Sensitivity Analysis across Hyperparameters

In [1]:

import pandas as pd
import numpy as np
np.random.seed(20251023)

data = pd.read_json("https://raw.githubusercontent.com/krisrs1128/distortions-data/main/data/mammoth_3d.json")
data.columns = ["x", "y", "z"]

import pandas as pd import numpy as np np.random.seed(20251023) data = pd.read_json("https://raw.githubusercontent.com/krisrs1128/distortions-data/main/data/mammoth_3d.json") data.columns = ["x", "y", "z"]

In [2]:

data

data

Out[2]:

	x	y	z
0	430.826	106.864	24.492
1	397.734	99.293	14.370
2	480.505	80.876	131.561
3	422.736	87.715	13.504
4	414.840	113.363	78.061
...	...	...	...
9995	492.742	-114.477	107.954
9996	502.376	-94.579	117.065
9997	467.228	-168.295	97.806
9998	494.780	-111.272	115.444
9999	486.007	-150.081	114.390

10000 rows × 3 columns

In [3]:

from anndata import AnnData
import scanpy as sc

n_neighbors = 50
adata = AnnData(X=data, obs=pd.DataFrame(index=data.index))
sc.pp.neighbors(adata, n_neighbors=n_neighbors)
sc.tl.umap(adata)
embedding = adata.obsm["X_umap"].copy()

from anndata import AnnData import scanpy as sc n_neighbors = 50 adata = AnnData(X=data, obs=pd.DataFrame(index=data.index)) sc.pp.neighbors(adata, n_neighbors=n_neighbors) sc.tl.umap(adata) embedding = adata.obsm["X_umap"].copy()

In [4]:

import numpy as np

radius = 3 * np.mean(adata.obsp["distances"].data)
parameters = {
    "radius": 2 ** (np.linspace(-1, 1, 10)) * radius,
    "scaling_epps": np.linspace(3, 7, 10)
}

import numpy as np radius = 3 * np.mean(adata.obsp["distances"].data) parameters = { "radius": 2 ** (np.linspace(-1, 1, 10)) * radius, "scaling_epps": np.linspace(3, 7, 10) }

In [5]:

radius

radius

Out[5]:

32.194828033447266

In [6]:

from distortions.geometry import Geometry
from distortions.geometry import expand_geoms
from distortions.geometry import metric_sensitivity

n_neighbors = 50
geom = Geometry("brute", laplacian_method="geometric", affinity_kwds={"radius": radius}, adjacency_kwds={"n_neighbors": n_neighbors}, laplacian_kwds={"scaling_epps": 5})
geoms, params_df = expand_geoms(geom, parameters)
Hvv, Hs = metric_sensitivity(geoms, embedding, data)

from distortions.geometry import Geometry from distortions.geometry import expand_geoms from distortions.geometry import metric_sensitivity n_neighbors = 50 geom = Geometry("brute", laplacian_method="geometric", affinity_kwds={"radius": radius}, adjacency_kwds={"n_neighbors": n_neighbors}, laplacian_kwds={"scaling_epps": 5}) geoms, params_df = expand_geoms(geom, parameters) Hvv, Hs = metric_sensitivity(geoms, embedding, data)

In [7]:

plot_data = []
for i in range(Hs.shape[1]):
    for j in range(Hs.shape[0]):
        plot_data.append({
            "sample": i,
            "combination": j,
            "hv00": float(Hvv[j, i, 0, 0]),
            "hv11": float(Hvv[j, i, 1, 1]),
            "hv01": float(Hvv[j, i, 0, 1]),
            "hv10": float(Hvv[j, i, 1, 0]),
            "hs0": float(Hs[j, i, 0]),
            "hs1": float(Hs[j, i, 1])
        })

params_df["combination"] = params_df.index
plot_df = pd.DataFrame(plot_data)
plot_df = plot_df.merge(params_df)

plot_data = [] for i in range(Hs.shape[1]): for j in range(Hs.shape[0]): plot_data.append({ "sample": i, "combination": j, "hv00": float(Hvv[j, i, 0, 0]), "hv11": float(Hvv[j, i, 1, 1]), "hv01": float(Hvv[j, i, 0, 1]), "hv10": float(Hvv[j, i, 1, 0]), "hs0": float(Hs[j, i, 0]), "hs1": float(Hs[j, i, 1]) }) params_df["combination"] = params_df.index plot_df = pd.DataFrame(plot_data) plot_df = plot_df.merge(params_df)

This is variation in the coordinate $H_{00}$ across choices of radius and scaling epsilon.

