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plotting.py
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plotting.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from collections import defaultdict
import matplotlib.font_manager
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from adjustText import adjust_text
from sklearn.decomposition import KernelPCA
from sklearn.metrics import r2_score
from sklearn.metrics.pairwise import cosine_similarity
FONT_SIZE = 10
font = {"size": FONT_SIZE}
matplotlib.rc("font", **font)
matplotlib.rc("ytick", labelsize=FONT_SIZE)
matplotlib.rc("xtick", labelsize=FONT_SIZE)
class CPAVisuals:
"""
A wrapper for automatic plotting CompPert latent embeddings and dose-response
curve. Sets up prefix for all files and default dictionaries for atomic
perturbations and cell types.
"""
def __init__(
self,
cpa,
fileprefix=None,
perts_palette=None,
covars_palette=None,
plot_params={"fontsize": None},
):
"""
Parameters
----------
cpa : CompPertAPI
Variable from API class.
fileprefix : str, optional (default: None)
Prefix (with path) to the filename to save all embeddings in a
standartized manner. If None, embeddings are not saved to file.
perts_palette : dict (default: None)
Dictionary of colors for the embeddings of perturbations. Keys
correspond to perturbations and values to their colors. If None,
default dicitonary will be set up.
covars_palette : dict (default: None)
Dictionary of colors for the embeddings of covariates. Keys
correspond to covariates and values to their colors. If None,
default dicitonary will be set up.
"""
self.fileprefix = fileprefix
self.perturbation_key = cpa.perturbation_key
self.dose_key = cpa.dose_key
self.covariate_keys = cpa.covariate_keys
self.measured_points = cpa.measured_points
self.unique_perts = cpa.unique_perts
self.unique_covars = cpa.unique_covars
if perts_palette is None:
self.perts_palette = dict(
zip(self.unique_perts, get_palette(len(self.unique_perts)))
)
else:
self.perts_palette = perts_palette
if covars_palette is None:
self.covars_palette = {}
for cov in self.unique_covars:
self.covars_palette[cov] = dict(
zip(
self.unique_covars[cov],
get_palette(len(self.unique_covars[cov]), palette_name="tab10"),
)
)
else:
self.covars_palette = covars_palette
if plot_params["fontsize"] is None:
self.fontsize = FONT_SIZE
else:
self.fontsize = plot_params["fontsize"]
def plot_latent_embeddings(
self,
emb,
titlename="Example",
kind="perturbations",
palette=None,
labels=None,
dimred="KernelPCA",
filename=None,
show_text=True,
):
"""
Parameters
----------
emb : np.array
Multi-dimensional embedding of perturbations or covariates.
titlename : str, optional (default: 'Example')
Title.
kind : int, optional, optional (default: 'perturbations')
Specify if this is embedding of perturbations, covariates or some
other. If it is perturbations or covariates, it will use default
saved dictionaries for colors.
palette : dict, optional (default: None)
If embedding of kind not perturbations or covariates, the user can
specify color dictionary for the embedding.
labels : list, optional (default: None)
Labels for the embeddings.
dimred : str, optional (default: 'KernelPCA')
Dimensionality reduction method for plotting low dimensional
representations. Options: 'KernelPCA', 'UMAPpre', 'UMAPcos', None.
If None, uses first 2 dimensions of the embedding.
filename : str (default: None)
Name of the file to save the plot. If None, will automatically
generate name from prefix file.
