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+ Word embedding vector-space analogy. TEACH adapts the idea of comparing semantic vector representations to the mobility domain, where locations, sensors, or trajectory tokens are represented as embeddings. +
+ +In NLP, an embedding maps a discrete token to a continuous vector. + +For example: + +```text +word -> vector +location token -> vector +trajectory sequence -> vector representation ``` -conda activate teach_env + +TEACH follows the same conceptual direction, but instead of comparing words, it compares representations of trajectory-related elements. + +This allows researchers to ask questions such as: + +```text +Do nearby locations have similar embedding vectors? +Do trajectories with similar routes become close in embedding space? +Does a better intrinsic representation improve downstream prediction? +Which embedding should be coupled with a neural network model? ``` -Execute the following commands: +--- + +## πΊοΈ Trajectory Data Context + +
+ 
+
+ Example of GPS track visualization. TEACH focuses on trajectory data represented either as latitude/longitude points or as symbolic sequences of locations. +
+ +A trajectory can be represented as an ordered sequence: + +$$ +T = \langle p_1, p_2, \ldots, p_n \rangle +$$ + +where each point may contain: + +```text +time +latitude +longitude +trajectory_id +optional location_label ``` -conda install -c conda-forge voila pymove nltk=3.7 ipython=8.8.0 + +In a sequence-based representation, the same trajectory can also be modeled as: + +$$ +T = \langle l_1, l_2, \ldots, l_n \rangle +$$ + +where each $l_i$ is a location token, road segment, sensor identifier, or discretized spatial label. + +--- + +## β Main Usage per Script + +| Script | Main Role | Libraries | +|---|---|---| +| `app/dashboards_classes/view_trajectory_data.py` | Dataset statistics and trajectory visualization. | Pandas, NumPy, IPython, IPyWidgets, PyMove, Folium, OS, Pathlib, Random | +| `app/dashboards_classes/extrinsic_evaluation.py` | Evaluation of trained models on location prediction tasks. | Pandas, NumPy, scikit-learn, Keras, IPython, IPyWidgets, Pathlib | +| `app/dashboards_classes/intrinsic_evaluation.py` | Embedding quality inspection through distance matrices, MRR, and similarity analysis. | Pandas, NumPy, PyMove, OSMnx, NetworkX, Matplotlib, NLTK, TQDM, Pathlib, OS, Copy | +| `app/dashboards_classes/teach_main.py` | Main dashboard orchestration class. | Pandas, NumPy, IPython, IPyWidgets, Keras, OS, Pathlib, IO, Copy | +| `app/utils/distances.py` | Distance functions used by intrinsic comparison routines. | Math, NumPy | +| `app/utils/geographical.py` | Geographical and sequence utilities. | Keras Preprocessing | +| `app/utils/metrics.py` | Evaluation metrics. | NumPy | +| `app/utils/output.py` | Output widgets and display helpers. | IPython, IPyWidgets | + +--- + +## π Repository Structure + +```text +teach/ +β +βββ app/ +β βββ dashboards_classes/ +β β βββ extrinsic_evaluation.py +β β βββ intrinsic_evaluation.py +β β βββ teach_main.py +β β βββ view_trajectory_data.py +β β +β βββ utils/ +β β βββ distances.py +β β βββ geographical.py +β β βββ metrics.py +β β βββ output.py +β β +β βββ matrices/ +β βββ trajectories/ +β βββ trejectories/ +β βββ teach.ipynb +β +βββ Basic_User_Experience_Flow.png +βββ TEACH_Class_Diagram.png +βββ README.md +βββ requirements.txt ``` + +Depending on the execution flow, TEACH may also create or update metadata files such as: + +```text +Emb.csv +Model.csv +Emb#Data.csv +Model#Data.csv +Model#Emb.csv ``` -conda install tensorflow=2.9.1 matplotlib=3.6.2 + +--- + +## π§ Dependencies & Libraries + +TEACH combines scientific Python, geospatial processing, machine learning, visualization, and dashboard tools. + +| Tool / Library | Purpose | +|---|---| +| Python 3.8.3 | Main execution environment. | +| Pandas 1.5.3 | Tabular data loading, metadata tables, and dataframe operations. | +| NumPy 1.23.5 | Numerical operations and vector calculations. | +| Matplotlib 3.6.2 | Plotting evaluation curves and