Python pylab.yticks方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def plot_pr_curve(pr_curve_dml, pr_curve_base, title):
    """
      Function that plots the PR-curve.

      Args:
        pr_curve: the values of precision for each recall value
        title: the title of the plot
    """
    plt.figure(figsize=(16, 9))
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_base, color='r', marker='o', linewidth=3, markersize=10)
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_dml, color='b', marker='o', linewidth=3, markersize=10)
    plt.grid(True, linestyle='dotted')
    plt.xlabel('Recall', color='k', fontsize=27)
    plt.ylabel('Precision', color='k', fontsize=27)
    plt.yticks(color='k', fontsize=20)
    plt.xticks(color='k', fontsize=20)
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title(title, color='k', fontsize=27)
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def dispersion_plot(text, words, ignore_case=False, title="Lexical Dispersion Plot"):
    """
    Generate a lexical dispersion plot.

    :param text: The source text
    :type text: list(str) or enum(str)
    :param words: The target words
    :type words: list of str
    :param ignore_case: flag to set if case should be ignored when searching text
    :type ignore_case: bool
    """

    try:
        from matplotlib import pylab
    except ImportError:
        raise ValueError('The plot function requires matplotlib to be installed.'
                     'See http://matplotlib.org/')

    text = list(text)
    words.reverse()

    if ignore_case:
        words_to_comp = list(map(str.lower, words))
        text_to_comp = list(map(str.lower, text))
    else:
        words_to_comp = words
        text_to_comp = text

    points = [(x,y) for x in range(len(text_to_comp))
                    for y in range(len(words_to_comp))
                    if text_to_comp[x] == words_to_comp[y]]
    if points:
        x, y = list(zip(*points))
    else:
        x = y = ()
    pylab.plot(x, y, "b|", scalex=.1)
    pylab.yticks(list(range(len(words))), words, color="b")
    pylab.ylim(-1, len(words))
    pylab.title(title)
    pylab.xlabel("Word Offset")
    pylab.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def malt_demo(nx=False):
    """
    A demonstration of the result of reading a dependency
    version of the first sentence of the Penn Treebank.
    """
    dg = DependencyGraph("""Pierre  NNP     2       NMOD
Vinken  NNP     8       SUB
,       ,       2       P
61      CD      5       NMOD
years   NNS     6       AMOD
old     JJ      2       NMOD
,       ,       2       P
will    MD      0       ROOT
join    VB      8       VC
the     DT      11      NMOD
board   NN      9       OBJ
as      IN      9       VMOD
a       DT      15      NMOD
nonexecutive    JJ      15      NMOD
director        NN      12      PMOD
Nov.    NNP     9       VMOD
29      CD      16      NMOD
.       .       9       VMOD
""")
    tree = dg.tree()
    tree.pprint()
    if nx:
        # currently doesn't work
        import networkx
        from matplotlib import pylab

        g = dg.nx_graph()
        g.info()
        pos = networkx.spring_layout(g, dim=1)
        networkx.draw_networkx_nodes(g, pos, node_size=50)
        # networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
        networkx.draw_networkx_labels(g, pos, dg.nx_labels)
        pylab.xticks([])
        pylab.yticks([])
        pylab.savefig('tree.png')
        pylab.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def imshow_(x, **kwargs):
    if x.ndim == 2:
        plt.imshow(x, interpolation="nearest", **kwargs)
    elif x.ndim == 1:
        plt.imshow(x[:, None].T, interpolation="nearest", **kwargs)
        plt.yticks([])
    plt.axis("tight")


# ------------- Data ------------- 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def plot1D_mat(a, b, M, title=''):
    """ Plot matrix M  with the source and target 1D distribution

    Creates a subplot with the source distribution a on the left and
    target distribution b on the tot. The matrix M is shown in between.


    Parameters
    ----------
    a : ndarray, shape (na,)
        Source distribution
    b : ndarray, shape (nb,)
        Target distribution
    M : ndarray, shape (na, nb)
        Matrix to plot
    """
    na, nb = M.shape

    gs = gridspec.GridSpec(3, 3)

    xa = np.arange(na)
    xb = np.arange(nb)

    ax1 = pl.subplot(gs[0, 1:])
    pl.plot(xb, b, 'r', label='Target distribution')
    pl.yticks(())
    pl.title(title)

    ax2 = pl.subplot(gs[1:, 0])
    pl.plot(a, xa, 'b', label='Source distribution')
    pl.gca().invert_xaxis()
    pl.gca().invert_yaxis()
    pl.xticks(())

    pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2)
    pl.imshow(M, interpolation='nearest')
    pl.axis('off')

    pl.xlim((0, nb))
    pl.tight_layout()
    pl.subplots_adjust(wspace=0., hspace=0.2) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def imshow_(x, **kwargs):
	if x.ndim == 2:
		plt.imshow(x, interpolation="nearest", **kwargs)
	elif x.ndim == 1:
		plt.imshow(x[:,None].T, interpolation="nearest", **kwargs)
		plt.yticks([])
	plt.axis("tight")

