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{
"cells": [
{
"cell_type": "markdown",
"id": "f75725e1-097c-4400-ab72-7f166e60789c",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"id": "071a39a8-1a51-4498-9900-3e5d40b7db6f",
"metadata": {},
"source": [
"In this example, we will introduce the example of AggMap in feature restructuring on Randomized MNIST data. Specifically, we first permute the pixel randomly to generate the unordered data, this will destroy the structured MNIST images into randomly permuted pixels, then we used AggMap to reconstruct from these random pixels. \n",
"\n",
"To randomize the MNISt data, we first reshaped the 28x28 pixels into 684 feature points (FPs) and permuted these 684 FPs randomly, and then reshaped them into the new shuffled 28x28 images. The random permuted images OrgRP1 have destroyed the spatial correlation of the original images totally.\n",
"\n",
"\n",
"After the unsupervised learning from these randomized MNIST data by AggMap, the disrupted MNIST images can been reconstructed to the very structured images and very similar to the original images. Moreover, the reconstruction ability of AggMap is related to the number of randomized samples for unsupervised pre-learning. You can try to use different number of the randomized samples to fit AggMap and to transform the randomized data, and see what happens."
]
},
{
"cell_type": "markdown",
"id": "353d17c4-0518-4b16-bbb9-d17f46ffb084",
"metadata": {},
"source": [
"## Step0: Import AggMap and Orignal MNIST data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f0570896-5ebc-452f-b3f6-cb809738dfcd",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from sklearn.utils import shuffle\n",
"import matplotlib.pyplot as plt\n",
"from aggmap import AggMap\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "72eb532d-0cd5-45a7-b453-ed108fcef78d",
"metadata": {},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() #load data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e87802f-df13-4957-87fd-b0e562b2d9b3",
"metadata": {},
"outputs": [],
"source": [
"_, w, h = x_train.shape\n",
"orignal_cols = ['p-%s' % str((i+1)).zfill(len(str(w*h))) for i in range(w*h)]\n",
"x_train_df = pd.DataFrame(x_train.reshape(x_train.shape[0], w*h), columns=orignal_cols)\n",
"x_test_df = pd.DataFrame(x_test.reshape(x_test.shape[0], w*h), columns=orignal_cols)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3ed7d521-0c83-4e03-808b-1d9559f11363",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+Q9JTkjZoKFizO9TbQg0dom+QtL72d36n37tCX21537hcFkiCE3RAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kMT/A65XcTMQuIbWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = plt.imshow(x_train_df.iloc[0].values.reshape(w,h))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5e3d1cb8-a894-4626-a8f2-e6a96778d7c8",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_test_df.iloc[0].values.reshape(w,h))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7363c597-3df6-41ce-820f-b5ffadd3bf80",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "66189c02-746a-4652-8956-90021183e6e7",
"metadata": {},
"source": [
"## Step1: MNIST pixel random permutation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9b75a33b-86c1-4aa1-beb3-aacfa0838dff",
"metadata": {},
"outputs": [],
"source": [
"shuffled_cols = shuffle(orignal_cols, random_state=111)\n",
"x_train_df_shuffled = x_train_df[shuffled_cols]\n",
"x_test_df_shuffled = x_test_df[shuffled_cols]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "50a60f1d-f38f-4a01-90f9-de9fbbddd5bb",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_train_df_shuffled.iloc[0].values.reshape(w,h))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "970f5165-da92-4cca-af8f-6ff6f20b19c6",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_test_df_shuffled.iloc[0].values.reshape(w,h))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e2d3b34-4ba0-4f03-aaec-dc727dc3f10c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "5ff55cb0-d6a7-4490-b8e9-3f2a7a5b82ae",
"metadata": {},
"source": [
"## Step2: AggMap pre-fitting on training set"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ba9efed8-11f9-42dd-9819-a171551a5c6b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-08-01 15:00:08,013 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Calculating distance ...\u001b[0m\n",
"2022-08-01 15:00:08,041 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - the number of process is 16\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|#######################################################################################################################################| 306936/306936 [00:35<00:00, 8665.40it/s]\n",
"100%|####################################################################################################################################| 306936/306936 [00:00<00:00, 5186856.19it/s]\n",
