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notebooks/gpu_tfidf_demo.ipynb
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# NLP preprocessing and vectorizing at rocking speed with RAPIDS cuML"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Author: Simon Andersen\nIn this notebook, we will show how to preprocess and vectorize a text dataset on GPU using cuML to get **performance boost up to 5x** compared to CPU.\n\nHere the vectorizing method is **TF-IDF**. Our GPU TfidfVectorizer is largely dirived from scikit-learn [TfidfVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer).\n\nThis notebook was tested on an NVIDIA Tesla V100-32Gb GPU using RAPIDS 0.15 and CUDA 10.0. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuML](https://github.com/rapidsai/cuml/issues).\n\n### Data\nWe will be working with a dataset of 8M Tweets related to the covid-19, or about 3Gb text data (credit goes to [Shane Smith](https://www.kaggle.com/smid80) for gathering the data from Twitter and publishing it on [Kaggle](https://www.kaggle.com/smid80/coronavirus-covid19-tweets-early-april)). If you want to run this notebook on your own setup, you will have to download the dataset [here](https://www.kaggle.com/smid80/coronavirus-covid19-tweets-early-april)."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Imports and definitions"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:49:53.043129Z",
"end_time": "2020-07-10T17:49:53.045767Z"
},
"trusted": true
},
"cell_type": "code",
"source": "import os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]='1'",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:49:53.639639Z",
"end_time": "2020-07-10T17:49:54.885872Z"
},
"trusted": true
},
"cell_type": "code",
"source": "import cudf\nfrom cudf import Series\nfrom cudf import DataFrame\n\nfrom cuml.feature_extraction.text import TfidfVectorizer",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:49:54.887488Z",
"end_time": "2020-07-10T17:49:54.890434Z"
},
"trusted": true
},
"cell_type": "code",
"source": "import numpy as np\nimport pandas as pd\nimport warnings\n\n\npd.set_option('display.max_colwidth', 1000000)\nwarnings.filterwarnings('ignore')",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:49:54.892119Z",
"end_time": "2020-07-10T17:49:54.895974Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def join_df(path, df_lib=cudf):\n data = df_lib.DataFrame()\n for file in os.listdir(path):\n print(f\"In path : {path}{file}\")\n temp = df_lib.read_csv(path+file)\n temp = temp[temp.lang=='en']\n data = df_lib.concat([data,temp])\n return data",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Taking advantage of sparsity"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "With the [dataset](https://www.kaggle.com/smid80/coronavirus-covid19-tweets-early-april) downloaded and extracted in a folder `/tweets`, we can load it into a dataframe using cudf:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:49:55.778905Z",
"end_time": "2020-07-10T17:50:01.433168Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = join_df('tweets/')\ntweets = Series(df['text'])\nlen(tweets)",
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": "In path : tweets/2020-03-29 Coronavirus Tweets.CSV\nIn path : tweets/2020-03-30 Coronavirus Tweets.CSV\nIn path : tweets/2020-03-31 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-11 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-12 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-03 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-10 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-01 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-07 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-13 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-14 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-15 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-09 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-02 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-05 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-06 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-08 Coronavirus Tweets.CSV\nIn path : tweets/2020-04-04 Coronavirus Tweets.CSV\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
"text/plain": "4827372"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "After filtering non-english tweets, we are left with almost 5M tweets."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can then preprocess and vectorize this data using our **TfidfVectorizer**:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:01.434694Z",
"end_time": "2020-07-10T17:50:25.129170Z"
},
