Python seaborn.lmplot方法代码示例

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def noisy_adversary_opponent_subset_plot(
    original_df, subset_specs, transform_specs, logistic=True, plot_line=True, savefile=None
):
    subset_df = subset(original_df, subset_specs)
    if len(subset_df) == 0:
        return
    transformed_df = transform(subset_df, transform_specs)
    plt.figure(figsize=(10, 7))
    if plot_line:
        sns.lmplot(data=transformed_df, x="log_noise", y="agent0_win_perc", logistic=logistic)
    else:
        sns.scatterplot(data=transformed_df, x="log_noise", y="agent0_win_perc")
    plt.title(
        "{}: Noisy Zoo{} Observations vs Adversary".format(
            subset_specs["env"], subset_specs["agent0_path"]
        )
    )
    if savefile is not None:
        plt.savefig(savefile)
    else:
        plt.show()
    plt.close() 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def noisy_multiple_opponent_subset_plot(
    original_df, subset_specs, transform_specs, logistic=True, savefile=None
):
    subset_df = subset(original_df, subset_specs)
    if len(subset_df) == 0:
        return
    transformed_df = transform(subset_df, transform_specs)
    plt.figure(figsize=(10, 7))
    sns.lmplot(
        data=transformed_df,
        x="log_noise",
        y="agent0_win_perc",
        hue="agent1_path",
        logistic=logistic,
    )
    plt.title(
        "{}: Noisy Zoo{} Observations vs Normal Zoos".format(
            subset_specs["env"], subset_specs["agent0_path"]
        )
    )
    if savefile is not None:
        plt.savefig(savefile)
    else:
        plt.show()
    plt.close() 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def make_biplot(self, pc_x=1, pc_y=2, outpath=None, dpi=150, custom_markers=None, custom_order=None):
        if not custom_order:
            custom_order = sorted(self.observations_df[self.observation_colname].unique().tolist())
        if not custom_markers:
            custom_markers = self.markers
        plot = sns.lmplot(data=self.principal_observations_df,
                              x=self.principal_observations_df.columns[pc_x - 1],
                              y=self.principal_observations_df.columns[pc_y - 1],
                              hue=self.observation_colname,
                              hue_order=custom_order,
                              fit_reg=False,
                              size=6,
                              markers=custom_markers,
                            scatter_kws={'alpha': 0.5})
        plot = (plot.set(title='PC{} vs. PC{}'.format(pc_x, pc_y)))
        if outpath:
            plot.savefig(outpath, dpi=dpi)
        else:
            plt.show()
        plt.close() 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def scatterplot(stats_output):
    """ Plot Prediction Scatterplot

    Args:
        stats_output: a pandas file with prediction output

    Returns:
        fig: Will return a seaborn scatterplot

    """

    if "yfit_xval" in stats_output.columns:
        sns.lmplot("Y", "yfit_xval", data=stats_output)
    else:
        sns.lmplot("Y", "yfit_all", data=stats_output)
    plt.xlabel("Y", fontsize=16)
    plt.ylabel("Predicted Value", fontsize=16)
    plt.title("Prediction", fontsize=18)
    return 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def pca_scatter(self, classifs=None, light=False):
        import seaborn as sns
        foo = self.get_pca_transf()
        if classifs is None:
            if light:
                plt.scatter(foo[:, 0], foo[:, 1])
            else:
                bar = pd.DataFrame(
                    list(zip(foo[:, 0], foo[:, 1])), columns=["PC1", "PC2"])
                sns.lmplot("PC1", "PC2", bar, fit_reg=False)
        else:
            if light:
                plt.scatter(foo[:, 0], foo[:, 1], color=cm.Scalar)
            else:
                bar = pd.DataFrame(list(zip(foo[:, 0], foo[:, 1], classifs)), columns=[
                                   "PC1", "PC2", "Class"])
                sns.lmplot("PC1", "PC2", bar, hue="Class", fit_reg=False) 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def visualize_can_eng_MLUw(child_name, can_reader, eng_reader, legend=True):
    x_label = '{}\'s age in months'.format(child_name)
    
    can_filenames = can_reader.filenames(sorted_by_age=True)
    can_ages = can_reader.age(months=True)
    can_MLUs = can_reader.MLUw()
    
    eng_filenames = eng_reader.filenames(sorted_by_age=True)
    eng_ages = eng_reader.age(months=True)
    eng_MLUs = eng_reader.MLUw()
    
    df = pd.DataFrame({x_label: [can_ages[fn] for fn in can_filenames] + [eng_ages[fn] for fn in eng_filenames],
                       'MLUw': [can_MLUs[fn] for fn in can_filenames] + [eng_MLUs[fn] for fn in eng_filenames],
                       'Language': ['Cantonese']*len(can_reader) + ['English']*len(eng_reader)})
    
