<reponame>fostroll/junky
# -*- coding: utf-8 -*-
# junky lib: plotter
#
# Copyright (C) 2020-present by <NAME>, <NAME>
# License: BSD, see LICENSE for details
"""
Provides a bunch of tools to visualize train results.
"""
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix

import matplotlib as mpl
import matplotlib.style
from matplotlib import pyplot as plt
import seaborn as sns


def plot_losses(train_losses=None, test_losses=None, accuracies=None,
                plot_title='Train/Dev Loss', figsize=(7, 4), legend_loc='best',
                legend_labels=['train', 'dev', '1 - acc'], save_name=None):
    """Plots train and dev losses obtained during training.
    The plot image is saved to disk.
    
    Args:
    
      train_losses:   list of floats, train losses throughout epochs.
                      If `None`, not plotted.
      test_losses:    list of floats, test losses throughout epochs.
                      If `None`, not plotted.
      accuracies:     optional, list of floats, accuracies throughout epochs.
                      Here, used to count `(1 - accuracy)`.
                      If `None`, not plotted.
      plot_title:     plot title, `str`. Default value - 'Train/Dev Loss'.
      figsize:        the size of the figure plotted. Default size is `(5,3)`.
      legend_loc:     location of the legend according to matplotlib.
                      Default: 'best'. If invalid `legend_loc` is entered,
                      matplotlib will place the legend in the `best` location and
                      show a warning with all possible valid loc definitions.
      legend_labels:  set legend labels. Default: `['train', 'dev', '1 - acc']`
      save_name:      optional, if `None`, plot is not saved.
                      Used as `fname` in `plt.savefig()`.
                      Default file extention is '.png', if other extention is needed,
                      please specify extention in save_name as well.
                      Example: save_name='plot.pdf'
    """
    mpl.style.use('default')
    plt.figure(figsize=figsize)

    if train_losses:
        plt.plot([None] + train_losses)
    if test_losses:
        plt.plot([None] + test_losses)
    if accuracies is not None:
        plt.plot([None] + [1 - x for x in accuracies])
    plt.title(plot_title)
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend(legend_labels, loc=legend_loc)
    plt.grid()
    if save_name is not None:
        plt.savefig(save_name, bbox_inches='tight')
    plt.show()

def plot_confusion_matrix(y_true, y_pred,
                          pad_index=None, ymap=None, figsize=(20, 10),
                          show_total=['x', 'y'], show_zeros=True, show_empty_tags=False,
                          plot_title='Confusion Matrix', save_name=None):
    """
    Generate matrix plot of confusion matrix with pretty annotations.
    The plot image is saved to disk.
    
    Args:
    
      y_true:           true labels of the data, with shape (nsamples,)

      y_pred:           label predictions, with shape (nsamples,)

      pad_index:        if not `None` and not present in `y_pred`, `pad_index`
                        will not be included in the plot.

      ymap:             dict: index -> tag.
                        if not `None`, map the predictions to their categorical labels.
                        if `None`, `range(1, len(set(y_true+y_pred))`
                        is used for labels.

      figsize:          tuple: the size of the figure plotted.

      show_total:       list of `str`. Where to display total number of
                        class occurrences in the corpus: diagonal and/or axes.
                        Up to all from `['diag', 'x', 'y']` can be chosen.
                        Default = `['x', 'y']` (horizontal and vertical axes respectively).
                        If `None`, total values are not displayed on the plot.

      show_zeros:       bool: whether to show zeros in the confusion matrix.

      show_empty_tags:  only active when when `ymap` is specified.
                        If `True`, all tags, including those that weren't met
                        neither in `y_true` or `y_pred` are displayed
                        (filled with `0` in both axes).
                        NB! If `True`, `pad_idx` will also be displayed even if specified.
                        Default: `False`, 'empty' tags are skipped.

      plot_title:       str: plot title, default title - 'Confusion Matrix'.