In [8]:

import altair as alt
alt.data_transformers.enable("vegafusion")

random_samples = np.random.choice(plot_df["sample"].unique(), size=20, replace=False)

charts = []
for i in random_samples:
    random_subset = plot_df[plot_df["sample"] == i]
    chart_ = alt.Chart(random_subset).mark_rect().encode(
        x=alt.X('radius:N', title='Radius', axis=alt.Axis(format='.2f')),
        y=alt.Y('scaling_epps:N', title='Epsilon', axis=alt.Axis(format='.2f')),
        fill=alt.Fill('hv00:Q', title='h')
    )
    charts.append(chart_)

#[display(p) for p in charts]

import altair as alt alt.data_transformers.enable("vegafusion") random_samples = np.random.choice(plot_df["sample"].unique(), size=20, replace=False) charts = [] for i in random_samples: random_subset = plot_df[plot_df["sample"] == i] chart_ = alt.Chart(random_subset).mark_rect().encode( x=alt.X('radius:N', title='Radius', axis=alt.Axis(format='.2f')), y=alt.Y('scaling_epps:N', title='Epsilon', axis=alt.Axis(format='.2f')), fill=alt.Fill('hv00:Q', title='h') ) charts.append(chart_) #[display(p) for p in charts]

Here is change in the singular values across the same parameters.

In [9]:

charts = []
for i in random_samples:
    random_subset = plot_df[plot_df["sample"] == i]
    chart_ = alt.Chart(random_subset).mark_rect().encode(
        x=alt.X('radius:N', title='Radius', axis=alt.Axis(format='.2f')),
        y=alt.Y('scaling_epps:N', title='Epsilon', axis=alt.Axis(format='.2f')),
        fill=alt.Fill('hs0:Q', title='h')
    )
    charts.append(chart_)

#[display(p) for p in charts]

charts = [] for i in random_samples: random_subset = plot_df[plot_df["sample"] == i] chart_ = alt.Chart(random_subset).mark_rect().encode( x=alt.X('radius:N', title='Radius', axis=alt.Axis(format='.2f')), y=alt.Y('scaling_epps:N', title='Epsilon', axis=alt.Axis(format='.2f')), fill=alt.Fill('hs0:Q', title='h') ) charts.append(chart_) #[display(p) for p in charts]

Here are some line plots to visualize sensitivity across all samples.

In [10]:

import altair as alt
alt.data_transformers.enable("vegafusion")

ixl = np.random.choice(plot_df["sample"].unique(), size=500, replace=False)
sub_df = plot_df.loc[plot_df["sample"].isin(ixl)]

line_plots = []
p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'),
    y=alt.Y('hv00:Q', title='Hvv[0,0]'),
    detail=['sample:N', 'radius:Q']
)
line_plots.append(p)

p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('radius:Q', title='Radius'),
    y=alt.Y('hv00:Q', title='Hvv[0,0]'),
    detail=['sample:N', 'scaling_epps:Q']
)
line_plots.append(p)

p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'),
    y=alt.Y('hs0:Q', title='first singular value'),
    color=alt.Color('radius:Q'),
    detail=['sample:N', "radius:Q"]
)
line_plots.append(p)

p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('radius:Q', title='Laplacian Radius'),
    y=alt.Y('hs0:Q', title='first singular value'),
    color=alt.Color('scaling_epps:Q'),
    detail=['sample:N', "scaling_epps:Q"]
)
line_plots.append(p)