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "vec = TfidfVectorizer(stop_words='english')\n\ntfidf_matrix = vec.fit_transform(tweets)\ntfidf_matrix.shape",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": "(4827372, 5435390)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Which takes 25.9 seconds on a Tesla V100-32Gb. In comparison, scikit-learn’s TfidfVectorizer takes 2 minutes and 54.6 seconds on CPU. Notice the shape of the matrix: (4827372, 5435706). In other words 4.8M by 5.4M floats. This is huge! The only reason it can fit in GPU memory is because we store this data as a sparse matrix."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Document search\nNow that we have our TF-IDF matrix, we want to query it with some keywords to find the most relevant documents. To do this, we will rely on the fact that our TfidfVectorizer has the capability to vectorize any document according to the vocabulary from the input corpus. We will therefore vectorize our query of keywords to get a corresponding vector in the same space as our TF-IDF matrix. Then, all we have to do is to compute the distance between our query vector and each document of the TF-IDF matrix. The document with the smallest distance (or highest similarity) to our query is the most relevant document to our keywords. We chose to use the cosine similarity. When both vectors are normalized, the cosine similarity is just the dot product of those vectors which correspond to the cosine of the angle between the two vectors. \n\nHere is an efficient way to compute the cosine similarity between a vector and each row of a matrix, first normalize both the vector and matrix according to the L2 norm and take the dot product between the matrix and the vector:\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:25.131058Z",
"end_time": "2020-07-10T17:50:25.136858Z"
},
"trusted": true
},
"cell_type": "code",
"source": "from cuml.common.sparsefuncs import csr_row_normalize_l2\n\n\ndef efficient_csr_cosine_similarity(query, tfidf_matrix, matrix_normalized=False):\n query = csr_row_normalize_l2(query, inplace=False)\n if not matrix_normalized:\n tfidf_matrix = csr_row_normalize_l2(tfidf_matrix, inplace=False)\n \n return tfidf_matrix.dot(query.T)\n\n\ndef document_search(text_df, query, vectorizer, tfidf_matrix, top_n=3):\n query_vec = vectorizer.transform(Series([query]))\n similarities = efficient_csr_cosine_similarity(query_vec, tfidf_matrix, matrix_normalized=True)\n similarities = similarities.todense().reshape(-1)\n best_idx = similarities.argsort()[-top_n:][::-1]\n \n pp = cudf.DataFrame({\n 'text': text_df['text'].iloc[best_idx],\n 'similarity': similarities[best_idx]\n })\n return pp",
"execution_count": 7,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Let’s try our simple keyword search with a few queries:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:25.138558Z",
"end_time": "2020-07-10T17:50:25.532169Z"
},
"trusted": true
},
"cell_type": "code",
"source": "document_search(df, 'computer science and NLP', vec, tfidf_matrix)",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": " text \\\n207793 Computer Science has been diverging as CS + cross platform domain knowledge. #covid19 is only going to accelerate that. We will have traditional computer science as well as computer science + domain emphasis. Could be bioinformatics, drug, speech (NLP) and even agriculture. \n161295 #CoronaVirus is not a computer virus!! #COVID19 \n334632 COVID-19: what can we do with NLP? https://t.co/N31tW5glKY #NLProc #NLP #ArtificialIntelligence #COVID19 #MachineLearning https://t.co/8tsnyluruC \n\n similarity \n207793 0.596569 \n161295 0.479981 \n334632 0.429284 ",
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},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:25.533465Z",
"end_time": "2020-07-10T17:50:25.978638Z"
},
"trusted": true
},
"cell_type": "code",
"source": "document_search(df, 'nvidia gpu', vec, tfidf_matrix)",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": " text \\\n404210 NVIDIA Brings GPU, HPC and AI Expertise to COVID-19 Battle -- https://t.co/7scxpFrPrY #AI #GPU #COVID19 @nvidia \n420528 Yes! #STEM in action, #NVIDIA assisting in science regarding #Coronavirus 😷. #CoronaVirusUpdate @nvidia NVIDIA Brings GPU, HPC and AI Expertise to COVID-19 Battle https://t.co/FxZ5VDmDMI \n433161 Today #supercomputing resources \\n\\n are crucial to help find a treatment for the novel \\n\\n#Covid19.NVIDIA GPU can help!! \n\n similarity \n404210 0.755702 \n420528 0.659085 \n433161 0.630629 ",
