From 4ee2788f326c8474e551b53aae577eb676c981ef Mon Sep 17 00:00:00 2001 From: GretaVilla Date: Fri, 9 Sep 2022 12:45:11 +0000 Subject: [PATCH] probablities density plot images added --- README.md | 25 +++++++++++++++--- resources/images/01-ROC-curve-test.png | Bin 30514 -> 23718 bytes resources/images/02-PR-isoF1-plot-test.png | Bin 40719 -> 31237 bytes .../04-interactive-density-plot-test.png | Bin 0 -> 39599 bytes ...05-interactive-confusion-matrix-train.png} | Bin ... 06-interactive-confusion-matrix-test.png} | Bin ...> 07-interactive-confusion-line-chart.png} | Bin ...08-interactive-amount-cost-line-chart.png} | Bin 8 files changed, 21 insertions(+), 4 deletions(-) create mode 100644 resources/images/04-interactive-density-plot-test.png rename resources/images/{04-interactive-confusion-matrix-train.png => 05-interactive-confusion-matrix-train.png} (100%) rename resources/images/{05-interactive-confusion-matrix-test.png => 06-interactive-confusion-matrix-test.png} (100%) rename resources/images/{06-interactive-confusion-line-chart.png => 07-interactive-confusion-line-chart.png} (100%) rename resources/images/{07-interactive-amount-cost-line-chart.png => 08-interactive-amount-cost-line-chart.png} (100%) diff --git a/README.md b/README.md index 902e637..e16eeb4 100644 --- a/README.md +++ b/README.md @@ -91,6 +91,7 @@ train_predicted_proba = cls.predict_proba(X_train)[:,1] test_predicted_proba = cls.predict_proba(X_test)[:,1] ``` Let's generate some known graphs with the functions in the binclass-tools package to check the overall behavior of the model on the test set. +Note that it's possible to customize the main title and choose whether to display the plotly bar mode through the parameters 'title' and 'show_display_modebar' in every graphical function of this library. We can start by visualizing the _Receiver Operating Characteristic (ROC) Curve_, using the following function, which also returns the value of the area under the curve: @@ -147,6 +148,22 @@ Here the interactive plot generated: ![Interactive Probabilities Violin Plot for the Test Set](/resources/images/03-interactive-violin-plot-test.png) +Another useful tool to visualize the probabilities density is the predicted_proba_density_curve_plot function, that plots for each true class either the kernel density estimation curve (default) or the normal distribution curve, depending on the curve_type parameter. For each threshold, that can be selected through a slider, we can see the regions that are correctly or incorrectly classified: + +```python +threshold_step = 0.05 +curve_type = 'kde' #'kde' is the default value, can also be set to 'normal' + +bc.predicted_proba_density_curve_plot(true_y = y_test, + predicted_proba = test_predicted_proba, + threshold_step = threshold_step, + curve_type = curve_type) +``` + +Here the interactive plot: + +![Interactive Probabilities Density Plot for the Test Set](/resources/images/04-interactive-density-plot-test.png) + Afterwards, we can conduct a more detailed threshold-related analysis of the model's performance. Let's set up a set of variables to pass as parameters in the subsequent binclass-tools functions we will use. Considering that we are going to do first an analysis of how the model performs on the training dataset in order to get also the optimal threshold values, these are the variables we will calculate: @@ -196,7 +213,7 @@ var_metrics_df, invar_metrics_df, opt_thresh_df = bc.confusion_matrix_plot( Here the output: -![Interactive Confusion Matrix for the Training Set](/resources/images/04-interactive-confusion-matrix-train.png) +![Interactive Confusion Matrix for the Training Set](/resources/images/05-interactive-confusion-matrix-train.png) As you can see, the interactive confusion matrix plot also returns metric dataframes that can be used in your code if needed. One is the _threshold dependent metrics dataframe_: @@ -273,7 +290,7 @@ var_metrics_df, invar_metrics_df, __ = bc.confusion_matrix_plot( Evidently, the Interactive Confusion Matrix plot will not present the table of optimal threshold values for the various metrics: -![Interactive Confusion Matrix for the Test Set](/resources/images/05-interactive-confusion-matrix-test.png) +![Interactive Confusion Matrix for the Test Set](/resources/images/06-interactive-confusion-matrix-test.png) As you can see from the code, this time the dataframes returned are only the first two. @@ -370,7 +387,7 @@ amount_cost_df, total_amount = bc.confusion_linechart_plot( Here the output: -![Interactive Confusion Line Chart](/resources/images/06-interactive-confusion-line-chart.png) +![Interactive Confusion Line Chart](/resources/images/07-interactive-confusion-line-chart.png) You can see that there are also black "diamonds" indicating the first threshold value in which there is a swap of the amount and cost curves. 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