nlp-lab/Project/advanced_approach/README.md
2018-07-27 14:50:47 +02:00

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# Advanced Approach
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## Folder Overview
| Filename | short_description |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [twitter_learning.py](twitter_learning.py) | module containing the main classes for the learning process |
| [Learner.ipynb](Learner.ipynb) | notebook containing a user interface to control the learn process |
| [Evaluation_sentiment_dataset.ipynb](Evaluation_sentiment_dataset.ipynb) | notebook creating an evaluation on the sentiment dataset |
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## twitter_learning.py
We wanted the possibility to load and save classifier and also to continue the train process of a loaded classifier on other datasets. Since this functionality doesn't fit well in a single linear processed notebook, we wrote this module containing three classes for managing train data, classifier and train process:
### sample_data_manager
class for preprocessing twitter data provided as json files. Creates samples with sentiment labels and provides train and validation samples
### pipeline_manager
creates, saves or loads a sklean-pipeline with vectorizer and keras classifier. When a new classifier is created, the vectorizer is fit by all samples that a given [sample_data_manager](#sample_data_manager) has stored at that moment.
### trainer
expects a [pipeline_manager](#pipeline_manager) and a [sample_data_manager](#sample_data_manager). It controls the train process and feeds the pipeline with data from the [sample_data_manager](#sample_data_manager). Since we want the possibility to continue training on loaded classifiers, this class modifies the pipeline during runtime to prevent a reset of the vectorizers while fitting new data.
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## Learner.ipynb
In short: the user interface for methods provided in [twitter_learning.py](#twitter_learning.py) This file provides all controls for the train process and feeds the classifier with data. To use it just run all cells and jump to the user interface Part. In order to train on a dataset, just use it in the following way:
### load datasets
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/>
* setup the root folder for the json files containing our twitter samples
* then you can set the range of files that will be loaded. Also you can setup more preprocessing steps, like top-emoji usage filtering or only load samples containing a specific set of emojis (given as string)
### create/save/load classifier
<img 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/img>
* create a new keras pipeline including a vectorizer. you can setup the number of neurons and the activation function per layer (the last layer will be automatically adjusted to fit the label-dimension). if doc2vec is deactivated, tfidf is used. In the image above is the configuration we used for our final classifier.
* you can also save a trained classifier and load it later again by a .pipeline configuration file (either by selecting them in the file selector box or by give the full path to the `clf_file` box)
### train classifier
<img 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/>
* if sample data and classifier are loaded, the classifier can be trained here
### Test single predictions
<img src="data:image/png;base64,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" />
* in the playground tab you can predict single sentences and get the nearest emoji in sentiment space of a given emoji set. Also you can plot the predictions of samples with given labels (as emoji) of the validation set of the currently loaded twitter data
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## Evaluation_sentiment_dataset.ipynb
this is just a notebook to perform and plot the predictions on our hand labeled validation set