td>175</td>\n",
" <td>147</td>\n",
" <td>139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fresh potatoes</th>\n",
" <td>720</td>\n",
" <td>874</td>\n",
" <td>566</td>\n",
" <td>1033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fresh Veg</th>\n",
" <td>253</td>\n",
" <td>265</td>\n",
" <td>171</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other Veg</th>\n",
" <td>488</td>\n",
" <td>570</td>\n",
" <td>418</td>\n",
" <td>355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Processed potatoes</th>\n",
" <td>198</td>\n",
" <td>203</td>\n",
" <td>220</td>\n",
" <td>187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Processed Veg</th>\n",
" <td>360</td>\n",
" <td>365</td>\n",
" <td>337</td>\n",
" <td>334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fresh fruit</th>\n",
" <td>1102</td>\n",
" <td>1137</td>\n",
" <td>957</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cereals</th>\n",
" <td>1472</td>\n",
" <td>1582</td>\n",
" <td>1462</td>\n",
" <td>1494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beverages</th>\n",
" <td>57</td>\n",
" <td>73</td>\n",
" <td>53</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soft drinks</th>\n",
" <td>1374</td>\n",
" <td>1256</td>\n",
" <td>1572</td>\n",
" <td>1506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alcoholic drinks</th>\n",
" <td>375</td>\n",
" <td>475</td>\n",
" <td>458</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Confectionary</th>\n",
" <td>54</td>\n",
" <td>64</td>\n",
" <td>62</td>\n",
" <td>41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" England Wales Scotland N. Ireland\n",
"Cheese 105 103 103 66\n",
"Carcass meat 245 227 242 267\n",
"Other meat 685 803 750 586\n",
"Fish 147 160 122 93\n",
"Fats and oils 193 235 184 209\n",
"Sugars 156 175 147 139\n",
"Fresh potatoes 720 874 566 1033\n",
"Fresh Veg 253 265 171 143\n",
"Other Veg 488 570 418 355\n",
"Processed potatoes 198 203 220 187\n",
"Processed Veg 360 365 337 334\n",
"Fresh fruit 1102 1137 957 674\n",
"Cereals 1472 1582 1462 1494\n",
"Beverages 57 73 53 47\n",
"Soft drinks 1374 1256 1572 1506\n",
"Alcoholic drinks 375 475 458 135\n",
"Confectionary 54 64 62 41"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"food_df = pd.DataFrame(food_data, columns=column_labels, index=row_labels)\n",
"food_df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17, 4)\n"
]
}
],
"source": [
"print(np.shape(food_df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Running PCA"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"# instantiate PCA class\n",
"single_dimension_pca = PCA(n_components=1)\n",
"# use our pca to fit and transform the whole dataset\n",
"single_dimension_food_data = single_dimension_pca.fit_transform(food_data.T)\n",
"\n",
"# matplotlib doesn't have a built-in 1D scatter plot but we can\n",
"# just use a 2D scatter plot with y-axis values all set to 0\n",
"y_axis_all_zeros = np.zeros(len(single_dimension_food_data))\n",
"plt.scatter(single_dimension_food_data, y_axis_all_zeros)\n",
"\n",
"for idx in range(len(single_dimension_food_data)):\n",
" plt.annotate(column_labels[idx], (single_dimension_food_data[idx] - 15, y_axis_all_zeros[idx]-0.011), rotation=-30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explaining the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this we can plot the data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8,8))\n",
"\n",
"ax1.bar(range(len(row_labels)), food_data[:, 0])\n",
"ax1.set_title(column_labels[0])\n",
"ax1.set_xticks(range(len(row_labels)))\n",
"ax1.set_xticklabels(row_labels, rotation=90)\n",
"\n",
"ax2.bar(range(len(row_labels)), food_data[:, 1])\n",
"ax2.set_title(column_labels[1])\n",
"ax2.set_xticks(range(len(row_labels)))\n",
"ax2.set_xticklabels(row_labels, rotation=90)\n",
"\n",
"ax3.bar(range(len(row_labels)), food_data[:, 2])\n",
"ax3.set_title(column_labels[2])\n",
"ax3.set_xticks(range(len(row_labels)))\n",
"ax3.set_xticklabels(row_labels, rotation=90)\n",
"\n",
"ax4.bar(range(len(row_labels)), food_data[:, 3])\n",
"ax4.set_title(column_labels[3])\n",
"ax4.set_xticks(range(len(row_labels)))\n",