      save_name:        str: filename of figure file to save.
                        if `None`, image is not saved to disk.
    """
    # if pad_index is not None and does not exist in y_pred, it's excluded
    # from labels
    mpl.style.use('default')

    cm = confusion_matrix(y_true, y_pred)

    if ymap:
        if show_empty_tags:
            for i in ymap.keys():
                if i not in set(y_true+y_pred):
                    cm = np.insert(cm, i, 0, axis=0)
                    cm = np.insert(cm, i, 0, axis=1)
            labels = ymap.values()

        if not show_empty_tags:

            labels = [ymap[i] if pad_index is not None and i != pad_index
                                         and pad_index not in y_pred else
                      ymap[i]
                      for i in set(y_true+y_pred)]

    else:
        labels = [i if pad_index is not None and i != pad_index
                                         and pad_index not in y_pred else
              i
              for i in set(y_true+y_pred)]

    cm_sum = np.sum(cm, axis=1, keepdims=True)
    cm_perc = np.divide(cm, cm_sum.astype(float), where=cm_sum!=0)*100


    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(nrows):
        for j in range(ncols):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                if show_total and 'diag' in show_total:
                    s = cm_sum[i]
                    annot[i, j] = '%.2f%%\n%d/%d' % (p, c, s)
                else:
                    annot[i, j] = '%.2f%%\n%d' % (p, c)
            elif c == 0:
                if show_zeros:
                    annot[i, j] = '0'
                else:
                    annot[i, j] = ''
            else:
                annot[i, j] = '%.2f%%\n%d' % (p, c)

    total_labels = [str(i)+'\n'+str(n[0]) for i, n in zip(labels, cm_sum)]

    cm = pd.DataFrame(cm,
                      index=total_labels if show_total and 'y' in show_total else labels,
                      columns=total_labels if show_total and 'x' in show_total else labels)
    cm.index.name = 'Actual'
    cm.columns.name = 'Predicted'

    fig, ax = plt.subplots(figsize=figsize)
    sns.heatmap(cm, annot=annot, fmt='', ax=ax)
    plt.title(plot_title)

    if save_name:
        plt.savefig(save_name, bbox_inches='tight')

    plt.show()

def plot_metrics(metrics=[], legend_loc='best',
                 labels=['accuracy', 'precision', 'recalls', 'f1_score'],
                 plot_title='Metrics', figsize=(7, 4), save_name=None):
    """Plots metrics obtained during training.
    Default: ['accuracy', 'precision', 'recalls', 'f1_score'].
    The plot image is saved to disk.
    
    Args:
    
      metrics:        tuple or list of metrics, where each metric is
                      a list of floats, len(metric)==num_epochs.
      legend_loc:     location of the legend according to matplotlib.
                      Default: 'best'. If invalid `legend_loc` is entered,
                      matplotlib will place the legend in the `best` location and
                      show a warning with all possible valid loc definitions.
      labels:         list of str, labels for metrics plotted.
      figsize:        tuple: the size of the figure plotted.
      plot_title:     str: plot title, default title - 'Metrics'.
      save_name:      str: filename of figure file to save.
                      if `None`, image is not saved to disk.
    """
    mpl.style.use('default')
    plt.figure(figsize=figsize)
    for metric in metrics:
        plt.plot([None] + metric)
    plt.grid()
    plt.title(plot_title)
    plt.xlabel('Epochs')
    plt.ylabel('Score')
    plt.legend(labels, loc=legend_loc)
    if save_name is not None:
        plt.savefig(save_name, bbox_inches='tight')
    plt.show()

def plot_seqlens(corpus, bins=20, figsize=(7, 2), **plot_kwargs):
    """Plots histogram of sentence lengths from the corpus.

