本文整理汇总了Python中seaborn.scatterplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.scatterplot方法的具体用法?Python seaborn.scatterplot怎么用?Python seaborn.scatterplot使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类seaborn
的用法示例。
在下文中一共展示了seaborn.scatterplot方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: make_reddit_prop_plt
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def make_reddit_prop_plt():
sns.set()
prop_expt = pd.DataFrame(att.process_propensity_experiment())
prop_expt = prop_expt[['exog', 'plugin', 'one_step_tmle', 'very_naive']]
prop_expt = prop_expt.rename(index=str, columns={'exog': 'Exogeneity',
'very_naive': 'Unadjusted',
'plugin': 'Plug-in',
'one_step_tmle': 'TMLE'})
prop_expt = prop_expt.set_index('Exogeneity')
plt.figure(figsize=(4.75, 3.00))
# plt.figure(figsize=(2.37, 1.5))
sns.scatterplot(data=prop_expt, legend='brief', s=75)
plt.xlabel("Exogeneity", fontfamily='monospace')
plt.ylabel("NDE Estimate", fontfamily='monospace')
plt.tight_layout()
fig_dir = '../output/figures'
os.makedirs(fig_dir, exist_ok=True)
plt.savefig(os.path.join(fig_dir,'reddit_propensity.pdf'))
示例2: noisy_adversary_opponent_subset_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [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()
示例3: scatter_plot_intent_dist
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def scatter_plot_intent_dist(workspace_pd):
"""
takes the workspace_pd and generate a scatter distribution of the intents
:param workspace_pd:
:return:
"""
label_frequency = Counter(workspace_pd["intent"]).most_common()
frequencies = list(reversed(label_frequency))
counter_list = list(range(1, len(frequencies) + 1))
df = pd.DataFrame(data=frequencies, columns=["Intent", "Number of User Examples"])
df["Intent"] = counter_list
sns.set(rc={"figure.figsize": (15, 10)})
display(
Markdown(
'## <p style="text-align: center;">Sorted Distribution of User Examples \
per Intent</p>'
)
)
plt.ylabel("Number of User Examples", fontdict=LABEL_FONT)
plt.xlabel("Intent", fontdict=LABEL_FONT)
ax = sns.scatterplot(x="Intent", y="Number of User Examples", data=df, s=100)
示例4: _plot_results_accuracy_comparison
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def _plot_results_accuracy_comparison(results_df, save_cfg):
"""Plot the comparison between the best model and best baseline.
"""
fig, ax = plt.subplots(figsize=(save_cfg['text_width'],
save_cfg['text_height'] * 0.5))
sns.scatterplot(data=results_df, x='Baseline (traditional)', y='Proposed',
ax=ax)
ax.plot([0, 1.1], [0, 1.1], c='k', alpha=0.2)
plt.axis('square')
ax.set_xlim([0, 1.1])
ax.set_ylim([0, 1.1])
plt.tight_layout()
if save_cfg is not None:
savename = 'reported_accuracy_comparison'
fname = os.path.join(save_cfg['savepath'], savename)
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例5: plot_embeddings
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot_embeddings(self, embeddings):
embeddings = MinMaxScaler((0, 1)).fit_transform(self.embeddings)
fig = plt.figure()
buf = io.BytesIO()
sns.scatterplot(x=embeddings[:, 0], y=embeddings[:, 1], s=1,
hue=self.labels,
palette=sns.color_palette("hls", self.n_classes),
linewidth=0)
plt.savefig(buf, format='png', dpi=300)
plt.close(fig)
buf.seek(0)
image = tf.Summary.Image(encoded_image_string=buf.getvalue())
return image
示例6: plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot(x, y, plot_id, names=None):
viz_df = pd.DataFrame(data=x[:5000])
viz_df['Label'] = y[:5000]
if names is not None:
viz_df['Label'] = viz_df['Label'].map(names)
viz_df.to_csv(args.save_dir + '/' + args.dataset + '.csv')
plt.subplots(figsize=(8, 5))
sns.scatterplot(x=0, y=1, hue='Label', legend='full', hue_order=sorted(viz_df['Label'].unique()),
palette=sns.color_palette("hls", n_colors=args.n_clusters),
alpha=.5,
data=viz_df)
