Coalesce all data_type attributes of frame into one

lux/action/filter.py

@@ -45,7 +45,7 @@ def filter(ldf):
         # get unique values for all categorical values specified and creates corresponding filters
         fltr = filters[0]
 
-        if ldf.data_type_lookup[fltr.attribute] == "nominal":
+        if ldf.data_type[fltr.attribute] == "nominal":
             recommendation = {
                 "action": "Filter",
                 "description": f"Changing the <p class='highlight-intent'>{fltr.attribute}</p> filter to an alternative value.",
@@ -60,7 +60,7 @@ def filter(ldf):
                     new_spec.append(new_filter)
                     temp_vis = Vis(new_spec)
                     output.append(temp_vis)
-        elif ldf.data_type_lookup[fltr.attribute] == "quantitative":
+        elif ldf.data_type[fltr.attribute] == "quantitative":
             recommendation = {
                 "action": "Filter",
                 "description": f"Changing the <p class='highlight-intent'>{fltr.attribute}</p> filter to an alternative inequality operation.",

lux/action/univariate.py

@@ -48,9 +48,7 @@ def univariate(ldf, *args):
         possible_attributes = [
             c
             for c in ldf.columns
-            if ldf.data_type_lookup[c] == "quantitative"
-            and ldf.cardinality[c] > 5
-            and c != "Number of Records"
+            if ldf.data_type[c] == "quantitative" and ldf.cardinality[c] > 5 and c != "Number of Records"
         ]
         intent = [lux.Clause(possible_attributes)]
         intent.extend(filter_specs)

lux/core/frame.py

@@ -21,6 +21,8 @@
 from lux.utils.message import Message
 from lux.utils.utils import check_import_lux_widget
 from typing import Dict, Union, List, Callable
+
+# from lux.executor.Executor import *
 import warnings
 import traceback
 import lux
@@ -35,10 +37,7 @@ class LuxDataFrame(pd.DataFrame):
     _metadata = [
         "_intent",
         "_inferred_intent",
-        "data_type_lookup",
         "data_type",
-        "data_model_lookup",
-        "data_model",
         "unique_values",
         "cardinality",
         "_rec_info",
@@ -77,10 +76,7 @@ def __init__(self, *args, **kw):
         self._message = Message()
         self._pandas_only = False
         # Metadata
-        self.data_type_lookup = None
         self.data_type = None
-        self.data_model_lookup = None
-        self.data_model = None
         self.unique_values = None
         self.cardinality = None
         self._min_max = None
@@ -126,10 +122,7 @@ def expire_recs(self):
     def expire_metadata(self):
         # Set metadata as null
         self._metadata_fresh = False
-        self.data_type_lookup = None
         self.data_type = None
-        self.data_model_lookup = None
-        self.data_model = None
         self.unique_values = None
         self.cardinality = None
         self._min_max = None
@@ -294,15 +287,11 @@ def compute_SQL_dataset_metadata(self):
         self.get_SQL_attributes()
         for attr in list(self.columns):
             self[attr] = None
-        self.data_type_lookup = {}
         self.data_type = {}
         #####NOTE: since we aren't expecting users to do much data processing with the SQL database, should we just keep this
         #####      in the initialization and do it just once
         self.compute_SQL_data_type()
         self.compute_SQL_stats()
-        self.data_model_lookup = {}
-        self.data_model = {}
-        self.compute_data_model()
 
     def compute_SQL_stats(self):
         # precompute statistics
@@ -312,7 +301,7 @@ def compute_SQL_stats(self):
         self.get_SQL_unique_values()
         # self.get_SQL_cardinality()
         for attribute in self.columns:
-            if self.data_type_lookup[attribute] == "quantitative":
+            if self.data_type[attribute] == "quantitative":
                 self._min_max[attribute] = (
                     self[attribute].min(),
                     self[attribute].max(),
@@ -349,7 +338,7 @@ def get_SQL_unique_values(self):
         self.unique_values = unique_vals
 
     def compute_SQL_data_type(self):
-        data_type_lookup = {}
+        data_type = {}
         sql_dtypes = {}
         self.get_SQL_cardinality()
         if "." in self.table_name:
@@ -362,20 +351,17 @@ def compute_SQL_data_type(self):
             datatype = list(pd.read_sql(query, lux.config.SQLconnection)["data_type"])[0]
             sql_dtypes[attr] = datatype
 