"ax4.set_xticklabels(row_labels, rotation=90)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['Fresh fruit' 'Alcoholic drinks' 'Fresh potatoes' 'Other meat'\n",
" 'Other Veg' 'Soft drinks' 'Fresh Veg' 'Fish' 'Cheese' 'Carcass meat'\n",
" 'Cereals' 'Sugars' 'Processed Veg' 'Confectionary' 'Processed potatoes'\n",
" 'Beverages' 'Fats and oils']]\n"
]
}
],
"source": [
"importance_idx = np.argsort(-abs(single_dimension_pca.components_))\n",
"# print row_labels in descending importance order\n",
"print(np.asarray(row_labels)[importance_idx])\n",
"# if interested, you could refer to the bar chart to verify"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some reasons?\n",
"\n",
"- Northern Ireland eat way more grams of fresh potatoes and way fewer of fresh fruits, cheese, fish and alcoholic drinks\n",
"- It turns out that Northern Ireland is the only of the four countries not on the island of Great Britain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using PCA with 2 components instead of two"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x800 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# similarly we define a PCA with two components\n",
"two_dimension_pca = PCA(n_components=2)\n",
"two_dimension_food_data = two_dimension_pca.fit_transform(food_data.T)\n",
"\n",
"# Notice that this is another way of plotting subplots\n",
"# ----------------------------------------------------\n",
"plt.figure(figsize=(12,8))\n",
"\n",
"plt.subplot(2,2,1) #upper left figure\n",
"plt.scatter(two_dimension_food_data[:,0], two_dimension_food_data[:,1])\n",
"for idx in range(len(two_dimension_food_data)):\n",
" plt.annotate(column_labels[idx], (two_dimension_food_data[:,0][idx], two_dimension_food_data[:,1][idx]), rotation=0)\n",
"plt.axis('equal')\n",
"plt.xlabel(\"PC1\")\n",
"plt.ylabel(\"PC2\")\n",
"\n",
"# note this is the first PC, and it is completely the same with the one with only one PC.\n",
"plt.subplot(2,2,3) #lower left figure\n",
"plt.scatter(two_dimension_food_data[:,0], y_axis_all_zeros)\n",
"for idx in range(len(two_dimension_food_data)):\n",
" plt.annotate(column_labels[idx], (two_dimension_food_data[:,0][idx], y_axis_all_zeros[idx]), rotation=90)\n",
"plt.axis('equal')\n",
"plt.xlabel(\"PC1\")\n",
"\n",
"plt.subplot(2,2,2) #upper right figure\n",
"plt.scatter(y_axis_all_zeros, two_dimension_food_data[:,1])\n",
"for idx in range(len(two_dimension_food_data)):\n",
" plt.annotate(column_labels[idx], (y_axis_all_zeros[idx], two_dimension_food_data[:,1][idx]), rotation=0)\n",
"plt.axis('equal')\n",
"plt.ylabel(\"PC2\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PCA Results"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data points for decomposition into 1 dimension:\n",
"\n",
"[[ 144.99315218]\n",
" [ 240.52914764]\n",
" [ 91.869339 ]\n",
" [-477.39163882]]\n",
"\n",
"\n",
"Data points for decomposition into 2 dimensions:\n",
"\n",
"[[ 144.99315218 2.53299944]\n",
" [ 240.52914764 224.64692488]\n",
" [ 91.869339 -286.08178613]\n",
" [-477.39163882 58.90186182]]\n"
]
}
],
"source": [
"print('Data points for decomposition into 1 dimension:\\n')\n",
"print(single_dimension_food_data)\n",
"print('\\n\\nData points for decomposition into 2 dimensions:\\n')\n",
"print(two_dimension_food_data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The explained ratio for decomposition into 1 dimension is 0.6744434639658383\n",
"\n",
"The explained ratio for decomposition into 2 dimensions is 0.6744434639658383 and 0.2905247457687651\n"
]
}
],
"source": [
"print('The explained ratio for decomposition into 1 dimension is', single_dimension_pca.explained_variance_ratio_[0])\n",
"print('\\nThe explained ratio for decomposition into 2 dimensions is', two_dimension_pca.explained_variance_ratio_[0], \n",
