Merge pull request #88 from jvivian/jvivian/issue87

Make fixes based on validation data

covid19_drdfm/streamlit/pages/0_Dynamic_Factor_Model.py

@@ -53,7 +53,8 @@ def file_uploader(self) -> "DataHandler":
             st.stop()
         self.batch_col = st.sidebar.selectbox("Select a batch column (optional):", ["None", *list(self.df.columns)])
         if self.batch_col == "None":
-            self.batch_col = None
+            self.df["Batch"] = "Batch1"
+            # self.batch_col = None
         self.non_batch_cols = [col for col in self.df.columns if col != self.batch_col]
         return self
 
@@ -84,9 +85,7 @@ def load_data(file) -> pd.DataFrame:
         return read_function(file)
 
     def apply_transforms(self) -> "DataHandler":
-        options = st.multiselect(
-            "Select columns to apply transformations:", self.non_batch_cols, format_func=lambda x: f"Transform {x}"
-        )
+        options = st.multiselect("Select columns to apply transformations:", self.non_batch_cols)
         transforms = {}
         for i, opt in enumerate(options):
             if i % 2 == 0:

covid19_drdfm/streamlit/pages/1_Factor_Analysis.py

@@ -63,68 +63,31 @@ def get_factors(res_dir):
 # Grab first state to fetch valid variables
 state_df = pd.read_csv(res_dir / state / "df.csv")
 cols = [x for x in df.columns if x in state_df.columns] + ["State"]
-# st.write(cols)
-# Get factors and columns
-# factor_vars = [x for x in FACTORS_GROUPED[factor.split("_")[1]] if x in valid_cols]  # and '_' in x]
-# columns = [*factor_vars, "State", "Time"]
-
-# Make df from res_dir
-dfs = []
-for subdir in res_dir.iterdir():
-    if not subdir.is_dir():
-        continue
-    path = subdir / "df.csv"
-    if not path.exists():
-        st.write(f"Skipping {path}, not found")
-        continue
-    sub = pd.read_csv(path, index_col=0)
-    sub["State"] = subdir.stem
-    dfs.append(sub)
-
-new = pd.concat(dfs)
+new = pd.read_csv(res_dir / state / "df.csv", index_col=0)
 
 # Normalize original data for state / valid variables
 ad = ann.read_h5ad(res_dir / "data.h5ad")
 factor_map = ad.var["factor"].to_frame()
 factor_set = factor_map["factor"].unique().to_list() + [x for x in df.columns if "Global" in x]
-# st.dataframe(factor_map)
 factor = st.sidebar.selectbox("Factor", factor_set)
-# new = ad.to_df().reset_index()
-# new["State"] = ad.obs["State"].to_list()
-# new = normalize(new[new.State == state])
+
 
 # Normalize factors and add to new dataframe
 if st.sidebar.checkbox("Invert Factor"):
     df[factor] = df[factor] * -1
-df = normalize(df[df.State == state])  # .reset_index(drop=True)
+df = normalize(df[df.State == state])
 
 df = df[df["State"] == state]
 df = df[[factor]].join(new, on="Time")
 
-# st.write(factor_map)
-# st.dataframe(df.head())
-# st.dataframe(new.head())
-
-# Coerce time bullshit to get dates standardized
-# df["Time"] = pd.to_datetime(df["Time"]).dt.date
-# new["Time"] = pd.to_datetime(new["Time"]).dt.date
 col_opts = [x for x in df.columns.to_list() if x != "State"]
 cols = st.multiselect("Variables to plot", col_opts, default=col_opts)
 with st.expander("Graph Data"):
-    # factor_cols = factor_map[factor_map["factor"] == factor]
-    # if factor_cols.empty:
-    #     factor_cols = new.columns
-    # else:
-    #     factor_cols = factor_cols.index.to_list()
-    #     factor_cols += [factor]
-    # factor_cols = [x for x in factor_cols if x in df.columns]
-    # st.write(factor_cols)
     st.dataframe(df[cols])
 
 df = df[cols].reset_index()
 
 # Melt into format for plotting
-# melted_df = df.drop(columns="State").melt(id_vars=["Time"], value_name="value")
 melted_df = df.melt(id_vars=["Time"], value_name="value")
 melted_df["Label"] = [5 if x == factor else 1 for x in melted_df.variable]
 
@@ -136,6 +99,7 @@ def get_factors(res_dir):
 results_path = res_dir / state / "results.csv"
 model_path = res_dir / state / "model.csv"
 
+# Metrics for run
 values = pd.Series()
 with open(results_path) as f:
     for line in f.readlines():