l = plt.legend(bbox_to_anchor=(-.1, 1.00, 1.1, .5), loc="lower left", markerfirst=True,
mode="expand", borderaxespad=0, ncol=args.n_clusters + 1, handletextpad=0.01, )
l.texts[0].set_text("")
plt.ylabel("")
plt.xlabel("")
plt.tight_layout()
plt.savefig(args.save_dir + '/' + args.dataset +
'-' + plot_id + '.png', dpi=300)
plt.clf()
示例7: plot_2d
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot_2d(tsne, fnar, palette, outpng, verbose):
"""
Create the 2d plot
:param tsne: tSNE array
:param fnar: functions list
:param outpng: base name for pg output
:return: nothing
"""
if verbose:
sys.stderr.write(f"{bcolors.GREEN}Plotting 2D tSNE{bcolors.ENDC}\n")
snsplot = sns.scatterplot(tsne[:,0], tsne[:,1], legend="full", hue=fnar, palette=palette)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.tight_layout()
plt.savefig(outpng + ".png")
示例8: plot_2d_sz
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot_2d_sz(tsne, fnar, palette, outpng, verbose):
"""
Create the 2d plot
:param tsne: tSNE array
:param fnar: functions list
:param outpng: base name for pg output
:return: nothing
"""
if verbose:
sys.stderr.write(f"{bcolors.GREEN}Plotting 2D tSNE by size{bcolors.ENDC}\n")
sp = sns.scatterplot(x=tsne[:,0], y=tsne[:,1], s=tsne[:,2], legend="full", hue=fnar, palette=palette)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.tight_layout()
plt.savefig(outpng + ".sz.png")
示例9: plot_vs_ttest
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot_vs_ttest(self, log10=False):
import matplotlib.pyplot as plt
import seaborn as sns
from .tests import t_test
grouping = self.grouping
ttest = t_test(
data=self.x,
grouping=grouping,
gene_names=self.gene_ids,
)
if log10:
ttest_pvals = ttest.log10_pval_clean()
pvals = self.log10_pval_clean()
else:
ttest_pvals = ttest.pval
pvals = self.pval
fig, ax = plt.subplots()
sns.scatterplot(x=ttest_pvals, y=pvals, ax=ax)
ax.set(xlabel="t-test", ylabel='rank test')
return fig, ax
示例10: finish
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def finish(self):
dict = self.getDestinationItems()
df = self.getDataFrame()
try:
x = dict['X Variable'][0]
y = dict['Y Variable'][0]
c = dict['Color By'][0]
except IndexError:
c = None
sns.scatterplot(x, y, c, data=df)
plt.show()
示例11: cluster_tsne
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def cluster_tsne(data, clusters, target, plot_name=None, **kwargs):
"""
Plots TSNE projection of user stories colored by clusters. Each point represents a user session or whole user trajectory.
Parameters
--------
data: pd.DataFrame
Feature matrix.
clusters: np.array
Array of cluster IDs.
target: np.array
Boolean vector, if ``True``, then user has `positive_target_event` in trajectory.
plot_name: str, optional
Name of plot to save. Default: ``'clusters_tsne_{timestamp}.svg'``
Returns
-------
Saves plot to ``retention_config.experiments_folder``
Return type
-------
PNG
"""
if hasattr(data.retention, '_tsne') and not kwargs.get('refit'):
tsne2 = data.retention._tsne.copy()
else:
tsne2 = data.retention.learn_tsne(clusters, **kwargs)
tsne = tsne2.values
if np.unique(clusters).shape[0] > 10:
f, ax = sns.mpl.pyplot.subplots()
points = ax.scatter(tsne[:, 0], tsne[:, 1], c=clusters, cmap="BrBG")
f.colorbar(points)
scatter = ___FigureWrapper__(f)
else:
scatter = sns.scatterplot(tsne[:, 0], tsne[:, 1], hue=clusters, legend='full',
palette=sns.color_palette("bright")[0:np.unique(clusters).shape[0]])
plot_name = plot_name or 'cluster_tsne_{}'.format(datetime.now()).replace(':', '_').replace('.', '_') + '.svg'
plot_name = data.retention.retention_config['experiments_folder'] + '/' + plot_name
return scatter, plot_name, tsne2, data.retention.retention_config
示例12: plot_chemical_trajectory
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def plot_chemical_trajectory(self, environment, filename):
"""
Plot the trajectory through chemical space.
