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The Frenchy answered on November 8, 2020 Popularity 10/10 Helpfulness 8/10

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pca python

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import numpy as np
from sklearn.decomposition import PCA

pca = PCA(n_components = 3) # Choose number of components
pca.fit(X) # fit on X_train if train/test split applied

print(pca.explained_variance_ratio_)

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Contributed on Nov 08 2020

The Frenchy

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from sklearn.decomposition import PCA

pca = PCA(n_components=3)
pca.fit(features)
features_pca = pca.transform(features)
print("original shape:   ", features.shape)
print("transformed shape:", features_pca.shape)
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)

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Contributed on Nov 15 2021

JJSSEEXX

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pca python

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from sklearn.decomposition import PCA
samples = df.drop("target", axis = 1).values
model = PCA(n_components=2)
model.fit(samples)
# Get the mean of the grain samples: mean
mean = model.mean_
# Get the first principal component: first_pc
first_pc = model.components_[0,:]
# Visualize direction of the component
plt.arrow(mean[0] , mean[1], first_pc[0], first_pc[1], color='red', width=0.01)
plt.axis('equal')
plt.show()

transformed = model.transform(samples)
# Principal Components
principal_components = model.components_
# Visualize principal components contribution
features = range(model.n_components_)
plt.bar(features, model.explained_variance_)
plt.show()
# Visualize how the dataset looks after transformation
xs = transformed[:,0]
ys = transformed[:,1]
plt.scatter(xs, ys, c=df["target"].values)
plt.show()
# Contribution of Original Features to Principal Components
for i, pc in enumerate(principal_components):
    plt.bar(list(df.columns), np.abs(pc), label=f'PC {i + 1}', alpha=0.7)
plt.xlabel('Original Features')
plt.ylabel('Absolute Loadings')
plt.legend()
plt.show()


#### TruncatedSVD : PCA on sparse dataset (most entries are zero, remembers entries by saving columns that have values to save space)
from sklearn.decomposition import TruncatedSVD
# Apply TruncatedSVD
svd_model = TruncatedSVD(n_components=2)
transformed_svd = svd_model.fit_transform(samples.values) # samples is scipy.sparse.csr_matrix
# Visualize how the dataset looks after transformation
xs_svd = transformed_svd[:, 0]
ys_svd = transformed_svd[:, 1]
plt.scatter(xs_svd, ys_svd)
plt.show()
# Principal Components
svd_principal_component = svd_model.components_
# Visualize principal components contribution
plt.bar(range(1, svd_model.n_components + 1), svd_model.explained_variance_ratio_)
plt.show()
# Contribution of Original Features to Principal Components
for i, loading in enumerate(svd_principal_component):
    plt.bar(df.columns, np.abs(loading), label=f'PC {i + 1}', alpha=0.7)
plt.show()

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Contributed on Dec 25 2023

Innocent Iguana

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pca python

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from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)

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Contributed on Nov 25 2021

Adventurous Addax

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pca in python

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def pca(data, n):
    data = np.array(data)

    # 均值
    mean_vector = np.mean(data, axis=0)

    # 协方差
    cov_mat = np.cov(data - mean_vector, rowvar=0)

    # 特征值 特征向量
    fvalue, fvector = np.linalg.eig(cov_mat)

    # 排序
    fvaluesort = np.argsort(-fvalue)

    # 取前几大的序号
    fValueTopN = fvaluesort[:n]

    # 保留前几大的数值
    newdata = fvector[:, fValueTopN]

    new = np.dot(data, newdata)

    return new

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Source: zhuanlan.zhihu.com

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Contributed on Jul 20 2022

Spotless Swiftlet

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