pca

pca_analysis(df, df_name='', k=10, scaler=StandardScaler(), max_scree=None, figsize=(12, 6))

Perform PCA analysis on a DataFrame with specified scaling.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input data frame to perform PCA on.	required
df_name	str	The name of the data frame.	''
k	int	The number of top features to select based on their influence on PC1.	10
scaler	obj	An instance of a Scaler from sklearn (e.g., StandardScaler()).	StandardScaler()
max_scree	int	The maximum number of principal components to plot in the scree plot. Default is None, which means all components will be plotted.	None
figsize	tuple	The size of the figure for the plots (width, height).	(12, 6)

Returns:

Name	Type	Description
tuple	tuple	Two pd.Index objects containing the names of the top k features most influential on PC1 and PC2, respectively.

Examples:

>>> import pandas as pd
>>> from spotpython.utils import pca_analysis
>>> df = pd.DataFrame({
...     "A": [1, 2, 3],
...     "B": [1, 2, 3],
...     "C": [4, 5, 6]
... })
>>> pca_analysis(df)

Source code in spotpython/utils/pca.py

def pca_analysis(
    df,
    df_name="",
    k=10,
    scaler=StandardScaler(),
    max_scree=None,
    figsize=(12, 6),
) -> tuple:
    """
    Perform PCA analysis on a DataFrame with specified scaling.

    Args:
        df (pd.DataFrame):
            The input data frame to perform PCA on.
        df_name (str):
            The name of the data frame.
        k (int):
            The number of top features to select based on their influence on PC1.
        scaler (obj):
            An instance of a Scaler from sklearn (e.g., StandardScaler()).
        max_scree (int):
            The maximum number of principal components to plot in the scree plot. Default is None, which means all components will be plotted.
        figsize (tuple):
            The size of the figure for the plots (width, height).

    Returns:
        tuple: Two pd.Index objects containing the names of the top k features most influential on PC1 and PC2, respectively.

    Examples:
        >>> import pandas as pd
        >>> from spotpython.utils import pca_analysis
        >>> df = pd.DataFrame({
        ...     "A": [1, 2, 3],
        ...     "B": [1, 2, 3],
        ...     "C": [4, 5, 6]
        ... })
        >>> pca_analysis(df)
    """
    # Scale the data
    scaled_data = scaler.fit_transform(df)
    feature_names = df.columns
    sample_names = df.index

    # Perform PCA
    pca = PCA()
    pca.fit(scaled_data)
    pca_data = pca.transform(scaled_data)

    # Scree plot
    per_var = np.round(pca.explained_variance_ratio_ * 100, decimals=1)
    full_labels = ["PC" + str(x) for x in range(1, len(per_var) + 1)]

    # Limit the number of PCs in the scree plot
    if max_scree is not None:
        per_var = per_var[:max_scree]
        scree_labels = full_labels[:max_scree]
    else:
        scree_labels = full_labels

    plt.figure(figsize=figsize)  # Set the figure size for the scree plot
    plt.bar(x=range(1, len(per_var) + 1), height=per_var, tick_label=scree_labels)
    plt.ylabel("Percentage of Explained Variance")
    plt.xlabel("Principal Component")
    plt.title(f"Scree Plot. {df_name}")
    plt.show()

    # PCA plot
    plt.figure(figsize=figsize)  # Set the figure size for the PCA plot
    pca_df = pd.DataFrame(pca_data, index=sample_names, columns=full_labels)

    plt.scatter(pca_df.PC1, pca_df.PC2)
    plt.title(f"PCA Graph. {df_name}")
    plt.xlabel("PC1 - {0}%".format(per_var[0]))
    plt.ylabel("PC2 - {0}%".format(per_var[1]))

    for sample in pca_df.index:
        plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]))

    plt.show()

    # Determine top k features influencing PC1 and PC2
    loading_scores_pc1 = pd.Series(pca.components_[0], index=feature_names)
    loading_scores_pc2 = pd.Series(pca.components_[1], index=feature_names)

    sorted_loading_scores_pc1 = loading_scores_pc1.abs().sort_values(ascending=False)
    sorted_loading_scores_pc2 = loading_scores_pc2.abs().sort_values(ascending=False)

    top_k_features_pc1 = sorted_loading_scores_pc1.head(k).index
    top_k_features_pc2 = sorted_loading_scores_pc2.head(k).index

    return top_k_features_pc1, top_k_features_pc2