"""
if filename is None:
if self.fileprefix is None:
filename = None
file_name_similarity = None
else:
filename = f"{self.fileprefix}_emebdding.png"
file_name_similarity = f"{self.fileprefix}_emebdding_similarity.png"
else:
file_name_similarity = filename.split(".")[0] + "_similarity.png"
if labels is None:
if kind == "perturbations":
palette = self.perts_palette
labels = self.unique_perts
elif kind in self.unique_covars:
palette = self.covars_palette[kind]
labels = self.unique_covars[kind]
if len(emb) < 2:
print(f"Embedding contains only {len(emb)} vectors. Not enough to plot.")
else:
plot_embedding(
fast_dimred(emb, method=dimred),
labels,
show_lines=True,
show_text=show_text,
col_dict=palette,
title=titlename,
file_name=filename,
fontsize=self.fontsize,
)
plot_similarity(
emb,
labels,
col_dict=palette,
fontsize=self.fontsize,
file_name=file_name_similarity,
)
def plot_contvar_response2D(
self,
df_response2D,
df_ref=None,
levels=15,
figsize=(4, 4),
xlims=(0, 1.03),
ylims=(0, 1.03),
palette="coolwarm",
response_name="response",
title_name=None,
fontsize=None,
postfix="",
filename=None,
alpha=0.4,
sizes=(40, 160),
logdose=False,
):
"""
Parameters
----------
df_response2D : pd.DataFrame
Data frame with responses of combinations with columns=(dose1, dose2,
response).
levels: int, optional (default: 15)
Number of levels for contour plot.
response_name : str (default: 'response')
Name of column in df_response to plot as response.
alpha: float (default: 0.4)
Transparency of the background contour.
figsize: tuple (default: (4,4))
Size of the figure in inches.
palette : dict, optional (default: None)
Colors dictionary for perturbations to plot.
title_name : str, optional (default: None)
Title for the plot.
postfix : str, optional (defualt: '')
Postfix to add to the output file name to save the model.
filename : str, optional (defualt: None)
Name of the file to save the plot. If None, will automatically
generate name from prefix file.
logdose: bool (default: False)
If True, dose values will be log10. 0 values will be mapped to
minumum value -1,e.g.
if smallest non-zero dose was 0.001, 0 will be mapped to -4.
"""
sns.set_style("white")
if (filename is None) and not (self.fileprefix is None):
filename = f"{self.fileprefix}_{postfix}response2D.png"
if fontsize is None:
fontsize = self.fontsize
x_name, y_name = df_response2D.columns[:2]
x = df_response2D[x_name].values
y = df_response2D[y_name].values
if logdose:
x = log10_with0(x)
y = log10_with0(y)
z = df_response2D[response_name].values
n = int(np.sqrt(len(x)))
X = x.reshape(n, n)
Y = y.reshape(n, n)
Z = z.reshape(n, n)
fig, ax = plt.subplots(figsize=figsize)
CS = ax.contourf(X, Y, Z, cmap=palette, levels=levels, alpha=alpha)
CS = ax.contour(X, Y, Z, levels=15, cmap=palette)
ax.clabel(CS, inline=1, fontsize=fontsize)
ax.set(xlim=(0, 1), ylim=(0, 1))
ax.axis("equal")
ax.axis("square")
ax.yaxis.set_tick_params(labelsize=fontsize)
ax.xaxis.set_tick_params(labelsize=fontsize)
ax.set_xlabel(x_name, fontsize=fontsize, fontweight="bold")
ax.set_ylabel(y_name, fontsize=fontsize, fontweight="bold")
ax.set_xlim(xlims)
ax.set_ylim(ylims)
# sns.despine(left=False, bottom=False, right=True)
sns.despine()
if not (df_ref is None):
sns.scatterplot(
x=x_name,
y=y_name,
hue="split",
size="num_cells",
sizes=sizes,
alpha=1.0,
palette={"train": "#000000", "training": "#000000", "ood": "#e41a1c"},
data=df_ref,
ax=ax,
)
ax.legend_.remove()
ax.set_title(title_name, fontweight="bold", fontsize=fontsize)
plt.tight_layout()
if filename:
save_to_file(fig, filename)
def plot_contvar_response(
self,
df_response,
response_name="response",
var_name=None,
df_ref=None,
palette=None,
title_name=None,
postfix="",
xlabelname=None,
filename=None,
logdose=False,
fontsize=None,
measured_points=None,
bbox=(1.35, 1.0),
figsize=(7.0, 4.0),
):
"""
Parameters
----------
df_response : pd.DataFrame
Data frame of responses.