#[display(p) for p in line_plots]

import altair as alt alt.data_transformers.enable("vegafusion") ixl = np.random.choice(plot_df["sample"].unique(), size=500, replace=False) sub_df = plot_df.loc[plot_df["sample"].isin(ixl)] line_plots = [] p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'), y=alt.Y('hv00:Q', title='Hvv[0,0]'), detail=['sample:N', 'radius:Q'] ) line_plots.append(p) p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('radius:Q', title='Radius'), y=alt.Y('hv00:Q', title='Hvv[0,0]'), detail=['sample:N', 'scaling_epps:Q'] ) line_plots.append(p) p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'), y=alt.Y('hs0:Q', title='first singular value'), color=alt.Color('radius:Q'), detail=['sample:N', "radius:Q"] ) line_plots.append(p) p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('radius:Q', title='Laplacian Radius'), y=alt.Y('hs0:Q', title='first singular value'), color=alt.Color('scaling_epps:Q'), detail=['sample:N', "scaling_epps:Q"] ) line_plots.append(p) #[display(p) for p in line_plots]

In [11]:

p = alt.Chart(sub_df).mark_line(size=0.2, clip=True).encode(
    x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'),
    y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(domain=[0, 0.5])),
    color=alt.Color('radius:Q'),
    detail=['sample:N', 'radius:Q']
)
line_plots.append(p)

p = alt.Chart(sub_df).mark_line(size=0.2, clip=True).encode(
    x=alt.X('radius:Q', title='Laplacian Radius'),
    y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(domain=[0, 0.5])),
    color=alt.Color('scaling_epps:Q'),
    detail=['sample:N', 'scaling_epps:Q']
)
line_plots.append(p)

#[display(p) for p in line_plots[-2:]]

p = alt.Chart(sub_df).mark_line(size=0.2, clip=True).encode( x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'), y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(domain=[0, 0.5])), color=alt.Color('radius:Q'), detail=['sample:N', 'radius:Q'] ) line_plots.append(p) p = alt.Chart(sub_df).mark_line(size=0.2, clip=True).encode( x=alt.X('radius:Q', title='Laplacian Radius'), y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(domain=[0, 0.5])), color=alt.Color('scaling_epps:Q'), detail=['sample:N', 'scaling_epps:Q'] ) line_plots.append(p) #[display(p) for p in line_plots[-2:]]

In [12]:

p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'),
    y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(type='log')),
    color=alt.Color('radius:Q'),
    detail=['sample:N', 'radius:Q']
)
line_plots.append(p)

p = alt.Chart(sub_df).mark_line(size=0.2).encode(
    x=alt.X('radius:Q', title='Laplacian Radius'),
    y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(type='log')),
    color=alt.Color('scaling_epps:Q'),
    detail=['sample:N', 'scaling_epps:Q']
)
line_plots.append(p)

#[display(p) for p in line_plots[-2:]]

p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('scaling_epps:Q', title='Rescaling Epsilon'), y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(type='log')), color=alt.Color('radius:Q'), detail=['sample:N', 'radius:Q'] ) line_plots.append(p) p = alt.Chart(sub_df).mark_line(size=0.2).encode( x=alt.X('radius:Q', title='Laplacian Radius'), y=alt.Y('hs0:Q', title='first singular value', scale=alt.Scale(type='log')), color=alt.Color('scaling_epps:Q'), detail=['sample:N', 'scaling_epps:Q'] ) line_plots.append(p) #[display(p) for p in line_plots[-2:]]

In [13]:

sub_df.to_csv("data/sensitivity_sub_df.csv")

sub_df.to_csv("data/sensitivity_sub_df.csv")

In [14]:

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib as mpl

def ellipse_sequence(single_sample):
    Hvv_i, Hs_i = [], []
    for i in range(len(single_sample)):
        h = np.array([
            [single_sample["hv00"].values[i],
            single_sample["hv01"].values[i]],
            [single_sample["hv10"].values[i],
            single_sample["hv11"].values[i]]
        ])
        s = np.array([
            single_sample["hs0"].values[i],
            single_sample["hs1"].values[i]
        ])

        Hvv_i.append(h)
        Hs_i.append(s)
    return Hvv_i, Hs_i

def plot_ellipses(Hvv_i, Hs_i, sample_df, max_cols=5, cmap_name='cividis'):
    """
    Facet by unique scaling_epps values and color ellipses by radius.