    MLU_plot = sns.lmplot(x=x_label, y='MLUw', hue='Language', data=df, markers=['o', 'x'],
                          legend=legend, legend_out=False)
    MLU_plot.set(xlim=(10, 45), ylim=(0, 4.5))
    MLU_plot.savefig('{}-MLU.pdf'.format(child_name))


# In[11]: 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def visualize_data(self):
        """
        Transform the DataFrame to the 2-dimensional case and visualizes the data. The first tags are used as labels.
        :return:
        """
        logging.debug("Preparing visualization of DataFrame")
        # Reduce dimensionality to 2 features for visualization purposes
        X_visualization = self.reduce_dimensionality(self.X, n_features=2)
        df = self.prepare_dataframe(X_visualization)
        # Set X and Y coordinate for each articles
        df['X coordinate'] = df['coordinates'].apply(lambda x: x[0])
        df['Y coordinate'] = df['coordinates'].apply(lambda x: x[1])
        # Create a list of markers, each tag has its own marker
        n_tags_first = len(self.df['tags_first'].unique())
        markers_choice_list = ['o', 's', '^', '.', 'v', '<', '>', 'D']
        markers_list = [markers_choice_list[i % 8] for i in range(n_tags_first)]
        # Create scatter plot
        sns.lmplot("X coordinate",
                   "Y coordinate",
                   hue="tags_first",
                   data=df,
                   fit_reg=False,
                   markers=markers_list,
                   scatter_kws={"s": 150})
        # Adjust borders and add title
        sns.set(font_scale=2)
        sns.plt.title('Visualization of TMT articles in a 2-dimensional space')
        sns.plt.subplots_adjust(right=0.80, top=0.90, left=0.12, bottom=0.12)
        # Show plot
        sns.plt.show()

    # Train recommender 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def probability_plot(stats_output):
    """ Plot Classification Probability

    Args:
        stats_output: a pandas file with prediction output

    Returns:
        fig: Will return a seaborn scatterplot

    """
    if "Probability_xval" in stats_output.columns:
        sns.lmplot("Y", "Probability_xval", data=stats_output, logistic=True)
    else:
        sns.lmplot("Y", "Probability_all", data=stats_output, logistic=True)
    plt.xlabel("Y", fontsize=16)
    plt.ylabel("Predicted Probability", fontsize=16)
    plt.title("Prediction", fontsize=18)
    return 

    # # and plot the result
    # plt.figure(1, figsize=(4, 3))
    # plt.clf()
    # plt.scatter(X.ravel(), y, color='black', zorder=20)
    # X_test = np.linspace(-5, 10, 300)

    # def model(x):
    #     return 1 / (1 + np.exp(-x))
    # loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
    # plt.plot(X_test, loss, color='blue', linewidth=3) 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def plot_com_properties_relation(com_clusters, com_fitness_x, com_fitness_y, **kwargs):
    """
    Plot the relation between two properties/fitness function of a clustering

    :param com_clusters:  clustering(s) to analyze (cluster or cluster list)
    :param com_fitness_x: first fitness/community property
    :param com_fitness_y: first fitness/community property
    :param kwargs: parameters for the seaborn lmplot
    :return: a seaborn lmplot

    Example:

    >>> from cdlib import algorithms, viz, evaluation
    >>> import networkx as nx
    >>> g = nx.karate_club_graph()
    >>> coms = algorithms.louvain(g)
    >>> coms2 = algorithms.walktrap(g)
    >>> lmplot = viz.plot_com_properties_relation([coms,coms2],evaluation.size,evaluation.internal_edge_density)
    """
    if isinstance(com_clusters, cdlib.classes.clustering.Clustering):
        com_clusters = [com_clusters]

    for_df = []

    for c in com_clusters:
        x = com_fitness_x(c.graph, c, summary=False)
        y = com_fitness_y(c.graph, c, summary=False)
        for i, vx in enumerate(x):
            for_df.append([c.get_description(), vx, y[i]])

    df = pd.DataFrame(columns=["Method", com_fitness_x.__name__, com_fitness_y.__name__], data=for_df)
    ax = sns.lmplot(x=com_fitness_x.__name__, y=com_fitness_y.__name__, data=df, hue="Method", fit_reg=False,legend=False, x_bins=100,**kwargs)
    plt.legend(loc='best')