" 'and', two_dimension_pca.explained_variance_ratio_[1])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.9649682097346034)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_dimension_pca.explained_variance_ratio_[0] + two_dimension_pca.explained_variance_ratio_[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Differences among fit, transform, and fit_transform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we fit the data before by doing `single_dimension_pca.fit_transform(food_data.T)` we actually runned two methods `fit()` and `transform()`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Usually this is really helpfull when we create machine learning models because we can fit the model and then inject new data to be \"transformed\" or predicted. That is `fit()` fits the model to the data we sent as a parameter."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"one_dim_pca = PCA(n_components=1)\n",
"one_dim_pca_fitted_model = one_dim_pca.fit(food_data.T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can look at results by using our original data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 144.99315218]\n",
" [ 240.52914764]\n",
" [ 91.869339 ]\n",
" [-477.39163882]]\n"
]
}
],
"source": [
"one_dim_pca_results = one_dim_pca_fitted_model.transform(food_data.T)\n",
"print(one_dim_pca_results)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[144.99315218],\n",
" [240.52914764]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_dim_pca_fitted_model.transform([food_data[:, 0], food_data[:, 1]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, we could actually plug in new data that we didn't fit within the PCA model (for example, if we collected the 17 measurements for Adelaide, we could use it with transform as well, etc)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1463.14536953 484.06253401 1026.04095959 1423.06323832 613.8601646\n",
" 1230.06955441 622.08729612 468.08165388 1197.47234605 1119.54593613\n",
" 677.6311532 1164.93039436 985.30009739 1283.01217997 312.3646879\n",
" 1107.82607575 900.13233599]\n"
]
}
],
"source": [
"# Let's imagine this is the data for Adelaide\n",
"adelaide_data = np.random.uniform(low=100, high=1500, size=(17,))\n",
"print(adelaide_data)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The results for using our moodel with Adelaide's dataset is: 1391.0111600258065\n"
]
}
],
"source": [
"# Now let's see what are the results on this\n",
"print(\"The results for using our moodel with Adelaide's dataset is: \", \n",
" one_dim_pca_fitted_model.transform([adelaide_data])[0][0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Interpretation\n",
"\n",
"How do we interpret the low-dimensional representation? Why is North Ireland so far away from the other points? One way to try to answer this question is to first look at what features (i.e., what specific food/drink items) are being assigned high weight by PCA:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.05695538 -0.04792763 0.25891666 0.08441498 0.00519362 0.03762098\n",
" -0.40140206 0.15184994 0.24359373 0.02688623 0.03648827 0.6326409\n",
" 0.04770286 0.02618776 -0.23224414 0.46396817 0.0296502 ]]\n",
"(1, 17)\n"
]
}
],
"source": [
"print(single_dimension_pca.components_) # index 0 is for the 1st principal component\n",
"print(np.shape(single_dimension_pca.components_))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.05695538 -0.04792763 0.25891666 0.08441498 0.00519362 0.03762098\n",
" -0.40140206 0.15184994 0.24359373 0.02688623 0.03648827 0.6326409\n",
" 0.04770286 0.02618776 -0.23224414 0.46396817 0.0296502 ]\n",
"[-0.01601285 -0.01391582 0.01533114 0.05075495 0.09538866 0.0430217\n",
" 0.71501708 0.14490027 0.22545092 -0.04285076 0.0454518 0.17774074\n",
" 0.21259968 0.03056054 -0.55512431 -0.11353652 -0.00594992]\n"
]
}
],
"source": [
"print(two_dimension_pca.components_[0])\n",
"print(two_dimension_pca.components_[1])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Fresh fruit' 'Alcoholic drinks' 'Fresh potatoes' 'Other meat'\n",
" 'Other Veg' 'Soft drinks' 'Fresh Veg' 'Fish' 'Cheese' 'Carcass meat'\n",
" 'Cereals' 'Sugars' 'Processed Veg' 'Confectionary' 'Processed potatoes'\n",
" 'Beverages' 'Fats and oils']\n"
]
}
],
"source": [
"importance_idx = np.argsort(-abs(two_dimension_pca.components_[0]))\n",
"# print row_labels in descending importance order\n",
"print(np.asarray(row_labels)[importance_idx])\n",
