Merge pull request #85 from jvivian/jvivian/issue78

Dynamically generate Dashboard (resolves #78)

covid19_drdfm/data/processed/Test_Data.csv

@@ -0,0 +1,37 @@
+Time,Pandemic_10,Pandemic_9,Pandemic_1,Pandemic_7,Factor
+1/1/2020,0,0,0,0,0.2045454545
+2/1/2020,0,0,0,0,0
+3/1/2020,0.0058740602,0.0397350993,0.0042229655,0.0609357998,0
+4/1/2020,0.0693139098,0.0198675497,0.0024131231,0.0282916213,0.0619834711
+5/1/2020,0.1379229323,0.0264900662,0.0025728151,0.0304678999,0.0289256198
+6/1/2020,0.1734022556,0.0264900662,0.0108767976,0.0631120783,0.0309917355
+7/1/2020,0.4814379699,0.1059602649,0.040277864,0.1828073993,0.0640495868
+8/1/2020,0.313674812,0.1258278146,0.0370130504,0.204570185,0.1838842975
+9/1/2020,0.1459116541,0.1125827815,0.0532484009,0.1566920566,0.2045454545
+10/1/2020,0.0773026316,0.3112582781,0.1582192571,0.4243743199,0.1570247934
+11/1/2020,0.155075188,0.5894039735,0.3026162868,0.752992383,0.423553719
+12/1/2020,0.5227913534,0.5033112583,0.1744191205,0.5571273123,0.7541322314
+1/1/2021,1,0.178807947,0.0982904087,0.2176278564,0.5578512397
+2/1/2021,0.6719924812,0.0993377483,0.0558034724,0.1523394995,0.2190082645
+3/1/2021,0.2319078947,0.1125827815,0.072748565,0.2241566921,0.152892562
+4/1/2021,0.0838815789,0.0066225166,0.0778320928,0.289445049,0.2252066116
+5/1/2021,0.0714285714,0.1390728477,0.0299511165,0.1284004353,0.2892561983
+6/1/2021,0.0723684211,0.0529801325,0.0149666865,0.0957562568,0.1301652893
+7/1/2021,0.0728383459,0.0794701987,0.0817711614,0.39390642,0.0950413223
+8/1/2021,0.1268796992,0.3245033113,0.26157545,0.8019586507,0.3925619835
+9/1/2021,0.2814849624,0.8278145695,0.4060079669,1,0.8037190083
+10/1/2021,0.274906015,0.9470198675,0.3689594294,0.8335146899,1
+11/1/2021,0.2709116541,1,0.1806914662,0.4494015234,0.8347107438
+12/1/2021,0.4515977444,0.1059602649,0.1362261238,0.2655059848,0.4504132231
+1/1/2022,0.4642857143,0.2847682119,1,0.478781284,0.2665289256
+2/1/2022,0.4090695489,0.5165562914,0.2854316563,0.2491838955,0.479338843
+3/1/2022,0.3106203008,0.4304635762,0.1077388504,0.1284004353,0.25
+4/1/2022,0.1604793233,0.1589403974,0.0940142126,0.0718171926,0.1280991736
+5/1/2022,0.0817669173,0.2185430464,0.1257574279,0.1099020675,0.0723140496
+6/1/2022,0.0507518797,0,0.1572433617,0.1077257889,0.1095041322
+7/1/2022,0.0594454887,0.1523178808,0.1569505931,0.1088139282,0.1074380165
+8/1/2022,0.0812969925,0.1920529801,0.0951143128,0.112078346,0.1095041322
+9/1/2022,0.0601503759,0.1655629139,0.0211769298,0.0402611534,0.1115702479
+10/1/2022,0,0,0.0135738176,0.0293797606,0.041322314
+11/1/2022,0,0,0.0134850999,0.0054406964,0.0289256198
+12/1/2022,0,0,0.0121365899,0.0087051143,0.0061983471

covid19_drdfm/streamlit/Dashboard.py

@@ -1,10 +1,10 @@
-import yaml
 import time
 from pathlib import Path
 
 import pandas as pd
 import plotly.io as pio
 import streamlit as st
+import yaml
 
 from covid19_drdfm.constants import FACTORS
 from covid19_drdfm.covid19 import get_df, get_project_h5ad
@@ -32,7 +32,7 @@ def get_data():
 var_df["Variables"] = var_df.index
 ad.obs["Time"] = pd.to_datetime(ad.obs.index)
 
-center_title("Dynamic Factor Model Runner")
+center_title("Legacy Dynamic Factor Model Runner for Covid-19")
 
 with st.expander("Variable correlations"):
     st.write("Data is normalized between [0, 1] before calculating correlation")