matrices. | +| scikit-learn 1.0.2 | Machine learning metrics and evaluation utilities. | +| Keras 2.9.0 | Loading and evaluating neural models. | +| TensorFlow 2.9.1 | Backend for Keras models. | +| TQDM 4.66.1 | Progress bars for long computations. | +| OSMnx 1.2.2 | Road network extraction and spatial network analysis. | +| NetworkX 2.8.5 | Graph structures and network algorithms. | +| IPyWidgets 8.0.6 | Interactive dashboard controls. | +| IPython 8.8.0 | Notebook display and output utilities. | +| PyMove 3.1.2 | Trajectory processing and visualization support. | +| NLTK 3.7 | NLP utilities. | +| Folium 0.14.0 | Interactive map visualization. | +| Keras Preprocessing 1.1.2 | Sequence padding utilities. | +| VoilΓ 0.5.2 | Converts the notebook into an interactive dashboard application. | + +--- + +# βΆοΈ Activating TEACH + +## 1. Create a Conda Environment + +Create an environment named `teach_env`: + +```bash +conda create -n teach_env python=3.8.3 ``` + +Activate the environment: + +```bash +conda activate teach_env ``` -conda install -c conda-forge osmnx=1.2.2 networkx=2.8.5 scikit-learn=1.0.2 + +--- + +## 2. Install Dependencies + +Install dashboard and mobility-related dependencies: + +```bash +conda install -c conda-forge voila pymove nltk=3.7 ipython=8.8.0 ``` +Install TensorFlow and Matplotlib: -Clone the repository: +```bash +conda install tensorflow=2.9.1 matplotlib=3.6.2 +``` +Install geospatial and machine learning dependencies: + +```bash +conda install -c conda-forge osmnx=1.2.2 networkx=2.8.5 scikit-learn=1.0.2 ``` -git clone https://github.com/InsightLab/teach.git + +If needed, install remaining packages with `pip`: + +```bash +pip install pandas==1.5.3 numpy==1.23.5 folium==0.14.0 ipywidgets==8.0.6 tqdm==4.66.1 keras-preprocessing==1.1.2 ``` +--- -Enter in the *teach* directory, so enter in the *app* directory: +## 3. Clone the Repository +```bash +git clone https://github.com/InsightLab/teach.git ``` + +Enter the repository and application folder: + +```bash cd teach +cd app ``` + +--- + +## 4. Run the Dashboard + +```bash +voila teach.ipynb ``` -cd app + +VoilΓ will serve the notebook as an interactive web application. + +--- + +# π§ Basic User Experience Flow + +The TEACH interface is organized as a set of tabs that guide the user through data upload, visualization, intrinsic evaluation, and extrinsic evaluation. + +
+ 
+
+ Basic user experience flow of the TEACH dashboard. +
+ +Conceptually, the dashboard follows this flow: + +```mermaid +flowchart LR + upload["Upload Data, Embeddings, and Models"] --> link["Create Dataset-Embedding-Model Links"] + link --> inspect["Inspect Trajectory Data"] + inspect --> intrinsic["Run Intrinsic Evaluation"] + intrinsic --> extrinsic["Run Extrinsic Evaluation"] + extrinsic --> compare["Compare Representations and Models"] +``` + +--- + +# 𧩠TEACH Dashboard Tabs + +## 1. Upload Tab + +The first tab contains three main upload sections: + +```text +Datasets Import +Embeddings Import +Models Import +``` + +These sections define the objects that will be compared across the rest of the dashboard. + +--- + +### Datasets Import + +This section uploads trajectory datasets in CSV format. + +A dataset should contain at least: + +```text +time +lat +lon +trajectory_id +``` + +Optionally, a dataset may also include: + +```text +location_label +``` + +The dashboard allows users to: + +```text +upload datasets +rename datasets +remove datasets +make datasets available to other tabs +``` + +Removing or renaming a dataset may affect links with embeddings and models. + +--- + +### Embeddings Import + +This section uploads embedding files in CSV format. + +An embedding file should contain: + +```text +a tokenization table +an embedding matrix associated with the tokens ``` -Finally, to use the tool just run the following command: +Users can: + +```text +upload embeddings +rename embeddings +remove embeddings +link embeddings to datasets +``` + +The embedding metadata is saved in: + +```text +Emb.csv +``` + +Dataset-embedding links are saved in: + +```text +Emb#Data.csv +``` + +--- + +### Models Import + +This section uploads trained models in `.h5` format. + +Users can: + +```text +upload models +rename models +remove models +link models to datasets +``` + +Model metadata is saved in: + +```text +Model.csv +``` + +Dataset-model links are saved in: + +```text +Model#Data.csv +``` + +Model-embedding links are saved in: + +```text +Model#Emb.csv +``` + +--- + +## 2. View Trajectory Data Tab + +This tab helps users understand the structure of an uploaded dataset. + +It includes two main functions: + +```text +Show Statistics +Sampling +``` + +--- + +### Show Statistics + +The statistics output may include: +```text +number_of_trajectories +maximum_length_of_trajectories +minimum_length_of_trajectories +average_length_of_trajectories +number_of_distinct_locations ``` + +The `number_of_distinct_locations` attribute is available when the dataset contains `location_label`. + +--- + +### Sampling + +The sampling function plots a subset of trajectories. + +If the user chooses $k$ trajectories, TEACH samples and visualizes $k$ trajectories from the selected dataset. + +This allows quick inspection of: + +```text +trajectory distribution +spatial coverage +trajectory length variation +possible outliers +route concentration +``` + +--- + +## 3. Intrinsic Evaluation Tab + +Intrinsic evaluation studies embedding quality independently of a downstream predictive task. + +In TEACH, intrinsic evaluation asks whether embedding-space similarity is consistent with mobility-space similarity. + +--- + +## Intrinsic Evaluation Workflow + +```mermaid +flowchart LR + choose_embedding["Choose Embedding"] --> choose_dataset["Choose Linked Dataset"] + choose_dataset --> build_matrices["Build or Load Distance Matrices"] + build_matrices --> location_eval["Location-Level Evaluation"] + build_matrices --> trajectory_eval["Trajectory-Level Evaluation"] + location_eval --> mrr_location["MRR Location"] + location_eval --> similar_locations["Similar Locations"] + trajectory_eval --> mrr_trajectory["MRR Trajectory"] + trajectory_eval --> similar_trajectories["Similar Trajectories"] +``` + +--- + +### MRR Location + +This evaluation compares distance matrices associated with each location in the trajectory dataset. + +TEACH compares: + +```text +Cosine Matrix +Euclidean Matrix +Road Matrix +``` + +The goal is to determine whether locations that are close in embedding space are also close according to geographic or road-network distance. + +--- + +### Similar Locations + +This function allows the user to select a tokenized location and visualize the most similar locations according to embedding similarity. + +The comparison is usually based on cosine distance between embedding vectors. + +This helps answer: + +```text +Does the embedding preserve spatial proximity? +Are similar tokens located in nearby regions? +Are unexpected neighbors appearing in embedding space? +``` + +--- + +### MRR Trajectory + +Trajectory-level MRR evaluates similarity between trajectory representations. + +A trajectory vector can be defined as the average of the embedding vectors of its locations: + +$$ +\mathbf{v}_T = \frac{1}{n}\sum_{i=1}^{n}\mathbf{v}_{l_i} +$$ + +TEACH can compare trajectory distance matrices such as: + +```text +Cosine Matrix +DTW Matrix +Edit Distance Matrix +``` + +--- + +### Similar Trajectories + +This function allows the user to choose a trajectory and visualize the most similar trajectories according to embedding similarity. + +This helps evaluate whether the embedding can group trajectories with similar movement patterns. + +--- + +## Mean Reciprocal Rank + +Mean Reciprocal Rank, or MRR, evaluates how highly the first relevant item appears in a ranked list. + +For a set of queries $Q$: + +$$ +MRR = \frac{1}{|Q|}\sum_{i=1}^{|Q|}\frac{1}{rank_i} +$$ + +where $rank_i$ is the rank position of the first relevant result for query $i$. + +Higher MRR indicates that relevant items tend to appear near the top of the ranking. + +--- + +## 4. Extrinsic Evaluation Tab + +Extrinsic evaluation compares embeddings