"# if interested, you could refer to the bar chart to verify"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importantly, how PCA (that has already been fitted) actually projects a data point to 1D is to take a weighted combination using the above weights (although it first subtracts off the feature means). Specifically, here are the calculations for England and Wales:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Single dimension PCA means:\n",
" [ 94.25 245.25 706. 130.5 205.25 154.25 798.25 208. 457.75\n",
" 202. 349. 967.5 1502.5 57.5 1427. 360.75 55.25]\n",
"\n",
"Two dimensions PCA means:\n",
" [ 94.25 245.25 706. 130.5 205.25 154.25 798.25 208. 457.75\n",
" 202. 349. 967.5 1502.5 57.5 1427. 360.75 55.25]\n"
]
}
],
"source": [
"print('Single dimension PCA means:\\n', single_dimension_pca.mean_)\n",
"print('\\nTwo dimensions PCA means:\\n', two_dimension_pca.mean_)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(144.99315218207673)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.inner(single_dimension_pca.components_[0], food_data[:, 0] - single_dimension_pca.mean_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(240.52914763517674)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.inner(single_dimension_pca.components_[0],\n",
" food_data[:, 1] - single_dimension_pca.mean_)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(2.5329994370406084)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.inner(two_dimension_pca.components_[1],\n",
" food_data[:, 0] - two_dimension_pca.mean_)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(224.6469248812689)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.inner(two_dimension_pca.components_[1],\n",
" food_data[:, 1] - two_dimension_pca.mean_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Argsort"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous lecture we saw the `sorted` function; now we introduce numpy's `argsort`, which does *not* return the sorted list but instead returns the rearranged indices that would sort the list (put another way, it returns rankings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Going back to our previous example with the food data, in PCA, weights with larger absolute value correspond to features that lead to the largest spread along the projected 1D axis. Here's some code to rank the weights by largest absolute value to smallest absolute value:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index Food Absolute Value\n",
"----- -------------------- ----------------------\n",
"11 Fresh fruit 0.6326408978722377 \n",
"15 Alcoholic drinks 0.4639681679767063 \n",
"6 Fresh potatoes -0.4014020602962481 \n",
"2 Other meat 0.25891665833612115 \n",
"8 Other Veg 0.24359372899027432 \n",
"14 Soft drinks -0.23224414047289454 \n",
"7 Fresh Veg 0.1518499415623022 \n",
"3 Fish 0.08441498252508357 \n",
"0 Cheese 0.05695537978568527 \n",
"1 Carcass meat -0.04792762813468528 \n",
"12 Cereals 0.04770285837364895 \n",
"5 Sugars 0.03762098283940196 \n",
"10 Processed Veg 0.03648826911159385 \n",
"16 Confectionary 0.029650201087993874 \n",
"9 Processed potatoes 0.026886232536746928 \n",
"13 Beverages 0.02618775590853346 \n",
"4 Fats and oils 0.005193622660047751 \n"
]
}
],
"source": [
"abs_1PC_weights = np.abs(single_dimension_pca.components_[0])\n",
"\n",
"ranking_abs_1PC_weights = np.argsort(-abs_1PC_weights) # use negative to get largest to smallest\n",
"\n",
"# Printing out the food items from highest to lowest absolute value weight\n",
"print(\"{0:5} {1:20} {2:10}\".format('Index', 'Food', 'Absolute Value'))\n",
"print(\"{0:5} {1:20} {2:22}\".format('-----', '--------------------', '----------------------'))\n",
"for index in ranking_abs_1PC_weights:\n",
" print(\"{0:5} {1:20} {2:22}\".format(str(index), row_labels[index], str(single_dimension_pca.components_[0][index])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using argsort with our example"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 144.99315218],\n",
" [ 240.52914764],\n",
" [ 91.869339 ],\n",
" [-477.39163882]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_dimension_food_data"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wales : 240.5291476351767\n",
"England : 144.9931521820767\n",
"Scotland : 91.86933899886354\n",