"100%|##############################################################################################################################################| 784/784 [00:00<00:00, 961.65it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-08-01 15:00:44,524 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - applying hierarchical clustering to obtain group information ...\u001b[0m\n",
"2022-08-01 15:00:46,360 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Applying grid assignment of feature points, this may take several minutes(1~30 min)\u001b[0m\n",
"2022-08-01 15:00:46,799 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Finished\u001b[0m\n"
]
}
],
"source": [
"mp = AggMap(x_train_df_shuffled, metric='correlation')\n",
"mp = mp.fit(cluster_channels=1, var_thr=0, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "816902d4-0d94-441f-a577-1e0cc472d6ab",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "6cfc6f5d-7bae-409e-9789-ef371cbc7d06",
"metadata": {},
"source": [
"## Step3: AggMap transformation on training and test test"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0aabf684-e8c7-4c5e-b170-c335ea07828d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|#########################################################################################################################################| 60000/60000 [00:10<00:00, 5493.30it/s]\n",
"100%|#########################################################################################################################################| 10000/10000 [00:01<00:00, 7557.54it/s]\n"
]
}
],
"source": [
"x_train_restructured = mp.batch_transform(x_train_df_shuffled.values)\n",
"x_test_restructured = mp.batch_transform(x_test_df_shuffled.values)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "72ff0bd8-80f8-4ec4-bf1d-9fc5ea242915",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_train_restructured[0].reshape(*mp.fmap_shape))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7299d60d-470c-4cbc-b617-a9515b270319",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_test_restructured[0].reshape(*mp.fmap_shape))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ae18f764-dd38-412b-9b48-512a47bfce6d",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_test_restructured[1].reshape(*mp.fmap_shape))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d0ab2371-373e-4606-8994-331f9103fbb0",
"metadata": {},
"outputs": [
{
"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": [
"ax = plt.imshow(x_test_restructured[2].reshape(*mp.fmap_shape))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "609fb493-0c0e-4eed-a7bb-8d72cf506e6d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "205af5cb-ec61-4687-b15c-f385dfed62c0",
"metadata": {},
"source": [
"## Step4: AggMap visualization"
]
},
{
"cell_type": "markdown",
"id": "83614aae-d752-451a-95a2-84d86c2ea6f3",
"metadata": {},
"source": [
"In the scatter and grid plot, we will get the final optimized position for each pixel that is in arbitrary order"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7b53d409-1d18-4720-8cdd-6f68d3952ea4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-08-01 15:04:55,078 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - generate file: ./feature points_717_correlation_umap_scatter\u001b[0m\n",
"2022-08-01 15:04:55,084 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - save html file to ./feature points_717_correlation_umap_scatter\u001b[0m\n"
]
},
{
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],
"text/plain": [
"<highcharts.highcharts.highcharts.Highchart at 0x7f2e42c6dad0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the scatter plot\n",
"mp.plot_scatter(radius = 6, enabled_data_labels=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c3a00631-6431-4cba-b736-190e35a20405",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-08-01 15:05:06,872 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - generate file: ./feature points_717_correlation_umap_mp\u001b[0m\n",
"2022-08-01 15:05:06,885 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - save html file to ./feature points_717_correlation_umap_mp\u001b[0m\n"
]
},
{
"data": {
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],
"text/plain": [
"<highcharts.highcharts.highcharts.Highchart at 0x7f2e42e7ced0>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the regular 2D grid plot\n",
"mp.plot_grid(enabled_data_labels=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05e2e8ae-41f4-4b5a-8a6b-49dd4d4c69c0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "95d6b55c-50e2-410c-a5c5-d51e18181336",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.11"
}
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"nbformat": 4,
"nbformat_minor": 5
}