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Copy file name to clipboardExpand all lines: example1/0-Workflow.html
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<p><strong>For example, this notebook demonstrates:</strong></p>
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<p><strong>Rule 1: Tell a Story for an Audience.</strong> This notebook was developed for biologists to learn how to apply a simple machine learning model to protein sequences.</p>
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<p><strong>Rule 3: Document the Entire Workflow.</strong> This notebook describes the entire workflow from data preparation, feature calculation, model fitting, to prediction. The modularity makes it easy to replace one of the steps, for example, use a different method to calculate features or apply a different machine learning model.</p>
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<p><strong>Rule 5: Use Cell and Notebook Boundaries to Organize Code and Reduce Clutter.</strong> We broke the workflow into separate notebooks and use this top-level notebook to explain and orchestrate the workflow.</p>
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<p><strong>Rule 3: Build a Pipeline.</strong> This notebook describes the entire workflow from data preparation, feature calculation, model fitting, to prediction. The modularity makes it easy to replace one of the steps, for example, use a different method to calculate features or apply a different machine learning model.</p>
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<p><strong>Rule 5: Use Cell, Section adn Notebook Divisions to Make Steps Clear.</strong> We broke the workflow into separate notebooks and use this top-level notebook to explain and orchestrate the workflow.</p>
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<p><strong>Rule 6: Declare Your Dependencies.</strong> Here we use the watermark extension to print software, operating system, and hardware version information.</p>
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<p><strong>Rule 6: Declare Dependencies.</strong> Here we use the watermark extension to print software, operating system, and hardware version information.</p>
Copy file name to clipboardExpand all lines: example1/0-Workflow.ipynb
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"\n",
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"**Rule 1: Tell a Story for an Audience.** This notebook was developed for biologists to learn how to apply a simple machine learning model to protein sequences.\n",
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"\n",
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"**Rule 3: Document the Entire Workflow.** This notebook describes the entire workflow from data preparation, feature calculation, model fitting, to prediction. The modularity makes it easy to replace one of the steps, for example, use a different method to calculate features or apply a different machine learning model.\n",
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"**Rule 3: Build a Pipeline.** This notebook describes the entire workflow from data preparation, feature calculation, model fitting, to prediction. The modularity makes it easy to replace one of the steps, for example, use a different method to calculate features or apply a different machine learning model.\n",
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"\n",
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"**Rule 5: Use Cell and Notebook Boundaries to Organize Code and Reduce Clutter.** We broke the workflow into separate notebooks and use this top-level notebook to explain and orchestrate the workflow.\n",
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"**Rule 5: Use Cell, Section adn Notebook Divisions to Make Steps Clear.** We broke the workflow into separate notebooks and use this top-level notebook to explain and orchestrate the workflow.\n",
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"---"
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"\n",
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"**Rule 6: Declare Your Dependencies.** Here we use the watermark extension to print software, operating system, and hardware version information.\n",
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"**Rule 6: Declare Dependencies.** Here we use the watermark extension to print software, operating system, and hardware version information.\n",
<p><strong>Rule 2: Document Your Process, Not Just Results.</strong> Here we describe the steps how to produce the dataset.</p>
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<p><strong>Rule 3: Make Your Notebook a Pipeline.</strong> Besides documenting all steps, the entire process of dataset creation from the original data files in the /data directory is automated. There are no manual steps.</p>
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<p><strong>Rule 2: Document Process, Not Just Results.</strong> Here we describe the steps how to produce the dataset.</p>
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<p><strong>Rule 3: Build a Pipeline.</strong> Besides documenting all steps, the entire process of dataset creation from the original data files in the /data directory is automated. There are no manual steps.</p>
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<p><strong>Rule 7: Share and Explain Your Data.</strong> To enable reproducibility we provide a /data directory with data files and a file that describes the datasets with download locations and dates.</p>
Copy file name to clipboardExpand all lines: example1/1-CreateDataset.ipynb
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"metadata": {},
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"**Rule 2: Document Your Process, Not Just Results.** Here we describe the steps how to produce the dataset.\n",
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"**Rule 2: Document Process, Not Just Results.** Here we describe the steps how to produce the dataset.\n",
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"\n",
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"**Rule 3: Make Your Notebook a Pipeline.** Besides documenting all steps, the entire process of dataset creation from the original data files in the /data directory is automated. There are no manual steps.\n",
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"**Rule 3: Build a Pipeline.** Besides documenting all steps, the entire process of dataset creation from the original data files in the /data directory is automated. There are no manual steps.\n",
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"\n",
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"**Rule 7: Share and Explain Your Data.** To enable reproducibility we provide a /data directory with data files and a file that describes the datasets with download locations and dates.\n",
<p><strong>Rule 4: Turn Reusable Code into Functions.</strong> To avoid duplicating code, we have collected several functions in protvectors.py. These functions are also used in 4-Predict.</p>
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<p><strong>Rule 5: Use Celland Notebook Boundaries to Organize Code and Reduce Clutter.</strong> We use one cell for each distinct task.</p>
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<p><strong>Rule 4: Modularize Code.</strong> To avoid duplicating code, we have collected several functions in protvectors.py. These functions are also used in 4-Predict.</p>
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<p><strong>Rule 5: Use Cell, Section and Notebook Divisions to Make Steps Clear.</strong> We use one cell for each distinct task.</p>
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<p><strong>Rule 7: Share and Explain Your Data.</strong> To enable reproducibility we provide a local copy of a Word2vec model in the /data directory and a file that describes the datasets with download locations and dates.</p>
Copy file name to clipboardExpand all lines: example1/2-CalculateFeatures.ipynb
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"**Rule 4: Turn Reusable Code into Functions.** To avoid duplicating code, we have collected several functions in protvectors.py. These functions are also used in 4-Predict.\n",
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"**Rule 4: Modularize Code.** To avoid duplicating code, we have collected several functions in protvectors.py. These functions are also used in 4-Predict.\n",
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"\n",
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"**Rule 5: Use Celland Notebook Boundaries to Organize Code and Reduce Clutter.** We use one cell for each distinct task.\n",
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"**Rule 5: Use Cell, Section and Notebook Divisions to Make Steps Clear.** We use one cell for each distinct task.\n",
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"\n",
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"**Rule 7: Share and Explain Your Data.** To enable reproducibility we provide a local copy of a Word2vec model in the /data directory and a file that describes the datasets with download locations and dates.\n",
<p><strong>Rule 8: Prepare Your Notebooks to be Read, Run, and Explored.</strong> We use ipywidgets to present the user with a pull-down menu to select a machine learning model.</p>
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<p><strong>Rule 8: Expect Your Notebooks to be Read, Run, and Explored.</strong> We use ipywidgets to present the user with a pull-down menu to select a machine learning model.</p>
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Copy file name to clipboardExpand all lines: example1/3-FitModel.ipynb
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"**Rule 8: Prepare Your Notebooks to be Read, Run, and Explored.** We use ipywidgets to present the user with a pull-down menu to select a machine learning model.\n",
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"**Rule 8: Expect Your Notebooks to be Read, Run, and Explored.** We use ipywidgets to present the user with a pull-down menu to select a machine learning model.\n",
<p><strong>Rule 8: Prepare Your Notebooks to be Read, Run, and Explored.</strong> We use ipywidgets to present the user with a text box to make a prediction for a protein sequence of their choice. We provide a default sequence to generate a reproducible result.</p>
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<p><strong>Rule 8: Expect Your Notebooks to be Read, Run, and Explored.</strong> We use ipywidgets to present the user with a text box to make a prediction for a protein sequence of their choice. We provide a default sequence to generate a reproducible result.</p>
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