"N. Ireland : -477.39163881611705\n"
]
}
],
"source": [
"ranking_of_region_from_large_to_small_1st_component = \\\n",
"np.argsort(-(single_dimension_food_data[:,0] - np.average(single_dimension_food_data[:,0])))\n",
"\n",
"for index in ranking_of_region_from_large_to_small_1st_component:\n",
" print(column_labels[index], \":\", single_dimension_food_data[index,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using argsort with a dictionary"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from collections import Counter\n",
"dict_fruits = {\"apple\":10, \"pear\":7, \"banana\":11, \"grape\":20, \"orange\":12}\n",
"stock = Counter(dict_fruits)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('grape', 20), ('orange', 12), ('banana', 11), ('apple', 10), ('pear', 7)]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(stock.items(), reverse=True, key = lambda x:x[1])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1, -2])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.dot(-1, [1,2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task:** Try to return a list in descending order based on the stock with argsort.\n",
"\n",
"Useful methods:\n",
"- Counter.keys()\n",
"- Counter.values()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['grape', 'orange', 'banana', 'apple', 'pear'], dtype='<U6')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_index = np.argsort(np.dot(-1, list(stock.values())))\n",
"\n",
"# another way to do it in desecending order\n",
"# sorted_index = np.argsort(list(stock.values()))[::-1]\n",
"\n",
"sorted_stock_keys = np.array(list(stock.keys()))\n",
"sorted_stock_keys[sorted_index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using argsort with matrices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we have a list of fruits with their respective prices. These prices correspond to 4 states in the Australia.\n",
"\n",
"**Tasks:** \n",
"- Give a list of the fruits from the most expensive to the cheapest. This thinking that each row correspond to one state.\n",
"- Now, do the same, but now think that the states are actually the columns of the matrix."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"fruits = np.array([['apple', 'banana', 'kiwi', 'passionfruit'], \n",
" ['mango', 'orange', 'mandarin', 'citrus'], \n",
" ['watermelon', 'rockmelon', 'papaya', 'grape'], \n",
" ['plum', 'peach', 'apricot', 'lychee']])\n",
"\n",
"fruit_prices = np.array([[5,3,12,1],\n",
" [12,5,3,9],\n",
" [2,6,1,19],\n",
" [1,5,4,14]])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[3 0 2 0]\n",
" [2 1 1 1]\n",
" [0 3 3 3]\n",
" [1 2 0 2]]\n",
"\n",
"[[3 1 0 2]\n",
" [2 1 3 0]\n",
" [2 0 1 3]\n",
" [0 2 1 3]]\n"
]
}
],
"source": [
"#return index matrix sorting by column\n",
"print(np.argsort(fruit_prices, axis=0))\n",
"print()\n",
"\n",
"#return index matrix sorting by row\n",
"print(np.argsort(fruit_prices, axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 1, 0, 2],\n",
" [2, 1, 3, 0],\n",
" [2, 0, 1, 3],\n",
" [0, 2, 1, 3]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_fruit_indices = np.argsort(fruit_prices, axis=1)\n",
"sorted_fruit_indices"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array(['passionfruit', 'banana', 'apple', 'kiwi'], dtype='<U12'),\n",
" array(['mandarin', 'orange', 'citrus', 'mango'], dtype='<U12'),\n",
" array(['papaya', 'watermelon', 'rockmelon', 'grape'], dtype='<U12'),\n",
" array(['plum', 'apricot', 'peach', 'lychee'], dtype='<U12')]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To show the results of this in terms of the labels you can do as follow\n",
"[fruit[sorted_fruit_indices[idx]] for idx, fruit in enumerate(fruits)]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['passionfruit', 'banana', 'apple', 'kiwi'],\n",
" ['mandarin', 'orange', 'citrus', 'mango'],\n",
" ['papaya', 'watermelon', 'rockmelon', 'grape'],\n",
" ['plum', 'apricot', 'peach', 'lychee']], dtype='<U12')"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You can also use \n",
"np.take_along_axis(fruits, sorted_fruit_indices, axis=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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