covid19_drdfm/streamlit/pages/0_Dynamic_Factor_Model.py

@@ -0,0 +1,179 @@
+from pathlib import Path
+from typing import Optional
+
+import anndata as ann
+import pandas as pd
+import plotly.io as pio
+import streamlit as st
+
+from covid19_drdfm.dfm import ModelRunner
+
+st.set_page_config(layout="wide")
+pio.templates.default = "plotly_white"
+
+
+def center_title(text):
+    return st.markdown(f"<h1 style='text-align: center; color: grey;'>{text}</h1>", unsafe_allow_html=True)
+
+
+class DataHandler:
+    """
+    Handles data loading and preprocessing for a Streamlit application.
+    """
+
+    def __init__(self):
+        self.df: Optional[pd.DataFrame] = None
+        self.ad: Optional[ann.AnnData] = None
+        self.batch_col: Optional[str] = None
+        self.non_batch_cols: Optional[list[str]] = None
+
+    def get_data(self) -> "DataHandler":
+        self.file_uploader().get_factor_mappings().apply_transforms().create_anndata()
+        return self
+
+    def file_uploader(self) -> "DataHandler":
+        """
+        Uploads a file and reads it into a DataFrame. Supported file types are CSV, TSV, and XLSX.
+
+        Returns:
+            A pandas DataFrame loaded from the uploaded file.
+
+        Raises:
+            RuntimeError: If no file is uploaded.
+        """
+        file = st.file_uploader("Upload a data file (CSV, TSV, XLSX)", type=["csv", "tsv", "xlsx"])
+        if file is None:
+            st.error("Please provide input file")
+            st.stop()
+        self.df = self.load_data(file)
+        with st.expander("Raw Input Data"):
+            st.dataframe(self.df)
+        if self.df is None:
+            st.error("DataFrame is empty! Check input data")
+            st.stop()
+        batch_col = st.sidebar.selectbox("Select a batch column (optional):", ["None", *list(self.df.columns)])
+        if batch_col == "None":
+            self.batch_col = None
+        self.non_batch_cols = [col for col in self.df.columns if col != batch_col]
+        return self
+
+    @staticmethod
+    def load_data(file) -> pd.DataFrame:
+        """
+        Loads a DataFrame from an uploaded file based on its MIME type.
+
+        Args:
+            file: UploadedFile object from Streamlit.
+
+        Returns:
+            A DataFrame containing the data from the file.
+
+        Raises:
+            ValueError: If the file type is unsupported.
+        """
+        file_type = file.type.split("/")[-1]
+        read_function = {
+            "csv": lambda f: pd.read_csv(f, index_col=0),
+            "tsv": lambda f: pd.read_csv(f, index_col=0, sep="\t"),
+            "xlsx": lambda f: pd.read_excel(f, index_col=0),
+        }.get(file_type, lambda _: None)
+
+        if read_function is None:
+            raise ValueError(f"Unsupported file type: {file_type}")
+
+        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}"
+        )
+        transforms = {}
+        for i, opt in enumerate(options):
+            if i % 2 == 0:
+                cols = st.columns(2)
+            transform = cols[i % 2].radio(
+                f"Select transform type for {opt}:", ("difference", "logdiff"), key=f"trans_{opt}"
+            )
+            transforms[opt] = transform
+            self.ad.var[transform] = None
+            self.ad.var.loc[opt, transform] = True
+        return self
+
+    def get_factor_mappings(self) -> "DataHandler":
+        factor_input = st.text_input("Enter all factor options separated by space:")
+        factor_options = factor_input.split()
+        if not factor_options:
+            st.warning("Enter at least one factor to assign to variables")
+            st.stop()
+        factor_mappings = {}
+        for i, col in enumerate(self.non_batch_cols):
+            if i % 2 == 0:
+                cols = st.columns(2)
+            col_factor = cols[i % 2].radio(
+                f"Select factor for {col}:",
+                options=factor_options,
+                key=col,
+                format_func=lambda x: f"{x}",
+                horizontal=True,
+            )
+            if col_factor:
+                factor_mappings[col] = col_factor
+
+        if len(factor_mappings) != len(self.non_batch_cols):
+            st.warning("Select a factor for each variable!")
+            st.stop()
+        self.factor_mappings = factor_mappings
+        return self
+
+    def create_anndata(self) -> ann.AnnData:
+        """
+        Creates an AnnData object from the loaded DataFrame with optional batch column handling.
+
+        Args:
+            factor_mappings: A dictionary mapping column names to their respective factors.
+            batch_col: Optional; the name of the column to use as the batch category.
+
+        Returns:
+            An AnnData object with additional metadata.
+        """
+        if self.batch_col and self.batch_col in self.df.columns:
+            ad = ann.AnnData(self.df.drop(columns=self.batch_col))
+            ad.obs[self.batch_col] = self.df[self.batch_col]
+        else:
+            ad = ann.AnnData(self.df)
+
+        ad.var["factor"] = [self.factor_mappings[x] for x in ad.var.index]
+        self.ad = ad
+        return ad
+
+
+def additional_params():
+    global_multiplier = st.sidebar.slider("Global Multiplier", min_value=0, max_value=4, value=0)
+    out_dir = st.sidebar.text_input("Output Directory", value=None)
+    if not out_dir:
+        st.warning("Specify output directory (in sidebar) to continue")
+        st.stop()
+    return global_multiplier, out_dir
+
+
+def run_model(ad, out_dir, batch, global_multiplier) -> ModelRunner:
+    dfm = ModelRunner(ad, Path(out_dir), batch=batch)
+    dfm.run(global_multiplier=global_multiplier)
+    st.subheader("Results")
+    for result in dfm.results:
+        if batch is not None:
+            st.subheader(result.name)
+        st.write(result.result.summary())
+        st.divider()
+        st.write(result.model.summary())
+    return dfm
+
+
+center_title("Dynamic Factor Model Runner")
+data = DataHandler().get_data()
+ad = data.ad
+global_multiplier, out_dir = additional_params()
+batch = None if ad.obs.empty else ad.obs.columns[0]
+dfm = run_model(ad, out_dir, batch, global_multiplier)
+st.balloons()
+st.stop()