    Hvv_i and Hs_i must align with rows of sample_df.
    """
    epsilons = np.unique(sample_df["scaling_epps"].values)
    n_e = len(epsilons)
    ncols = min(max_cols, n_e)
    nrows = int(np.ceil(n_e / ncols))
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
                             figsize=(4 * ncols, 4 * nrows),
                             squeeze=False, constrained_layout=True)
    axes_flat = axes.ravel()

    # Colormap normalized over radius in this sample_df
    vmin = float(sample_df["radius"].min())
    vmax = float(sample_df["radius"].max())
    # Use a raw palette sequence
    palette = [
        "#7E1700", "#862D06", "#8F3F0D", "#984E14", "#9F5D1A", "#A56B1F", "#AC7926",
        "#B4872F", "#BB9739", "#C3A848", "#CAB95C", "#CFCB74", "#D1D98E", "#CFE4A6",
        "#C6E9BA", "#B9EAC8", "#A6E6D1", "#91DED6", "#7AD3D6", "#63C5D4", "#51B7D0",
        "#41A7CA", "#3698C4", "#2E8BBE", "#287DB8", "#246FB3", "#1F60AD", "#1952A6",
        "#11419F", "#023198"
    ]
    cmap = mpl.colors.ListedColormap(palette)

    # Map each epsilon value to an axis (use enumeration order)
    theta = np.linspace(0, 2 * np.pi, 200)
    epsilon_to_ax = {e: axes_flat[i] for i, e in enumerate(epsilons)}

    for idx, (vals, vecs) in enumerate(zip(Hs_i, Hvv_i)):
        circle = np.array([np.cos(theta), np.sin(theta)])
        ellipse = vecs @ np.diag(np.sqrt(np.abs(vals))) @ circle
        epsilon_val = sample_df["scaling_epps"].values[idx]
        radius_val = float(sample_df["radius"].values[idx])
        # Normalize radius to [0, 1] for colormap
        norm_radius = (radius_val - vmin) / (vmax - vmin) if vmax > vmin else 0.0
        color = cmap(norm_radius)
        ax = epsilon_to_ax[epsilon_val]
        ax.plot(ellipse[0, :], ellipse[1, :], lw=1, color=color, alpha=0.9)
        ax.set_title(f"epsilon={epsilon_val:.3g}")
        ax.set_aspect('equal')

    # Hide any unused axes
    for j in range(len(epsilons), len(axes_flat)):
        axes_flat[j].axis('off')

    # Add a colorbar for radius
    norm = mcolors.Normalize(vmin=vmin, vmax=vmax)
    sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
    sm.set_array([])  # Required for colorbar

    cbar = fig.colorbar(sm, ax=axes.ravel().tolist(),
                        orientation='horizontal', fraction=0.02, pad=0.04)
    cbar.set_label('radius')
    plt.show()

import matplotlib.pyplot as plt import matplotlib.colors as mcolors import matplotlib as mpl def ellipse_sequence(single_sample): Hvv_i, Hs_i = [], [] for i in range(len(single_sample)): h = np.array([ [single_sample["hv00"].values[i], single_sample["hv01"].values[i]], [single_sample["hv10"].values[i], single_sample["hv11"].values[i]] ]) s = np.array([ single_sample["hs0"].values[i], single_sample["hs1"].values[i] ]) Hvv_i.append(h) Hs_i.append(s) return Hvv_i, Hs_i def plot_ellipses(Hvv_i, Hs_i, sample_df, max_cols=5, cmap_name='cividis'): """ Facet by unique scaling_epps values and color ellipses by radius. Hvv_i and Hs_i must align with rows of sample_df. """ epsilons = np.unique(sample_df["scaling_epps"].values) n_e = len(epsilons) ncols = min(max_cols, n_e) nrows = int(np.ceil(n_e / ncols)) fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(4 * ncols, 4 * nrows), squeeze=False, constrained_layout=True) axes_flat = axes.ravel() # Colormap normalized over radius in this sample_df vmin = float(sample_df["radius"].min()) vmax = float(sample_df["radius"].max()) # Use a raw palette sequence palette = [ "#7E1700", "#862D06", "#8F3F0D", "#984E14", "#9F5D1A", "#A56B1F", "#AC7926", "#B4872F", "#BB9739", "#C3A848", "#CAB95C", "#CFCB74", "#D1D98E", "#CFE4A6", "#C6E9BA", "#B9EAC8", "#A6E6D1", "#91DED6", "#7AD3D6", "#63C5D4", "#51B7D0", "#41A7CA", "#3698C4", "#2E8BBE", "#287DB8", "#246FB3", "#1F60AD", "#1952A6", "#11419F", "#023198" ] cmap = mpl.colors.ListedColormap(palette) # Map each epsilon value to an axis (use enumeration order) theta = np.linspace(0, 2 * np.pi, 200) epsilon_to_ax = {e: axes_flat[i] for i, e in enumerate(epsilons)} for idx, (vals, vecs) in enumerate(zip(Hs_i, Hvv_i)): circle = np.array([np.cos(theta), np.sin(theta)]) ellipse = vecs @ np.diag(np.sqrt(np.abs(vals))) @ circle epsilon_val = sample_df["scaling_epps"].values[idx] radius_val = float(sample_df["radius"].values[idx]) # Normalize radius to [0, 1] for colormap norm_radius = (radius_val - vmin) / (vmax - vmin) if vmax > vmin else 0.0 color = cmap(norm_radius) ax = epsilon_to_ax[epsilon_val] ax.plot(ellipse[0, :], ellipse[1, :], lw=1, color=color, alpha=0.9) ax.set_title(f"epsilon={epsilon_val:.3g}") ax.set_aspect('equal') # Hide any unused axes for j in range(len(epsilons), len(axes_flat)): axes_flat[j].axis('off') # Add a colorbar for radius norm = mcolors.Normalize(vmin=vmin, vmax=vmax) sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap) sm.set_array([]) # Required for colorbar cbar = fig.colorbar(sm, ax=axes.ravel().tolist(), orientation='horizontal', fraction=0.02, pad=0.04) cbar.set_label('radius') plt.show()

