dsci_310_group_11_pkg.grapher
Module Contents
Functions
DESCRIPTION: Displays a correlation table (correlation coefficient value |
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DESCRIPTION: Displays a simple bar chart of the count of the quality variable. |
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DESCRIPTION: Displays a heatmap of the count of the predicted case. |
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DESCRIPTION: Displays a visual example of a decision tree for conceptual |
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DESCRIPTION: Displays a bar chart comparing the accuracy scores of each |
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DESCRIPTION: Displays a dataframe with the coefficients of the Logistic |
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DESCRIPTION: Displays a dataframe with the True Positive + True Negative |
- dsci_310_group_11_pkg.grapher.correlation_table(df)
DESCRIPTION: Displays a correlation table (correlation coefficient value of each variable to each other variable).
INPUTS: df - A dataframe object containing prediction features.
ACTION: Inputs a dataframe and displays the correlation coefficients in a square grid.
RETURNS: The table as a display.
- dsci_310_group_11_pkg.grapher.bar_chart(df)
DESCRIPTION: Displays a simple bar chart of the count of the quality variable.
ACTION: Inputs a dataframe and displays the bar chart.
INPUTS: df - A dataframe object
RETURNS: The bar chart as a display.
TODO: 1. Modularize the variables that you can input into the chart 2. Move from altair to matplotlib
- dsci_310_group_11_pkg.grapher.class_report(pipe, X_test, y_test)
DESCRIPTION: Displays a heatmap of the count of the predicted case.
ACTION: Inputs a model, testing data and displays the heatmap.
- INPUTS: pipe - a model
X_test - testing features data y_test - testing label data
RETURNS: The heatmap as figure
- dsci_310_group_11_pkg.grapher.vis_tree(X_train, y_train)
DESCRIPTION: Displays a visual example of a decision tree for conceptual purposes. The max_depth variable is limited to 3 so that the visualization is interpretable.
- INPUTS: X_train - a dataframe object containing prediction features
y_train - a series object containing target variables.
ACTION: Inputs an X_train dataframe and y_train series and displays the decision tree model and each of its chosen parameter splits.
RETURNS: The decisision tree model as a display.
- dsci_310_group_11_pkg.grapher.compare_scores(lst)
DESCRIPTION: Displays a bar chart comparing the accuracy scores of each ML model in the ‘lst’ list.
INPUTS: lst - a list of floats (accuracy scores) of each model.
ACTION: Inputs a list (lst) of ML model accuracy scores, generates a dataframe named ‘report’ and turns this dataframe into a bar chart.
RETURNS: The bar chart where the highlighted bar is the highest score.
- dsci_310_group_11_pkg.grapher.show_coefficients(pipe, X_train)
DESCRIPTION: Displays a dataframe with the coefficients of the Logistic Regression model.
- INPUTS: pipe - a pipeline object containing scikit-learn model transformers, and a scikit-learn model.
X_train - a dataframe object containing prediction features.
ACTION: Inputs a LogisticRegression model, and an X_train dataset. Names the pipe variables given the named_steps in the logistic regression in an array called ‘flatten’. Returns the dataframe ‘coeffs’ with the model’s features versus their coefficients. Sorts the values descendingly.
RETURNS: The dataframe, sorted descending by coefficients value.
Printing out coefficients of the regression model for values influencing the model.
- dsci_310_group_11_pkg.grapher.show_correct(pipe, X_test, y_test)
DESCRIPTION: Displays a dataframe with the True Positive + True Negative versus the False Positive + False Negative ratio of the classifier model.
- INPUTS: pipe - a pipeline object containing scikit-learn model transformers, and a scikit-learn model.
X_test - a dataframe object containing prediction features. y_test - a series object containing target variables.
ACTION: Inputs a model (pipe), and testing data; calls predict on the test data and reports the correct classifications versus the incorrect classifications.
RETURNS: A dataframe with the correct classifications versus incorrect classifications.