    Args:

    **corpus** (`list([list([str])])`): pretokenized corpus.

    **figsize** (`tuple(x, y)`): figure size.

    **\*\*plot_kwargs**: any other `pandas.DataFrame.hist` 
    keyword arguments.
    """
    mpl.style.use('default')
    lens = [len(x) for x in corpus]
    _ = pd.Series(lens).hist(bins=bins, figsize=figsize, **plot_kwargs)
    print('Minimum sequence length:', min(lens))
    print('Maximum sequence length:', max(lens))

# -*- coding: utf-8 -*-
# junky lib: plotter
#
# Copyright (C) 2020-present by <NAME>, <NAME>
# License: BSD, see LICENSE for details
"""
Provides a bunch of tools to visualize train results.
"""
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix

import matplotlib as mpl
import matplotlib.style
from matplotlib import pyplot as plt
import seaborn as sns


def plot_losses(train_losses=None, test_losses=None, accuracies=None,
                plot_title='Train/Dev Loss', figsize=(7, 4), legend_loc='best',
                legend_labels=['train', 'dev', '1 - acc'], save_name=None):
    """Plots train and dev losses obtained during training.
    The plot image is saved to disk.
    
    Args:
    
      train_losses:   list of floats, train losses throughout epochs.
                      If `None`, not plotted.
      test_losses:    list of floats, test losses throughout epochs.
                      If `None`, not plotted.
      accuracies:     optional, list of floats, accuracies throughout epochs.
                      Here, used to count `(1 - accuracy)`.
                      If `None`, not plotted.
      plot_title:     plot title, `str`. Default value - 'Train/Dev Loss'.
      figsize:        the size of the figure plotted. Default size is `(5,3)`.
      legend_loc:     location of the legend according to matplotlib.
                      Default: 'best'. If invalid `legend_loc` is entered,
                      matplotlib will place the legend in the `best` location and
                      show a warning with all possible valid loc definitions.
      legend_labels:  set legend labels. Default: `['train', 'dev', '1 - acc']`
      save_name:      optional, if `None`, plot is not saved.
                      Used as `fname` in `plt.savefig()`.
                      Default file extention is '.png', if other extention is needed,
                      please specify extention in save_name as well.
                      Example: save_name='plot.pdf'
    """
    mpl.style.use('default')
    plt.figure(figsize=figsize)

    if train_losses:
        plt.plot([None] + train_losses)
    if test_losses:
        plt.plot([None] + test_losses)
    if accuracies is not None:
        plt.plot([None] + [1 - x for x in accuracies])
    plt.title(plot_title)
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend(legend_labels, loc=legend_loc)
    plt.grid()
    if save_name is not None:
        plt.savefig(save_name, bbox_inches='tight')
    plt.show()

def plot_confusion_matrix(y_true, y_pred,
                          pad_index=None, ymap=None, figsize=(20, 10),
                          show_total=['x', 'y'], show_zeros=True, show_empty_tags=False,
                          plot_title='Confusion Matrix', save_name=None):
    """
    Generate matrix plot of confusion matrix with pretty annotations.
    The plot image is saved to disk.
    
    Args:
    
      y_true:           true labels of the data, with shape (nsamples,)

      y_pred:           label predictions, with shape (nsamples,)

      pad_index:        if not `None` and not present in `y_pred`, `pad_index`
                        will not be included in the plot.

      ymap:             dict: index -> tag.
                        if not `None`, map the predictions to their categorical labels.
                        if `None`, `range(1, len(set(y_true+y_pred))`
                        is used for labels.

      figsize:          tuple: the size of the figure plotted.

      show_total:       list of `str`. Where to display total number of
                        class occurrences in the corpus: diagonal and/or axes.
                        Up to all from `['diag', 'x', 'y']` can be chosen.
                        Default = `['x', 'y']` (horizontal and vertical axes respectively).
                        If `None`, total values are not displayed on the plot.

      show_zeros:       bool: whether to show zeros in the confusion matrix.

      show_empty_tags:  only active when when `ymap` is specified.
                        If `True`, all tags, including those that weren't met
                        neither in `y_true` or `y_pred` are displayed
                        (filled with `0` in both axes).
                        NB! If `True`, `pad_idx` will also be displayed even if specified.
                        Default: `False`, 'empty' tags are skipped.

      plot_title:       str: plot title, default title - 'Confusion Matrix'.