Parameters
----------
environment : str
the name of the environment for which the chemical space trajectory is desired
"""
chemical_state_trajectory = self.extract_state_trajectory(environment)
visited_states = list(set(chemical_state_trajectory))
state_trajectory = np.zeros(len(chemical_state_trajectory))
for idx, chemical_state in enumerate(chemical_state_trajectory):
state_trajectory[idx] = visited_states.index(chemical_state)
with PdfPages(filename) as pdf:
sns.set(font_scale=2)
fig = plt.figure(figsize=(28, 12))
plt.subplot2grid((1,2), (0,0))
ax = sns.scatterplot(np.arange(len(state_trajectory)), state_trajectory)
plt.yticks(np.arange(len(visited_states)), visited_states)
plt.title("Trajectory through chemical space in {}".format(environment))
plt.xlabel("iteration")
plt.ylabel("chemical state")
plt.tight_layout()
plt.subplot2grid((1,2), (0,1))
ax = sns.countplot(y=state_trajectory)
pdf.savefig(fig)
plt.close()
示例13: create_scatterplot_from_df
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def create_scatterplot_from_df(
df, x: str, y: str, output_path: str = ".", fig_x: int = 16, fig_y: int = 8
):
"""Loads an executive summary df and creates a scatterplot from some pre-specified variables.
Args:
df ([type]): [description]
x (str): [description]
y (str): [description]
output_path (str, optional): [description]. Defaults to '.'.
fig_x (int, optional): [description]. Defaults to 16.
fig_y (int, optional): [description]. Defaults to 8.
"""
if not os.path.exists(output_path):
os.makedirs(output_path)
# create graph dirs
graph_dir = str(fig_x) + "_" + str(fig_y)
out_dir = os.path.join(output_path, graph_dir)
df[x] = df[x].astype(float)
df[y] = df[y].astype(float)
os.makedirs(out_dir, exist_ok=True)
a4_dims = (14, 9)
fig, ax = plt.subplots(figsize=a4_dims)
graph = sns.scatterplot(
ax=ax, x=x, y=y, data=df, s=325, alpha=0.5, hue="Experiment", legend="brief"
) # , palette="Set1")
box = ax.get_position()
plt.legend(markerscale=2)
# ax.set_position([box.x0,box.y0,box.width*0.83,box.height])
# plt.legend(loc='upper left',bbox_to_anchor=(1,1.15))
# plt.ylim(bottom=0.0)
# plt.legend(loc='lower right')
# Use regplot to plot the regression line for the whole points
# sns.regplot(x="FPOs", y=args.y_axis_var, data=df, sizes=(250, 500), alpha=.5, scatter=False, ax=graph.axes[2])
path_name = os.path.join(out_dir, "{}v{}.png".format(x, y))
plt.savefig(path_name)
plt.close("all")
return path_name
示例14: scatter_with_legend
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def scatter_with_legend(
fig, ax, df, font_color, x, y, c, cmap, legend, **scatter_kwargs
):
import seaborn as sns
import matplotlib.patheffects as PathEffects
unique_labels = np.unique(c)
if legend == "on data":
g = sns.scatterplot(
x, y, hue=c, palette=cmap, ax=ax, legend=False, **scatter_kwargs
)
for i in unique_labels:
color_cnt = np.nanmedian(df.iloc[np.where(c == i)[0], :2], 0)
txt = ax.text(
color_cnt[0],
color_cnt[1],
str(i),
color=font_color,
zorder=1000,
verticalalignment="center",
horizontalalignment="center",
weight="bold",
) # c
txt.set_path_effects(
[
PathEffects.Stroke(
linewidth=1.5, foreground=font_color, alpha=0.8
), # 'w'
PathEffects.Normal(),
]
)
else:
g = sns.scatterplot(
x, y, hue=c, palette=cmap, ax=ax, legend="full", **scatter_kwargs
)
ax.legend(loc=legend, ncol=unique_labels // 15)
return fig, ax
示例15: visualize_distribution_static
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import scatterplot [as 别名]
def visualize_distribution_static(X,prediction,score, path=None):
"""
Visualize the original distribution of the data in 2-dimension space, which outliers/inliers are colored as differnet scatter plot.
Parameters
----------
X: numpy array of shape (n_test, n_features)
Test data.
prediction: numpy array of shape (n_test, )
The prediction result of the test data.
score: umpy array of shape (n_test, )
The outlier score of the test data.
path: string
The saving path for result figures.
"""
sns.set(style="darkgrid")
X=X.to_numpy()
X_embedding = TSNE(n_components=2).fit_transform(X)
outlier_label=[]
for i in range(len(X_embedding)):
if prediction[i]==1:
outlier_label.append('inlier')
else:
outlier_label.append('outlier')
X_outlier = pd.DataFrame({'x_emb':X_embedding[:,0],'y_emb':X_embedding[:,1],'outlier_label':np.array(outlier_label),'score':np.array(score)})
new_sns = sns.scatterplot(x="x_emb", y="y_emb",hue = "score", sizes =20, palette = 'BuGn_r',legend = False, data = X_outlier)
if path:
new_sns.get_figure().savefig(path+'/distribution_withoutlier.png')
plt.show()