response_name : str (default: 'response')
Name of column in df_response to plot as response.
var_name : str, optional (default: None)
Name of conditioning variable, e.g. could correspond to covariates.
df_ref : pd.DataFrame, optional (default: None)
Reference values. Fields for plotting should correspond to
df_response.
palette : dict, optional (default: None)
Colors dictionary for perturbations to plot.
title_name : str, optional (default: None)
Title for the plot.
postfix : str, optional (defualt: '')
Postfix to add to the output file name to save the model.
filename : str, optional (defualt: None)
Name of the file to save the plot. If None, will automatically
generate name from prefix file.
logdose: bool (default: False)
If True, dose values will be log10. 0 values will be mapped to
minumum value -1,e.g.
if smallest non-zero dose was 0.001, 0 will be mapped to -4.
figsize: tuple (default: (7., 4.))
Size of output figure
"""
if (filename is None) and not (self.fileprefix is None):
filename = f"{self.fileprefix}_{postfix}response.png"
if fontsize is None:
fontsize = self.fontsize
if logdose:
dose_name = f"log10-{self.dose_key}"
df_response[dose_name] = log10_with0(df_response[self.dose_key].values)
if not (df_ref is None):
df_ref[dose_name] = log10_with0(df_ref[self.dose_key].values)
else:
dose_name = self.dose_key
if var_name is None:
if len(self.unique_covars) > 1:
var_name = self.covars_key
else:
var_name = self.perturbation_key
if palette is None:
if var_name == self.perturbation_key:
palette = self.perts_palette
elif var_name in self.covariate_keys:
palette = self.covars_palette[var_name]
plot_dose_response(
df_response,
dose_name,
var_name,
xlabelname=xlabelname,
df_ref=df_ref,
response_name=response_name,
title_name=title_name,
use_ref_response=(not (df_ref is None)),
col_dict=palette,
plot_vertical=False,
f1=figsize[0],
f2=figsize[1],
fname=filename,
logscale=measured_points,
measured_points=measured_points,
bbox=bbox,
fontsize=fontsize,
figformat="png",
)
def plot_scatter(
self,
df,
x_axis,
y_axis,
hue=None,
size=None,
style=None,
figsize=(4.5, 4.5),
title=None,
palette=None,
filename=None,
alpha=0.75,
sizes=(30, 90),
text_dict=None,
postfix="",
fontsize=14,
):
sns.set_style("white")
if (filename is None) and not (self.fileprefix is None):
filename = f"{self.fileprefix}_scatter{postfix}.png"
if fontsize is None:
fontsize = self.fontsize
fig = plt.figure(figsize=figsize)
ax = plt.gca()
sns.scatterplot(
x=x_axis,
y=y_axis,
hue=hue,
style=style,
size=size,
sizes=sizes,
alpha=alpha,
palette=palette,
data=df,
)
ax.legend_.remove()
ax.set_xlabel(x_axis, fontsize=fontsize)
ax.set_ylabel(y_axis, fontsize=fontsize)
ax.xaxis.set_tick_params(labelsize=fontsize)
ax.yaxis.set_tick_params(labelsize=fontsize)
ax.set_title(title)
if not (text_dict is None):
texts = []
for label in text_dict.keys():
texts.append(
ax.text(
text_dict[label][0],
text_dict[label][1],
label,
fontsize=fontsize,
)
)
adjust_text(
texts, arrowprops=dict(arrowstyle="-", color="black", lw=0.1), ax=ax
)
plt.tight_layout()
if filename:
save_to_file(fig, filename)
def log10_with0(x):
mx = np.min(x[x > 0])
x[x == 0] = mx / 10
return np.log10(x)
def get_palette(n_colors, palette_name="Set1"):
try:
palette = sns.color_palette(palette_name)
except:
print("Palette not found. Using default palette tab10")
palette = sns.color_palette()
while len(palette) < n_colors:
palette += palette
return palette
def fast_dimred(emb, method="KernelPCA"):
"""
Takes high dimensional embeddings and produces a 2-dimensional representation
for plotting.
emb: np.array
Embeddings matrix.
method: str (default: 'KernelPCA')
Method for dimensionality reduction: KernelPCA, UMAPpre, UMAPcos, tSNE.