-        data_type = {"quantitative": [], "nominal": [], "temporal": []}
         for attr in list(self.columns):
             if str(attr).lower() in ["month", "year"]:
-                data_type_lookup[attr] = "temporal"
-                data_type["temporal"].append(attr)
+                data_type[attr] = "temporal"
             elif sql_dtypes[attr] in [
                 "character",
                 "character varying",
                 "boolean",
                 "uuid",
                 "text",
             ]:
-                data_type_lookup[attr] = "nominal"
-                data_type["nominal"].append(attr)
+                data_type[attr] = "nominal"
             elif sql_dtypes[attr] in [
                 "integer",
                 "real",
@@ -384,15 +370,11 @@ def compute_SQL_data_type(self):
                 "serial",
             ]:
                 if self.cardinality[attr] < 13:
-                    data_type_lookup[attr] = "nominal"
-                    data_type["nominal"].append(attr)
+                    data_type[attr] = "nominal"
                 else:
-                    data_type_lookup[attr] = "quantitative"
-                    data_type["quantitative"].append(attr)
+                    data_type[attr] = "quantitative"
             elif "time" in sql_dtypes[attr] or "date" in sql_dtypes[attr]:
-                data_type_lookup[attr] = "temporal"
-                data_type["temporal"].append(attr)
-        self.data_type_lookup = data_type_lookup
+                data_type[attr] = "temporal"
         self.data_type = data_type
 
     def _append_rec(self, rec_infolist, recommendations: Dict):
@@ -419,8 +401,9 @@ def maintain_recs(self):
             rec_df._message = Message()
         # Add warning message if there exist ID fields
         id_fields_str = ""
-        if len(rec_df.data_type["id"]) > 0:
-            for id_field in rec_df.data_type["id"]:
+        inverted_data_type = lux.config.executor.invert_data_type(rec_df.data_type)
+        if len(inverted_data_type["id"]) > 0:
+            for id_field in inverted_data_type["id"]:
                 id_fields_str += f"<code>{id_field}</code>, "
             id_fields_str = id_fields_str[:-2]
             rec_df._message.add(f"{id_fields_str} is not visualized since it resembles an ID field.")

lux/core/series.py

@@ -21,10 +21,7 @@
 class LuxSeries(pd.Series):
     _metadata = [
         "_intent",
-        "data_type_lookup",
         "data_type",
-        "data_model_lookup",
-        "data_model",
         "unique_values",
         "cardinality",
         "_rec_info",

lux/executor/Executor.py

@@ -51,9 +51,9 @@ def compute_stats(self):
     def compute_data_type(self):
         return NotImplemented
 
-    @staticmethod
-    def compute_data_model(self):
-        return NotImplemented
+    # @staticmethod
+    # def compute_data_model(self):
+    #     return NotImplemented
 
     def mapping(self, rmap):
         group_map = {}
@@ -67,3 +67,19 @@ def reverseMapping(self, map):
             for val in map[valKey]:
                 reverse_map[val] = valKey
         return reverse_map
+
+    def invert_data_type(self, data_type):
+        return self.mapping(data_type)
+
+    def compute_data_model(self, data_type):
+        data_type_inverted = self.invert_data_type(data_type)
+        data_model = {
+            "measure": data_type_inverted["quantitative"],
+            "dimension": data_type_inverted["nominal"]
+            + data_type_inverted["temporal"]
+            + data_type_inverted["id"],
+        }
+        return data_model
+
+    def compute_data_model_lookup(self, data_type):
+        return self.reverseMapping(self.compute_data_model(data_type))

lux/executor/PandasExecutor.py

@@ -374,64 +374,59 @@ def execute_2D_binning(vis: Vis):
     ############ Metadata: data type, model #############
     #######################################################
     def compute_dataset_metadata(self, ldf: LuxDataFrame):
-        ldf.data_type_lookup = {}
         ldf.data_type = {}
         self.compute_data_type(ldf)
-        ldf.data_model_lookup = {}
-        ldf.data_model = {}
-        self.compute_data_model(ldf)
 