In [15]:

for i in random_samples:
    sample_df = plot_df[(plot_df["sample"] == i)]
    Hvv_i, Hs_i = ellipse_sequence(sample_df)
    plot_ellipses(Hvv_i, Hs_i, sample_df)

for i in random_samples: sample_df = plot_df[(plot_df["sample"] == i)] Hvv_i, Hs_i = ellipse_sequence(sample_df) plot_ellipses(Hvv_i, Hs_i, sample_df)

Almost all the singular vectors were stable -- but a few were not. What might be responsible for this? Let's pick a few examples and analyze in more depth.

In [16]:

sub_df

sub_df

Out[16]:

	sample	combination	hv00	hv11	hv01	hv10	hs0	hs1	radius	scaling_epps
1100	11	0	-0.889882	0.889882	0.456191	0.456191	2.655257	0.037078	16.097414	3.000000
1101	11	1	-0.889882	0.889882	0.456191	0.456191	2.655257	0.037078	16.097414	3.444444
1102	11	2	-0.889882	0.889882	0.456191	0.456191	2.655257	0.037078	16.097414	3.888889
1103	11	3	-0.889882	0.889882	0.456191	0.456191	2.655257	0.037078	16.097414	4.333333
1104	11	4	-0.889882	0.889882	0.456191	0.456191	2.655257	0.037078	16.097414	4.777778
...	...	...	...	...	...	...	...	...	...	...
997095	9970	95	-0.484864	0.484864	0.874590	0.874590	0.054277	0.049231	64.389656	5.222222
997096	9970	96	-0.484864	0.484864	0.874590	0.874590	0.054277	0.049231	64.389656	5.666667
997097	9970	97	-0.484864	0.484864	0.874590	0.874590	0.054277	0.049231	64.389656	6.111111
997098	9970	98	-0.484864	0.484864	0.874590	0.874590	0.054277	0.049231	64.389656	6.555556
997099	9970	99	-0.484864	0.484864	0.874590	0.874590	0.054277	0.049231	64.389656	7.000000

50000 rows × 10 columns

In [17]:

def sample_adj_diffs(df, col='hv00', sort_by='radius'):
    # returns DataFrame of adjacent absolute diffs per row (aligned with the
    # later row in the pair)
    out = []
    for s, g in df.groupby('sample'):
        g_sorted = g.sort_values(sort_by).reset_index(drop=True)
        diffs = g_sorted[col].diff().abs()
        for i in range(1, len(g_sorted)):
            out.append({
                'sample': s,
                'abs_diff': float(diffs.iloc[i]),
                'radius_prev': float(g_sorted.loc[i-1, sort_by]),
                'radius_curr': float(g_sorted.loc[i, sort_by]),
                'hv00_prev': float(g_sorted.loc[i-1, col]),
                'hv00_curr': float(g_sorted.loc[i, col])
            })
    return pd.DataFrame(out)