      save_name:        str: filename of figure file to save.
                        if `None`, image is not saved to disk.
    """
    # if pad_index is not None and does not exist in y_pred, it's excluded
    # from labels
    mpl.style.use('default')

    cm = confusion_matrix(y_true, y_pred)

    if ymap:
        if show_empty_tags:
            for i in ymap.keys():
                if i not in set(y_true+y_pred):
                    cm = np.insert(cm, i, 0, axis=0)
                    cm = np.insert(cm, i, 0, axis=1)
            labels = ymap.values()

        if not show_empty_tags:

            labels = [ymap[i] if pad_index is not None and i != pad_index
                                         and pad_index not in y_pred else
                      ymap[i]
                      for i in set(y_true+y_pred)]

    else:
        labels = [i if pad_index is not None and i != pad_index
                                         and pad_index not in y_pred else
              i
              for i in set(y_true+y_pred)]

    cm_sum = np.sum(cm, axis=1, keepdims=True)
    cm_perc = np.divide(cm, cm_sum.astype(float), where=cm_sum!=0)*100


    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(nrows):
        for j in range(ncols):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                if show_total and 'diag' in show_total:
                    s = cm_sum[i]
                    annot[i, j] = '%.2f%%\n%d/%d' % (p, c, s)
                else:
                    annot[i, j] = '%.2f%%\n%d' % (p, c)
            elif c == 0:
                if show_zeros:
                    annot[i, j] = '0'
                else:
                    annot[i, j] = ''
            else:
                annot[i, j] = '%.2f%%\n%d' % (p, c)

    total_labels = [str(i)+'\n'+str(n[0]) for i, n in zip(labels, cm_sum)]

    cm = pd.DataFrame(cm,
                      index=total_labels if show_total and 'y' in show_total else labels,
                      columns=total_labels if show_total and 'x' in show_total else labels)
    cm.index.name = 'Actual'
    cm.columns.name = 'Predicted'

    fig, ax = plt.subplots(figsize=figsize)
    sns.heatmap(cm, annot=annot, fmt='', ax=ax)
    plt.title(plot_title)

    if save_name:
        plt.savefig(save_name, bbox_inches='tight')

    plt.show()

def plot_metrics(metrics=[], legend_loc='best',
                 labels=['accuracy', 'precision', 'recalls', 'f1_score'],
                 plot_title='Metrics', figsize=(7, 4), save_name=None):
    """Plots metrics obtained during training.
    Default: ['accuracy', 'precision', 'recalls', 'f1_score'].
    The plot image is saved to disk.
    
    Args:
    
      metrics:        tuple or list of metrics, where each metric is
                      a list of floats, len(metric)==num_epochs.
      legend_loc:     location of the legend according to matplotlib.
                      Default: 'best'. If invalid `legend_loc` is entered,
                      matplotlib will place the legend in the `best` location and
                      show a warning with all possible valid loc definitions.
      labels:         list of str, labels for metrics plotted.
      figsize:        tuple: the size of the figure plotted.
      plot_title:     str: plot title, default title - 'Metrics'.
      save_name:      str: filename of figure file to save.
                      if `None`, image is not saved to disk.
    """
    mpl.style.use('default')
    plt.figure(figsize=figsize)
    for metric in metrics:
        plt.plot([None] + metric)
    plt.grid()
    plt.title(plot_title)
    plt.xlabel('Epochs')
    plt.ylabel('Score')
    plt.legend(labels, loc=legend_loc)
    if save_name is not None:
        plt.savefig(save_name, bbox_inches='tight')
    plt.show()

def plot_seqlens(corpus, bins=20, figsize=(7, 2), **plot_kwargs):
    """Plots histogram of sentence lengths from the corpus.