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},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Clustering\nLet’s try to find clusters in our tweets and see if we can discover general topics related to the covid-19. Note that here we limited the number of features to 18,000, which means that only the top 18,000 most frequent terms will be represented by our vectors. "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:25.980055Z",
"end_time": "2020-07-10T17:50:50.396768Z"
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"trusted": true,
"scrolled": true
},
"cell_type": "code",
"source": "vec = TfidfVectorizer(max_features=18000, stop_words='english')\ntfidf_matrix = vec.fit_transform(tweets)\ntfidf_matrix.shape",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": "(4827372, 18000)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### KMeans\nKMeans clustering is an algorithm that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest center. Cluster centers (or cluster centroid) are simply the cluster’s mean. KMeans require us to provide the number of cluster k beforehand. Ideally, we want to choose the number of clusters carefully. One technique to find a good number of clusters is the Elbow Method. Essentially, the method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters. However, for simplicity, I’ll arbitrarily set `k = 10` as it seems to be decent for this dataset. \n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:50.398018Z",
"end_time": "2020-07-10T17:50:50.456970Z"
},
"trusted": true
},
"cell_type": "code",
"source": "from cuml.cluster import KMeans\n\n\nnum_clusters = 10\nsample_size = 100_000\nkmeans_model = KMeans(n_clusters=num_clusters, n_init=1, max_iter=1000)\nsample_data = tfidf_matrix[:sample_size].todense()\nsample_tweets = tweets[:sample_size].reset_index(drop=True)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "You might have noticed that we only use 100,000 samples here instead of the full dataset. As we discussed earlier, sparsity is very important and the `tfidf_matrix` for this dataset cannot fit into GPU memory unless we keep it sparse. Since cuML KMeans does not support sparse input yet, we have to use a subset of the dataset which can fit in GPU memory. This is not a blocker for this demonstration as 100,000 samples is enough to find interesting clusters on this dataset."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:50.458713Z",
"end_time": "2020-07-10T17:50:52.038759Z"
},
"trusted": true
},
"cell_type": "code",
"source": "kmeans = kmeans_model.fit(sample_data)\nkmeans_clusters = kmeans.predict(sample_data)\nkmeans_distances = kmeans.transform(sample_data)",
"execution_count": 12,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Using the previously computed TF-IDF, we can look at the most important words for each of our clusters (which are the words with the highest tf-idf value in each of the cluster centers). We filter terms that are also present in other clusters as they do not help us distinguish different topics. For instance, words such as “covid” or “coronavirus” are top words in almost all clusters so we filter them out. Hopefully this will be a good indication of the topic of each cluster."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:50:52.040138Z",
"end_time": "2020-07-10T17:50:52.116171Z"
},
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "import numpy as np\n\n\nsorted_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]\nterms = vec.get_feature_names()\n\nclusters_terms = sorted_centroids[:, :100].get()\n\nfor i, c1 in enumerate(clusters_terms):\n cluster = set(c1)\n for j, c2 in enumerate(clusters_terms):\n if i == j:\n continue\n cluster -= set(c2)\n cluster = c1[np.isin(c1, list(cluster))][:5]\n print(f'Cluster {i}:', ' | '.join(terms[cluster].tolist()))",
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": "Cluster 0: end | soon | weeks | april | week\nCluster 1: workers | pm | testing | masks | hospital\nCluster 2: news | fake | siamo | westandwithitaly | voi\nCluster 3: sooner | identify | download | slow | reporting\nCluster 4: save | healthy | hands | wash | staysafe\nCluster 5: earthhour2020 | untilltomorrow | dontrushchallenge | sundaymotivation | socialdistanacing\nCluster 6: york | jersey | connecticut | yorkers | travel\nCluster 7: believe | hard | ppl | won | faith\nCluster 8: total | confirmed | number | 28 | toll\nCluster 9: hour | forward | earth | estimated | tweeted\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Looking at the top words from each cluster we can guess topics for most of them. For instance, cluster 0 seems to be tweets of people trying to guess when the pandemic (or social distancing) will end. Cluster 6 seems focused about the area around New York city which was one of the first epicenters of the pandemic in the US. Cluster 4 seems to be about safety rules like washing your hands and staying at home, let’s sample a few tweets from this cluster:"
