fix experimental charts in DA.

python/pdstools/app/decision_analyzer/pages/10_Business_Value_Analysis.py

@@ -1,52 +1,61 @@
-import polars as pl
 import streamlit as st
 
-from da_streamlit_utils import get_current_scope_index, st_value_distribution
-from utils import NBADScope_Mapping, ensure_data
-
-# TODO Finish up to show effect on proposition distribution (side to side)
-
-"# Business Value Analysis"
-
-"""
-A closer look at the values associated with actions.
-
-* Is my value distribution very skewed? Are there actions with significantly different values than the others?
-* What's the range of the values?
-
-"""
-ensure_data()
 st.warning(
-    "Current sample data action values are artificial so the analysis is just an example."
-)
-
-st.session_state["sidebar"] = st.sidebar
-
-scope_options = st.session_state.decision_data.getPossibleScopeValues()
-if "scope" not in st.session_state:
-    st.session_state.scope = scope_options[0]
-
-valueData = st.session_state.decision_data.getValueDistributionData()
-
-with st.container(border=True):
-    st.plotly_chart(
-        st_value_distribution(valueData, st.session_state.scope),
-        use_container_width=True,
-    )
-
-    scope_index = get_current_scope_index(scope_options)
-    st.selectbox(
-        "Granularity:",
-        options=scope_options,
-        format_func=lambda option: NBADScope_Mapping[option],
-        index=scope_index,
-        key="scope",
-    )
-
-"Actions having different values:"
-
-st.dataframe(
-    valueData.filter(pl.col("Value_min") != pl.col("Value_max")).collect(),
-    hide_index=True,
-    column_config=NBADScope_Mapping,
+    "In maintenance!!, please see: https://streamlit-dev.dsmcloud.io/Business%20Value%20Analysis for the older version. If the link doesn't work, contact Yusuf Uyanik."
 )
+# import polars as pl
+# import streamlit as st
+
+# from da_streamlit_utils import (
+#     get_current_scope_index,
+#     st_value_distribution,
+#     ensure_data,
+# )
+# from pdstools.decision_analyzer.utils import NBADScope_Mapping
+
+# # TODO Finish up to show effect on proposition distribution (side to side)
+
+# "# Business Value Analysis"
+
+# """
+# A closer look at the values associated with actions.
+
+# * Is my value distribution very skewed? Are there actions with significantly different values than the others?
+# * What's the range of the values?
+
+# """
+# ensure_data()
+# st.warning(
+#     "Current sample data action values are artificial so the analysis is just an example."
+# )
+
+# st.session_state["sidebar"] = st.sidebar
+
+# scope_options = st.session_state.decision_data.getPossibleScopeValues()
+# if "scope" not in st.session_state:
+#     st.session_state.scope = scope_options[0]
+
+# valueData = st.session_state.decision_data.getValueDistributionData()
+
+# with st.container(border=True):
+#     st.plotly_chart(
+#         st_value_distribution(valueData, st.session_state.scope),
+#         use_container_width=True,
+#     )
+
+#     scope_index = get_current_scope_index(scope_options)
+#     st.selectbox(
+#         "Granularity:",
+#         options=scope_options,
+#         format_func=lambda option: NBADScope_Mapping[option],
+#         index=scope_index,
+#         key="scope",
+#     )
+
+# "Actions having different values:"
+
+# st.dataframe(
+#     valueData.filter(pl.col("Value_min") != pl.col("Value_max")).collect(),
+#     hide_index=True,
+#     column_config=NBADScope_Mapping,
+# )

python/pdstools/app/decision_analyzer/pages/11_Business_Lever_Analysis.py

@@ -3,7 +3,10 @@
 import polars as pl
 import streamlit as st
 
-from utils import ensure_data, find_lever_value
+from da_streamlit_utils import (
+    ensure_data,
+)
+from pdstools.decision_analyzer.utils import find_lever_value
 
