def register_new_action(validator: bool = True):
df = pd.read_csv("lux/data/car.csv")
def random_categorical(ldf):
intent = [lux.Clause("?", data_type="nominal")]
vlist = VisList(intent, ldf)
for vis in vlist:
vis.score = 10
vlist = vlist.topK(15)
return {
"action": "bars",
"description": "Random list of Bar charts",
"collection": vlist
}
def contain_horsepower(df):
for clause in df.intent:
if clause.get_attr() == "Horsepower":
return True
return False
if validator:
lux.register_action("bars", random_categorical, contain_horsepower)
else:
lux.register_action("bars", random_categorical)
return df
def test_invalid_validator():
df = pd.read_csv("lux/data/car.csv")
def random_categorical(ldf):
intent = [lux.Clause("?", data_type="nominal")]
vlist = VisList(intent, ldf)
for vis in vlist:
vis.score = 10
vlist = vlist.topK(15)
return {
"action": "bars",
"description": "Random list of Bar charts",
"collection": vlist
}
with pytest.raises(ValueError, match="Value must be a callable"):
lux.register_action("bars", random_categorical, "not a Callable")
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata() # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = 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"]:
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.")
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
rec_infolist = []
from lux.action.custom import custom
from lux.action.custom import custom_actions
from lux.action.correlation import correlation
from lux.action.univariate import univariate
from lux.action.enhance import enhance
from lux.action.filter import filter
from lux.action.generalize import generalize
from lux.action.row_group import row_group
from lux.action.column_group import column_group
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if rec_df.recommendation == {}:
# display conditions for default actions
no_vis = lambda ldf: (ldf.current_vis is None) or (
ldf.current_vis is not None and len(ldf.current_vis) == 0
)
one_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) == 1
)
multiple_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) > 1
)
# globally register default actions
lux.register_action("correlation", correlation, no_vis)
lux.register_action("distribution", univariate, no_vis, "quantitative")
lux.register_action("occurrence", univariate, no_vis, "nominal")
lux.register_action("temporal", univariate, no_vis, "temporal")
lux.register_action("Enhance", enhance, one_current_vis)
lux.register_action("Filter", filter, one_current_vis)
lux.register_action("Generalize", generalize, one_current_vis)
lux.register_action("Custom", custom, multiple_current_vis)
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df.recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
self._widget = rec_df.render_widget()
self._recs_fresh = True
def test_invalid_function():
df = pd.read_csv("lux/data/car.csv")
with pytest.raises(ValueError, match="Value must be a callable"):
lux.register_action("bars", "not a Callable")