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utils.py
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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
class DataWrangling:
def __init__(self, file):
self.file = file
if isinstance(file, pd.DataFrame):
self.df = file
elif file.endswith('.csv'):
self.df = pd.read_csv(file)
self.columns = list(self.get_columns())
self.label_font = 30
self.legend_font = 20
self.title_font = 35
self.linewidth = 4
def get_columns(self):
return self.df.columns
def get_levels(self, col):
return self.df[col].unique()
def get_data_by_level(self, col, level):
if level not in self.get_levels(col):
print('Error: level not in the column')
return None
else:
return self.df[self.df[col] == level]
def remove_columns(self, col_to_remove):
self.df = self.df.drop(columns=col_to_remove)
self.columns = list(self.get_columns())
def get_averaged_data(self, col_to_average):
columns = self.columns.copy()
for col in col_to_average:
if col not in columns:
print('Error: column not in the dataframe')
return None
columns.remove(col)
averaged_df = self.df.groupby(columns, as_index=False).agg({
col_to_average[i]: 'mean' for i in range(len(col_to_average))
})
# get the standard deviation
std_df = self.df.groupby(columns, as_index=False).agg({
col_to_average[i]: 'std' for i in range(len(col_to_average))
})
for col in col_to_average:
averaged_df[col + '_std'] = std_df[col]
return averaged_df
def switch_to_averaged_data(self, col_to_average):
columns = self.columns.copy()
for col in col_to_average:
if col not in columns:
print('Error: column not in the dataframe')
return None
columns.remove(col)
averaged_df = self.df.groupby(columns, as_index=False).agg({
col_to_average[i]: 'mean' for i in range(len(col_to_average))
})
std_df = self.df.groupby(columns, as_index=False).agg({
col_to_average[i]: 'std' for i in range(len(col_to_average))
})
for col in col_to_average:
averaged_df[col + '_std'] = std_df[col]
self.df = averaged_df
self.columns = list(self.get_columns())
def plot_y_vs_x_by_cat(self, y, x, categories, ymin=None, ymax=None, std = True, alpha=0.2, xticks=False, title=None, save=False, save_path=None, startidx=0):
sns.set_theme(style="whitegrid", palette="bright", color_codes=True)
plt.figure(figsize=(13, 10))
plt.grid(alpha=0.5)
if categories is None:
print(self.df)
plt.plot(self.df[x][startidx:], self.df[y][startidx:], linewidth=self.linewidth)
if std:
# chek if the std column exists
if y + '_std' not in self.columns:
print('Error: no standard deviation column')
return None
else:
# fill the area between mean - std and mean + std
plt.fill_between(self.df[x][startidx:], self.df[y][startidx:] - self.df[y + '_std'], self.df[y][startidx:] + self.df[y + '_std'], alpha=alpha)
elif len(categories) == 1:
cat = categories[0]
levels = self.get_levels(cat)
for level in levels:
data = self.get_data_by_level(cat, level)
data = data[startidx:]
print(data)
plt.plot(data[x], data[y], label=level, linewidth=self.linewidth)
if std:
# chek if the std column exists
if y + '_std' not in self.columns:
print('Error: no standard deviation column')
return None
else:
# fill the area between mean - std and mean + std
plt.fill_between(data[x], data[y] - data[y + '_std'], data[y] + data[y + '_std'], alpha=alpha)
# put a title for the legend
if cat == 'k':
plt.legend(title='Size (k)', fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
plt.legend(title=cat, fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else: # 2 nested categories
cat = categories[0]
nest_cat = categories[1]
levels = self.get_levels(cat)
nest_levels = self.get_levels(nest_cat)
for level in levels:
for nest_level in nest_levels:
data = self.get_data_by_level(cat, level)
data = data[data[nest_cat] == nest_level]
data = data[startidx:]
plt.plot(data[x], data[y], label=level + ' - ' + nest_level, linewidth=self.linewidth)
if std:
# chek if the std column exists
if y + '_std' not in self.columns:
print('Error: no standard deviation column')
return None
else:
# fill the area between mean - std and mean + std
plt.fill_between(data[x], data[y] - data[y + '_std'], data[y] + data[y + '_std'], alpha=alpha)
