python词云（一）简单的英语词云

内容简介：python词云（一）简单的英语词云

python的一个蛮酷炫的功能是可以轻松地实现词云。

github上有关于这个项目的开源代码：

https://github.com/amueller/word_cloud

注意跑例程时要删除里面的wordcloud文件夹

词云的功能有部分是基于NLP，有部分是基于图像的，

下面以一段github wordcloud上面的代码为例

from os import path
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

from wordcloud import WordCloud, STOPWORDS

d = path.dirname(__file__)

# Read the whole text.
text = open(path.join(d, 'alice.txt')).read()

# read the mask image
# taken from
# http://www.stencilry.org/stencils/movies/alice%20in%20wonderland/255fk.jpg
alice_mask = np.array(Image.open(path.join(d, "alice_mask.png")))

stopwords = set(STOPWORDS)
stopwords.add("said")

wc = WordCloud(background_color="white", max_words=2000, mask=alice_mask,
               stopwords=stopwords)
# generate word cloud
wc.generate(text)

# store to file
wc.to_file(path.join(d, "alice.png"))

# show
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
plt.figure()
plt.imshow(alice_mask, cmap=plt.cm.gray, interpolation='bilinear')
plt.axis("off")
plt.show()

Alice与兔子的图片

其中：

text打开文档

alice_mask是以数组的形式加载图画

stopwords设置停止显示的词语

WordCloud设置词云的属性

generate生成词云

to_file储存图片

进入wordcloud.py可以看到WordCloud类的相关属性：

"""Word cloud object for generating and drawing.

    Parameters
    ----------
    font_path : string
        Font path to the font that will be used (OTF or TTF).
        Defaults to DroidSansMono path on a Linux machine. If you are on
        another OS or don't have this font, you need to adjust this path.

    width : int (default=400)
        Width of the canvas.

    height : int (default=200)
        Height of the canvas.

    prefer_horizontal : float (default=0.90)
        The ratio of times to try horizontal fitting as opposed to vertical.
        If prefer_horizontal < 1, the algorithm will try rotating the word
        if it doesn't fit. (There is currently no built-in way to get only vertical
        words.)

    mask : nd-array or None (default=None)
        If not None, gives a binary mask on where to draw words. If mask is not
        None, width and height will be ignored and the shape of mask will be
        used instead. All white (#FF or #FFFFFF) entries will be considerd
        "masked out" while other entries will be free to draw on. [This
        changed in the most recent version!]

    scale : float (default=1)
        Scaling between computation and drawing. For large word-cloud images,
        using scale instead of larger canvas size is significantly faster, but
        might lead to a coarser fit for the words.

    min_font_size : int (default=4)
        Smallest font size to use. Will stop when there is no more room in this
        size.

    font_step : int (default=1)
        Step size for the font. font_step > 1 might speed up computation but
        give a worse fit.

    max_words : number (default=200)
        The maximum number of words.

    stopwords : set of strings or None
        The words that will be eliminated. If None, the build-in STOPWORDS
        list will be used.

    background_color : color value (default="black")
        Background color for the word cloud image.

    max_font_size : int or None (default=None)
        Maximum font size for the largest word. If None, height of the image is
        used.

    mode : string (default="RGB")
        Transparent background will be generated when mode is "RGBA" and
        background_color is None.

    relative_scaling : float (default=.5)
        Importance of relative word frequencies for font-size.  With
        relative_scaling=0, only word-ranks are considered.  With
        relative_scaling=1, a word that is twice as frequent will have twice
        the size.  If you want to consider the word frequencies and not only
        their rank, relative_scaling around .5 often looks good.

        .. versionchanged: 2.0
            Default is now 0.5.

    color_func : callable, default=None
        Callable with parameters word, font_size, position, orientation,
        font_path, random_state that returns a PIL color for each word.
        Overwrites "colormap".
        See colormap for specifying a matplotlib colormap instead.

    regexp : string or None (optional)
        Regular expression to split the input text into tokens in process_text.
        If None is specified, ``r"\w[\w']+"`` is used.

    collocations : bool, default=True
        Whether to include collocations (bigrams) of two words.

        .. versionadded: 2.0

    colormap : string or matplotlib colormap, default="viridis"
        Matplotlib colormap to randomly draw colors from for each word.
        Ignored if "color_func" is specified.

        .. versionadded: 2.0

    normalize_plurals : bool, default=True
        Whether to remove trailing 's' from words. If True and a word
        appears with and without a trailing 's', the one with trailing 's'
        is removed and its counts are added to the version without
        trailing 's' -- unless the word ends with 'ss'.

    Attributes
    ----------
    ``words_`` : dict of string to float
        Word tokens with associated frequency.

        .. versionchanged: 2.0
            ``words_`` is now a dictionary

    ``layout_`` : list of tuples (string, int, (int, int), int, color))
        Encodes the fitted word cloud. Encodes for each word the string, font
        size, position, orientation and color.

    Notes
    -----
    Larger canvases with make the code significantly slower. If you need a
    large word cloud, try a lower canvas size, and set the scale parameter.