# compute all adjacent diffs
thresh = 0.1
adj = sample_adj_diffs(plot_df, col='hv00', sort_by='radius')
adj = adj[adj['abs_diff'] >= thresh].sort_values('abs_diff', ascending=False)
adj

def sample_adj_diffs(df, col='hv00', sort_by='radius'): # returns DataFrame of adjacent absolute diffs per row (aligned with the # later row in the pair) out = [] for s, g in df.groupby('sample'): g_sorted = g.sort_values(sort_by).reset_index(drop=True) diffs = g_sorted[col].diff().abs() for i in range(1, len(g_sorted)): out.append({ 'sample': s, 'abs_diff': float(diffs.iloc[i]), 'radius_prev': float(g_sorted.loc[i-1, sort_by]), 'radius_curr': float(g_sorted.loc[i, sort_by]), 'hv00_prev': float(g_sorted.loc[i-1, col]), 'hv00_curr': float(g_sorted.loc[i, col]) }) return pd.DataFrame(out) # compute all adjacent diffs thresh = 0.1 adj = sample_adj_diffs(plot_df, col='hv00', sort_by='radius') adj = adj[adj['abs_diff'] >= thresh].sort_values('abs_diff', ascending=False) adj

Out[17]:

	sample	abs_diff	radius_prev	radius_curr	hv00_prev	hv00_curr
27947	282	1.658717	21.905200	25.553052	0.668439	-0.990278
797751	8058	1.644829	16.097414	18.778101	0.644997	-0.999832
332580	3359	1.600961	25.553052	29.808377	0.635254	-0.965707
248608	2511	1.537157	18.778101	21.905200	0.648442	-0.888714
145658	1471	1.522120	21.905200	25.553052	0.660227	-0.861893
...	...	...	...	...	...	...
118413	1196	0.103036	16.097414	18.778101	-0.338080	-0.235044
245133	2476	0.102851	16.097414	18.778101	-0.794161	-0.897013
132887	1342	0.101218	21.905200	25.553052	-0.729922	-0.831140
148123	1496	0.100459	18.778101	21.905200	-0.686773	-0.787232
722640	7299	0.100093	25.553052	29.808377	-0.627275	-0.527182

274 rows × 6 columns

In [18]:

for i in adj["sample"].values[:5]:
    sample_df = plot_df[(plot_df["sample"] == i)]
    Hvv_i, Hs_i = ellipse_sequence(sample_df)
    plot_ellipses(Hvv_i, Hs_i, sample_df)

for i in adj["sample"].values[:5]: sample_df = plot_df[(plot_df["sample"] == i)] Hvv_i, Hs_i = ellipse_sequence(sample_df) plot_ellipses(Hvv_i, Hs_i, sample_df)

Overlaying Ellipses¶

In [19]:

from distortions.geometry import bind_metric

embedding = adata.obsm["X_umap"].copy()
embedding_list = []

for i in range(len(Hvv)):
    embedding_i = bind_metric(embedding, Hvv[i], Hs[i])
    embedding_i["parameter_group"] = i
    embedding_i["sample"] = range(len(embedding_i))
    embedding_list.append(embedding_i)

from distortions.geometry import bind_metric embedding = adata.obsm["X_umap"].copy() embedding_list = [] for i in range(len(Hvv)): embedding_i = bind_metric(embedding, Hvv[i], Hs[i]) embedding_i["parameter_group"] = i embedding_i["sample"] = range(len(embedding_i)) embedding_list.append(embedding_i)

In [20]:

combined_embedding = pd.concat(embedding_list)

ix = np.random.choice(embedding_list[0]["sample"], size=500, replace=False)
combined_embedding = combined_embedding[combined_embedding["sample"].isin(ix)]
combined_embedding = combined_embedding.merge(params_df, left_on="parameter_group", right_on="combination")
combined_embedding

combined_embedding = pd.concat(embedding_list) ix = np.random.choice(embedding_list[0]["sample"], size=500, replace=False) combined_embedding = combined_embedding[combined_embedding["sample"].isin(ix)] combined_embedding = combined_embedding.merge(params_df, left_on="parameter_group", right_on="combination") combined_embedding