through a downstream task. + +In TEACH, the downstream task is related to location prediction. + +The user selects trained models that were imported and linked to datasets. + +For each selected model, TEACH loads the sequence-type trajectory dataset generated from the linked lat/lon data and evaluates predictive performance. + +--- + +## Extrinsic Evaluation Workflow + +```mermaid +flowchart LR + select_models["Select Linked Models"] --> load_sequences["Load Sequence Trajectories"] + load_sequences --> load_embeddings["Load Associated Embeddings"] + load_embeddings --> run_prediction["Run Location Prediction"] + run_prediction --> compute_metrics["Compute Accuracy and F1-Score"] + compute_metrics --> comparison_table["Generate Comparative DataFrame"] +``` + +--- + +## Metrics + +The extrinsic evaluation output includes: + +```text +three accuracy variants +F1-Score +``` + +The exact interpretation of each accuracy variant depends on the model and evaluation implementation. + +The F1-score combines precision and recall: + +$$ +F_1 = 2 \cdot \frac{precision \cdot recall}{precision + recall} +$$ + +--- + +# 𧱠TEACH Architecture + +The `Teach_Main` class is imported into the Jupyter Notebook and orchestrates the dashboard. + +The following classes are used as components: + +```text +View_Trajectory_Output +Intrinsic_Evaluation_Output +Extrinsic_Evaluation_Output +``` + +These classes are connected because changes in uploaded data, embeddings, or models affect multiple tabs. + +
+ 
+
+ TEACH class diagram showing the relationship between the main dashboard class and the output/evaluation components. +
+ +--- + +## Architecture Flow + +```mermaid +classDiagram + class Teach_Main { + +datasets + +embeddings + +models + +load_metadata() + +update_tabs() + } + + class View_Trajectory_Output { + +show_statistics() + +sample_trajectories() + } + + class Intrinsic_Evaluation_Output { + +mrr_location() + +similar_locations() + +mrr_trajectory() + +similar_trajectories() + } + + class Extrinsic_Evaluation_Output { + +evaluate_models() + +generate_metrics_dataframe() + } + + Teach_Main --> View_Trajectory_Output + Teach_Main --> Intrinsic_Evaluation_Output + Teach_Main --> Extrinsic_Evaluation_Output +``` + +--- + +# π Distance and Similarity Measures + +TEACH compares embeddings and trajectories using multiple distance concepts. + +## Cosine Similarity + +For two vectors $\mathbf{a}$ and $\mathbf{b}$: + +$$ +cos(\theta) = \frac{\mathbf{a}\cdot\mathbf{b}}{\|\mathbf{a}\|\|\mathbf{b}\|} +$$ + +Cosine distance can be defined as: + +$$ +d_{cos}(\mathbf{a}, \mathbf{b}) = 1 - cos(\theta) +$$ + +--- + +## Euclidean Distance + +$$ +d_E(\mathbf{a}, \mathbf{b}) = \sqrt{\sum_{i=1}^{n}(a_i - b_i)^2} +$$ + +--- + +## Road Network Distance + +Road distance measures how far two locations are along the road network rather than through straight-line distance. + +This is useful because two locations may be geographically close but far apart by road connectivity. + +--- + +## Dynamic Time Warping + +DTW compares sequences that may have different speeds or temporal alignments. + +It is useful for comparing trajectories with similar shapes but different sampling rates or movement speeds. + +--- + +## Edit Distance + +Edit distance compares sequences according to insertions, deletions, and substitutions. + +For trajectory sequences, this can be used to compare symbolic movement patterns. + +--- + +# β±οΈ Computational Complexity Notes + +Let: + +```text +N = number of trajectories +L = average trajectory length +M = number of unique locations or tokens +D = embedding dimension +K = number of selected models +``` + +| Task | Typical Time Complexity | Typical Space Complexity | Notes | +|---|---:|---:|---| +| Dataset statistics | $O(N \cdot L)$ | $O(1)$ extra | Scans trajectories and lengths. | +| Sampling trajectories | $O(K_s \cdot L)$ | $O(K_s \cdot L)$ | $K_s$ is the number of sampled trajectories. | +| Location cosine matrix | $O(M^2 \cdot D)$ | $O(M^2)$ | Pairwise vector similarity. | +| Location Euclidean matrix | $O(M^2 \cdot D)$ | $O(M^2)$ | Pairwise vector distance. | +| Road distance matrix | Depends on graph shortest paths | $O(M^2)$ | Cost depends on road network size and algorithm. | +| Trajectory vector averaging | $O(N \cdot L \cdot D)$ | $O(N \cdot D)$ | Builds trajectory embeddings by averaging token vectors. | +| Trajectory cosine matrix | $O(N^2 \cdot D)$ | $O(N^2)$ | Pairwise trajectory vector similarity. | +| DTW matrix | $O(N^2 \cdot L^2)$ | $O(N^2)$ | Pairwise sequence alignment. | +| Edit distance matrix | $O(N^2 \cdot L^2)$ | $O(N^2)$ | Pairwise symbolic sequence distance. | +| Extrinsic model evaluation | Depends on model architecture | Depends on model | Evaluates selected neural models. | + +--- + +# π§ͺ Behavior Summary + +The TEACH workflow can be summarized as: + +```text +1. Start the VoilΓ dashboard. +2. Upload trajectory datasets. +3. Upload embedding files. +4. Upload trained neural models. +5. Link datasets to embeddings and models. +6. Inspect dataset statistics and sampled trajectories. +7. Run intrinsic evaluation over locations and trajectories. +8. Run extrinsic evaluation over prediction models. +9. Compare representations and model performance. +10. Select the most appropriate trajectory embedding for future experiments. +``` + +--- + +# π§ Suggested Study Path + +A good study order for this repository is: + +```text +1. Trajectory data representation +2. Latitude/longitude trajectory format +3. Sequence trajectory format +4. Tokenization of locations +5. Word embeddings and NLP analogy +6. Trajectory embeddings +7. Cosine and Euclidean distance +8. Road-network distance +9. Dynamic Time Warping +10. Edit distance +11. Mean Reciprocal Rank +12. Intrinsic evaluation +13. Neural location prediction +14. Accuracy and F1-score +15. Extrinsic evaluation +16. Dashboard workflow with VoilΓ +``` + +--- + +# π§° Technologies and Tools + +| Tool / Library | Purpose | +|---|---| +| Python | Main programming language. | +| Jupyter Notebook | Interactive development environment. | +| VoilΓ | Turns `teach.ipynb` into a dashboard. | +| IPyWidgets | Interactive UI controls. | +| Pandas | Metadata tables and datasets. | +| NumPy | Vector and matrix operations. | +| TensorFlow / Keras | Neural model loading and evaluation. | +| scikit-learn | Metrics and machine learning utilities. | +| PyMove | Mobility data processing and visualization. | +| Folium | Interactive trajectory maps. | +| OSMnx | Road network extraction and analysis. | +| NetworkX | Graph algorithms and network structures. | +| NLTK | NLP support utilities. | +| Matplotlib | Plots and metric visualizations. | + +--- + +# β οΈ Troubleshooting + +## VoilΓ does not start + +Check whether VoilΓ is installed inside the active environment: + +```bash +conda activate teach_env +voila --version +``` + +Then run again: + +```bash voila teach.ipynb ``` -## Using the TEACH +--- + +## TensorFlow or Keras model loading fails + +Make sure the model was saved in a compatible `.h5` format and that the TensorFlow/Keras versions match the expected environment. + +--- + +## OSMnx or road network loading fails + +Road network operations may require internet access when downloading OpenStreetMap data. -### Upload +If the network was previously cached, check whether the cache folder is available and readable. -With the tool is activated, when opening the tool, four tabs will be shown, the first tab is the *Data Upload*, *Embeddings Upload* and *Models Upload* tab, separated into three accordions +--- -1. **Datasets Import**:In this sector it is possible to upload data in *lat/lon* format (*.csv*) considering them with a format containing at least three attributes: time, lat, lon and trajectory_id. In addition, it is possible, if necessary, to change the name of the imported data as well as to remove it with consequences for other accordions and other tabs. -2. **Embeddings Import**:This is the sector responsible for importing *embedding* files, which are *.csv* files containing a tokenization table and the embedding matrix associated with the tokens of that tokenization