index

API Overview

Modules

• ml
• ml.dataframes: The dataframes module provides a lot of common operations for dataframe handling
• ml.evaluation
• ml.evaluation.classification: The classification module allows users to evaluate and visualize classifiers
• ml.images
• ml.images.conversion: The conversion module allows users to transform images freely
• ml.images.explorer: The image explorer module provides standard helper functions to explore and visualize images
• ml.images.io: The io module provides standard helper functions to load images from disk
• ml.neuralnetworks
• ml.neuralnetworks.keras: The keras module provides additions to work and visualize Keras neural networks
• ml.timeseries
• ml.timeseries.timeops: The timeops module provides helpful functionality for timeseries datasets

Classes

• No classes

Functions

• dataframes.distribute_class: Makes sure a DataFrame is returned with an equal class distribution
• dataframes.keep_numeric_features: Takes the DataFrame and removes all non-numeric columns or features
• dataframes.one_hot_encode: Take a categorical column and pivots the DataFrame to add columns (0 or 1 value) for every category
• dataframes.plot_features: Plots the distribution of the relevant columns of a DataFrame
• dataframes.shuffle: Shuffles the DataFrame and returns it
• dataframes.to_timeseries: This is deprecated and it is advised to use the timeseries.set_timeseries function for this
• classification.evaluate_model: Will predict and evaluate a model against a test set
• classification.plot_roc_curve: Will plot the Receiver Operating Characteristic (ROC) Curve for binary classifiers
• conversion.crop: Crops an image based on the specified size
• conversion.get_fragments: Scans an image and return the resulted parts as a list of image sections
• conversion.prepare: Takes an image and applies preformatting
• conversion.to_blackwhite: Transforms an image to a black & white image
• explorer.show_image: Visualizes an image
• explorer.visualize: Visualizes the images in the image_sets in a grid
• explorer.visualize_classes: Visualizes the images from the image_set in a grid and print the corresponding class on the charts
• io.load_image_from_disk: Loads an image from file, applying preformatting
• io.load_image_from_url: Loads an image from a given url, applying preformatting and supporting file caching
• io.load_images: Loads the images from a specific folder
• io.load_images_from_dataframe: Loads a set images from disk, based on the file name in a Data Frame.
• keras.enable_gpu: Enables Keras to run on the GPU
• timeops.add_time_reference: This method will add a reference column to the DataFrame that contains the value of reference column of n items before
• timeops.combine_time_ranges: This method combines multiple timeseries (as DataFrame) and removes the overlapping time sections
• timeops.get_windows: This method take a DataFrame and returns a set of time windows of a specific length and a given column, eventually grouped by another column
• timeops.set_timeseries: Transforms the dataframe to a timeseries enabled dataframe
• timeops.time_slice: This method takes a time series DataFrame and only returns the time slice, based on the start & end date

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