    The algorithm might give more weight to the ranking of the words
    than their actual frequencies, depending on the ``max_font_size`` and the
    scaling heuristic.
    """

其中：

font_path表示用到字体的路径

width和height表示画布的宽和高

prefer_horizontal可以调整词云中字体水平和垂直的多少

mask即掩膜，产生词云背景的区域

scale:计算和绘图之间的缩放

min_font_size设置最小的字体大小

max_words设置字体的多少

stopwords设置禁用词

background_color设置词云的背景颜色

max_font_size设置字体的最大尺寸

mode设置字体的颜色 但设置为RGBA时背景透明

relative_scaling设置有关字体大小的相对字频率的重要性

regexp设置正则表达式

collocations 是否包含两个词的搭配

在generate函数中调试进去可以看到函数：

words=process_text（text）可以返回文本中的词频

generate_from_frequencies根据单词和词频创造一个词云

下面是generate_from_frequencies函数的实现步骤

def generate_from_frequencies(self, frequencies, max_font_size=None):
        """Create a word_cloud from words and frequencies.

        Parameters
        ----------
        frequencies : dict from string to float
            A contains words and associated frequency.

        max_font_size : int
            Use this font-size instead of self.max_font_size

        Returns
        -------
        self

        """
        # make sure frequencies are sorted and normalized
        frequencies = sorted(frequencies.items(), key=item1, reverse=True)
        if len(frequencies) <= 0:
            raise ValueError("We need at least 1 word to plot a word cloud, "
                             "got %d." % len(frequencies))
        frequencies = frequencies[:self.max_words]

        # largest entry will be 1
        max_frequency = float(frequencies[0][1])

        frequencies = [(word, freq / max_frequency)
                       for word, freq in frequencies]

        if self.random_state is not None:
            random_state = self.random_state
        else:
            random_state = Random()

        if self.mask is not None:
            mask = self.mask
            width = mask.shape[1]
            height = mask.shape[0]
            if mask.dtype.kind == 'f':
                warnings.warn("mask image should be unsigned byte between 0"
                              " and 255. Got a float array")
            if mask.ndim == 2:
                boolean_mask = mask == 255
            elif mask.ndim == 3:
                # if all channels are white, mask out
                boolean_mask = np.all(mask[:, :, :3] == 255, axis=-1)
            else:
                raise ValueError("Got mask of invalid shape: %s"
                                 % str(mask.shape))
        else:
            boolean_mask = None
            height, width = self.height, self.width
        occupancy = IntegralOccupancyMap(height, width, boolean_mask)

        # create image
        img_grey = Image.new("L", (width, height))
        draw = ImageDraw.Draw(img_grey)
        img_array = np.asarray(img_grey)
        font_sizes, positions, orientations, colors = [], [], [], []

        last_freq = 1.

        if max_font_size is None:
            # if not provided use default font_size
            max_font_size = self.max_font_size

        if max_font_size is None:
            # figure out a good font size by trying to draw with
            # just the first two words
            if len(frequencies) == 1:
                # we only have one word. We make it big!
                font_size = self.height
            else:
                self.generate_from_frequencies(dict(frequencies[:2]),
                                               max_font_size=self.height)
                # find font sizes
                sizes = [x[1] for x in self.layout_]
                font_size = int(2 * sizes[0] * sizes[1] / (sizes[0] + sizes[1]))
        else:
            font_size = max_font_size

        # we set self.words_ here because we called generate_from_frequencies
        # above... hurray for good design?
        self.words_ = dict(frequencies)

        # start drawing grey image
        for word, freq in frequencies:
            # select the font size
            rs = self.relative_scaling
            if rs != 0:
                font_size = int(round((rs * (freq / float(last_freq))
                                       + (1 - rs)) * font_size))
            if random_state.random() < self.prefer_horizontal:
                orientation = None
            else:
                orientation = Image.ROTATE_90
            tried_other_orientation = False
            while True:
                # try to find a position
                font = ImageFont.truetype(self.font_path, font_size)
                # transpose font optionally
                transposed_font = ImageFont.TransposedFont(
                    font, orientation=orientation)
                # get size of resulting text
                box_size = draw.textsize(word, font=transposed_font)
                # find possible places using integral image:
                result = occupancy.sample_position(box_size[1] + self.margin,
                                                   box_size[0] + self.margin,
                                                   random_state)
                if result is not None or font_size < self.min_font_size:
                    # either we found a place or font-size went too small
                    break
                # if we didn't find a place, make font smaller
                # but first try to rotate!
                if not tried_other_orientation and self.prefer_horizontal < 1:
                    orientation = (Image.ROTATE_90 if orientation is None else
                                   Image.ROTATE_90)
                    tried_other_orientation = True
                else:
                    font_size -= self.font_step
                    orientation = None

            if font_size < self.min_font_size:
                # we were unable to draw any more
                break

            x, y = np.array(result) + self.margin // 2
            # actually draw the text
            draw.text((y, x), word, fill="white", font=transposed_font)
            positions.append((x, y))
            orientations.append(orientation)
            font_sizes.append(font_size)
            colors.append(self.color_func(word, font_size=font_size,
                                          position=(x, y),
                                          orientation=orientation,
                                          random_state=random_state,
                                          font_path=self.font_path))
            # recompute integral image
            if self.mask is None:
                img_array = np.asarray(img_grey)
            else:
                img_array = np.asarray(img_grey) + boolean_mask
            # recompute bottom right
            # the order of the cumsum's is important for speed ?!
            occupancy.update(img_array, x, y)
            last_freq = freq

        self.layout_ = list(zip(frequencies, font_sizes, positions,
                                orientations, colors))
        return self

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