},
{
"metadata": {
"deletable": false,
"editable": false,
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2020-07-10T17:51:40.929863Z",
"end_time": "2020-07-10T17:51:40.944695Z"
},
"run_control": {
"frozen": true
}
},
"cell_type": "code",
"source": "sample_tweets[kmeans_clusters == 4].to_pandas().sample()",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": "62623 While you stay at home to stay safe due the COVID-19 outbreak, ensure you are safe at home.\\n\\n#staysafe #stayathome #socialdistancing #corona #coronavirus #covid_19 #goodhealth #goodhealthandwellbeing #sdgs #sdg… https://t.co/AFFtusHj6v\nName: text, dtype: object"
},
"metadata": {}
}
]
},
{
"metadata": {
"deletable": false,
"editable": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2020-07-10T17:52:10.579523Z",
"end_time": "2020-07-10T17:52:10.592469Z"
},
"run_control": {
"frozen": true
}
},
"cell_type": "code",
"source": "sample_tweets[kmeans_clusters == 4].to_pandas().sample()",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": "4338 #StayHome to stay unexposed and not expose others to #COVID19. Only go out for essential services or if you are an essential worker. Stay 6 feet or more away from others. Don't gather in groups. https://t.co/fnXN7UAm4V\nName: text, dtype: object"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Pretty good! This is coherent with our guess for the topic of this cluster, hinting that tweets are indeed linearly separable using this method."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### t-SNE\nKMeans helps us find clusters for our data, but the resulting matrix is highly-dimensional with 30 components which is hard to represent on a 2D plane. In order to visualize those clusters, we will use the t-SNE algorithm to reduce the dimensionality of our data. t-SNE models each high-dimensional object by a n-dimensional point (for instance `n=2`) in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability. In our case, t-SNE will help us find a low dimensional data representation that keeps close points together in the 2D space. With this 2D representation, we will see more clearly if our KMeans worked as clusters should be visible on the plot."
},
{
"metadata": {
"deletable": false,
"editable": false,
"run_control": {
"frozen": true
},
"trusted": true
},
"cell_type": "code",
"source": "from cuml.manifold import TSNE\n\n\ntsne_model = TSNE(n_components=2, verbose=1, random_state=42, n_iter=1000)\ntsne_kmeans = tsne_model.fit_transform(kmeans_distances)",
"execution_count": 51,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can now plot our data with colors according to their cluster assignment to visualize the clusters discovered by KMeans:"
},
{
"metadata": {
"deletable": false,
"editable": false,
"run_control": {
"frozen": true
},
"scrolled": false,
"trusted": true
},
"cell_type": "code",
"source": "from bokeh.palettes import Turbo256\n\n\n# setup data\nstep = len(Turbo256) / num_clusters\nkmeans_df = DataFrame(tsne_kmeans, columns=['x', 'y'])\nkmeans_df['cluster'] = kmeans_clusters\nkmeans_df['tweets'] = sample_tweets\nkmeans_df['color'] = [Turbo256[int(i * step)] for i in kmeans_clusters.tolist()]\nkmeans_df = kmeans_df.to_pandas()\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure\n\n\nfig, ax = plt.subplots(figsize=(7, 6), dpi=100)\n\nax.scatter(kmeans_df['x'], kmeans_df['y'], color=kmeans_df['color'], s = 5)",
"execution_count": 54,
"outputs": [
{
"data": {
"text/plain": "<matplotlib.collections.PathCollection at 0x7fa220d64f10>"
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
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{
"data": {
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\n",
"text/plain": "<Figure size 700x600 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can clearly see different groups of clusters, pretty good! Of course it’s not perfect as some clusters seem to be merging with others like deep blue with deep orange, or deep blue with green, but others are completely separated like deep red, black or cyan. This is expected as we could have done a better job of choosing the number of clusters cleverly instead of picking 10 arbitrarily. Added to the fact that this dataset is made of real tweets, which means uncleaned data and outliers, this result seems pretty good. Let’s draw markers for the two earlier tweets from cluster 4 with a black colored X and add a tweet from cluster 6 with a red cross."
