Python matplotlib.pylab 模块,bar() 实例源码

我们从Python开源项目中,提取了以下5个代码示例,用于说明如何使用matplotlib.pylab.bar()

项目:Building-Machine-Learning-Systems-With-Python-Second-Edition    作者:PacktPublishing    | 项目源码 | 文件源码
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")
项目:Building-Machine-Learning-Systems-With-Python-Second-Edition    作者:PacktPublishing    | 项目源码 | 文件源码
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")
项目:Building-Machine-Learning-Systems-With-Python-Second-Edition    作者:PacktPublishing    | 项目源码 | 文件源码
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")
项目:ML    作者:saurabhsuman47    | 项目源码 | 文件源码
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")
项目:ML    作者:saurabhsuman47    | 项目源码 | 文件源码
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")