{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Test using the new _Utility Script_-feature"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nfrom lwlwrap import *","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Test code from [original lwlwrap-notebook](https://colab.research.google.com/drive/1AgPdhSp7ttY18O3fEoHOQKlt_3HJDLi8#scrollTo=Aq8VVHsTohAy):"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# Random test data.\nnum_samples = 100\nnum_labels = 20\n\ntruth = np.random.rand(num_samples, num_labels) > 0.5\n# Ensure at least some samples with no truth labels.\ntruth[0:1, :] = False\n\nscores = np.random.rand(num_samples, num_labels)","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"per_class_lwlrap, weight_per_class = calculate_per_class_lwlrap(truth, scores)\nprint(\"lwlrap from per-class values=\", np.sum(per_class_lwlrap * weight_per_class))\nprint(\"lwlrap from sklearn.metrics =\", calculate_overall_lwlrap_sklearn(truth, scores))","execution_count":6,"outputs":[{"output_type":"stream","text":"lwlrap from per-class values= 0.5917284143689108\nlwlrap from sklearn.metrics = 0.5917284143689108\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Test of accumulator version.\naccumulator = lwlrap_accumulator()\nbatch_size = 12\nfor base_sample in range(0, scores.shape[0], batch_size):\n  accumulator.accumulate_samples(\n      truth[base_sample : base_sample + batch_size, :], \n      scores[base_sample : base_sample + batch_size, :])\nprint(\"cumulative_lwlrap=\", accumulator.overall_lwlrap())\nprint(\"total_num_samples=\", accumulator.total_num_samples)\n\n","execution_count":7,"outputs":[{"output_type":"stream","text":"cumulative_lwlrap= 0.5917284143689108\ntotal_num_samples= 100\n","name":"stdout"}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}