Out[20]:

	embedding_0	embedding_1	x0	y0	x1	y1	s0	s1	angle	parameter_group	sample	radius	scaling_epps	combination
0	7.375961	-0.011776	-0.983779	-0.179386	-0.179386	0.983779	0.185832	0.067388	-79.665990	0	8	16.097414	3.0	0
1	7.354166	8.815589	-0.871437	0.490507	0.490507	0.871437	0.166502	0.057035	60.626081	0	16	16.097414	3.0	0
2	10.309316	4.898260	-0.983480	0.181017	0.181017	0.983480	0.226255	0.039947	79.570990	0	72	16.097414	3.0	0
3	16.243284	6.437485	-0.831112	-0.556105	-0.556105	0.831112	0.078166	0.008150	-56.213120	0	122	16.097414	3.0	0
4	10.166615	4.376842	0.283942	0.958842	0.958842	-0.283942	0.092243	0.047333	-16.495593	0	140	16.097414	3.0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49995	25.687422	9.248513	-0.868609	0.495498	0.495498	0.868609	0.091448	0.045784	60.297414	99	9947	64.389656	7.0	99
49996	21.379816	10.046772	0.438932	0.898520	0.898520	-0.438932	0.062808	0.036358	-26.035779	99	9956	64.389656	7.0	99
49997	21.190882	10.283684	-0.830467	-0.557068	-0.557068	0.830467	0.095534	0.028097	-56.146751	99	9971	64.389656	7.0	99
49998	21.102766	10.866966	-0.241780	0.970331	0.970331	0.241780	0.066084	0.052895	13.991624	99	9975	64.389656	7.0	99
49999	25.977240	10.568069	-0.963954	0.266068	0.266068	0.963954	0.120325	0.034445	74.569565	99	9976	64.389656	7.0	99

50000 rows × 14 columns

In [33]:

from distortions.visualization import dplot

pal = ["#CC533F", "#3C64D4"]
p = {}
p["subset_rcol"] = dplot(combined_embedding, height = 350, width=450)\
    .mapping(x="embedding_0", y="embedding_1", color="radius")\
    .geom_ellipse(opacity=0.1, radiusMin=1, radiusMax=20, stroke=True)\
    .scale_color(stroke=True, scheme=pal)\
    .labs(x="UMAP1", y="UMAP2")

from distortions.visualization import dplot pal = ["#CC533F", "#3C64D4"] p = {} p["subset_rcol"] = dplot(combined_embedding, height = 350, width=450)\ .mapping(x="embedding_0", y="embedding_1", color="radius")\ .geom_ellipse(opacity=0.1, radiusMin=1, radiusMax=20, stroke=True)\ .scale_color(stroke=True, scheme=pal)\ .labs(x="UMAP1", y="UMAP2")

In [34]:

p["subset_ecol"] = dplot(combined_embedding, height = 350, width=450)\
    .mapping(x="embedding_0", y="embedding_1", color="scaling_epps")\
    .geom_ellipse(opacity=0.1, radiusMin=1, radiusMax=20, stroke=True)\
    .scale_color(stroke=True)\
    .labs(x="UMAP1", y="UMAP2")

p["subset_ecol"] = dplot(combined_embedding, height = 350, width=450)\ .mapping(x="embedding_0", y="embedding_1", color="scaling_epps")\ .geom_ellipse(opacity=0.1, radiusMin=1, radiusMax=20, stroke=True)\ .scale_color(stroke=True)\ .labs(x="UMAP1", y="UMAP2")

In [41]:

[v.save(f"{k}.svg") for (k, v) in p.items()]

[v.save(f"{k}.svg") for (k, v) in p.items()]

Out[41]:

[None, None]

In [40]:

[display(p_i) for p_i in p.values()]

[display(p_i) for p_i in p.values()]

dplot(dataset=[{'embedding_0': 7.375960826873779, 'embedding_1': -0.01177565474063158, 'x0': -0.98377873024196…

dplot(dataset=[{'embedding_0': 7.375960826873779, 'embedding_1': -0.01177565474063158, 'x0': -0.98377873024196…

Out[40]:

[None, None]

In [ ]: