fix layout size

app.py

@@ -62,7 +62,7 @@ with gr.Blocks() as demo:
         with gr.Accordion("See model explanability", open=False):
             with gr.Row():
                 with gr.Column():
-                    waterfall_plot = gr.Plot()
+                    waterfall_plot = gr.Plot(container=True)
                 with gr.Column():
                     ploott = gr.Plot()
             with gr.Row():
@@ -150,7 +150,8 @@ with gr.Blocks() as demo:
             feature_names=df.columns.tolist(),
         )
 
-        fig2 = plt.figure(figsize=(10, 5))  # Create a new figure for SHAP plot
+        fig2 = plt.figure(figsize=(8, 4))  # Create a new figure for SHAP plot
+        fig2.tight_layout()
         plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
         plt.tight_layout()
         shap.waterfall_plot(

test.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import shap
+import hopsworks
+import pandas as pd
+import joblib
+
+# Assuming you have your model and data defined elsewhere
+project = hopsworks.login(
+    project="SonyaStern_Lab1",
+    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
+)
+mr = project.get_model_registry()
+model = mr.get_model("diabetes_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/diabetes_model.pkl")
+rf_model = model.steps[-1][1]  # Load your model
+df = pd.DataFrame(
+    [[20, 20, 30, 40]],
+    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
+)
+
+
+def generate_plots():
+    # Create the first plot as before
+    fig1, ax1 = plt.subplots()
+    ax1.plot([1, 2, 3], [4, 5, 6])
+    ax1.set_title("Plot 1")
+
+    # Generate the SHAP waterfall plot for fig2
+    fig2 = shap.plots.waterfall(
+        shap.Explanation(
+            values=shap.Explainer(rf_model).shap_values(df)[1][0],
+            base_values=shap.Explainer(rf_model).expected_value[1],
+        )
+    )
+
+    return fig1, fig2
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Plot(generate_plots()[0])  # Display first plot in the first row
+
+    with gr.Row():
+        gr.Plot(generate_plots()[1])  # Display SHAP waterfall plot in the second row
+
+demo.launch()

testchat.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import shap
+import hopsworks
+import pandas as pd
+import joblib
+
+project = hopsworks.login(
+    project="SonyaStern_Lab1",
+    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
+)
+mr = project.get_model_registry()
+model = mr.get_model("diabetes_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/diabetes_model.pkl")
+rf_model = model.steps[-1][1]  # Load your model
+df = pd.DataFrame(
+    [[20, 20, 30, 40]],
+    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
+)
+
+
+def generate_plots():
+    # Create the first plot as before
+    fig1, ax1 = plt.subplots()
+    ax1.plot([1, 2, 3], [4, 5, 6])
+    ax1.set_title("Plot 1")
+
+    # Generate the SHAP waterfall plot for fig2
+    explainer = shap.Explainer(rf_model)
+    shap_values = explainer.shap_values(df)[1]  # Select SHAP values for class 1
+    shap_values_exp = shap.Explanation(
+        values=shap_values[0], base_values=explainer.expected_value[1]
+    )
+    ax2 = shap.plots.waterfall(
+        shap_values_exp, show=False
+    )  # Get the axis for the waterfall plot
+
+    return fig1, ax2
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Plot(generate_plots()[0])  # Display first plot in the first row
+
+    with gr.Row():
+        _, ax2 = generate_plots()
+        gr.Plot(ax2)  # Display SHAP waterfall plot in the second row
+
+demo.launch()

