def main():
parser = argparse.ArgumentParser()
parser.add_argument("notefile", help="raw notes file")
parser.add_argument("outfile", help="raw notes file")
parser.add_argument("--thr",
type=float,
default=0.74,
help="threshold frequency")
parser.add_argument("--lb",
type=int,
default=10,
help="lower bound on number of topics")
parser.add_argument("--ub",
type=int,
default=100,
help="upper bound on number of topics")
parser.add_argument("--step",
type=int,
default=5,
help="step size to explore")
args = parser.parse_args()
aboveThr = args.thr
progNoteDF = pd.read_csv(args.notefile)
progScoreDF = lu.compute_lda_scores(progNoteDF,
'procNote',
args.lb,
args.ub,
args.step,
nAbove=aboveThr)
# find the pareto front, or the points that are not dominated by others.
aggPF = pareto.eps_sort([list(progScoreDF.itertuples(False))], [1, 6])
print(pd.DataFrame(aggPF, columns=progScoreDF.columns.get_values()))
progScoreDF.to_csv(args.outfile, index=False)
def pareto_front(self, step_name=None, nums=None, records=None):
"""Get parent front. pareto."""
if records is None:
records = self.all_records
records = list(
filter(
lambda x: x.step_name == step_name and x.performance is
not None, records))
in_pareto = [
record.rewards
if isinstance(record.rewards, list) else [record.rewards]
for record in records
]
if not in_pareto:
return None, None
try:
fitness = np.array(in_pareto)
if fitness.shape[1] != 1 and nums is not None and len(
in_pareto) > nums:
# len must larger than nums, otherwise dead loop
_, res, selected = SortAndSelectPopulation(fitness.T, nums)
else:
outs = pareto.eps_sort(fitness,
maximize_all=True,
attribution=True)
res, selected = np.array(outs)[:, :-2], np.array(
outs)[:, -1].astype(np.int32)
return res.tolist(), selected.tolist()
except Exception as ex:
logging.error('No pareto_front_records found, ex=%s', ex)
return [], []
def best_config(self):
"""Get current config list located in pareto front.
:return: list of dict
{'config_id': int,
'score': float,
'configs': dict}
config_id, score, and configs of the current best config.
"""
if self.total_propose == 0:
idx = random.randint(0, len(self.config_list))
result = {'config_id': idx,
'score': -1 * float('inf'),
'configs': self.config_list[idx]}
return [result]
else:
pareto_board = self.sieve_board.copy()
pareto_board = pareto_board.dropna()
nondominated = pareto.eps_sort([list(pareto_board.itertuples(False))],
objectives=self.pareto_cols,
epsilons=None,
maximize=self.max_object_ids)
pareto_list = []
for tmp_list in nondominated:
result = {}
for i, value in enumerate(tmp_list):
if i == 1:
result['config_id'] = value
result['configs'] = self.config_list[int(value)]
elif i >= 3:
result[self.sieve_columns[i]] = value
pareto_list.append(result)
return pareto_list
def pareto_frontier(df: pd.DataFrame, colx: str, coly: str) -> pd.DataFrame:
cxi = list(df.columns).index(colx)
cyi = list(df.columns).index(coly)
rows = eps_sort([list(df.itertuples(False))],
objectives=[cxi, cyi],
maximize=[cxi, cyi])
return pd.DataFrame(rows, columns=df.columns).sort_values([colx])
def approximate_pareto(y, epsilons=None, margin=0):
"""
Uses pareto.py from https://github.com/matthewjwoodruff/pareto.py
Returns the same data as prpt.
