metric-visualizer 0.3.12

No project description provided

MetricVisualizer - for easy managing performance metric

Install

pip install metric_visualizer

Usage

If you need to run trial experiments, you can use this tool to make simple plots then fix it manually.

from metric_visualizer import MetricVisualizer
import numpy as np

MV = MetricVisualizer()

trial_num = 10  # number of different trials, the trial in this repo means controlled experiments
repeat = 10  # number of repeats
metric_num = 3  # number of metrics

for trial in range(trial_num):
    for r in range(repeat):
        t = 0  # metric scale factor        # repeat the experiments to plot violin or box figure
        metrics = [(np.random.random()+n) * 100 for n in range(metric_num)]
        for i, m in enumerate(metrics):
            MV.add_metric('Metric-{}'.format(i + 1), round(m, 2))
    MV.next_trial()

MV.summary(save_path=None)  # plot fig via matplotlib
MV.traj_plot(save_path=None)  # plot fig via matplotlib
MV.violin_plot(save_path=None)  # plot fig via matplotlib
MV.box_plot(save_path=None)  # plot fig via matplotlib
MV.avg_bar_plot(save_path=None)  # plot fig via matplotlib
MV.sum_bar_plot(save_path=None)  # plot fig via matplotlib

save_path = 'example'
MV.traj_plot(save_path=save_path)  #  save fig into tikz and .pdf format
MV.violin_plot(save_path=save_path)  # save fig into tikz and .pdf format
MV.box_plot(save_path=save_path)  # save fig into tikz and .pdf format
MV.avg_bar_plot(save_path=save_path)  # save fig into tikz and .pdf format
MV.sum_bar_plot(save_path=save_path)  # save fig into tikz and .pdf format

 -------------------- Metric Summary --------------------
╒══════════╤═════════╤══════════════════════════════════════════════════════════════╤═════════════════════════════════════════════════════════════╕
│ Metric   │ Trail   │ Values                                                       │ Summary                                                     │
╞══════════╪═════════╪══════════════════════════════════════════════════════════════╪═════════════════════════════════════════════════════════════╡
│ Metric-1 │ trial-0 │ [0.35, 0.65, 0.67, 0.51, 0.04, 0.43, 0.46, 0.58, 0.11, 0.66] │ ['Avg:0.45, Median: 0.48, IQR: 0.22, Max: 0.67, Min: 0.04'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-1 │ trial-1 │ [0.52, 0.1, 0.11, 0.86, 0.49, 0.7, 0.77, 0.96, 0.16, 0.65]   │ ['Avg:0.53, Median: 0.58, IQR: 0.41, Max: 0.96, Min: 0.1']  │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-1 │ trial-2 │ [0.73, 0.99, 0.13, 0.72, 0.63, 0.61, 0.14, 0.85, 0.71, 0.86] │ ['Avg:0.64, Median: 0.72, IQR: 0.17, Max: 0.99, Min: 0.13'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-1 │ trial-3 │ [0.99, 0.69, 0.86, 0.2, 0.4, 0.1, 0.05, 0.07, 0.95, 0.31]    │ ['Avg:0.46, Median: 0.36, IQR: 0.62, Max: 0.99, Min: 0.05'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-1 │ trial-4 │ [0.58, 0.95, 0.73, 0.63, 0.04, 0.19, 0.5, 0.9, 0.64, 0.89]   │ ['Avg:0.6, Median: 0.64, IQR: 0.27, Max: 0.95, Min: 0.04']  │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-2 │ trial-0 │ [1.58, 1.32, 1.98, 1.76, 1.31, 1.6, 1.6, 1.22, 1.3, 1.19]    │ ['Avg:1.49, Median: 1.45, IQR: 0.29, Max: 1.98, Min: 1.19'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-2 │ trial-1 │ [1.88, 1.67, 1.77, 1.94, 1.01, 1.6, 1.25, 1.63, 1.62, 1.91]  │ ['Avg:1.63, Median: 1.65, IQR: 0.21, Max: 1.94, Min: 1.01'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-2 │ trial-2 │ [1.4, 1.94, 1.28, 1.78, 1.01, 1.08, 1.21, 1.82, 1.78, 1.18]  │ ['Avg:1.45, Median: 1.34, IQR: 0.59, Max: 1.94, Min: 1.01'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-2 │ trial-3 │ [1.79, 1.35, 1.14, 1.5, 1.73, 1.06, 1.98, 1.75, 1.07, 1.49]  │ ['Avg:1.49, Median: 1.5, IQR: 0.49, Max: 1.98, Min: 1.06']  │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-2 │ trial-4 │ [1.93, 1.81, 1.18, 1.08, 1.57, 1.85, 1.95, 1.94, 1.58, 1.35] │ ['Avg:1.62, Median: 1.7, IQR: 0.43, Max: 1.95, Min: 1.08']  │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-3 │ trial-0 │ [2.85, 2.87, 2.3, 2.05, 2.86, 2.34, 2.85, 2.3, 2.95, 2.53]   │ ['Avg:2.59, Median: 2.69, IQR: 0.54, Max: 2.95, Min: 2.05'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-3 │ trial-1 │ [2.31, 2.41, 2.34, 2.96, 2.48, 2.68, 2.99, 2.94, 2.01, 2.46] │ ['Avg:2.56, Median: 2.47, IQR: 0.44, Max: 2.99, Min: 2.01'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-3 │ trial-2 │ [2.65, 2.5, 2.68, 2.34, 2.32, 2.61, 2.61, 2.88, 2.86, 2.36]  │ ['Avg:2.58, Median: 2.61, IQR: 0.24, Max: 2.88, Min: 2.32'] │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-3 │ trial-3 │ [2.29, 2.12, 2.4, 2.81, 2.5, 2.54, 2.82, 2.61, 2.45, 2.44]   │ ['Avg:2.5, Median: 2.48, IQR: 0.16, Max: 2.82, Min: 2.12']  │
├──────────┼─────────┼──────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────┤
│ Metric-3 │ trial-4 │ [2.41, 2.12, 2.31, 2.29, 2.46, 2.95, 2.74, 2.66, 2.34, 2.65] │ ['Avg:2.49, Median: 2.44, IQR: 0.33, Max: 2.95, Min: 2.12'] │
╘══════════╧═════════╧══════════════════════════════════════════════════════════════╧═════════════════════════════════════════════════════════════╛
 -------------------- Metric Summary --------------------