If None return first 2 dimensions of the embedding vector.
"""
if method is None:
return emb[:, :2]
elif method == "KernelPCA":
similarity_matrix = cosine_similarity(emb)
np.fill_diagonal(similarity_matrix, 1.0)
X = KernelPCA(n_components=2, kernel="precomputed").fit_transform(
similarity_matrix
)
else:
raise NotImplementedError
return X
def plot_dose_response(
df,
contvar_key,
perturbation_key,
df_ref=None,
response_name="response",
use_ref_response=False,
palette=None,
col_dict=None,
fontsize=8,
measured_points=None,
interpolate=True,
f1=7,
f2=3.0,
bbox=(1.35, 1.0),
ref_name="origin",
title_name="None",
plot_vertical=True,
fname=None,
logscale=None,
xlabelname=None,
figformat="png",
):
"""Plotting decoding of the response with respect to dose.
Params
------
df : `DataFrame`
Table with columns=[perturbation_key, contvar_key, response_name].
The last column is always "response".
contvar_key : str
Name of the column in df for values to use for x axis.
perturbation_key : str
Name of the column in df for the perturbation or covariate to plot.
response_name: str (default: response)
Name of the column in df for values to use for y axis.
df_ref : `DataFrame` (default: None)
Table with the same columns as in df to plot ground_truth or another
condition for comparison. Could
also be used to just extract reference values for x-axis.
use_ref_response : bool (default: False)
A flag indicating if to use values for y axis from df_ref (True) or j
ust to extract reference values for x-axis.
col_dict : dictionary (default: None)
Dictionary with colors for each value in perturbation_key.
bbox : tuple (default: (1.35, 1.))
Coordinates to adjust the legend.
plot_vertical : boolean (default: False)
Flag if to plot reference values for x axis from df_ref dataframe.
f1 : float (default: 7.0))
Width in inches for the plot.
f2 : float (default: 3.0))
Hight in inches for the plot.
fname : str (default: None)
Name of the file to export the plot. The name comes without format
extension.
format : str (default: png)
Format for the file to export the plot.
"""
sns.set_style("white")
if use_ref_response and not (df_ref is None):
df[ref_name] = "predictions"
df_ref[ref_name] = "observations"
if interpolate:
df_plt = pd.concat([df, df_ref])
else:
df_plt = df
else:
df_plt = df
df_plt = df_plt.reset_index()
atomic_drugs = np.unique(df[perturbation_key].values)
if palette is None:
current_palette = get_palette(len(list(atomic_drugs)))
if col_dict is None:
col_dict = dict(zip(list(atomic_drugs), current_palette))
fig = plt.figure(figsize=(f1, f2))
ax = plt.gca()
if use_ref_response:
sns.lineplot(
x=contvar_key,
y=response_name,
palette=col_dict,
hue=perturbation_key,
style=ref_name,
dashes=[(1, 0), (2, 1)],
legend="full",
style_order=["predictions", "observations"],
data=df_plt,
ax=ax,
)
df_ref = df_ref.replace("training_treated", "train")
sns.scatterplot(