"""
tagalongs = np.array(pareto.eps_sort(y, epsilons=epsilons, maximize_all=True, attribution=True))
pareto_solutions = tagalongs[:, :y.shape[1]]
pareto_idx = tagalongs[:, y.shape[1] + 1].astype(int)
if margin > 0:
miny = np.min(y, axis=0)
ptp = pareto_solutions - miny
margin = ptp * margin
pareto_idx = range(y.shape[0])
for s, m in zip(pareto_solutions, margin):
pareto_idx = np.intersect1d(pareto_idx, np.where(np.any(y >= s - m, axis=1))[0], assume_unique=True)
pareto_solutions = y[pareto_idx, :]
pareto_scores = compute_scores(y, pareto_solutions)
return pareto_solutions, pareto_idx, pareto_scores
def _min_max(study):
"""
Multi-objective function to find best trial index with minimum deviation and max correlation
:param study: Optuna study
:return:
"""
# Iterate pareto-front trials storing mean correlation and std dev
df = []
for trial in study.best_trials:
df.append([trial.number, np.mean(trial.values), np.std(trial.values)])
# Sort dataframe ascending by mean correlation
df = pd.DataFrame(df).sort_values(by=2, ascending=True)
# Sort df with best trial in first row
if len(df) > 1 and len(df.iloc[:, 1:3].drop_duplicates()) > 1:
# Create second pareto to maximize correlation and minimize stddev
# Epsilons define precision, ie dominance over other candidates
# Dominance is defined as x percent of stddev of stddev
try:
nd = pareto.eps_sort([list(df.itertuples(False))], objectives=[1, 2],
epsilons=[1e-09, np.std(df[1])*.5], maximize=[1])
except:
# Something went wrong, return df
nd = df
# Sort remaining candidates
nd = pd.DataFrame(nd).sort_values(by=2, ascending=True)
# Only 1st trial so return it
else:
nd = df
# Return "best" trial index
return nd.iloc[0, 0]
def get_pareto_front(self):
"""Propose the Pareto front from the board.
:return: The row count of the Pareto board.
:rtype: dict
"""
pareto_list = []
pareto_dict = {}
pareto_board = self.sieve_board.copy()
pareto_board = pareto_board.dropna()
if not pareto_board.empty:
nondominated = pareto.eps_sort(
[list(pareto_board.itertuples(False))],
objectives=self.pareto_cols,
epsilons=None,
maximize=self.max_object_ids)
for tmp_list in nondominated:
for i, value in enumerate(tmp_list):
if i == 2:
pareto_list.append(copy.deepcopy(value))
break
if len(tmp_list) > 2:
pareto_dict[tmp_list[0]] = copy.deepcopy(tmp_list[2])
return pareto_dict
def plot_nondominated_sets(df,
mdf,
x_axis=None,
y_axis=None,
z_axis=None,
hoverlabel=None,
output='plot'):
"""
Takes in parameters and returns a figure object to be plotted by plotly
:param df: Input objectives table as a pandas dataframe
:param mdf: Input metadata file as a pandas dataframe
:param x_axis: Objective name to be plotted on the X-axis [STRING]
:param y_axis: Objective name to be plotted on the Y-axis [STRING]
:param z_axis: Objective name to be plotted on the Y-axis [STRING]
:param hoverlabel: Objective name to be used to tag all the points [STRING]
:param output: specifies the output of the fucntion [STRING]
:return: Plotly Figure object [use plotly.offline.plot() ]
"""
if output == 'table':
objective_names = []
max_col = []
eps_vals = []
for i in range(len(mdf)):
row = mdf.iloc[i]
default_epsilon = 1e-9
objective_names.append(row['Col_Name'])
if pd.isna(row['Epsilon']):
eps_vals.append(default_epsilon)
else:
eps_vals.append((row['Epsilon']))
if row['Max_Min'] == 'Max':
max_col.append(row['Col_Name'])
objective_cols = [
df.columns.get_loc(c) for c in objective_names if c in df
] # Converts col-names to col_ids
max_col_ids = [df.columns.get_loc(c) for c in max_col if c in df]
