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patternproject/wk1-submission.ipynb

Created March 6, 2020 03:13
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"colab_type": "text"
},
"source": [
"Manning LP \n",
"\"Classifying Customer Feedback with Imbalanced Text Data\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fi-68P7Ht6H3",
"colab_type": "text"
},
"source": [
"### 1.Import Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Zw2vCkcfts6e",
"colab_type": "code",
"outputId": "a6835c93-f652-4ba2-b116-f4af9bac31d3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 621
}
},
"source": [
"from __future__ import absolute_import, division, print_function, unicode_literals\n",
"\n",
"import numpy as np\n",
"\n",
"# for frequency tables\n",
"import collections\n",
"\n",
"# for plotting\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
"except Exception:\n",
" pass\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"!pip install tensorflow-hub\n",
"!pip install tfds-nightly\n",
"import tensorflow_hub as hub\n",
"import tensorflow_datasets as tfds\n",
"\n",
"print(\"Version: \", tf.__version__)\n",
"print(\"Eager mode: \", tf.executing_eagerly())\n",
"print(\"Hub version: \", hub.__version__)\n",
"print(\"GPU is\", \"available\" if tf.config.experimental.list_physical_devices(\"GPU\") else \"NOT AVAILABLE\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 2.x selected.\n",
"Requirement already satisfied: tensorflow-hub in /usr/local/lib/python3.6/dist-packages (0.7.0)\n",
"Requirement already satisfied: six>=1.10.0 in /tensorflow-2.1.0/python3.6 (from tensorflow-hub) (1.14.0)\n",
"Requirement already satisfied: protobuf>=3.4.0 in /tensorflow-2.1.0/python3.6 (from tensorflow-hub) (3.11.3)\n",
"Requirement already satisfied: numpy>=1.12.0 in /tensorflow-2.1.0/python3.6 (from tensorflow-hub) (1.18.1)\n",
"Requirement already satisfied: setuptools in /tensorflow-2.1.0/python3.6 (from protobuf>=3.4.0->tensorflow-hub) (45.2.0)\n",
"Collecting tfds-nightly\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/43/24/94fde758681af73d67cee468b9cafdc8c9cd1111cf550a773aa61ade3640/tfds_nightly-2.1.0.dev202003030105-py3-none-any.whl (3.1MB)\n",
"\u001b[K |████████████████████████████████| 3.1MB 2.8MB/s \n",
"\u001b[?25hRequirement already satisfied: absl-py in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (0.9.0)\n",
"Requirement already satisfied: wrapt in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (1.12.0)\n",
"Requirement already satisfied: requests>=2.19.0 in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (2.22.0)\n",
"Requirement already satisfied: six in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (1.14.0)\n",
"Requirement already satisfied: protobuf>=3.6.1 in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (3.11.3)\n",
"Requirement already satisfied: promise in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (2.3)\n",
"Requirement already satisfied: termcolor in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (1.1.0)\n",
"Requirement already satisfied: numpy in /tensorflow-2.1.0/python3.6 (from tfds-nightly) (1.18.1)\n",
"Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (0.21.1)\n",
"Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (0.3.1.1)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (4.28.1)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (0.16.0)\n",
"Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.6/dist-packages (from tfds-nightly) (19.3.0)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /tensorflow-2.1.0/python3.6 (from requests>=2.19.0->tfds-nightly) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /tensorflow-2.1.0/python3.6 (from requests>=2.19.0->tfds-nightly) (2019.11.28)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /tensorflow-2.1.0/python3.6 (from requests>=2.19.0->tfds-nightly) (1.25.8)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /tensorflow-2.1.0/python3.6 (from requests>=2.19.0->tfds-nightly) (2.8)\n",
"Requirement already satisfied: setuptools in /tensorflow-2.1.0/python3.6 (from protobuf>=3.6.1->tfds-nightly) (45.2.0)\n",
"Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tfds-nightly) (1.51.0)\n",
"Installing collected packages: tfds-nightly\n",
"Successfully installed tfds-nightly-2.1.0.dev202003030105\n",
"Version: 2.1.0\n",
"Eager mode: True\n",
"Hub version: 0.7.0\n",
"GPU is NOT AVAILABLE\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C1_0Kjw9uFY0",
"colab_type": "text"
},
"source": [
"### 2.Load Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xQAydIjduFyT",
"colab_type": "code",
"outputId": "13dd1ab5-78ba-4904-c579-4c7c2b0aaf45",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368,
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}
},
"source": [
"imdb_1, info = tfds.load('imdb_reviews/subwords32k', with_info=True, as_supervised=True)\n",
