modular code and more features

app.py

@@ -8,6 +8,16 @@ from datetime import datetime
 import plotly.express as px
 
 
+def select_plot_data(df, quantile_low, qunatile_high):
+    df.fillna(0, inplace=True)
+    df_plot = df.set_index('Model').T
+    df_plot.index = date_range(df_plot)
+    df_stats = df_plot.describe()
+    quantile_lvalue = df_stats.quantile(quantile_low, axis=1)['mean']
+    quantile_hvalue = df_stats.quantile(qunatile_high, axis=1)['mean']  
+    df_plot_data = df_plot.loc[:,[(df_plot[col].mean() > quantile_lvalue and df_plot[col].mean() < quantile_hvalue) for col in df_plot.columns]]
+    return df_plot_data
+
 def read_file_to_df(file):
    return pd.read_csv(file)
 
@@ -15,7 +25,7 @@ def date_range(df):
     time = df.index.to_list()
     time_range = []
     for t in time:
-        time_range.append(str(datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%fZ').date().month) +'/' +  str(datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%fZ').date().day))
+        time_range.append(str(datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%fZ').date().month) +'/' +  str(datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%fZ').date().day)  + '/' + str(datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%fZ').date().year)[-2:])
     return time_range
 
 
@@ -25,42 +35,29 @@ if __name__ == "__main__":
 
     st.header("Model Usage Visualization")
     with st.expander("How to read and interact with the plot:"):
-        st.markdown("The plots below visualize model usage for HF models created in mid 2021 (top) vs. models created in mid 2022 (bottom). Note the y-axis range is different for each plot.")
-        st.markdown("The plots are categorized based on model popularity. I first created a histogram of weekly mean usage across all models and then grouped them into these categorizes so that the plots are easier to read.")
+        st.markdown("The plots below visualize weekly usage for HF models categorized by the model creation time.")
+        st.markdown("Select the model creation time range you want to visualize using the dropdown menu below.")
+        st.markdown("Choose the quantile range to filter out models with high or low usage.")
         st.markdown("The plots are interactive. Hover over the points to see the model name and the number of weekly mean usage. Click on the legend to hide/show the models.")
 
-    popularity = st.radio("Model popularity", ('Low', 'Moderate', 'High'), key = "popularity", index=2, horizontal = True)
-
-    with st.container():
-        df_2021 = read_file_to_df("./assets/2021/model_init_time.csv")
-        df_2021.fillna(0, inplace=True)
-        df_plot = df_2021.set_index('Model').T
-        df_plot.index = date_range(df_plot)
-        df_plot_2021 = pd.DataFrame()
-        if popularity == 'Low':
-            df_plot_2021 = df_plot[df_plot.columns[(df_plot.mean(axis=0)<=5000) & (df_plot.mean(axis=0)>=3500)]]
-        elif popularity == 'Moderate':
-            df_plot_2021 =  df_plot[df_plot.columns[(df_plot.mean(axis=0)<=40000) & (df_plot.mean(axis=0)>=5000)]]
-        else:
-            df_plot_2021 = df_plot[df_plot.columns[df_plot.mean(axis=0)>=40000]]
-        fig = px.line(df_plot_2021, title="Models created in 2021", labels={"index": "Weeks", "value": "Usage", "variable": "Model"})
-        st.plotly_chart(fig, use_container_width=True)
-
-    with st.container():
-        df_2022 = read_file_to_df("./assets/2022/model_init_time.csv")
-        df_2022.fillna(0, inplace=True)
-        df_plot = df_2022.set_index('Model').T
-        df_plot.index = date_range(df_plot)
-        df_plot_2022 = pd.DataFrame()
-        if popularity == 'Low':
-            df_plot_2022 = df_plot[df_plot.columns[(df_plot.mean(axis=0)<500) & (df_plot.mean(axis=0)>=300)]]
-        elif popularity == 'Moderate':
-            df_plot_2022 =  df_plot[df_plot.columns[(df_plot.mean(axis=0)<=1500) & (df_plot.mean(axis=0)>=500)]]
-        else:
-            df_plot_2022 = df_plot[df_plot.columns[df_plot.mean(axis=0)>=1500]]
-        fig = px.line(df_plot_2022, title="Models created in 2022", labels={"index": "Weeks", "value": "Usage", "variable": "Model"})
-        st.plotly_chart(fig, use_container_width=True)
 
+    model_init_year = st.multiselect("Model creation year", ["before_2021", "2021", "2022"], key = "model_init_year", default = "2022")
 
+    popularity_low = st.slider("Model popularity quantile (lower limit) ",  min_value=0.0, max_value=1.0, step=0.01, value=0.90, key = "popularity_low")
+    popularity_high = st.slider("Model popularity quantile (upper limit) ",  min_value=0.0, max_value=1.0, step=0.01, value=0.99, key = "popularity_high")
 
+    if 'model_init_year' not in st.session_state:
+        st.session_state['model_init_year'] = model_init_year
+    if 'popularity_low' not in st.session_state:
+        st.session_state['popularity_low'] = popularity_low
+    if 'popularity_high' not in st.session_state:
+        st.session_state['popularity_high'] = popularity_high
 
+    with st.container():
+        for year in st.session_state['model_init_year']:
+            plotly_spot = st.empty()
+            df = read_file_to_df("./assets/"+year+"/model_usage.csv")
+            df_plot_data = select_plot_data(df, st.session_state['popularity_low'], st.session_state['popularity_high'])
+            fig = px.line(df_plot_data, title="Models created in "+year, labels={"index": "Weeks", "value": "Usage", "variable": "Model"})
+            with plotly_spot:
+                st.plotly_chart(fig, use_container_width=True)

assets/2022/model_usage.csv

@@ -0,0 +1,312 @@
+Model,2022-01-03T00:00:00.000Z,2022-01-10T00:00:00.000Z,2022-01-17T00:00:00.000Z,2022-01-24T00:00:00.000Z,2022-01-31T00:00:00.000Z,2022-02-07T00:00:00.000Z,2022-02-14T00:00:00.000Z,2022-02-21T00:00:00.000Z,2022-02-28T00:00:00.000Z,2022-03-07T00:00:00.000Z,2022-03-14T00:00:00.000Z,2022-03-21T00:00:00.000Z,2022-03-28T00:00:00.000Z,2022-04-04T00:00:00.000Z,2022-04-11T00:00:00.000Z,2022-04-18T00:00:00.000Z,2022-04-25T00:00:00.000Z,2022-05-02T00:00:00.000Z,2022-05-09T00:00:00.000Z,2022-05-16T00:00:00.000Z,2022-05-23T00:00:00.000Z,2022-05-30T00:00:00.000Z,2022-06-06T00:00:00.000Z,2022-06-13T00:00:00.000Z,2022-06-20T00:00:00.000Z,2022-06-27T00:00:00.000Z,2022-07-04T00:00:00.000Z,2022-07-11T00:00:00.000Z,2022-07-18T00:00:00.000Z,2022-07-25T00:00:00.000Z,2022-08-01T00:00:00.000Z,2022-08-08T00:00:00.000Z,2022-08-15T00:00:00.000Z,2022-08-22T00:00:00.000Z,2022-08-29T00:00:00.000Z,2022-09-05T00:00:00.000Z,2022-09-12T00:00:00.000Z,2022-09-19T00:00:00.000Z
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