我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用seaborn.pointplot()。
def plot_averages_by_type(df, figname, fignum, use_en_source=True, pointplot=True, layer0=True): plt.figure(fignum) if use_en_source: df_side = df[(df.source == 'en') & (df.target != 'en')] else: df_side = df[(df.source != 'en') & (df.target == 'en')] if not layer0: df_side = df_side[df_side.layer != '0'] plotfunc = sns.pointplot if pointplot else sns.boxplot if pointplot: plotfunc(x='accuracy', y='relation', hue='layer', data=df_side, join=False) else: plotfunc(x='accuracy', y='relation', hue='layer', data=df_side) plt.xlabel('Accuracy') plt.ylabel('') plt.tight_layout() plt.savefig(figname) return fignum + 1
def save_mapping_cmp(is_power_point = False): dfs = [] for filename in glob.glob("../output/cifar10classifier_resnet32_*.csv"): target = filename.split("_")[-1].split(".csv")[0] if target in ["momentum", "bnafteraddition", "fullpreactivation", "noactivation", "relubeforeaddition", "reluonlypreactivation"]: df = pd.read_csv(filename) df["train_error"] = 1 - df["train_accuracy"] df["test_error"] = 1 - df["test_accuracy"] dfs.append(df) total_df = pd.concat(dfs) total_df["name"] = total_df["name"].str.split("_").str.get(-1).str.replace("Momentum", "Nesterov(Original Paper)") ax = sns.pointplot(x="epoch", y="test_error", hue="name", data=total_df, scale=0.2) if is_power_point: ax.legend(loc="lower left", markerscale=9.0, fontsize=20) else: ax.legend(markerscale=3.0) ax.set(ylim=(0, 0.2)) ax.set_xticklabels([i if i % 10 == 0 else "" for i in range(200)]) ax.set(xlabel='epoch', ylabel='error(%)') ax.get_figure().savefig("../figures/resnet.mapping.png") sns.plt.close()
def save_solvers_cmp(is_power_point = False): dfs = [] for filename in glob.glob("../output/cifar10classifier_resnet32_*.csv"): target = filename.split("_")[-1].split(".csv")[0] if target in ["adadelta", "adagrad", "adam", "momentum", "rmsprop"]: df = pd.read_csv(filename) df["train_error"] = 1 - df["train_accuracy"] df["test_error"] = 1 - df["test_accuracy"] dfs.append(df) total_df = pd.concat(dfs) total_df["name"] = total_df["name"].str.split("_").str.get(-1).str.replace("Momentum", "Nesterov(Original Paper)") ax = sns.pointplot(x="epoch", y="test_error", hue="name", data=total_df, scale=0.2) if is_power_point: ax.legend(loc="lower left", markerscale=9.0, fontsize=20) else: ax.legend(loc="lower left", markerscale=3.0) ax.set(ylim=(0, 0.2)) ax.set_xticklabels([i if i % 10 == 0 else "" for i in range(200)]) ax.set(xlabel='epoch', ylabel='error(%)') ax.get_figure().savefig("../figures/resnet.solvers.png") sns.plt.close()
def save_layers_cmp(is_power_point = False): total_df = None for layer in [20, 32, 44, 56, 110]: df = pd.read_csv("../output/cifar10classifier_resnet%d.csv" % layer) df["train_error"] = 1 - df["train_accuracy"] df["test_error"] = 1 - df["test_accuracy"] df = df[df["epoch"] < 150] if total_df is None: total_df = df else: total_df = pd.concat([total_df, df]) total_df["name"] = total_df["name"].str.split("_").str.get(-1) ax = sns.pointplot(x="epoch", y="test_error", hue="name", data=total_df, scale=0.2) if is_power_point: ax.legend(loc="lower left", markerscale=9.0, fontsize=20) else: ax.legend(markerscale=3.0) ax.set(ylim=(0, 0.2)) ax.set_xticklabels([i if i % 10 == 0 else "" for i in range(150)]) ax.set(xlabel='epoch', ylabel='error(%)') ax.get_figure().savefig("../figures/resnet.layers.png") sns.plt.close()
def plotDailyStatsSleep(stats, columns=None): """ Plot daily stats. Fill all data range, and put NaN for days without measures :param data: data to plot """ MEASURE_NAME = 'date' if not columns: columns = ['sleep_inefficiency', 'sleep_hours'] dataToPlot = _prepareDailyStats(stats, columns) f, axes = getAxes(2,1) xTicksDiv = min(10, len(dataToPlot)) #xticks = [(x-pd.DateOffset(years=1, day=2)).date() for x in stats.date] xticks = [x.date() for x in dataToPlot.date] keptticks = xticks[::int(len(xticks)/xTicksDiv)] xticks = ['' for _ in xticks] xticks[::int(len(xticks)/xTicksDiv)] = keptticks for i, c in enumerate(columns): g =sns.pointplot(x=MEASURE_NAME, y=NAMES[c], data=dataToPlot, ax=axes[i]) g.set_xticklabels([]) g.set_xlabel('') g.set_xticklabels(xticks, rotation=45) sns.plt.show()