table. The name of this file can be changed, as well as this file can be removed if necessary, with consequences for other accordions and tabs. In the second tab of that same sector, we have a screen responsible for linking an embedding file to a data set imported in *Dataset Import*. -3. **Models Import**: Finally, we have the sector responsible for uploading models in *.h5* format, where it is possible to rename the uploaded file or remove this file, with consequences for accordions and other tabs. Furthermore, it is possible to make the link between these models and the data set imported in *Dataset Import*. +## Distance matrices take too long -Both the imported embedding files and the imported models are saved in a *.csv* file with the names *Emb.csv* and *Model.csv* respectively, while the links of the embedding files are saved in the *Emb#Data.csv* file , as well as the link to the models saved in the *Model#Data.csv* file. The links between models and embeddings files are saved in *Model#Emb.csv* in a fixed way without having to interact with the user, the names are fixed, if you want to use a model with a specific embedding you need to rename the embedding to the name placed in this table. +Pairwise matrices can be expensive for large datasets. -### View Trajectory Data +Consider: -The second tab is a dataset study tab. At first you choose the dataset you want to understand its data disposition, after that there are two functionalities: +```text +sampling trajectories +reducing the number of locations +using cached matrices +running heavy computations before using the dashboard +``` + +--- + +# πΌοΈ Image Credits and Licenses + +| Image | Author / Source | License information | Link | +|---|---|---|---| +| Word Embedding Illustration | Fschwarzentruber / Wikimedia Commons | CC BY-SA 4.0 | [File page](https://commons.wikimedia.org/wiki/File:Word_embedding_illustration.svg) | +| GPS Track Visualization Example | Wikimedia Commons | See file page for licensing details | [File path](https://commons.wikimedia.org/wiki/Special:FilePath/GpsTrackKonstanzReichenauPhorLeaflet.png) | +| Basic User Experience Flow | Local repository image | Repository documentation asset | `Basic_User_Experience_Flow.png` | +| TEACH Class Diagram | Local repository image | Repository documentation asset | `TEACH_Class_Diagram.png` | -1. **Show Statistics**: This functionality returns a dataframe containing the *number_of_trajectories*, the *maximum_length_of_trajectories*, the *minimum_length_of_trajectories* and the *average_length_of_trajectories* at least and even has the *number_of_distinct_locations* attribute if the dataset has the *location_label* attribute. -2. **Sampling**:This functionality is responsible for plotting a sample of trajectories from the selected dataset with *k* trajectories. +--- -### Intrinsic Evaluation +# π References and Further Reading -This is the tab responsible for studying the ability of an embedding trained by an NLP model to represent trajectories. In this tab, an embedding file is chosen that was imported in the first tab and a dataset linked with it initially and from this link I can: +## Main Paper -1. **Mean Reciprocal Rank (MRR) Location**: Plot the MRR comparing three distance matrices referring to the embedding vectors (Cosine Matrix, Euclidean Matrix, Road Matrix) associated with each location of the trajectories dataset. -2. **Similar Locations**: Choose a tokenizer location and plot the locations most similar to that location, taking into account the cosine distance matrix obtained through the embedding vector associated with that location, analyzing whether the proximity associated with this embedding resembles spatial proximity, studying how much this embedding is representative. -3. **Mean Reciprocal Rank (MRR) Trajectory**: Plot the MRR comparing three distance matrices referring to the embedding vectors (Cossine Matrix, DTW Matrix, Edit Distance Matrix) defined as the vector obtained from the average of the embedding vectors of each location of a specific