testcorcel.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import shap
+import hopsworks
+import pandas as pd
+import joblib
+from sklearn.pipeline import make_pipeline
+
+df = pd.DataFrame(
+    [[20, 20, 30, 40]],
+    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
+)
+
+
+# Assuming the hopsworks login and model retrieval code works as expected
+project = hopsworks.login(
+    project="SonyaStern_Lab1",
+    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
+)
+mr = project.get_model_registry()
+model = mr.get_model("diabetes_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/diabetes_model.pkl")
+print("printing model pipeline:", model)
+
+rf_classifier = model.named_steps["randomforestclassifier"]
+
+transformer_pipeline = make_pipeline(
+    *[
+        step
+        for name, step in model.named_steps.items()
+        if name != "randomforestclassifier"
+    ]
+)
+
+transformed_df = transformer_pipeline.transform(df)
+
+
+# rf_model = model.steps[-1][1]  # Load your model
+
+
+def generate_plots():
+    # Create the first plot as before
+    fig1, ax1 = plt.subplots()
+    ax1.plot([1, 2, 3], [4, 5, 6])
+    ax1.set_title("Plot 1")
+
+    # Generate the SHAP waterfall plot for fig2
+    explainer = shap.TreeExplainer(rf_classifier)
+
+    shap_values = explainer.shap_values(transformed_df)
+    predicted_class = rf_classifier.predict(transformed_df)[0]
+    shap_values_for_predicted_class = shap_values[predicted_class]
+    # base_value = explainer.expected_value[1]
+
+    fig2 = plt.figure()  # Create a new figure for SHAP plot
+    shap_explanation = shap.Explanation(
+        values=shap_values_for_predicted_class[0],
+        base_values=explainer.expected_value[predicted_class],
+        data=transformed_df[0],
+        feature_names=df.columns.tolist(),
+    )
+    shap.waterfall_plot(shap_explanation)
+    plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
+
+    return fig1, fig2
+
+
+# Generate plots once and store them
+fig1, fig2 = generate_plots()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Plot(fig1)  # Display first plot in the first row
+
+    with gr.Row():
+        gr.Plot(fig2)  # Display SHAP waterfall plot in the second row
+
+demo.launch()

testcorcel2.py

@@ -0,0 +1,75 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import shap
+import hopsworks
+import pandas as pd
+import joblib
+from sklearn.pipeline import make_pipeline
+
+df = pd.DataFrame(
+    [[20, 20, 30, 40]],
+    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
+)
+
+
+# Assuming the hopsworks login and model retrieval code works as expected
+project = hopsworks.login(
+    project="SonyaStern_Lab1",
+    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
+)
+mr = project.get_model_registry()
+model = mr.get_model("diabetes_gan_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/diabetes_gan_model.pkl")
+print("printing model pipeline:", model)
+
+rf_classifier = model.named_steps["randomforestclassifier"]
+
+transformer_pipeline = make_pipeline(
+    *[
+        step
+        for name, step in model.named_steps.items()
+        if name != "randomforestclassifier"
+    ]
+)
+
+transformed_df = transformer_pipeline.transform(df)
+
+
+# rf_model = model.steps[-1][1]  # Load your model
+
+
+def generate_plots():
+    # Create the first plot as before
+    fig1, ax1 = plt.subplots()
+    ax1.plot([1, 2, 3], [4, 5, 6])
+    ax1.set_title("Plot 1")
+
+    # Generate the SHAP waterfall plot for fig2
+    explainer = shap.TreeExplainer(rf_classifier)
+
+    shap_values = explainer.shap_values(transformed_df)
+    predicted_class = rf_classifier.predict(transformed_df)[0]
+    shap_values_for_predicted_class = shap_values[predicted_class]
+    # base_value = explainer.expected_value[1]
+
+    fig2 = plt.figure()  # Create a new figure for SHAP plot
+    shap.waterfall_plot(
+        explainer.expected_value[predicted_class], shap_values_for_predicted_class[0]
+    )
+    plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
+
+    return fig1, fig2
+
+
+# Generate plots once and store them
+fig1, fig2 = generate_plots()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Plot(fig1)  # Display first plot in the first row
+
+    with gr.Row():
+        gr.Plot(fig2)  # Display SHAP waterfall plot in the second row
+
+demo.launch()