add plots

app.py

@@ -5,17 +5,14 @@ import streamlit as st
 import seaborn as sns
 import matplotlib.pyplot as plt
 
-#from dotenv import load_dotenv
-#load_dotenv()
+from dotenv import load_dotenv
+load_dotenv()
 
 @st.experimental_memo
 def load_data():
     project = hopsworks.login()
     fs = project.get_feature_store()
 
-    #if not os.path.isfile("./cache/batch_data.pkl"):
-    #    if not os.path.isdir("./cache"):
-    #        os.mkdir("./cache")
     posts_fg = fs.get_feature_group("reddit_posts", version=os.getenv("POSTS_FG_VERSION", default=1))
     users_fg = fs.get_feature_group("reddit_users", version=os.getenv("USERS_FG_VERSION", default=1))
     subreddits_fg = fs.get_feature_group("reddit_subreddits", version=os.getenv("SUBREDDITS_FG_VERSION", default=1))
@@ -24,13 +21,9 @@ def load_data():
                             subreddits_fg.select(features=["subreddit_id", "snapshot_time"]), on=["subreddit_id", "snapshot_time"])
     df = full_join.read()
 
-    #    df.to_pickle("./cache/batch_data.pkl")
-    #else:
-    #    df = pd.read_pickle("./cache/batch_data.pkl")
-
     # Load model including the generated images and evaluation scores
     mr = project.get_model_registry()
-    model_hsfs = mr.get_model("reddit_predict", version=16)
+    model_hsfs = mr.get_model("reddit_predict", version=18)
     model_dir = model_hsfs.download()
     print("Model directory: {}".format(model_dir))
 
@@ -42,13 +35,19 @@ def load_data():
             metric_rows[target].append(model_hsfs.training_metrics[f"{metric}_{target}"])
     df_metrics = pd.DataFrame(metric_rows, index=metrics_avail)
 
-    img_predictions = plt.imread(f"{model_dir}/prediction_error.png")
-    img_predictions_logscale = plt.imread(f"{model_dir}/prediction_error_logscale.png")
+    plots = {
+        "predictions": plt.imread(f"{model_dir}/prediction_error.png"),
+        "predictions_logscale": plt.imread(f"{model_dir}/prediction_error_logscale.png"),
+        "confusion_matrix": plt.imread(f"{model_dir}/confusion_matrix.png"),
+        "shap_numlikes": plt.imread(f"{model_dir}/shap_summary_plot_num_likes.png"),
+        "shap_upvote_ratio": plt.imread(f"{model_dir}/shap_summary_plot_upvote_ratio.png"),
+        "shap_numlikes_compact": plt.imread(f"{model_dir}/shap_summary_plot_compact.png")
+    }
 
-    return df, img_predictions, img_predictions_logscale, df_metrics
+    return df, plots, df_metrics
 
 
-df, img_predictions, img_predictions_logscale, df_metrics = load_data()
+df, plots, df_metrics = load_data()
 
 # create a distribution plot of the number of likes using seaborn
 st.title("Like It or Not")
@@ -89,15 +88,22 @@ st.dataframe(df_metrics)
 st.markdown("## Prediction Error Plots")
 st.markdown("The green line indicates the perfect prediction while the blue lines show point densities. Every point represents a prediction. The model is optimized for the number of likes and provides an estimate for the minimum number of likes expected. The upvote ratio does not perform well and would profit from dedicated modeling with another objective function if it is important.")
 st.markdown("### Linear Scale")
-st.image(img_predictions)
+st.image(plots["predictions"])
 st.markdown("### Log Scale")
-st.image(img_predictions_logscale)
+st.image(plots["predictions_logscale"])
 
 # Confusion matrix
-#st.markdown("## Confusion Matrix")
-#st.markdown("The confusion matrix of the model is as follows:")
-#st.image("confusion_matrix.png")
-
-# display the evaluation scores table
-#st.title("Evaluation Scores")
-#st.dataframe(df[["metric1", "metric2", "metric3", "metric4"]])
+st.markdown("## Confusion Matrix")
+st.markdown("After mapping the predicted number of likes to categories, the following confusion matrix can be obtained:")
+st.image(plots["confusion_matrix"])
+
+# Shap plots
+st.markdown("## Shap Evaluation")
+st.markdown("Shap values are an approach to machine learning explainability where the magnitude and kind of impact (positive / negative) of all features is computed." +
+"Below, you see a beeswarm plot obtained on the predictions on the test data where every point represents a sample, its color tells if the feature had a high or low value " +
+"and its position tells if the feature had a positive or negative impact on the prediction.")
+st.image(plots["shap_numlikes"])
+
+st.markdown("In addition, it is possible to sum up and average the absolute impact of all features over all samples. " +
+            "The result can be interpreted as the feature importance. For the embedding features, we summed the values of the individual dimensions.")
+st.image(plots["shap_numlikes_compact"])