"train_data, test_data = imdb_1['train'], imdb_1['test']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset imdb_reviews/subwords32k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords32k/1.0.0...\u001b[0m\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d0d85b85d3ca4b70998565e9d36ace79",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8a9b2ae18a8417bb16a09c0ab5720dd",
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"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords32k/1.0.0.incomplete2UJXVB/imdb_reviews-train.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"application/vnd.jupyter.widget-view+json": {
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"HBox(children=(IntProgress(value=0, max=25000), HTML(value='')))"
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"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89a91e104a7b489a88e4363833181fc1",
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"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords32k/1.0.0.incomplete2UJXVB/imdb_reviews-test.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb49f4643c494151973a83246aaa27df",
"version_minor": 0,
"version_major": 2
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]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords32k/1.0.0.incomplete2UJXVB/imdb_reviews-unsupervised.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a496d1ca2d2b4872b5d224d9239ca4c8",
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"version_major": 2
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"HBox(children=(IntProgress(value=0, max=50000), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9d806b1122f46a0a1c4e2e7e2f3d81b",
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"version_major": 2
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"text/plain": [
"HBox(children=(IntProgress(value=0, description='Computing statistics...', max=3, style=ProgressStyle(descript…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78de9463467d4520863e857553d298d7",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e2b82f6e504d4bc69dc2ba90472a2037",
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},
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"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords32k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_R35iz4ouMAR",
"colab_type": "text"
},
"source": [
"### 3.Explore Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "62hNBdG_uMZy",
"colab_type": "code",
"outputId": "ef48ace1-82ca-4213-a024-197b4bd65e1d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 587
}
},
"source": [
"print(info)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"tfds.core.DatasetInfo(\n",
" name='imdb_reviews',\n",
" version=1.0.0,\n",
" description='Large Movie Review Dataset.\n",
"This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.',\n",
" homepage='http://ai.stanford.edu/~amaas/data/sentiment/',\n",
" features=FeaturesDict({\n",
" 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
" 'text': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=32650>),\n",
" }),\n",
" total_num_examples=100000,\n",
" splits={\n",
" 'test': 25000,\n",
" 'train': 25000,\n",
" 'unsupervised': 50000,\n",
" },\n",
" supervised_keys=('text', 'label'),\n",
" citation=\"\"\"@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n",
" author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},\n",
" title = {Learning Word Vectors for Sentiment Analysis},\n",
" booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n",
" month = {June},\n",
" year = {2011},\n",
" address = {Portland, Oregon, USA},\n",
" publisher = {Association for Computational Linguistics},\n",
" pages = {142--150},\n",
" url = {http://www.aclweb.org/anthology/P11-1015}\n",
" }\"\"\",\n",
" redistribution_info=,\n",
")\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z9wgcraBuOU7",
"colab_type": "code",
"outputId": "2319a91b-77b3-49bf-c4ed-8631b3b37e47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336
}
},
"source": [
"for imdb_example in train_data.take(1): # Only take a single example\n",
" text, label = imdb_example[0], imdb_example[1]\n",
"\n",
"print(\"text\")\n",
"print(text)\n",
"print('\\n')\n",
"print(\"label\")\n",
"print(label)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"text\n",
"tf.Tensor(\n",
"[ 56 18 41 496 674 59 3 574 32433 25 34 21905\n",
" 10 39 1626 8210 49 548 17768 3 1730 29 112 520\n",
" 2 24 14 256 382 34 72 312 335 10 1216 3\n",
" 422 72 112 170 106 33 8157 14 59 32433 11 1133\n",
" 1964 3 56 27 8 41 476 15843 32426 1504 4185 2692\n",
" 3 19 96 2143 199 77 178 66 1 27162 381 15791\n",
" 77 281 72 5479 22 5693 32509 3 4147 24625 11296 24690\n",
" 50 1849 27610 2 5 54 4184 32439 157 2520 21 8210\n",
" 18 223 24 4 2143 1041 14185 10 4 27 13 18\n",
