From 2b2ee8d53a92368f5bcf46c146e701a3a0b34231 Mon Sep 17 00:00:00 2001 From: generall Date: Mon, 21 Oct 2024 23:54:14 +0200 Subject: [PATCH] wip: article --- .../articles/distance-based-exploration.md | 160 ++++++++++++++++++ .../data-on-chart.png | Bin 0 -> 22059 bytes .../no-context-data.png | Bin 0 -> 14059 bytes .../distance-based-exploration/preview.png | Bin 0 -> 6311902 bytes .../preview/preview.jpg | Bin 0 -> 24464 bytes .../preview/preview.webp | Bin 0 -> 21686 bytes .../preview/social_preview.jpg | Bin 0 -> 174147 bytes .../preview/title.jpg | Bin 0 -> 69392 bytes .../preview/title.webp | Bin 0 -> 59596 bytes .../social-preview.jpg | Bin 0 -> 578796 bytes .../umap-midlib.png | Bin 0 -> 110003 bytes .../distance-based-exploration/umap.png | Bin 0 -> 191923 bytes 12 files changed, 160 insertions(+) create mode 100644 qdrant-landing/content/articles/distance-based-exploration.md create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/data-on-chart.png create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/no-context-data.png create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview.png create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview/preview.jpg create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview/preview.webp create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview/social_preview.jpg create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview/title.jpg create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/preview/title.webp create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/social-preview.jpg create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/umap-midlib.png create mode 100644 qdrant-landing/static/articles_data/distance-based-exploration/umap.png diff --git a/qdrant-landing/content/articles/distance-based-exploration.md b/qdrant-landing/content/articles/distance-based-exploration.md new file mode 100644 index 000000000..6f137ec9d --- /dev/null +++ b/qdrant-landing/content/articles/distance-based-exploration.md @@ -0,0 +1,160 @@ +--- +title: "Distance-based data exploration" +short_description: "Efficient visualization and clusterization of high-dimensional data with Qdrant" +description: "Explore your data under a new angle with Qdrant's tools for dimensionality reduction, clusterization, and visualization." +social_preview_image: /articles_data/distance-based-exploration/social-preview.jpg +preview_dir: /articles_data/distance-based-exploration/preview +weight: -250 +author: Qdrant team +date: 2024-10-20T12:00:00+03:00 +draft: false +keywords: + - clusterization + - dimensionality reduction + - vizualization +--- + + +## Hidden Structure + +When working with large collections of documents, images or other arrays of unstructured data, it ofter becomes useful to understand the big picture. +Looking at datapoints one by one is not always the best way to understand the structure of the data. + +{{< figure src="/articles_data/distance-based-exploration/no-context-data.png" alt="Data visualization" caption="Datapoints without context, pretty much useless" >}} + +Similar to how numbers in a table obtain meaning when plotted on a graph, similarities between items of the unstructured data can reveal hidden structures and patterns. + +{{< figure src="/articles_data/distance-based-exploration/data-on-chart.png" alt="Data visualization" caption="Vizualized chart, very intuitive" >}} + +There are many tools to investigate data similarity, but with the latest release of Qdrant it became much easier to start working with them. +With the new [Distance Matrix API](/documentation/concepts/explore/#distance-matrix), Qdrant takes care of the most computationally expensive part of the process - calculating the distances between data points. + +In many implementations, the distance matrix was calculated on the same process that performed the clustering or visualization, but that either required a brute-force computation or building a temporary index. +With Qdrant, on the other hand, the data is already indexed and distance matrix can be calculated relatively cheaply. + +In this article we will take a look at a few ways to explore the data using the distance matrix API. + +## Dimensionality Reduction + +As a first instinct, we might want have an ability to look at the whole dataset, or at least a large part of it, with a single glance. +But when the data is high-dimensional, it is not possible to visualize it directly. We need to apply a technique called dimensionality reduction. +Dimensionality reduction allows us to convert high-dimensional data into a lower-dimensional representation, while preserving the most important properties of the data. + +In this article we will use the [UMAP](https://github.com/lmcinnes/umap) as our dimensionality reduction algorithm of choice. +It will be very easy to understand