x=contvar_key,
y=response_name,
hue="split",
size="num_cells",
sizes=(10, 100),
alpha=1.0,
palette={"train": "#000000", "training": "#000000", "ood": "#e41a1c"},
data=df_ref,
ax=ax,
)
sns.despine()
ax.legend_.remove()
else:
sns.lineplot(
x=contvar_key,
y=response_name,
palette=col_dict,
hue=perturbation_key,
data=df_plt,
ax=ax,
)
ax.legend(loc="upper right", bbox_to_anchor=bbox, fontsize=fontsize)
sns.despine()
if not (title_name is None):
ax.set_title(title_name, fontsize=fontsize, fontweight="bold")
ax.grid("off")
if xlabelname is None:
ax.set_xlabel(contvar_key, fontsize=fontsize)
else:
ax.set_xlabel(xlabelname, fontsize=fontsize)
ax.set_ylabel(f"{response_name}", fontsize=fontsize)
ax.xaxis.set_tick_params(labelsize=fontsize)
ax.yaxis.set_tick_params(labelsize=fontsize)
if not (logscale is None):
ax.set_xticks(np.log10(logscale))
ax.set_xticklabels(logscale, rotation=90)
if not (df_ref is None):
atomic_drugs = np.unique(df_ref[perturbation_key].values)
for drug in atomic_drugs:
x = df_ref[df_ref[perturbation_key] == drug][contvar_key].values
m1 = np.min(df[df[perturbation_key] == drug][response_name].values)
m2 = np.max(df[df[perturbation_key] == drug][response_name].values)
if plot_vertical:
for x_dot in x:
ax.plot(
[x_dot, x_dot],
[m1, m2],
":",
color="black",
linewidth=0.5,
alpha=0.5,
)
fig.tight_layout()
if fname:
plt.savefig(f"{fname}.{figformat}", format=figformat, dpi=600)
return fig
def plot_uncertainty_comb_dose(
cpa_api,
cov,
pert,
N=11,
metric="cosine",
measured_points=None,
cond_key="condition",
figsize=(4, 4),
vmin=None,
vmax=None,
sizes=(40, 160),
df_ref=None,
xlims=(0, 1.03),
ylims=(0, 1.03),
fixed_drugs="",
fixed_doses="",
title="",
filename=None,
):
"""Plotting uncertainty for a single perturbation at a dose range for a
particular covariate.
Params
------
cpa_api
Api object for the model class.
cov : dict
Name of covariate.
pert : str
Name of the perturbation.
N : int
Number of dose values.
metric: str (default: 'cosine')
Metric to evaluate uncertainty.
measured_points : dict (default: None)
A dicitionary of dictionaries. Per each covariate a dictionary with
observed doses per perturbation, e.g. {'covar1': {'pert1':
[0.1, 0.5, 1.0], 'pert2': [0.3]}
cond_key : str (default: 'condition')
Name of the variable to use for plotting.
filename : str (default: None)
Full path to the file to export the plot. File extension should be
included.
Returns
-------
pd.DataFrame of uncertainty estimations.
"""
cov_name = "_".join([cov[cov_key] for cov_key in cpa_api.covariate_keys])
df_list = []
for i in np.round(np.linspace(0, 1, N), decimals=2):
for j in np.round(np.linspace(0, 1, N), decimals=2):
df_list.append(
{
"covariates": cov_name,
"condition": pert + fixed_drugs,
"dose_val": str(i) + "+" + str(j) + fixed_doses,
}
)
df_pred = pd.DataFrame(df_list)
uncert_cos = []
uncert_eucl = []
closest_cond_cos = []