nondominated_set_multi = pareto.eps_sort([list(df.itertuples(False))],
objectives=objective_cols,
maximize=max_col_ids,
epsilons=eps_vals)
nondominated_df_multi = pd.DataFrame(nondominated_set_multi)
nondominated_df_multi.columns = df.columns
return nondominated_df_multi
else:
if z_axis:
plot_list = [x_axis, y_axis, z_axis]
else:
plot_list = [x_axis, y_axis]
plot_mdf = mdf.loc[mdf['Col_Name'].isin(plot_list)]
plot_col_ids = [df.columns.get_loc(c) for c in plot_list if c in df]
plot_max_cols = [
df.columns.get_loc(c)
for c in plot_mdf.query('Max_Min == "Max"')['Col_Name'].values
if c in df
]
col_eps = [1e-9 if pd.isna(e) else e for e in plot_mdf['Epsilon']]
nondominated_set = pareto.eps_sort([list(df.itertuples(False))],
objectives=plot_col_ids,
maximize=plot_max_cols,
epsilons=col_eps)
nondominated_df = pd.DataFrame(nondominated_set)
nondominated_df.columns = df.columns
full_df = pd.merge(df,
nondominated_df['row_index'],
how='left',
indicator='Optimal',
on='row_index')
full_df.Optimal.replace(to_replace=dict(both="True",
left_only="False"),
inplace=True)
if z_axis is None:
nd_points_list = [(nondominated_df[x_axis].values[i],
nondominated_df[y_axis].values[i])
for i in range(len(nondominated_df))]
nd_points_list.sort(key=lambda tup: tup[1])
x, y = map(list, zip(*nd_points_list))
if x_axis and y_axis and z_axis:
fig = px.scatter_3d(full_df,
x=x_axis,
y=y_axis,
z=z_axis,
color='Optimal',
hover_name=hoverlabel,
color_discrete_map={
"True": "#23B58A",
'False': "#B5234E"
})
fig.data[0].marker.size = 12
fig.data[0].marker.line.width = 2
fig.data[0].marker.line.color = "black"
fig.update_layout(scene=dict(xaxis_title=x_axis,
yaxis_title=y_axis,
zaxis_title=z_axis),
height=545,
margin=dict(r=0, b=0, l=0))
return fig
elif x_axis and y_axis:
fig = px.scatter(full_df,
x=x_axis,
y=y_axis,
color='Optimal',
hover_name=hoverlabel,
color_discrete_map={
"True": "#23B58A",
'False': "#B5234E"
})
fig.add_trace(
go.Scatter(x=x,
y=y,
mode="lines",
line=go.scatter.Line(shape='linear',
color='#3D9970'),
showlegend=False))
fig.data[0].marker.size = 12
fig.data[0].marker.line.width = 2
fig.data[0].marker.line.color = "black"
fig.update_layout(
height=545,
title=go.layout.Title(
xref="paper",
x=0,
),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(
text=x_axis, font=dict(size=18, color="#7f7f7f"))),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(
text=y_axis, font=dict(size=18, color="#7f7f7f"))))
return fig
else:
raise Exception('You must supply at least two axes')
data = pandas.read_csv("/Users/rshu/Downloads/pareto.py-picture/data.txt",
header=None,
sep=" ")
sets = {}
archives = {}
fig = matplotlib.figure.Figure(figsize=(15, 15))
agg.FigureCanvasAgg(fig)
counter = 0
resolutions = [1e-9, 0.03, 0.06, 0.1, 0.15, 0.2, 0.25, 0.4, 1.0]
for resolution in resolutions:
archives[resolution] = pareto.eps_sort([data.itertuples(False)], [0, 1],
[resolution] * 2)
sets[resolution] = pandas.DataFrame(data=archives[resolution].archive)
# print("debugging")
# print(sets[0.03])
#
# for idx, row in sets[0.03].iterrows():
# print((idx, row[0], row[1]))
for resolution in resolutions:
counter += 1
ax = fig.add_subplot(3, 3, counter)
ax.scatter(data[0], data[1], lw=0, facecolor=(0.7, 0.7, 0.7), zorder=-1)
for idx, row in sets[resolution].iterrows():
ax.scatter(row[0],
row[1],
def pr_plot(opensubs18_csv, eurosense_csv=None, out=None):
import matplotlib.pyplot as plt
import pareto
from brokenaxes import brokenaxes
import matplotlib as mpl
mpl.rcParams.update({
