Python matplotlib.pylab.bar() Examples

def plot_feat_importance(feature_names, clf, name):
    pylab.clf()
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(range(len(coef)))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight") 

def plot_feat_importance(feature_names, clf, name):
    pylab.clf()
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(range(len(coef)))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight") 

def plot_feat_importance(feature_names, clf, name):
    pylab.figure(num=None, figsize=(6, 5))
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(list(range(len(coef))))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight") 

def _plot_NWOE_bins(NWOE_dict, feats):
    """
    Plots the NWOE by bin for the subset of features interested in (form of list)

    Parameters
    ----------
    - NWOE_dict = dictionary output of `NWOE` function
    - feats = list of features to plot NWOE for

    Returns
    -------
    - plots of NWOE for each feature by bin
    """

    for feat in feats:
        fig, ax = _plot_defaults()
        feat_df = NWOE_dict[feat].reset_index()
        plt.bar(range(len(feat_df)), feat_df['NWOE'], tick_label=feat_df[str(feat)+'_bin'], color='k', alpha=0.5)
        plt.xticks(rotation='vertical')
        ax.set_title('NWOE by bin for '+str(feat))
        ax.set_xlabel('Bin Interval');
    return ax 

def _plot_NWOE_bins(NWOE_dict, feats):
    """
    Plots the NWOE by bin for the subset of features interested in (form of list)

    Parameters
    ----------
    - NWOE_dict = dictionary output of `NWOE` function
    - feats = list of features to plot NWOE for

    Returns
    -------
    - plots of NWOE for each feature by bin
    """

    for feat in feats:
        fig, ax = _plot_defaults()
        feat_df = NWOE_dict[feat].reset_index()
        plt.bar(range(len(feat_df)), feat_df['NWOE'], tick_label=feat_df[str(feat)+'_bin'], color='k', alpha=0.5)
        plt.xticks(rotation='vertical')
        ax.set_title('NWOE by bin for '+str(feat))
        ax.set_xlabel('Bin Interval');
    return ax 

def save_state_images(frame_idx, states, net, device="cpu", max_states=200):
    ofs = 0
    p = np.arange(Vmin, Vmax + DELTA_Z, DELTA_Z)
    for batch in np.array_split(states, 64):
        states_v = torch.tensor(batch).to(device)
        action_prob = net.apply_softmax(net(states_v)).data.cpu().numpy()
        batch_size, num_actions, _ = action_prob.shape
        for batch_idx in range(batch_size):
            plt.clf()
            for action_idx in range(num_actions):
                plt.subplot(num_actions, 1, action_idx+1)
                plt.bar(p, action_prob[batch_idx, action_idx], width=0.5)
            plt.savefig("states/%05d_%08d.png" % (ofs + batch_idx, frame_idx))
        ofs += batch_size
        if ofs >= max_states:
            break 

def save_transition_images(batch_size, predicted, projected, next_distr, dones, rewards, save_prefix):
    for batch_idx in range(batch_size):
        is_done = dones[batch_idx]
        reward = rewards[batch_idx]
        plt.clf()
        p = np.arange(Vmin, Vmax + DELTA_Z, DELTA_Z)
        plt.subplot(3, 1, 1)
        plt.bar(p, predicted[batch_idx], width=0.5)
        plt.title("Predicted")
        plt.subplot(3, 1, 2)
        plt.bar(p, projected[batch_idx], width=0.5)
        plt.title("Projected")
        plt.subplot(3, 1, 3)
        plt.bar(p, next_distr[batch_idx], width=0.5)
        plt.title("Next state")
        suffix = ""
        if reward != 0.0:
            suffix = suffix + "_%.0f" % reward
        if is_done:
            suffix = suffix + "_done"
        plt.savefig("%s_%02d%s.png" % (save_prefix, batch_idx, suffix)) 

def autolabel(ax, rects, strfrm='%.2f'):
    '''
    Automatically add value over each bar in bar chart
    http://matplotlib.org/1.4.2/examples/api/barchart_demo.html
    '''
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, strfrm % float(height),
                ha='center', va='bottom') 

def plotData(Data, nObsPlot=5000):
  ''' Plot data items, at most nObsPlot distinct points (for quick rendering)
  '''
  if type(Data) == bnpy.data.XData:
    PRNG = np.random.RandomState(nObsPlot)
    pIDs = PRNG.permutation(Data.nObs)[:nObsPlot]
    if Data.dim > 1:
      pylab.plot(Data.X[pIDs,0], Data.X[pIDs,1], 'k.')  
    else:
      hist, bin_edges = pylab.histogram(Data.X, bins=25)
      xs = bin_edges[:-1]
      ys = np.asarray(hist, dtype=np.float32) / np.sum(hist)
      pylab.bar(xs, ys, width=0.8*(bin_edges[1]-bin_edges[0]), color='k') 

def bar(self, key_word_sep=" ", title=None, **kwargs):
        """Generates a pylab bar plot from the result set.

        ``matplotlib`` must be installed, and in an
        IPython Notebook, inlining must be on::

            %%matplotlib inline

        The last quantitative column is taken as the Y values;
        all other columns are combined to label the X axis.

        :param title: plot title, defaults to names of Y value columns
        :param key_word_sep: string used to separate column values
                             from each other in labels

        Any additional keyword arguments will be passsed
        through to ``matplotlib.pylab.bar``.
        """
        if not plt:
            raise ImportError("Try installing matplotlib first.")
        self.guess_pie_columns(xlabel_sep=key_word_sep)
        plot = plt.bar(range(len(self.ys[0])), self.ys[0], **kwargs)
        if self.xlabels:
            plt.xticks(range(len(self.xlabels)), self.xlabels,
                       rotation=45)
        plt.xlabel(self.xlabel)
        plt.ylabel(self.ys[0].name)
        return plot