def plot(self): """ Graphical summary of pointwise pareto-k importance-sampling indices Pareto-k tail indices are plotted (on the y axis) for each observation unit (on the x axis) """ seaborn.pointplot( y = self.pointwise.pareto_k, x = self.pointwise.index, join = False) #pyplot.axhline(0.5)
def plot_averages_by_distance(df, figname, fignum, use_en_source=True, num_accs=24, pointplot=True, hue='Distance'): plt.figure(fignum) if use_en_source: df_side = df[(df.source == 'en') & (df.target != 'en')] layers = np.concatenate([[i]*5 for i in range(5)] * num_accs) else: df_side = df[(df.source != 'en') & (df.target == 'en')] layers = list(range(5))*5*num_accs accs = get_accs_from_df(df_side, col_pref='dist') flat_accs = np.concatenate(accs) dists = np.concatenate([[pretty_dist_names_list[i]]*75 for i in range(8)]) df_plot = pd.DataFrame({'Layer' : layers, 'Accuracy' : flat_accs, 'Distance' : dists }) #print(df_plot) plotfunc = sns.pointplot if pointplot else sns.boxplot if hue == 'Distance': plotfunc(x='Layer', y='Accuracy', data=df_plot, hue='Distance') else: plotfunc(x='Distance', y='Accuracy', data=df_plot, hue='Layer') plt.xticks(range(8), pretty_dist_names_list) plt.tight_layout() plt.savefig(figname) return fignum + 1
def line_plot(data ,title = "", x_title ="", y_title="", legend_label="",group_labels=None): plot_data = DataFrame() plot_data['x'] = data[:, 1].astype(int) plot_data['y'] = data[:, 0].astype(float) plot_data[legend_label] = data[:, 2] sns.set(style="whitegrid") g = sns.pointplot(x="x", y="y", hue=legend_label, data=plot_data, hue_order=np.unique(plot_data[legend_label])) plt.title(title, fontsize=25) plt.ylabel(y_title, fontsize=12) plt.xlabel(x_title, fontsize=12) plt.show()
def line_plot(data ,title = "", x_title ="", y_title="", legend_label="",group_labels=None): plot_data = DataFrame() plot_data['x'] = data[:, 1].astype(int) plot_data['y'] = data[:, 0].astype(float) plot_data[legend_label] = data[:, 2] sns.set(style="whitegrid") g = sns.pointplot(x="x", y="y", hue=legend_label, data=plot_data, hue_order=np.unique(plot_data[legend_label])) plt.title(title, fontsize=14) plt.ylabel(y_title, fontsize=12) plt.xlabel(x_title, fontsize=12) plt.show()
def plot_points(df, axes): for exp, ax in zip(["dots", "sticks", "rest"], axes): exp_df = pd.melt(df.query("exp == @exp"), "subj", ["within", "between"], "test", "corr") sns.pointplot(x="test", y="corr", hue="test", data=exp_df, dodge=.5, join=False, ci=95, palette=[".15", ".5"], ax=ax) plt.setp(ax.lines, linewidth=2) sns.pointplot(x="test", y="corr", hue="subj", data=exp_df, palette=[".75"], scale=.75, ax=ax) plt.setp(ax.collections[:], facecolor="w", zorder=20) ax.legend_ = None ax.set(ylabel="", xlabel="", xticks=[-.1, 1.1], xticklabels=["Same\ncontext", "Different\ncontext"]) axes[0].set(ylim=(0, .105), ylabel="Timeseries correlation (r)") axes[1].set(ylim=(0, .0525)) axes[2].set(ylim=(0, .0525)) for ax in axes: sns.despine(ax=ax, trim=True)
def plot_swarms(df, axes, palette): for exp, ax in zip(["dots", "sticks"], axes): exp_df = df.query("experiment == @exp") ax.axhline(.5, .1, .9, dashes=(5, 2), color=".6") ax.set(ylim=(.4, .9), yticks=[.4, .5, .6, .7, .8, .9]) sns.pointplot(x="roi", y="acc", data=exp_df, palette=palette, join=False, ci=None, ax=ax) points_to_lines(ax, lw=3) sns.swarmplot(x="roi", y="acc", data=exp_df, size=4, color=".85", # facecolor="none", linewidth=1, edgecolor=".4", ax=ax) ax.set(xlabel="", ylabel="", xticklabels=["IFS", "MFC"]) ax_l, ax_r = axes ax_l.set(ylabel="Decoding accuracy") ax_r.set(yticks=[]) ax_l.text(.5, .91, "Experiment 1", ha="center", va="center", size=7.5) ax_r.text(.5, .91, "Experiment 2", ha="center", va="center", size=7.5) sns.despine(ax=ax_l, trim=True) sns.despine(ax=ax_r, left=True, trim=True)
def _plotWeekdayByMonthStats(stats, stat_name): dataToPlot = _prepareWeekdayByMonthStats(stats) # Plot g = sns.pointplot(data=dataToPlot, x="day", y=stat_name, hue="month", order=dayOfWeekOrder) g.set(xlabel='') g.set_ylabel(NAMES[stat_name]) return g #sns.plt.show()