closest_cond_eucl = []
for i in range(df_pred.shape[0]):
(
uncert_cos_,
uncert_eucl_,
closest_cond_cos_,
closest_cond_eucl_,
) = cpa_api.compute_uncertainty(
cov=cov, pert=df_pred.iloc[i]["condition"], dose=df_pred.iloc[i]["dose_val"]
)
uncert_cos.append(uncert_cos_)
uncert_eucl.append(uncert_eucl_)
closest_cond_cos.append(closest_cond_cos_)
closest_cond_eucl.append(closest_cond_eucl_)
df_pred["uncertainty_cosine"] = uncert_cos
df_pred["uncertainty_eucl"] = uncert_eucl
df_pred["closest_cond_cos"] = closest_cond_cos
df_pred["closest_cond_eucl"] = closest_cond_eucl
doses = df_pred.dose_val.apply(lambda x: x.split("+"))
X = np.array(doses.apply(lambda x: x[0]).astype(float)).reshape(N, N)
Y = np.array(doses.apply(lambda x: x[1]).astype(float)).reshape(N, N)
Z = np.array(df_pred[f"uncertainty_{metric}"].values.astype(float)).reshape(N, N)
fig, ax = plt.subplots(1, 1, figsize=figsize)
CS = ax.contourf(X, Y, Z, cmap="coolwarm", levels=20, alpha=1, vmin=vmin, vmax=vmax)
ax.set_xlabel(pert.split("+")[0], fontweight="bold")
ax.set_ylabel(pert.split("+")[1], fontweight="bold")
if not (df_ref is None):
sns.scatterplot(
x=pert.split("+")[0],
y=pert.split("+")[1],
hue="split",
size="num_cells",
sizes=sizes,
alpha=1.0,
palette={"train": "#000000", "training": "#000000", "ood": "#e41a1c"},
data=df_ref,
ax=ax,
)
ax.legend_.remove()
if measured_points:
ticks = measured_points[cov_name][pert]
xticks = [float(x.split("+")[0]) for x in ticks]
yticks = [float(x.split("+")[1]) for x in ticks]
ax.set_xticks(xticks)
ax.set_xticklabels(xticks, rotation=90)
ax.set_yticks(yticks)
fig.colorbar(CS)
sns.despine()
ax.axis("equal")
ax.axis("square")
ax.set_xlim(xlims)
ax.set_ylim(ylims)
ax.set_title(title, fontsize=10, fontweight='bold')
plt.tight_layout()
if filename:
plt.savefig(filename, dpi=600)
return df_pred
def plot_uncertainty_dose(
cpa_api,
cov,
pert,
N=11,
metric="cosine",
measured_points=None,
cond_key="condition",
log=False,
min_dose=None,
filename=None,
):
"""Plotting uncertainty for a single perturbation at a dose range for a
particular covariate.
Params
------
cpa_api
Api object for the model class.
cov : str
Name of covariate.
pert : str
Name of the perturbation.
N : int
Number of dose values.
metric: str (default: 'cosine')
Metric to evaluate uncertainty.
measured_points : dict (default: None)
A dicitionary of dictionaries. Per each covariate a dictionary with
observed doses per perturbation, e.g. {'covar1': {'pert1':
[0.1, 0.5, 1.0], 'pert2': [0.3]}
cond_key : str (default: 'condition')
Name of the variable to use for plotting.
log : boolean (default: False)
A flag if to plot on a log scale.
min_dose : float (default: None)
Minimum dose for the uncertainty estimate.
filename : str (default: None)
Full path to the file to export the plot. File extension should be included.
Returns
-------
pd.DataFrame of uncertainty estimations.