# put a title for the legend
if cat == 'k':
plt.legend(title='Size (k)', fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
plt.legend(title=cat + ' - ' + nest_cat, fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
# ymin, ymax
if ymin is not None and ymax is not None:
plt.ylim(ymin, ymax)
# xlabel, yabel
if x == 'size':
plt.xlabel('# of processes', fontsize= self.label_font)
elif x == 'nthreads' or x == 'n_threads':
plt.xlabel('# of threads', fontsize= self.label_font)
elif x == 'k':
plt.xlabel('Size (k)', fontsize= self.label_font)
if y == 'time':
plt.ylabel('Time (s)', fontsize= self.label_font)
elif y == 'GFLOPS':
plt.ylabel('GFLOPS', fontsize= self.label_font)
# xticks
plt.xticks(fontsize= self.label_font)
plt.yticks(fontsize= self.label_font)
if isinstance(xticks, list):
plt.xticks(xticks)
elif xticks:
plt.xticks(self.df[x].unique())
# title
if title is not None:
plt.title( title, fontsize=self.title_font )
plt.tight_layout()
if save:
plt.savefig(save_path + '.png', dpi=300)
plt.show()
############################################
# speedup
############################################
def plot_speedup(self, y, x, categories, ymin=None, ymax=None, xticks=False, title=None, save=False, save_path=None):
sns.set_theme(style="whitegrid", palette="bright", color_codes=True)
plt.figure(figsize=(13, 10))
plt.grid(alpha=0.5)
if categories is None:
plt.plot(self.df[x], (self.df[y][0] / self.df[y]), label='speedup', linewidth=self.linewidth)
plt.plot(self.df[x], self.df[x], '--', label='ideal')
plt.legend(fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
elif len(categories) == 1:
cat = categories[0]
levels = self.get_levels(cat)
for level in levels:
data = self.get_data_by_level(cat, level)
data.reset_index(inplace=True)
base = data[y][0]
plt.plot(data[x], base/(data[y]), label=level, linewidth=self.linewidth)
# plot the ideal speedup
plt.plot(data[x], data[x], '--', label='ideal')
# put a title for the legend
if cat == 'k':
plt.legend(title='Size (k)', fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
plt.legend(title=cat, fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
cat = categories[0]
nest_cat = categories[1]
levels = self.get_levels(cat)
nest_levels = self.get_levels(nest_cat)
for level in levels:
for nest_level in nest_levels:
data = self.get_data_by_level(cat, level)
data = data[data[nest_cat] == nest_level]
data.reset_index(inplace=True)
base = data[y][0]
plt.plot(data[x], base/data[y], label=level + ' - ' + nest_level, linewidth=self.linewidth)
plt.plot(data[x], data[x], '--', label='ideal')
plt.legend(title=cat + ' - ' + nest_cat, fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
# ymin, ymax
if ymin is not None and ymax is not None:
plt.ylim(ymin, ymax)
# xlabel, yabel
if x == 'size':
plt.xlabel('# of processes', fontsize= self.label_font)
elif x == 'nthreads' or x == 'n_threads':
plt.xlabel('# of threads', fontsize= self.label_font)
elif x == 'k':
plt.xlabel('Size (k)', fontsize= self.label_font)
plt.ylabel('Speedup', fontsize= self.label_font)
# xticks
plt.xticks(fontsize= self.label_font)
plt.yticks(fontsize= self.label_font)
if isinstance(xticks, list):
plt.xticks(xticks)
elif xticks:
plt.xticks(self.df[x].unique())
# title
if title is not None:
plt.title('Speedup - ' + title, fontsize=self.title_font )
plt.tight_layout()
if save:
plt.savefig(save_path + '.png', dpi=300)
plt.show()
############################################
# efficiency
############################################
def plot_efficiency(self, y, x, categories, ymin=None, ymax=None, xticks=False, title=None, save=False, save_path=None, startidxeff=1):
sns.set_theme(style="whitegrid", palette="bright", color_codes=True)
plt.figure(figsize=(13, 10))
plt.grid(alpha=0.5)
if categories is None:
plt.plot(self.df[x][startidxeff:], (self.df[y][0] / self.df[y][startidxeff:])/self.df[x][startidxeff:], label='efficiency', linewidth=self.linewidth)
plt.plot(self.df[x][startidxeff:], np.ones(len(self.df[x])-startidxeff), '--', label='ideal')