why we chose UMAP, if we quickly recap how it works. + +Here is a **very** simplified, but intuitive explanation of UMAP: + +1. *Randomply generate points in the 2d space*: for each high-dimensional point, generate a random 2d point. +1. *Generate distance matrix for the the high-dimensional points*: calculate distances between all pairs of points. +1. *Generate distance matrix for the 2d points*: the same way as in the high-dimensional space. +1. *Try to match both distance matricies to each other*: move 2d points around until the difference is minimized. + +{{< figure src="/articles_data/distance-based-exploration/umap.png" alt="UMAP" caption="Canonical example of UMAP results, [source](https://github.com/lmcinnes/umap?tab=readme-ov-file#performance-and-examples)" >}} + +As you can see, UMAP tries to preserve the relative distances between points in the high-dimensional space, while the actual high-dimensional coordinates are not important. +In fact, we can skip the second step completely if we have the distance matrix already calculated. + +Let's see how we can use Qdrant to calculate the distance matrix and then apply UMAP to it. +We will one of the default datasets that come with Qdrant - Midjourney Styles dataset. + +Use this command to download the dataset and import it into Qdrant: + +```http +PUT /collections/midlib/snapshots/recover +{ + "location": "http://snapshots.qdrant.io/midlib.snapshot" +} +``` + +
+We also need to prepare our python enviroment + +```bash +pip install umap-learn seaborn matplotlib qdrant-client +``` + +Import the necessary libraries: + +```python +# Used to talk to Qdrant +from qdrant_client import QdrantClient +# Package with original UMAP implementation +from umap import UMAP +# Python implementation for sparse matrices +from scipy.sparse import csr_matrix +# For vizualization +import seaborn as sns +``` + +Establish connection to Qdrant: + +```python +qdrant = QdrantClient("http://localhost:6333") +``` + +
+ +After this is done, we can compute the distance matrix: + +```python + +# Request distances matrix from Qdrant +# `_offsets` suffix defines a format of the output matrix. +result = client.search_matrix_offsets( + collection_name="midlib", + sample=1000, # Select subset of the data, as whole dataset might be too large + limit=20, # For performance reasons, limit number of closest neighbors to consider +) + +# Convert distances matrix to python-native format +matrix = csr_matrix( + (result.scores, (result.offsets_row, result.offsets_col)) +) + +# Make matrix symmetric, as UMAP expects it. +# Distance matrix is always symmetric, but qdrant only computes half of it. +matrix = matrix + matrix.T +``` + +Now we can apply UMAP to the distance matrix: + +```python +umap = UMAP( + metric="precomputed", # We providing ready made distance matrix + n_components=2, # output dimension + n_neighbors=20, # Same as limit in the search_matrix_offsets +) + +vectors_2d = umap.fit_transform(matrix) +``` + +That's all that is needed to get the 2d representation of the data. + +{{< figure src="/articles_data/distance-based-exploration/umap-midlib.png" alt="UMAP on Midlib" caption="UMAP applied to Midlib dataset" >}} + + + +UMAP is not the only dimensionality reduction algorithm, compatible with Sparse Distance Matrix input. + +Sci-kit learn has a list of algorithms that can work with precomputed distances: + +- [Isomap](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html) - Non-linear dimensionality reduction through Isometric Mapping +- [SpectralEmbedding](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.SpectralEmbedding.html) - Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. +- [TSNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) - well-known algorithm for dimensionality reduction + +and multiple others. + +## Clustering + +ToDo + +## Graphs + +### Global Graph + +ToDo + +### Response Graph + +ToDo \ No newline at end of file diff --git a/qdrant-landing/static/articles_data/distance-based-exploration/data-on-chart.png b/qdrant-landing/static/articles_data/distance-based-exploration/data-on-chart.png new file mode 100644 index 0000000000000000000000000000000000000000..a62c281f6f66a604c4c86073cae44c69cbe30e86 GIT binary patch literal 22059 zcmbTe1yEJ(+b@hFC?cXrr-F1zcSwhTba!`miZlq)-5}lFA+YK06gCYTq#Mr7^Zeg; zzHiPuXXYGc4?9?Utvjyk`o*<|;IGo6C@*ne!ok6zh>Hoy!NEO+g4gjc5W(*@Sw%JQ z^vqsBT;T=y@pxen1m5E~2&*{ATN^nz>)IK@8CzLf8q(V9+Zh^K*_&899KyHpf`jND z4-&L9)O9emwtBB%YH0{31zs>RyqD2;c+dEWk@@{64o)U^@Ob}K>b;<_ynC${F;n*s+Es9sNOYfeed^-_+%wmr@(mp&LzyQnS_}tX1zKHq#bHw`>zW+R=zIW^8mj7WU?IyL-W_$V!p>AU^QJ}Dp zirxhJ?DLz)M`pv((NV|93~EcODg1;|QEcI^nXm7H8#>et?)ztY0gZo-`fL)Jg8Jcg z#?0OkBkrCpr|)u%@$<^d%M;bf{+SXNE&} zwIaJQ!1QzzWrQxbjb)3{Sx{Pdlt89uJ}$AaJDQ~ zCOCQc)YQ513_>(p!E}{^=a~5k_lL$bd6Z!po!P;!7g!$xQHj 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