},
{
"metadata": {
"deletable": false,
"editable": false,
"run_control": {
"frozen": true
},
"trusted": true,
"scrolled": false
},
"cell_type": "code",
"source": "# markers\ncluster_4_tweets = [4338, 62623]\ncluster_6_tweets = [55737]\n\ndef get_glyph_df(tweets_ids, color):\n glyph_df = kmeans_df.iloc[tweets_ids].copy()\n glyph_df['color'] = color\n glyph_df['size'] = 20\n return glyph_df\n\nglyph_4 = get_glyph_df(cluster_4_tweets, '#000000')\nglyph_6 = get_glyph_df(cluster_6_tweets, '#c72020')\n\nfig, ax = plt.subplots(figsize=(7, 6), dpi=100)\n\nax.scatter(kmeans_df['x'], kmeans_df['y'], color=kmeans_df['color'], s = 5)\nax.scatter(glyph_4['x'], glyph_4['y'], color=glyph_4['color'], marker='x', s=80)\nax.scatter(glyph_6['x'], glyph_6['y'], color=glyph_6['color'], marker='+', s=100)",
"execution_count": 55,
"outputs": [
{
"data": {
"text/plain": "<matplotlib.collections.PathCollection at 0x7fa22226af10>"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<matplotlib.collections.PathCollection at 0x7fa220cf6b50>"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<matplotlib.collections.PathCollection at 0x7fa220cf6950>"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 700x600 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "For reference, here are the tweets from cluster 4 (marked with a black X on the graph):"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:40:40.570709Z",
"end_time": "2020-07-10T17:40:40.584322Z"
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"frozen": true
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"cell_type": "code",
"source": "glyph_4[['tweets', 'cluster']]",
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 56,
"data": {
"text/plain": " tweets \\\n4338 #StayHome to stay unexposed and not expose others to #COVID19. Only go out for essential services or if you are an essential worker. Stay 6 feet or more away from others. Don't gather in groups. https://t.co/fnXN7UAm4V \n62623 While you stay at home to stay safe due the COVID-19 outbreak, ensure you are safe at home.\\n\\n#staysafe #stayathome #socialdistancing #corona #coronavirus #covid_19 #goodhealth #goodhealthandwellbeing #sdgs #sdg… https://t.co/AFFtusHj6v \n\n cluster \n4338 4 \n62623 4 ",
"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>tweets</th>\n <th>cluster</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>4338</th>\n <td>#StayHome to stay unexposed and not expose others to #COVID19. Only go out for essential services or if you are an essential worker. Stay 6 feet or more away from others. Don't gather in groups. https://t.co/fnXN7UAm4V</td>\n <td>4</td>\n </tr>\n <tr>\n <th>62623</th>\n <td>While you stay at home to stay safe due the COVID-19 outbreak, ensure you are safe at home.\\n\\n#staysafe #stayathome #socialdistancing #corona #coronavirus #covid_19 #goodhealth #goodhealthandwellbeing #sdgs #sdg… https://t.co/AFFtusHj6v</td>\n <td>4</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "And here is the tweet from cluster 6 (marked with a red + on the graph):"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T17:40:42.604958Z",
"end_time": "2020-07-10T17:40:42.618142Z"
},
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"frozen": true
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"cell_type": "code",
"source": "glyph_6[['tweets', 'cluster']]",
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 57,
"data": {
"text/plain": " tweets \\\n55737 New York, New Jersey, Connecticut residents must refrain from travel for 14 days : CDC\\n#COVID19 #CoronaVirusUpdates #CoronaVirusOutbreak #CoronaVirus #COVID_19 #COVID19outbreak #WuhanCoronavirus #COVID19Pandemic #SARSCOV2 #NewYork #NewJersey #UnitedStates \\nhttps://t.co/qgDGzKwfP0 \n\n cluster \n55737 6 ",
"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>tweets</th>\n <th>cluster</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>55737</th>\n <td>New York, New Jersey, Connecticut residents must refrain from travel for 14 days : CDC\\n#COVID19 #CoronaVirusUpdates #CoronaVirusOutbreak #CoronaVirus #COVID_19 #COVID19outbreak #WuhanCoronavirus #COVID19Pandemic #SARSCOV2 #NewYork #NewJersey #UnitedStates \\nhttps://t.co/qgDGzKwfP0</td>\n <td>6</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2020-07-10T16:30:58.620863Z",
"end_time": "2020-07-10T16:30:58.630068Z"
}
},
"cell_type": "markdown",
"source": "---\nCopyright (c) 2020, NVIDIA CORPORATION.\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/e96f2f48d1de35b21506a13cdc37a363"
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"gist": {
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"data": {
"description": "notebooks/gpu_tfidf_demo.ipynb",
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},
"kernelspec": {
"name": "cuml_dev",
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"language_info": {
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"mimetype": "text/x-python",
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"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
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"toc": {
"nav_menu": {},
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