Plot via Matplotlib (or Tikz)

Traj Plot tikz version

Box Plot tikz version

Violin Plot tikz version

Average Bar Plot tikz version

Sum Bar Plot tikz version

Real Usage Example in PyABSA

To analyze the impact of max_seq_len, we can use MetricVisualizer as following:

pip install pyabsa  # install pyabsa

import autocuda
import random

from metric_visualizer import MetricVisualizer

from pyabsa.functional import Trainer
from pyabsa.functional import APCConfigManager
from pyabsa.functional import ABSADatasetList
from pyabsa.functional import APCModelList

import warnings

device = autocuda.auto_cuda()
warnings.filterwarnings('ignore')

seeds = [random.randint(0, 10000) for _ in range(3)]

max_seq_lens = [60, 70, 80, 90, 100]

apc_config_english = APCConfigManager.get_apc_config_english()
apc_config_english.model = APCModelList.FAST_LCF_BERT
apc_config_english.lcf = 'cdw'
apc_config_english.max_seq_len = 80
apc_config_english.cache_dataset = False
apc_config_english.patience = 10
apc_config_english.seed = seeds

MV = MetricVisualizer()
apc_config_english.MV = MV

for eta in max_seq_lens:
    apc_config_english.eta = eta
    dataset = ABSADatasetList.Laptop14
    Trainer(config=apc_config_english,
            dataset=dataset,  # train set and test set will be automatically detected
            checkpoint_save_mode=0,  # =None to avoid save model
            auto_device=device  # automatic choose CUDA or CPU
            )
    apc_config_english.MV.next_trial()

apc_config_english.MV.summary(save_path=None, xticks=max_seq_lens)
apc_config_english.MV.traj_plot(save_path=None, xticks=max_seq_lens)
apc_config_english.MV.violin_plot(save_path=None, xticks=max_seq_lens)
apc_config_english.MV.box_plot(save_path=None, xticks=max_seq_lens)

save_path = '{}_{}'.format(apc_config_english.model_name, apc_config_english.dataset_name)
apc_config_english.MV.summary(save_path=save_path)
apc_config_english.MV.traj_plot(save_path=save_path, xticks=max_seq_lens, xlabel=r'$\eta$')
apc_config_english.MV.violin_plot(save_path=save_path, xticks=max_seq_lens, xlabel=r'$\eta$')
apc_config_english.MV.box_plot(save_path=save_path, xticks=max_seq_lens, xlabel=r'$\eta$')