    # if log_x:
    #     ax.set_xscale("log")
    # if log_y:
    #     ax.set_yscale("log")
    plt.tight_layout()

    return ax 

# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lmplot [as 别名]
def PerfMonPlotter(perf_mon_records, time_window = None):
    """
    For plotting performance monitoring records.
    """
    # Entire records
    pqos_records = perf_mon_records['pqos_records']
    # perf_records = perf_mon_records['perf_records']
    # # Select a time window if provided
    # if time_window is not None:
    #     test_start = pqos_records['timestamp'].min()
    #     time_window = [5, 10]
    #     selection_bounds = [test_start + timedelta(seconds=time_window[0]), \
    #                         test_start + timedelta(seconds=time_window[1])]
    #     pqos_records['In Test Bound'] = (pqos_records['timestamp']>selection_bounds[0]) \
    #                                     & (pqos_records['timestamp']<selection_bounds[1])
    #     perf_records['In Test Bound'] = (perf_records['timestamp']>time_window[0]) \
    #                                     & (perf_records['timestamp']<time_window[1])
    #     pqos_df = pqos_records[pqos_records['In Test Bound']==True]
    #     perf_df = perf_records[perf_records['In Test Bound']==True]

    palette = sns.color_palette("rocket_r", 16)
    
    # 'timestamp','Core','IPC','LLC Misses','LLC Util (KB)','MBL (MB/s)'
    fig, axs = plt.subplots(ncols=2, nrows=2, sharex=True)
    pqos_records_sum = pqos_records.groupby('timestamp').sum()
    pqos_records_sum.plot(y='IPC', ax=axs[0][0])
    pqos_records_sum.plot(y='MBL (MB/s)', ax=axs[0][1])
    pqos_records_sum.plot(y='LLC Util (KB)', ax=axs[1][0])
    pqos_records_sum.plot(y='LLC Misses', ax=axs[1][1])
    axs[0][0].set_ylim([0,20])

    # sns.relplot(data=pqos_records, x='timestamp', y='IPC', hue='Core', kind='line', palette=palette, alpha=0.75)
    # sns.relplot(data=pqos_records, x='timestamp', y='MBL (MB/s)', hue='Core', kind='scatter', palette=palette, alpha=0.75)
    # sns.lmplot(data=pqos_df.groupby('timestamp').sum(), x='IPC', y='MBL (MB/s)', palette=palette,
    #            truncate=True, order=5, fit_reg=False, scatter_kws={'alpha':0.5}, legend_out=False)
    # sns.jointplot(data=pqos_df.groupby('timestamp').sum(), x='LLC Util (KB)', y='MBL (MB/s)', kind="hex", zorder=0)
                #  .plot_joint(sns.kdeplot, zorder=10, n_levels=25, bw='silverman')

    # cpu-cycles,L1-dcache-loads,L1-dcache-load-misses,L1-icache-load-misses,dTLB-load-misses,dTLB-loads,
    # iTLB-load-misses,iTLB-loads,branch-misses,context-switches,cpu-migrations,page-faults
    # sns.relplot(data=perf_records, x='timestamp', y='context-switches', kind='line', palette=palette, alpha=0.75)
    # plt.stackplot(perf_records['timestamp'], perf_records['r4f1'], perf_records['r2f1'], perf_records['r1f1'])
    # sns.relplot(data=perf_df, x='context-switches', y='r1f1', kind='scatter', palette=palette, alpha=0.75)
    # perf_records['Branch Miss Rate (%)'] = 100.0*perf_records['branch-misses']/perf_records['branches']
    # sns.lmplot(data=perf_records, x='context-switches', y='block:block_plug',
    #            truncate=True, order=8, scatter_kws={'alpha':0.5}, legend_out=False)
    # sns.jointplot(data=perf_df, x='dTLB-loads', y='iTLB-loads', kind="hex", zorder=0)

    plt.show()
    plt.close()

    return True