 # TODO not so sure what to do with this tool - maybe generalize to work across a selection not just a single action and figure out a multiplier
 # TODO but do show the effect of levering right away (distributions side to side) just like we should do in the thresholding analysis (share code)

python/pdstools/app/decision_analyzer/pages/4_Action_Funnel.py

@@ -36,7 +36,6 @@
 st.session_state["sidebar"] = st.sidebar
 if "local_filters" in st.session_state:
     del st.session_state["local_filters"]
-
 with st.session_state["sidebar"]:
     scope_options = st.session_state.decision_data.getPossibleScopeValues()
     stage_options = st.session_state.decision_data.getPossibleStageValues()

python/pdstools/app/decision_analyzer/pages/8_Offer_Quality_Analysis.py

@@ -1,13 +1,13 @@
 import streamlit as st
 
-from plots import getTrendChart, offer_quality_piecharts
+from pdstools.decision_analyzer.plots import getTrendChart, offer_quality_piecharts
 from da_streamlit_utils import (
     get_current_scope_index,
     get_current_stage_index,
+    ensure_data,
 )
-from utils import (
+from pdstools.decision_analyzer.utils import (
     NBADScope_Mapping,
-    ensure_data,
     filtered_action_counts,
 )
 

python/pdstools/app/decision_analyzer/pages/9_Thresholding_Analysis.py

@@ -2,8 +2,7 @@
 import polars as pl
 import streamlit as st
 
-from plots import distribution, threshold_deciles
-from utils import ensure_data
+from da_streamlit_utils import ensure_data
 
 # TODO Interactive Thresholding isn't working properly yet. Also show the total numbers.
 # TODO Instead of priority/propensity side to side have a drop-down to select which property to show
@@ -80,7 +79,9 @@
 # st.dataframe(plotData)
 
 st.plotly_chart(
-    threshold_deciles(threshold_deciles_data, thresholding_mapping[thresholding_on]),
+    st.session_state.decision_data.plot.threshold_deciles(
+        thresholding_on, thresholding_mapping[thresholding_on]
+    ),
     use_container_width=True,
 )
 
@@ -98,13 +99,11 @@
     ),  # Hmm, probalby not the right way
     # additional_filters=((pl.col(thresholding_on).list.eval(pl.element() > current_threshold)).list.any()),
 )
-# st.write(xxx.head().collect())
 st.write(
-    distribution(
+    st.session_state.decision_data.plot.distribution(
         xxx,
         scope="pyIssue",
         breakdown="pyGroup",
-        title="Effect of Thresholding",
         horizontal=True,
     )
 )

python/pdstools/decision_analyzer/decision_data.py

@@ -81,7 +81,7 @@ def __init__(self, raw_data: pl.LazyFrame):
             "pyIssue",
             "pyGroup",
             "pyName",
-            # "pyTreatment", # should be in there dependent on what's in the data
+            "pyTreatment",  # should be in there dependent on what's in the data
             "pyChannel",
             "pyDirection",
             "pxComponentName",

python/pdstools/decision_analyzer/plots.py

@@ -13,8 +13,8 @@ class Plot:
     def __init__(self, decision_data):
         self._decision_data = decision_data
 
-    def threshold_deciles(self, thresholding_name, return_df=False):
-        df = self._decision_data.whatever_preprocessing
+    def threshold_deciles(self, thresholding_on, thresholding_name, return_df=False):
+        df = self._decision_data.getThresholdingData(thresholding_on)
         if return_df:
             return df
 

python/pdstools/decision_analyzer/table_definition.py

@@ -345,10 +345,6 @@ class TableConfig(TypedDict):
         "Arbitration.TopSelection",
         "TreatmentPlacements",
         "Channels.ExtensionPoint",
-        "Channels.ExtensionPoint",
-        "Channels.ExtensionPoint",
-        "Channels.ExtensionPoint",
-        "Channels.ExtensionPoint",
         "ContactPolicies.ChannelLimits",
         "ContactPolicies.ExtensionPoint",
         "FinalLimitsAndBundlingPostExtensionPoint",