SHAP Numpy

This function uses the SHAP library to visualize the SHAP values for a given model and data. It takes input features, target variable, model, and feature names as inputs and generates a visualization of the SHAP values.

The type of code 2024-12-16 12:18:00 35 views

Plotly Python

This code defines a function named random_bar_chart that accepts a dictionary as input, with the keys and values of the dictionary serving as the x-axis and y-axis data for the chart. The function uses the GraphObjects module from Plotly to create a bar chart and randomly generates data. Then, it updates the chart's layout and displays the chart.

The type of code 2024-12-16 12:17:55 30 views

PyVISA

This function uses the PyVISA library to connect to a device at a specified address and read its current values.

Function 2024-12-16 12:17:48 27 views

The code

This code defines a function named `visualize_shap_values` that visualizes SHAP values for a given dataset and model. It first creates a SHAP explainer, then computes SHAP values, and visualizes these values using the `waterfall` method from the SHAP library. In the main function `main`, a random dataset is generated, a simple linear regression model is created, and the `visualize_shap_values` function is called to visualize the SHAP values of the model on the data.

The type of code 2024-12-16 12:17:40 39 views

pymongo

This function uses MongoDB's aggregation pipeline to process data in the collection. It first uses the $match stage to filter records, then uses the $group stage to group records and calculate the sum of values for each category, and finally uses the $sort stage to sort by the sum.

The type of code 2024-12-16 12:17:39 15 views

Cartopy Matplotlib

This function uses the Cartopy library to create a map and plot points on the map with given longitude, latitude, and color values.

Map generation 2024-12-16 12:17:25 15 views

Eli5 scikit-learn

This function uses the PermutationImportance from the Eli5 library to calculate the feature importance for a given dataset. It does so by shuffling the feature values and retraining the model, then comparing the change in model performance to evaluate the importance of each feature.

Machine learning 2024-12-16 12:16:57 28 views

NumPy

This function generates a random matrix of specified size with random values from a specified distribution.

Function 2024-12-16 12:16:55 14 views

numpy sklearn.decomposition.PCA

This function first applies PCA (Principal Component Analysis) to reduce the dimensions of the dataset, and then uses PermutationImportance to explain the importance of the principal components. PermutationImportance evaluates the importance of features by randomly shuffling feature values and observing the change in model performance.

The type of code 2024-12-16 12:16:44 14 views

HoloViews NumPy

This function creates a 3D surface plot using the HoloViews library. It accepts three arguments: x and y values for the grid, and a function f that calculates the z value for each grid point.

Function 2024-12-16 12:16:42 12 views