{"action":"created","comment":{"url":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/pulls/comments/308012851","pull_request_review_id":267516956,"id":308012851,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDMwODAxMjg1MQ==","diff_hunk":"@@ -3,11 +3,66 @@\n ## Random Sampling of Generative Model\n place the grid aurocs here for generative model\n \n-## Discriminator Model Builds Off Generative Model\n-place the grid of aurocs here for discriminator model\n+### Discriminator Model Builds Off Generative Model\n+In this framework we used a generative model trained over label functions to produce probabilistic training labels for each sentence.\n+Then we train a discriminative model, which has full access to a representation of the text of the sentence, to predict the generated labels.\n+The discriminative model is a convolutional neural network trained over word embeddings.\n+We report the results of the discriminative model using area under the precision recall curve (AUPR) (Figure {@fig:discriminative_model_auprc}) and area under the receiver operating curve (AUROC) (Figure {@fig:discriminative_model_auroc}).    \n+Generally, the discriminative model under-performs compared to the generative model.\n+In the top left corner of figures {@fig:discriminative_model_auroc} and {@fig:discriminative_model_auprc} the discriminative model underperforms the generative model when only using distant supervision label functions.\n+As the number of label functions increase the discriminative model starts to have similar performance to the generative model.\n+Compound-treats-Disease (CtD) starts off with the opposite trend.\n+The discriminative model outcompetes the generative model; however, as more label functions are added performance starts to decline.\n+For the Compound-binds-Gene (CbG) edge type, the discriminative model consistently underperforms the generative model.\n+Gene-interacts-Gene (GiG) has a similar pattern to DaG.\n+The discriminative model starts off underperforming the generative model.\n+As soon as more label functions are incorporated performance improves to be on par with the generative model.  \n+ \n+With respect to the off-diagonals, the discriminative model uses annotations from the generative model, that is trained using label functions other than the targeted edge type (targeted edge type shown on the y-axes of figures {@fig:discriminative_model_auroc} and {@fig:discriminative_model_auprc}). \n+Overall, performance for the discriminative model is second-rate. \n+For the top row the discriminative model under performs the generative model.\n+When using CtD label functions the auroc dips as low as 50%.\n+Similar scores can be seen with CbG and All (Figure {@fig:discriminative_model_auprc}). \n+For the second row, there is an inverse effect.\n+The discriminative model starts off out-performing the generative model, but diminishes as the number of label functions increases.\n+In the last two rows performance spikes when using one label function, but plateaus or sharply declines as more are added.\n+Despite the decreasing trend, the discriminative model performs almost on par with the generative model when predicting the CbG edge type using GiG label functions.\n+The same trend can be found when predicting the GiG edge using CbG label functions.\n+Lastly only CtD and DaG slightly benefited from using all label functions at once.\n+For CbG and GiG they have performance increase using one label function then plateaus or declines when more are added.\n \n-## Random Noise Generative Model\n-place the results of random label function experiment\n+![\n+Grid of Area Under the Receiver Operating Curve (AUROC) scores for each discriminative model trained using generated labels from the generative models.\n+The rows depict the relationship each model is trying to predict and the columns are the relationship specific sources each label function was sampled from. \n+For example, the top-left most square depicts the discriminator model predicting Disease associates Gene (DaG) sentences, while randomly sampling label functions designed to predict the DaG relationship. \n+The square towards the right depicts the discriminative model predicting DaG sentences, while randomly sampling label functions designed to predict the Compound treats Disease (CtD) relationship.\n+This pattern continues filling out the rest of the grid.\n+The last most column consists of pooling every relationship specific label function and proceeding as above.