Load dataset

Let’s load the wine dataset to demonstrate the plots.

# Load library
from sklearn.datasets import load_wine
# Load dataset
data = load_wine()
X = data.data
y = data.target
labels = data.feature_names

from pca import pca
# Initialize
model = pca(normalize=True)

# Fit transform and include the column labels and row labels
results = model.fit_transform(X, col_labels=labels, row_labels=y)

# [pca] >Normalizing input data per feature (zero mean and unit variance)..
# [pca] >The PCA reduction is performed to capture [95.0%] explained variance using the [13] columns of the input data.
# [pca] >Fitting using PCA..
# [pca] >Computing loadings and PCs..
# [pca] >Computing explained variance..
# [pca] >Number of components is [10] that covers the [95.00%] explained variance.
# [pca] >The PCA reduction is performed on the [13] columns of the input dataframe.
# [pca] >Fitting using PCA..
# [pca] >Computing loadings and PCs..
# [pca] >Outlier detection using Hotelling T2 test with alpha=[0.05] and n_components=[10]
# [pca] >Outlier detection using SPE/DmodX with n_std=[2]

Scatter plot

# Make scatterplot
model.scatter()

# Make scatterplot
model.scatter(density=True)

# Gradient over the samples. High dense areas will be more colourful.
model.scatter(gradient='#FFFFFF', edgecolor=None)

# Include the outlier detection
model.scatter(SPE=True, density=True)

# Include the outlier detection
model.scatter(HT2=True, density=True)

# Look at different PCs: 1st PC=1  vs PC=3
model.scatter(PC=[0, 2])

Scatterplots

Biplot

# Make biplot
model.biplot()

# Here again, many other options can be turned on and off
model.biplot(SPE=True, HT2=True, legend=1)

Biplots

Biplot (only arrows)

# Make plot with parameters: set cmap to None and label and legend to False. Only directions will be plotted.
model.biplot(cmap=None, legend=False)

Explained variance plot

model.plot()

Alpha Transparency

fig, ax = model.scatter(alpha=1)

Markers

import numpy as np
from sklearn.datasets import make_friedman1
from pca import pca

# Make data set
X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)

# All available markers
markers = np.array(['.', 'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X'])
# Generate random integers
random_integers = np.random.randint(0, len(markers), size=X.shape[0])
# Draw markers
marker = markers[random_integers]

# Init
model = pca(verbose=3)
# Fit
model.fit_transform(X)
# Make plot with markers
fig, ax = model.biplot(c=[0, 0, 0],
                       marker=marker,
                       title='Demonstration of specifying markers per sample.',
                       n_feat=5,
                       legend=False)

Biplots with markers

Control color/marker/size per sample

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from sklearn.datasets import make_friedman1
from pca import pca

# Make data set
X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)

# All available markers
markers = np.array(['.', 'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X'])
# Create colors
cmap = plt.cm.get_cmap('tab20c', len(markers))
# Generate random integers
random_integers = np.random.randint(0, len(markers), size=X.shape[0])
# Draw markers
marker = markers[random_integers]
# Set colors
color = cmap.colors[random_integers, 0:3]
# Set Size
size = np.random.randint(50, 1000, size=X.shape[0])
# Set alpha
alpha = np.random.rand(1, X.shape[0])[0][random_integers]

# Init
model = pca(verbose=3)
# Fit
model.fit_transform(X)
# Make plot with blue arrows and text
fig, ax = model.biplot(
                        SPE=True,
                        HT2=True,
                        c=color,
                        s=size,
                        marker=marker,
                        alpha=alpha,
                        color_arrow='k',
                        title='Demonstration of specifying colors, markers, alpha, and size per sample.',
                        n_feat=5,
                        fontsize=20,
                        fontweight='normal',
                        arrowdict={'fontsize': 18},
                        density=True,
                        density_on_top=False,
                        )

Biplot with customized properties per sample.

3D plots

All plots can also be created in 3D by setting the d3=True parameter.

model.biplot3d()

Toggle visible status

The visible status for can be turned on and off.

# Make plot but not visible.
fig, ax = model.biplot(visible=False)

# Set the figure again to True and show the figure.
fig.set_visible(True)
fig

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