def show_more(self):
from lux.action.custom import custom
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
self._rec_info = []
if (self.pre_aggregated):
if (self.columns.name is not None):
self._append_recInfo(row_group(self))
if (self.index.name is not None):
self._append_recInfo(column_group(self))
else:
if (self.current_vis is None):
no_vis = True
one_current_vis = False
multiple_current_vis = False
else:
no_vis = len(self.current_vis) == 0
one_current_vis = len(self.current_vis) == 1
multiple_current_vis = len(self.current_vis) > 1
if (no_vis):
self._append_recInfo(correlation(self))
self._append_recInfo(univariate(self, "quantitative"))
self._append_recInfo(univariate(self, "nominal"))
self._append_recInfo(univariate(self, "temporal"))
elif (one_current_vis):
self._append_recInfo(enhance(self))
self._append_recInfo(filter(self))
self._append_recInfo(generalize(self))
elif (multiple_current_vis):
self._append_recInfo(custom(self))
# Store _rec_info into a more user-friendly dictionary form
self.recommendation = {}
for rec_info in self._rec_info:
action_type = rec_info["action"]
vc = rec_info["collection"]
if (self.plot_config):
for vis in self.current_vis:
vis.plot_config = self.plot_config
for vis in vc:
vis.plot_config = self.plot_config
if (len(vc) > 0):
self.recommendation[action_type] = vc
self.clear_filter()
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
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.append(
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.append(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
if (not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh
): # Check that recs has not yet been computed
rec_infolist = []
from lux.action.custom import custom
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))
if (rec_df.index.name is not None):
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if (rec_df.current_vis is None):
no_vis = True
one_current_vis = False
multiple_current_vis = False
else:
no_vis = len(rec_df.current_vis) == 0
one_current_vis = len(rec_df.current_vis) == 1
multiple_current_vis = len(rec_df.current_vis) > 1
if (no_vis):
rec_df._append_rec(rec_infolist, correlation(rec_df))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "quantitative"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "nominal"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "temporal"))
elif (one_current_vis):
rec_df._append_rec(rec_infolist, enhance(rec_df))
rec_df._append_rec(rec_infolist, filter(rec_df))
rec_df._append_rec(rec_infolist, generalize(rec_df))
elif (multiple_current_vis):
rec_df._append_rec(rec_infolist, custom(rec_df))
# 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 (rec_df._plot_config):
for vis in rec_df.current_vis:
vis._plot_config = rec_df.plot_config
for vis in vlist:
vis._plot_config = rec_df.plot_config
if (len(vlist) > 0):
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
elif (
show_prev
): # re-render widget for the current dataframe if previous rec is not recomputed
self._widget = rec_df.render_widget()
self._recs_fresh = True
lux.config.streaming = False
if dataset == "airbnb":
downsample_func = data_utils.downsample_airbnb
elif dataset == "communities":
downsample_func = data_utils.downsample_communities
trial = []
for nPts in trial_range:
nPts = int(nPts)
print(f"Start {nPts}")
df = downsample_func(nPts)
df.maintain_metadata()
################
start = time.perf_counter()
univariate(df, ["quantitative"])
end = time.perf_counter()
t_dist = end - start
################
start = time.perf_counter()
univariate(df, ["nominal"])
end = time.perf_counter()
t_nominal = end - start
################
start = time.perf_counter()
univariate(df, ["temporal"])
end = time.perf_counter()
t_temporal = end - start
################
start = time.perf_counter()
correlation(df)