"""
df_list = []
if log:
if min_dose is None:
min_dose = 1e-3
N_val = np.round(np.logspace(np.log10(min_dose), np.log10(1), N), decimals=10)
else:
if min_dose is None:
min_dose = 0
N_val = np.round(np.linspace(min_dose, 1.0, N), decimals=3)
cov_name = "_".join([cov[cov_key] for cov_key in cpa_api.covariate_keys])
for i in N_val:
df_list.append({"covariates": cov_name, "condition": pert, "dose_val": repr(i)})
df_pred = pd.DataFrame(df_list)
uncert_cos = []
uncert_eucl = []
closest_cond_cos = []
closest_cond_eucl = []
for i in range(df_pred.shape[0]):
(
uncert_cos_,
uncert_eucl_,
closest_cond_cos_,
closest_cond_eucl_,
) = cpa_api.compute_uncertainty(
cov=cov, pert=df_pred.iloc[i]["condition"], dose=df_pred.iloc[i]["dose_val"]
)
uncert_cos.append(uncert_cos_)
uncert_eucl.append(uncert_eucl_)
closest_cond_cos.append(closest_cond_cos_)
closest_cond_eucl.append(closest_cond_eucl_)
df_pred["uncertainty_cosine"] = uncert_cos
df_pred["uncertainty_eucl"] = uncert_eucl
df_pred["closest_cond_cos"] = closest_cond_cos
df_pred["closest_cond_eucl"] = closest_cond_eucl
x = df_pred.dose_val.values.astype(float)
y = df_pred[f"uncertainty_{metric}"].values.astype(float)
fig, ax = plt.subplots(1, 1)
ax.plot(x, y)
ax.set_xlabel(pert)
ax.set_ylabel("Uncertainty")
ax.set_title(cov_name)
if log:
ax.set_xscale("log")
if measured_points:
ticks = measured_points[cov_name][pert]
ax.set_xticks(ticks)
ax.set_xticklabels(ticks, rotation=90)
else:
plt.draw()
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
sns.despine()
plt.tight_layout()
if filename:
plt.savefig(filename)
return df_pred
def save_to_file(fig, file_name, file_format=None):
if file_format is None:
if file_name.split(".")[-1] in ["png", "pdf"]:
file_format = file_name.split(".")[-1]
savename = file_name
else:
file_format = "pdf"
savename = f"{file_name}.{file_format}"
else:
savename = file_name
fig.savefig(savename, format=file_format, dpi=600)
print(f"Saved file to: {savename}")
def plot_embedding(
emb,
labels=None,
col_dict=None,
title=None,
show_lines=False,
show_text=False,
show_legend=True,
axis_equal=True,
circle_size=40,
circe_transparency=1.0,
line_transparency=0.8,
line_width=1.0,
fontsize=9,
fig_width=4,
fig_height=4,
file_name=None,
file_format=None,
labels_name=None,
width_ratios=[7, 1],
bbox=(1.3, 0.7),
):
sns.set_style("white")
# create data structure suitable for embedding
df = pd.DataFrame(emb, columns=["dim1", "dim2"])
if not (labels is None):
if labels_name is None:
labels_name = "labels"
df[labels_name] = labels
fig = plt.figure(figsize=(fig_width, fig_height))
ax = plt.gca()
sns.despine(left=False, bottom=False, right=True)
if (col_dict is None) and not (labels is None):
col_dict = get_colors(labels)
sns.scatterplot(
x="dim1",
y="dim2",
hue=labels_name,
palette=col_dict,
alpha=circe_transparency,
edgecolor="none",
s=circle_size,
data=df,
ax=ax,
)
try:
ax.legend_.remove()
except:
pass
if show_lines:
for i in range(len(emb)):
if col_dict is None:
ax.plot(
[0, emb[i, 0]],
[0, emb[i, 1]],
alpha=line_transparency,
linewidth=line_width,
c=None,
)
else:
ax.plot(
[0, emb[i, 0]],
[0, emb[i, 1]],
alpha=line_transparency,
linewidth=line_width,
c=col_dict[labels[i]],
)
if show_text and not (labels is None):
texts = []
labels = np.array(labels)
unique_labels = np.unique(labels)
for label in unique_labels:
idx_label = np.where(labels == label)[0]
texts.append(
ax.text(
np.mean(emb[idx_label, 0]),
np.mean(emb[idx_label, 1]),
label,
#fontsize=fontsize,
)
)
adjust_text(
texts, arrowprops=dict(arrowstyle="-", color="black", lw=0.1), ax=ax
)
if axis_equal:
ax.axis("equal")
ax.axis("square")
if title:
ax.set_title(title, fontweight="bold")
ax.set_xlabel("dim1"),# fontsize=fontsize)
ax.set_ylabel("dim2"),# fontsize=fontsize)
#ax.xaxis.set_tick_params(labelsize=fontsize)