# ------------- Data ------------- 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def plot_confusion_matrix(y_true, y_pred, classes, figure_size=(8, 8)):
    """This function plots a confusion matrix."""
    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100

    # Build Laussen Labs colormap
    cmap = LinearSegmentedColormap.from_list('laussen_labs_green', ['w', '#43BB9B'], N=256)

    # Setup plot
    plt.figure(figsize=figure_size)

    # Plot confusion matrix
    plt.imshow(cm, interpolation='nearest', cmap=cmap)

    # Modify axes
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)
    thresh = cm.max() / 1.5
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, str(np.round(cm[i, j], 2)) + ' %', horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black", fontsize=20)
    plt.xticks(fontsize=16)
    plt.yticks(fontsize=16)
    plt.tight_layout()
    plt.ylabel('True Label', fontsize=25)
    plt.xlabel('Predicted Label', fontsize=25)

    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def interval_plot(label_id, labels, path, dataset):
    """Plot measure vs time."""
    # Label lookup
    label_lookup = {0: 'N', 1: 'A', 2: 'O', 3: '~'}

    # File name
    file_name = list(labels.keys())[label_id]

    # Get label
    label = labels[file_name]

    # Load data
    data = np.load(os.path.join(path, dataset, 'waveforms', file_name + '.npy'))

    # Time array
    time = np.arange(data.shape[0]) * 1 / 300

    # Setup figure
    fig = plt.figure(figsize=(15, 5), facecolor='w')
    fig.subplots_adjust(wspace=0, hspace=0.05)
    ax1 = plt.subplot2grid((1, 1), (0, 0))

    # ECG
    ax1.set_title('Dataset: {}\nFile Name: {}\nLabel: {}'.format(dataset, file_name, label_lookup[label]), fontsize=20)
    ax1.plot(time, data, '-k', lw=2)

    # Axes labels
    ax1.set_xlabel('Time, seconds', fontsize=20)
    ax1.set_ylabel('ECG', fontsize=20)
    ax1.set_xlim([time.min(), time.max()])
    plt.yticks(fontsize=12)

    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def plotCovMatFromHModel(hmodel, compListToPlot=None, compsToHighlight=None, wTHR=0.001):
  ''' Plot cov matrix visualization for each "significant" component in hmodel
      Args
      -------
      hmodel : bnpy HModel object
      compListToPlot : array-like of integer IDs of components within hmodel
      compsToHighlight : int or array-like of integer IDs to highlight
                          if None, all components get unique colors
                          if not None, only highlighted components get colors.
      wTHR : float threshold on minimum weight assigned to comp tobe "plottable"      
  '''
  if compsToHighlight is not None:
    compsToHighlight = np.asarray(compsToHighlight)
    if compsToHighlight.ndim == 0:
      compsToHighlight = np.asarray([compsToHighlight])
  else:
    compsToHighlight = list()  
  if compListToPlot is None:
    compListToPlot = np.arange(0, hmodel.allocModel.K)
  try:
    w = np.exp(hmodel.allocModel.Elogw)
  except Exception:
    w = hmodel.allocModel.w

  nRow = 2
  nCol = np.ceil(hmodel.obsModel.K/2.0)

