API reference / Other

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metbit.dev

Other module in metbit 5.2.2.

import metbit.dev

Classes

opls_da

Methods

__init__(self, X, y, features_name=None, n_components=2, scale='pareto', kfold=3, estimator='opls', random_state=42)

fit(self)

permutation_test(self, n_permutations=500, cv=3, n_jobs=-1, verbose=10)

vip_scores(self, model=None, features_name=None)

get_vip_scores(self)

vip_plot(self, x_range=9, threshold=2, size=12, width=1000, height=500, filter_=False)

plot_oplsda_scores(self, color_dict=None, symbol=None, symbol_dict=None, fig_height=900, fig_width=1300, marker_size=35, marker_opacity=0.7)

plot_hist(self, nbins_=50, height_=500, width_=1000)

plot_s_scores(self, height_=900, width_=2000, range_color_=[-0.05, 0.05], color_continuous_scale_='jet')

plot_loading(self, height_=900, width_=2000, range_color_=[-0.05, 0.05], color_continuous_scale_='jet')

pca

Principal Component Analysis (PCA) model.

Parameters

Xarray-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

labelarray-like, shape (n_samples,)

Target data, where n_samples is the number of samples.

features_namearray-like, shape (n_features,), default=None

Name of features.

n_componentsint, default=2

Number of components to keep.

scalestr, default='pareto'

Method of scaling. 'pareto' for pareto scaling, 'mean' for mean centering, 'uv' for unitvarian scaling.

random_stateint, default=42

Random state for permutation test.

test_sizefloat, default=0.3

Size of test set.

Examples

import pandas as pd import numpy as np from metbit import pca

# Create a dataset data = pd.DataFrame(np.random.rand(500, 50000)) class_ = pd.Series(np.random.choice(['A', 'B', 'C'], 500), name='Group') time = pd.Series(np.random.choice(['1-wk', '2-wk', '3-wk', '4-wk'], 500), name='Time point')

# Assign X and target X = datasets.iloc[:, 2:] y = datasets['Group'] time = datasets['Time point'] features_name = list(X.columns.astype(float))

## Perform PCA model pca_mod = pca(X=X, label=y, features_name=features_name, n_components=2, scale='pareto', random_state=42, test_size=0.3) pca_mod.fit()

# Visualisation of PCA model pca_mod.plot_observe_variance() pca_mod.plot_cumulative_observed() shape_ = {'1-wk': 'circle', '2-wk': 'square', '3-wk': 'diamond', '4-wk': 'cross'} pca_mod.plot_pca_scores(symbol=time, symbol_dict=shape_) pca_mod.plot_loading_() pca_mod.plot_pca_trajectory(time_=time, time_order={'1-wk': 0, '2-wk': 1, '3-wk': 2, '4-wk': 3}, color_dict={'A': '#636EFA', 'B': '#EF553B', 'C': '#00CC96'}, symbol_dict=shape_)