"font.family": "serif",
"font.serif": [],
"font.sans-serif": []
})
opensubs18_df = pd.read_csv(opensubs18_csv)
nondominated = pareto.eps_sort(
opensubs18_df[["precision", "recall"]],
[0, 1],
maximize_all=True,
attribution=True,
)
nondominated_idxs = [nd[-1] for nd in nondominated]
opensubs18_df["type"] = pd.Series([
"stiff-nondom" if idx in nondominated_idxs else "stiff"
for idx in opensubs18_df.index
])
type_markers = {"stiff": ("o", "k"), "stiff-nondom": ("D", "b")}
if eurosense_csv is not None:
type_markers["eurosense"] = ("s", "y")
eurosense_df = pd.read_csv(eurosense_csv)
eurosense_df["type"] = "eurosense"
eurosense_df["name"] = eurosense_df["name"].str.replace(
"high", "eurosense")
df = pd.concat([opensubs18_df, eurosense_df], ignore_index=True)
else:
df = opensubs18_df
df = df.drop(df[df.name == "N"].index)
print(df)
fig = plt.gcf()
fig.set_size_inches(645.0 / INCH_PTS, 441.0 / INCH_PTS)
bax = brokenaxes(
xlims=((0, 0.01), (0.24, 0.51), (0.69, 0.81), (0.99, 1)),
ylims=((0, 0.21), (0.39, 0.43), (0.99, 1)),
hspace=0.05,
wspace=0.05,
)
for type, (marker, c) in type_markers.items():
group_df = df[df.type == type]
bax.scatter(x=group_df["precision"],
y=group_df["recall"],
marker=marker,
c=c,
s=10)
texts = []
for idx, row in df.iterrows():
if row["precision"] > 0.6:
axnum = 10
elif row["recall"] > 0.3:
axnum = 5
else:
axnum = 9
texts.append(bax.axs[axnum].text(row["precision"],
row["recall"],
row["name"],
ha="center",
va="center"))
x = numpy.linspace(0, 0.6, 100)
for eurosense in ("EC", "EP"):
row = df[df.name == eurosense]
p = row.precision.item()
r = row.recall.item()
assert p > r
bax.plot(x, x * r / p, zorder=-1, color="#ffdddd")
from adjustText import adjust_text
adjust_text(texts)
bax.set_xlabel("Precision", labelpad=0)
bax.set_ylabel("Recall", labelpad=40)
if out is not None:
plt.savefig(out, bbox_inches="tight")
else:
plt.show()
def pr_plot(opensubs18_csv, eurosense_csv=None, out=None):
import matplotlib.pyplot as plt
from adjustText import adjust_text
import pareto
from brokenaxes import brokenaxes
import matplotlib as mpl
mpl.rcParams.update(
{"font.family": "serif", "font.serif": [], "font.sans-serif": []}
)
opensubs18_df = pd.read_csv(opensubs18_csv)
nondominated = pareto.eps_sort(
opensubs18_df[["precision", "recall"]],
[0, 1],
maximize_all=True,
attribution=True,
)
nondominated_idxs = [nd[-1] for nd in nondominated]
opensubs18_df["type"] = pd.Series(
[
"stiff-nondom" if idx in nondominated_idxs else "stiff"
for idx in opensubs18_df.index
]
)
type_markers = {"stiff": ("o", "k"), "stiff-nondom": ("D", "b")}
if eurosense_csv is not None:
type_markers["eurosense"] = ("s", "y")
eurosense_df = pd.read_csv(eurosense_csv)
eurosense_df["type"] = "eurosense"
eurosense_df["name"] = eurosense_df["name"].str.replace("high", "eurosense")
df = pd.concat([opensubs18_df, eurosense_df], ignore_index=True)
else:
df = opensubs18_df
print(df)
fig = plt.gcf()
fig.set_size_inches(645.0 / INCH_PTS, 441.0 / INCH_PTS)
bax = brokenaxes(
xlims=((0, 0.01), (0.29, 1)),
ylims=((0, 0.51), (0.99, 1)),
hspace=0.05,
wspace=0.05,
)
for type, (marker, c) in type_markers.items():
group_df = df[df.type == type]
bax.scatter(
x=group_df["precision"], y=group_df["recall"], marker=marker, c=c, s=10
)
texts = []
for idx, row in df.iterrows():
texts.append(
bax.axs[3].text(
row["precision"], row["recall"], row["name"], ha="center", va="center"
)
)
adjust_text(texts)
bax.set_xlabel("Precision")
bax.set_ylabel("Recall")
if out is not None:
plt.savefig(out, bbox_inches="tight")
else:
plt.show()