  colorID = 0
  for plotID, kk in enumerate(compListToPlot):
    if w[kk] < wTHR and kk not in compsToHighlight:
      Sigma = getEmptyCompSigmaImage(hmodel.obsModel.D)
      clim = [0, 1]
    else:
      Sigma = hmodel.obsModel.get_covar_mat_for_comp(kk)
      clim = [-.25, 1]
    pylab.subplot(nRow, nCol, plotID)
    pylab.imshow(Sigma, interpolation='nearest', cmap='hot', clim=clim)
    pylab.xticks([])
    pylab.yticks([])
    pylab.xlabel('%.2f' % (w[kk]))
    if kk in compsToHighlight:
      pylab.xlabel('***') 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def plotBarsFromHModel(hmodel, Data=None, doShowNow=True, figH=None,
                       compsToHighlight=None, sortBySize=False,
                       width=12, height=3, Ktop=None):
    if Data is None:
        width = width/2
    if figH is None:
      figH = pylab.figure(figsize=(width,height))
    else:
      pylab.axes(figH)
    K = hmodel.allocModel.K
    VocabSize = hmodel.obsModel.comp[0].lamvec.size
    learned_tw = np.zeros( (K, VocabSize) )
    for k in xrange(K):
        lamvec = hmodel.obsModel.comp[k].lamvec 
        learned_tw[k,:] = lamvec / lamvec.sum()
    if sortBySize:
        sortIDs = np.argsort(hmodel.allocModel.Ebeta[:-1])[::-1]
        sortIDs = sortIDs[:Ktop]
        learned_tw = learned_tw[sortIDs] 
    if Data is not None and hasattr(Data, "true_tw"):
        # Plot the true parameters and learned parameters
        pylab.subplot(121)
        pylab.imshow(Data.true_tw, **imshowArgs)
        pylab.colorbar()
        pylab.title('True Topic x Word')
        pylab.subplot(122)
        pylab.imshow(learned_tw,  **imshowArgs)
        pylab.title('Learned Topic x Word')
    else:
        # Plot just the learned parameters
        aspectR = learned_tw.shape[1]/learned_tw.shape[0]
        while imshowArgs['vmax'] > 2 * np.percentile(learned_tw, 97):
          imshowArgs['vmax'] /= 5
        pylab.imshow(learned_tw, aspect=aspectR, **imshowArgs)

    if compsToHighlight is not None:
        ks = np.asarray(compsToHighlight)
        if ks.ndim == 0:
          ks = np.asarray([ks])
        pylab.yticks( ks, ['**** %d' % (k) for k in ks])
    if doShowNow and figH is None:
      pylab.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def dispersion_plot(text, words, ignore_case=False, title="Lexical Dispersion Plot"):
    """
    Generate a lexical dispersion plot.

    :param text: The source text
    :type text: list(str) or enum(str)
    :param words: The target words
    :type words: list of str
    :param ignore_case: flag to set if case should be ignored when searching text
    :type ignore_case: bool
    """

    try:
        from matplotlib import pylab
    except ImportError:
        raise ValueError(
            'The plot function requires matplotlib to be installed.'
            'See http://matplotlib.org/'
        )

    text = list(text)
    words.reverse()

    if ignore_case:
        words_to_comp = list(map(str.lower, words))
        text_to_comp = list(map(str.lower, text))
    else:
        words_to_comp = words
        text_to_comp = text

    points = [
        (x, y)
        for x in range(len(text_to_comp))
        for y in range(len(words_to_comp))
        if text_to_comp[x] == words_to_comp[y]
    ]
    if points:
        x, y = list(zip(*points))
    else:
        x = y = ()
    pylab.plot(x, y, "b|", scalex=0.1)
    pylab.yticks(list(range(len(words))), words, color="b")
    pylab.ylim(-1, len(words))
    pylab.title(title)
    pylab.xlabel("Word Offset")
    pylab.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import yticks [as 别名]
def malt_demo(nx=False):
    """
    A demonstration of the result of reading a dependency
    version of the first sentence of the Penn Treebank.
    """
    dg = DependencyGraph(
        """Pierre  NNP     2       NMOD
Vinken  NNP     8       SUB
,       ,       2       P
61      CD      5       NMOD
years   NNS     6       AMOD
old     JJ      2       NMOD
,       ,       2       P
will    MD      0       ROOT
join    VB      8       VC
the     DT      11      NMOD
board   NN      9       OBJ
as      IN      9       VMOD
a       DT      15      NMOD
nonexecutive    JJ      15      NMOD
director        NN      12      PMOD
Nov.    NNP     9       VMOD
29      CD      16      NMOD
.       .       9       VMOD
"""
    )
    tree = dg.tree()
    tree.pprint()
    if nx:
        # currently doesn't work
        import networkx
        from matplotlib import pylab

        g = dg.nx_graph()
        g.info()
        pos = networkx.spring_layout(g, dim=1)
        networkx.draw_networkx_nodes(g, pos, node_size=50)
        # networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
        networkx.draw_networkx_labels(g, pos, dg.nx_labels)
        pylab.xticks([])
        pylab.yticks([])
        pylab.savefig('tree.png')
        pylab.show()