plt.legend(fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
elif len(categories) == 1:
cat = categories[0]
levels = self.get_levels(cat)
for level in levels:
data = self.get_data_by_level(cat, level)
data.reset_index(inplace=True)
base = data[y][0]
plt.plot(data[x][startidxeff:], (base/(data[y][startidxeff:]))/data[x][startidxeff:], label=level, linewidth=self.linewidth)
# plot the ideal efficiency
plt.plot(data[x][startidxeff:], np.ones(len(data[x])-startidxeff), '--', label='ideal')
# put a title for the legend
if cat == 'k':
plt.legend(title='Size (k)', fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
plt.legend(title=cat, fontsize=self.legend_font)
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
else:
cat = categories[0]
nest_cat = categories[1]
levels = self.get_levels(cat)
nest_levels = self.get_levels(nest_cat)
for level in levels:
for nest_level in nest_levels:
data = self.get_data_by_level(cat, level)
data = data[data[nest_cat] == nest_level]
data.reset_index(inplace=True)
base = data[y][0]
plt.plot(data[x][startidxeff:], (base/(data[y][startidxeff:]))/data[x][startidxeff:], label=level+ ' - ' + nest_level, linewidth=self.linewidth)
# plot the ideal efficiency
plt.plot(data[x][startidxeff:], np.ones(len(data[x])-startidxeff), '--', label='ideal')
# put a title for the legend
plt.legend(title=cat + ' - ' + nest_cat, fontsize=self.legend_font)
# font of title of the legend
plt.setp(plt.gca().get_legend().get_title(), fontsize=self.legend_font)
# ymin, ymax
if ymin is not None and ymax is not None:
plt.ylim(ymin, ymax)
# xlabel, yabel
if x == 'size':
plt.xlabel('# of processes', fontsize= self.label_font)
elif x == 'nthreads' or x == 'n_threads':
plt.xlabel('# of threads', fontsize= self.label_font)
elif x == 'k':
plt.xlabel('Size (k)', fontsize= self.label_font)
plt.ylabel('Efficiency', fontsize= self.label_font)
# xticks
plt.xticks(fontsize= self.label_font)
plt.yticks(fontsize= self.label_font)
if isinstance(xticks, list):
plt.xticks(xticks)
elif xticks:
plt.xticks(self.df[x][1:].unique())
# title
if title is not None:
plt.title('Efficiency - ' + title, fontsize=self.title_font )
plt.tight_layout()
if save:
plt.savefig(save_path + '.png', dpi=300)
plt.show()
def pipeline(file,
mode,
xaxis,
yaxis,
categories,
speedup=True,
efficiency=False,
ymin=None, ymax=None,
yminsp=None, ymaxsp=None,
ymineff=None, ymaxeff=None ,std = True, alpha=0.2,
xticks=False,
xtickssp=None,
xtickseff=None,
title=None,
save=False, save_path=None,
startidx=0,
startidxeff=1):
df = DataWrangling(file)
df = DataWrangling(df.get_data_by_level('mode', mode))
df.switch_to_averaged_data([yaxis])
df.plot_y_vs_x_by_cat(yaxis, xaxis, categories, ymin, ymax, std, alpha, xticks, title, save, save_path, startidx=startidx)
if speedup:
if save:
save_pathsp = save_path + '_speedup'
df.plot_speedup(yaxis, xaxis, categories, yminsp, ymaxsp, xtickssp, title, save, save_pathsp)
if efficiency:
if save:
save_pathef = save_path + '_efficiency'
df.plot_efficiency(yaxis, xaxis, categories, ymineff, ymaxeff, xtickseff, title, save, save_pathef, startidxeff=startidxeff)
def pipeline_2(file,
xaxis,
yaxis,
categories,
mode = None,
speedup=True,
efficiency=False,
ymin=None, ymax=None,
yminsp=None, ymaxsp=None,
ymineff=None, ymaxeff=None ,std = True, alpha=0.2,
xticks=False,
xtickssp=None,
xtickseff=None,
title=None,
save=False, save_path=None):
df = DataWrangling(file)
if mode != None:
df = DataWrangling(df.get_data_by_level('mode', mode))
df.remove_columns(['iteration'])
if yaxis == 'time':
df.remove_columns(['GFLOPS'])
df.switch_to_averaged_data([yaxis])
elif yaxis == 'GFLOPS':
df.remove_columns(['time'])
df.switch_to_averaged_data([yaxis])
print(df.columns)
df.plot_y_vs_x_by_cat(yaxis, xaxis, categories, ymin, ymax, std, alpha, xticks, title, save, save_path)
if speedup:
if save:
save_pathsp = save_path + '_speedup'
df.plot_speedup(yaxis, xaxis, categories, yminsp, ymaxsp, xtickssp, title, save, save_pathsp)
if efficiency:
if save:
save_pathef = save_path + '_efficiency'
df.plot_efficiency(yaxis, xaxis, categories, ymineff, ymaxeff, xtickseff, title, save, save_pathef)