     def compute_data_type(self, ldf: LuxDataFrame):
         from pandas.api.types import is_datetime64_any_dtype as is_datetime
 
         for attr in list(ldf.columns):
             temporal_var_list = ["month", "year", "day", "date", "time"]
             if is_datetime(ldf[attr]):
-                ldf.data_type_lookup[attr] = "temporal"
+                ldf.data_type[attr] = "temporal"
             elif self._is_datetime_string(ldf[attr]):
-                ldf.data_type_lookup[attr] = "temporal"
+                ldf.data_type[attr] = "temporal"
             elif isinstance(attr, pd._libs.tslibs.timestamps.Timestamp):
-                ldf.data_type_lookup[attr] = "temporal"
+                ldf.data_type[attr] = "temporal"
             elif str(attr).lower() in temporal_var_list:
-                ldf.data_type_lookup[attr] = "temporal"
+                ldf.data_type[attr] = "temporal"
             elif pd.api.types.is_float_dtype(ldf.dtypes[attr]):
                 # int columns gets coerced into floats if contain NaN
                 convertible2int = pd.api.types.is_integer_dtype(ldf[attr].convert_dtypes())
                 if convertible2int and ldf.cardinality[attr] != len(ldf) and ldf.cardinality[attr] < 20:
-                    ldf.data_type_lookup[attr] = "nominal"
+                    ldf.data_type[attr] = "nominal"
                 else:
-                    ldf.data_type_lookup[attr] = "quantitative"
+                    ldf.data_type[attr] = "quantitative"
             elif pd.api.types.is_integer_dtype(ldf.dtypes[attr]):
                 # See if integer value is quantitative or nominal by checking if the ratio of cardinality/data size is less than 0.4 and if there are less than 10 unique values
                 if ldf.pre_aggregated:
                     if ldf.cardinality[attr] == len(ldf):
-                        ldf.data_type_lookup[attr] = "nominal"
+                        ldf.data_type[attr] = "nominal"
                 if ldf.cardinality[attr] / len(ldf) < 0.4 and ldf.cardinality[attr] < 20:
-                    ldf.data_type_lookup[attr] = "nominal"
+                    ldf.data_type[attr] = "nominal"
                 else:
-                    ldf.data_type_lookup[attr] = "quantitative"
+                    ldf.data_type[attr] = "quantitative"
                 if check_if_id_like(ldf, attr):
-                    ldf.data_type_lookup[attr] = "id"
+                    ldf.data_type[attr] = "id"
             # Eliminate this clause because a single NaN value can cause the dtype to be object
             elif pd.api.types.is_string_dtype(ldf.dtypes[attr]):
                 if check_if_id_like(ldf, attr):
-                    ldf.data_type_lookup[attr] = "id"
+                    ldf.data_type[attr] = "id"
                 else:
-                    ldf.data_type_lookup[attr] = "nominal"
+                    ldf.data_type[attr] = "nominal"
             # check if attribute is any type of datetime dtype
             elif is_datetime_series(ldf.dtypes[attr]):
-                ldf.data_type_lookup[attr] = "temporal"
+                ldf.data_type[attr] = "temporal"
             else:
-                ldf.data_type_lookup[attr] = "nominal"
+                ldf.data_type[attr] = "nominal"
         # for attr in list(df.dtypes[df.dtypes=="int64"].keys()):
         #   if self.cardinality[attr]>50:
         if ldf.index.dtype != "int64" and ldf.index.name:
-            ldf.data_type_lookup[ldf.index.name] = "nominal"
-        ldf.data_type = self.mapping(ldf.data_type_lookup)
+            ldf.data_type[ldf.index.name] = "nominal"
 
         non_datetime_attrs = []
         for attr in ldf.columns:
-            if ldf.data_type_lookup[attr] == "temporal" and not is_datetime(ldf[attr]):
+            if ldf.data_type[attr] == "temporal" and not is_datetime(ldf[attr]):
                 non_datetime_attrs.append(attr)
         warn_msg = ""
         if len(non_datetime_attrs) == 1:
@@ -468,13 +463,6 @@ def _is_datetime_string(self, series):
                 return True
         return False
 