\n+](https://raw.githubusercontent.com/greenelab/snorkeling/master/figures/label_sampling_experiment/disc_performance_test_set_auroc.png){#fig:discriminative_model_auroc}\n+\n+![\n+Grid of Area Under the Receiver Operating Curve (AUROC) scores for each discriminative model trained using generated labels from the generative models.\n+The rows depict the relationship each model is trying to predict and the columns are the relationship specific sources each label function was sampled from. \n+For example, the top-left most square depicts the discriminator model predicting Disease associates Gene (DaG) sentences, while randomly sampling label functions designed to predict the DaG relationship. \n+The square towards the right depicts the discriminative model predicting DaG sentences, while randomly sampling label functions designed to predict the Compound treats Disease (CtD) relationship.\n+This pattern continues filling out the rest of the grid.\n+The last most column consists of pooling every relationship specific label function and proceeding as above.\n+](https://raw.githubusercontent.com/greenelab/snorkeling/master/figures/label_sampling_experiment/disc_performance_test_set_auprc.png){#fig:discriminative_model_auprc}\n+\n+### Discriminative Model Calibration","path":"content/04.results.md","position":54,"original_position":54,"commit_id":"418bf60a32f7f4fbee285be4923fae04a775caac","original_commit_id":"418bf60a32f7f4fbee285be4923fae04a775caac","user":{"login":"cgreene","id":542643,"node_id":"MDQ6VXNlcjU0MjY0Mw==","avatar_url":"https://avatars2.githubusercontent.com/u/542643?v=4","gravatar_id":"","url":"https://api.github.com/users/cgreene","html_url":"https://github.com/cgreene","followers_url":"https://api.github.com/users/cgreene/followers","following_url":"https://api.github.com/users/cgreene/following{/other_user}","gists_url":"https://api.github.com/users/cgreene/gists{/gist_id}","starred_url":"https://api.github.com/users/cgreene/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cgreene/subscriptions","organizations_url":"https://api.github.com/users/cgreene/orgs","repos_url":"https://api.github.com/users/cgreene/repos","events_url":"https://api.github.com/users/cgreene/events{/privacy}","received_events_url":"https://api.github.com/users/cgreene/received_events","type":"User","site_admin":false},"body":"```suggestion\r\n### Discriminative Model Calibration\r\n\r\n```","created_at":"2019-07-28T18:35:15Z","updated_at":"2019-07-28T18:37:20Z","html_url":"https://github.com/greenelab/text_mined_hetnet_manuscript/pull/15#discussion_r308012851","pull_request_url":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/pulls/15","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/pulls/comments/308012851"},"html":{"href":"https://github.com/greenelab/text_mined_hetnet_manuscript/pull/15#discussion_r308012851"},"pull_request":{"href":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/pulls/15"}}},"pull_request":{"url":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/pulls/15","id":298690617,"node_id":"MDExOlB1bGxSZXF1ZXN0Mjk4NjkwNjE3","html_url":"https://github.com/greenelab/text_mined_hetnet_manuscript/pull/15","diff_url":"https://github.com/greenelab/text_mined_hetnet_manuscript/pull/15.diff","patch_url":"https://github.com/greenelab/text_mined_hetnet_manuscript/pull/15.patch","issue_url":"https://api.github.com/repos/greenelab/text_mined_hetnet_manuscript/issues/15","number":15,"state":"open","locked":false,"title":"Results-discriminator model and Model Calibration","user":{"login":"danich1","id":3505417,"node_id":"MDQ6VXNlcjM1MDU0MTc=","avatar_url":"https://avatars0.githubusercontent.com/u/3505417?v=4","gravatar_id":"","url":"https://api.github.com/users/danich1","html_url":"https://github.com/danich1","followers_url":"https://api.github.com/users/danich1/followers","following_url":"https://api.github.com/users/danich1/following{/other_user}","gists_url":"https://api.github.com/users/danich1/gists{/gist_id}","starred_url":"https://api.github.com/users/danich1/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/danich1/subscriptions","organizations_url":"https://api.github.com/users/danich1/orgs","repos_url":"https://api.github.com/users/danich1/repos","events_url":"https://api.github.com/users/danich1/events{/privacy}","received_events_url":"https://api.github.com/users/danich1/received_events","type":"User","site_admin":false},"body":"This section describes the discriminative model's performance and talks about model calibration.\r\nCloses #20.\r\nAdded tables for confidence scores in Supplementary Figures section. 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