" 5414 6 111 188 4811 3 12 274 1357 13 84 29\n",
" 154 46 204 2 11717 758 32433 11 46 1626 8003 32433\n",
" 11 68 1417 3 12 106 1403 951 175 37 32440], shape=(143,), dtype=int64)\n",
"\n",
"\n",
"label\n",
"tf.Tensor(0, shape=(), dtype=int64)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aPdjMMwKuR8-",
"colab_type": "code",
"outputId": "38426378-c19f-47f8-fcd2-df4f0fd9ffac",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"print(info.features)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"FeaturesDict({\n",
" 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
" 'text': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=32650>),\n",
"})\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "blWeKd8vuafK",
"colab_type": "text"
},
"source": [
"### 4.Munge Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IqIjADWZuWEA",
"colab_type": "code",
"colab": {}
},
"source": [
"pos_count = 0\n",
"l_pos_count = []\n",
"neg_count = 0\n",
"l_neg_count = []"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6t_kNXbzudkv",
"colab_type": "code",
"colab": {}
},
"source": [
"# directly using len function \n",
"for imdb_example in train_data.take(-1): # -1 implies, all the samples\n",
" if imdb_example[1] == 1:\n",
" #print(len(imdb_example[0]))\n",
" l_pos_count.append(len(imdb_example[0])) # store length in a list\n",
" pos_count += 1 # increment count of pos\n",
" #print(pos_count)\n",
"\n",
" else:\n",
" #print(len(imdb_example[0]))\n",
" l_neg_count.append(len(imdb_example[0])) # store length in a list\n",
" neg_count += 1 # increment count of pos\n",
" #print(neg_count)\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pUjqu1HEunHE",
"colab_type": "text"
},
"source": [
"### 5.Frequency Table"
]
},
{
"cell_type": "code",
"metadata": {
"id": "k_ZO4gHDunj6",
"colab_type": "code",
"colab": {}
},
"source": [
"pos_counter = collections.Counter(l_pos_count)\n",
"#print(pos_counter)\n",
"#print(pos_counter.most_common(1))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "15LxtLaJusVe",
"colab_type": "code",
"colab": {}
},
"source": [
"pos_freq = list(pos_counter.keys()) # from dict_key to list\n",
"pos_val = list(pos_counter.values()) # from dict_values to list"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Ionzt0_JuvLZ",
"colab_type": "code",
"colab": {}
},
"source": [
"neg_counter = collections.Counter(l_neg_count)\n",
"#print(neg_counter)\n",
"#print(neg_counter.most_common(1))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E30AtrPAuwWG",
"colab_type": "code",
"colab": {}
},
"source": [
"neg_freq = list(neg_counter.keys()) # from dict_key to list\n",
"neg_val = list(neg_counter.values()) # from dict_values to list"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EeLchByIuxw5",
"colab_type": "code",
"outputId": "e856401b-7fb1-4cf6-c465-ee3a0249ab97",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(\"The most frequent length for postive reviews is\", pos_counter.most_common(1)[0][0], \"words and it occurs\", pos_counter.most_common(1)[0][1], \"times\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"The most frequent length for postive reviews is 162 words and it occurs 80 times\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BIAJO3ptuzEQ",
"colab_type": "code",
"outputId": "7169fffd-6d09-486f-8c75-83efe840aa7b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(\"The most frequent length for negative reviews is\", neg_counter.most_common(1)[0][0], \"words and it occurs\", neg_counter.most_common(1)[0][1], \"times\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"The most frequent length for negative reviews is 164 words and it occurs 84 times\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VMPWclAju663",
"colab_type": "text"
},
"source": [
"### 6.Plot Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zNz0TCMEu7Zb",
"colab_type": "code",
"colab": {}
},
"source": [
"sns.set_style('darkgrid')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6jgcmk0iu-As",
"colab_type": "code",
"outputId": "be7c3fb8-170e-4bfd-9582-4668d6885707",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
}
},
"source": [
"fig, ax = plt.subplots()\n",
"sns.distplot(neg_freq, label = \"negative review length\")\n",
"sns.distplot(pos_freq, label = \"positive review length\")\n",
"ax.legend()\n",
"ax.set(xlabel='total word count', ylabel = 'frequency of occurence')\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Text(0, 0.5, 'frequency of occurence'), Text(0.5, 0, 'total word count')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
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TrsSReLQm9EdFL0UQ2gNRUNqhIqeegZpCVM4qbPr2f4fXqTQqickJdraUhlDu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\n3wkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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