-    def compute_data_model(self, ldf: LuxDataFrame):
-        ldf.data_model = {
-            "measure": ldf.data_type["quantitative"],
-            "dimension": ldf.data_type["nominal"] + ldf.data_type["temporal"] + ldf.data_type["id"],
-        }
-        ldf.data_model_lookup = self.reverseMapping(ldf.data_model)
-
     def compute_stats(self, ldf: LuxDataFrame):
         # precompute statistics
         ldf.unique_values = {}

lux/processor/Compiler.py

@@ -159,6 +159,8 @@ def populate_data_type_model(ldf, vlist):
         # TODO: copy might not be neccesary
         from lux.utils.date_utils import is_datetime_string
 
+        data_model_lookup = lux.config.executor.compute_data_model_lookup(ldf.data_type)
+
         for vis in vlist:
             for clause in vis._inferred_intent:
                 if clause.description == "?":
@@ -167,11 +169,11 @@ def populate_data_type_model(ldf, vlist):
                 # and not is_datetime_string(clause.attribute):
                 if clause.attribute != "" and clause.attribute != "Record":
                     if clause.data_type == "":
-                        clause.data_type = ldf.data_type_lookup[clause.attribute]
+                        clause.data_type = ldf.data_type[clause.attribute]
                     if clause.data_type == "id":
                         clause.data_type = "nominal"
                     if clause.data_model == "":
-                        clause.data_model = ldf.data_model_lookup[clause.attribute]
+                        clause.data_model = data_model_lookup[clause.attribute]
                 if clause.value != "":
                     # If user provided title for Vis, then don't override.
                     if vis.title == "":
@@ -439,16 +441,19 @@ def populate_wildcard_options(_inferred_intent: List[Clause], ldf: LuxDataFrame)
         import copy
         from lux.utils.utils import convert_to_list
 
+        inverted_data_type = lux.config.executor.invert_data_type(ldf.data_type)
+        data_model = lux.config.executor.compute_data_model(ldf.data_type)
+
         intent = {"attributes": [], "filters": []}
         for clause in _inferred_intent:
             spec_options = []
             if clause.value == "":  # attribute
                 if clause.attribute == "?":
                     options = set(list(ldf.columns))  # all attributes
                     if clause.data_type != "":
-                        options = options.intersection(set(ldf.data_type[clause.data_type]))
+                        options = options.intersection(set(inverted_data_type[clause.data_type]))
                     if clause.data_model != "":
-                        options = options.intersection(set(ldf.data_model[clause.data_model]))
+                        options = options.intersection(set(data_model[clause.data_model]))
                     options = list(options)
                 else:
                     options = convert_to_list(clause.attribute)

lux/utils/date_utils.py

@@ -13,6 +13,7 @@
 #  limitations under the License.
 
 import pandas as pd
+import lux
 
 
 def date_formatter(time_stamp, ldf):
@@ -38,10 +39,13 @@ def date_formatter(time_stamp, ldf):
     date_str: str
             A reformatted version of the time_stamp according to granularity
     """
+
+    inverted_data_type = lux.config.executor.invert_data_type(ldf.data_type)
+    # TODO: method for data_type_lookup to data_type
     datetime = pd.to_datetime(time_stamp)
-    if ldf.data_type["temporal"]:
+    if inverted_data_type["temporal"]:
         # assumes only one temporal column, may need to change this function to recieve multiple temporal columns in the future
-        date_column = ldf[ldf.data_type["temporal"][0]]
+        date_column = ldf[inverted_data_type["temporal"][0]]
 
     granularity = compute_date_granularity(date_column)
     date_str = ""

tests/test_dates.py

@@ -96,15 +96,16 @@ def test_refresh_inplace():
     )
     with pytest.warns(UserWarning, match="Lux detects that the attribute 'date' may be temporal."):
         df._repr_html_()
-    assert df.data_type_lookup["date"] == "temporal"
+    assert df.data_type["date"] == "temporal"
 
     from lux.vis.Vis import Vis
 
     vis = Vis(["date", "value"], df)
 
     df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
     df.maintain_metadata()
-    assert df.data_type["temporal"][0] == "date"
+    inverted_data_type = lux.config.executor.invert_data_type(df.data_type)
+    assert inverted_data_type["temporal"][0] == "date"
 
     vis.refresh_source(df)
     assert vis.mark == "line"