trajectory. -4. **Similar Trajectories**: Choose a trajectory from the trajectory dataset and plot the trajectories most similar to this trajectory, taking into account the cosine distance matrix obtained through the embedding vector associated with that trajectory, analyzing whether the proximity associated with this embedding is similar to spatial proximity, studying the how representative this embedding is. +| Reference | Main Topic | Why it is useful | Link | +|---|---|---|---| +| Cruz et al. β *Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches* | Trajectory embeddings and location prediction | Main paper motivating the trajectory embedding comparison approach used by TEACH. | [MDPI Sensors](https://www.mdpi.com/1424-8220/22/19/7475) | -The matrices used in this study may or may not be present in the matrices folder, if they are not, they are created, as well as the dataset of sequence type trejectories (different from *lat/lon*), which is saved in the trejectories folder. The Road Matrix, if it is not in the matrices folder, is created as soon as the tool is started. +--- -### Extrinsic Evaluation +## Tools and Libraries -In the last tab, a comparison is made between the models imported in the first tab, analyzing their ability of - such as NLP transformer models, have the ability to predict texts - predict locations of a trajectory. The models will only appear to be selected if they have been linked in the first tab with a dataset. To generate the comparative dataframe of accuracy, for each model, a dataset of trajectories of the sequence type is loaded based on the dataset of trajectories *lat/lon* linked to it in the first tab and in the embedding file associated with it in the fixed table. In the dataframe appears for each model selected 3 types of accuracy plus the F1-Score. +| Resource | Main Topic | Why it is useful | Link | +|---|---|---|---| +| PyMove | Trajectory processing | Provides tools for trajectory and spatio-temporal data processing and visualization. | [GitHub repository](https://github.com/InsightLab/PyMove) | +| OSMnx | Street networks | Used to download, model, analyze, and visualize OpenStreetMap street networks and geospatial features. | [OSMnx docs](https://osmnx.readthedocs.io/) | +| NetworkX | Graph algorithms | Supports graph structures, graph algorithms, and network analysis. | [NetworkX docs](https://networkx.org/) | +| Folium | Interactive maps | Allows Python data to be visualized with Leaflet maps. | [Folium docs](https://python-visualization.github.io/folium/) | +| VoilΓ | Dashboard serving | Converts Jupyter notebooks into standalone interactive dashboards. | [VoilΓ docs](https://voila.readthedocs.io/en/stable/) | +| TensorFlow | Neural models | Backend for machine learning models. | [TensorFlow](https://www.tensorflow.org/) | +| Keras | Neural network API | Model loading and evaluation. | [Keras](https://keras.io/) | +| scikit-learn | ML metrics | Metrics and utilities for model evaluation. | [scikit-learn](https://scikit-learn.org/) | -### Basic User Experience Flow - +--- -## TEACH Class Diagram +# β Summary +TEACH is an interactive benchmark for comparing trajectory embeddings. -The Teach_Main class is imported into the Jupyter Notebook file. The View_Trajectory_Output, Intrinsic_Evaluation_Output and Extrinsic_Evaluation_Output classes make up this main class as attributes of the class since removing and renaming *lat/long* datasets, removing and renaming embedding files, and removing and renaming model files directly change the tabs associated with these classes. +It connects: -# - +```text +trajectory data +location tokens +trajectory embeddings +NLP-inspired representation learning +intrinsic evaluation +extrinsic evaluation +location prediction +interactive dashboards +road-network distances +sequence similarity +``` + +The main emphasis is: + +```text +Upload datasets, embeddings, and models. +Inspect trajectory data. +Compare embeddings intrinsically. +Evaluate models extrinsically. +Select better trajectory representations before downstream learning. +```