    Args:

    **corpus** (`list([list([str])])`): pretokenized corpus.

    **figsize** (`tuple(x, y)`): figure size.

    **\*\*plot_kwargs**: any other `pandas.DataFrame.hist` 
    keyword arguments.
    """
    mpl.style.use('default')
    lens = [len(x) for x in corpus]
    _ = pd.Series(lens).hist(bins=bins, figsize=figsize, **plot_kwargs)
    print('Minimum sequence length:', min(lens))
    print('Maximum sequence length:', max(lens))
# -*- coding: utf-8 -*- # junky lib: plotter # # Copyright (C) 2020-present by <NAME>, <NAME> # License: BSD, see LICENSE for details Provides a bunch of tools to visualize train results. Plots train and dev losses obtained during training.     The plot image is saved to disk.          Args:            train_losses:   list of floats, train losses throughout epochs.                       If `None`, not plotted.       test_losses:    list of floats, test losses throughout epochs.                       If `None`, not plotted.       accuracies:     optional, list of floats, accuracies throughout epochs.                       Here, used to count `(1 - accuracy)`.                       If `None`, not plotted.       plot_title:     plot title, `str`. Default value - 'Train/Dev Loss'.       figsize:        the size of the figure plotted. Default size is `(5,3)`.       legend_loc:     location of the legend according to matplotlib.                       Default: 'best'. If invalid `legend_loc` is entered,                       matplotlib will place the legend in the `best` location and                       show a warning with all possible valid loc definitions.       legend_labels:  set legend labels. Default: `['train', 'dev', '1 - acc']`       save_name:      optional, if `None`, plot is not saved.                       Used as `fname` in `plt.savefig()`.                       Default file extention is '.png', if other extention is needed,                       please specify extention in save_name as well.                       Example: save_name='plot.pdf' Generate matrix plot of confusion matrix with pretty annotations.     The plot image is saved to disk.          Args:            y_true:           true labels of the data, with shape (nsamples,)        y_pred:           label predictions, with shape (nsamples,)        pad_index:        if not `None` and not present in `y_pred`, `pad_index`                         will not be included in the plot.        ymap:             dict: index -> tag.                         if not `None`, map the predictions to their categorical labels.                         if `None`, `range(1, len(set(y_true+y_pred))`                         is used for labels.        figsize:          tuple: the size of the figure plotted.        show_total:       list of `str`. Where to display total number of                         class occurrences in the corpus: diagonal and/or axes.                         Up to all from `['diag', 'x', 'y']` can be chosen.                         Default = `['x', 'y']` (horizontal and vertical axes respectively).                         If `None`, total values are not displayed on the plot.        show_zeros:       bool: whether to show zeros in the confusion matrix.        show_empty_tags:  only active when when `ymap` is specified.                         If `True`, all tags, including those that weren't met                         neither in `y_true` or `y_pred` are displayed                         (filled with `0` in both axes).                         NB! If `True`, `pad_idx` will also be displayed even if specified.                         Default: `False`, 'empty' tags are skipped.        plot_title:       str: plot title, default title - 'Confusion Matrix'.        save_name:        str: filename of figure file to save.                         if `None`, image is not saved to disk. # if pad_index is not None and does not exist in y_pred, it's excluded # from labels Plots metrics obtained during training.     Default: ['accuracy', 'precision', 'recalls', 'f1_score'].     The plot image is saved to disk.          Args:            metrics:        tuple or list of metrics, where each metric is                       a list of floats, len(metric)==num_epochs.       legend_loc:     location of the legend according to matplotlib.                       Default: 'best'. If invalid `legend_loc` is entered,                       matplotlib will place the legend in the `best` location and                       show a warning with all possible valid loc definitions.       labels:         list of str, labels for metrics plotted.       figsize:        tuple: the size of the figure plotted.       plot_title:     str: plot title, default title - 'Metrics'.       save_name:      str: filename of figure file to save.                       if `None`, image is not saved to disk. Plots histogram of sentence lengths from the corpus.      Args:      **corpus** (`list([list([str])])`): pretokenized corpus.      **figsize** (`tuple(x, y)`): figure size.      **\*\*plot_kwargs**: any other `pandas.DataFrame.hist`      keyword arguments.