RandomForestAnalysis.py

#!/usr/bin/env python
# coding: utf-8

import pandas as pd

def import_data_and_separate_features_from_labels(csv_file_path):
    """
    Loads the data from a .csv file and returns
    
    Parameters
    ----------
    csv_file_path : string
    The input is a .csv file path that is used to create a Pandas dataframe
    Samples should be in rows and features in columns. 
    This function separates the data into feature names (e.g. gene names), features (gene expression values), and labels (sample classes). 
    The feature names are in a list, and both features and label are returned as numpy arrays of the values for feature values 
    and sample class. 
    
    Returns 
    ---------
    1) a N samples x p variables feature Pandas dataframe  
    2) a list with the sample class labels 
    3) a list with the feature names 
    

    Example of input .csv file
    ---------------------------
    sample_id,sample_class,clump,uniformity_cell_size,uniformity_cell_shape,adhesion
    sample_01,benign,5,1,1,1
    sample_02,benign,5,4,4,5
    sample_03,benign,3,1,1,1
    sample_04,benign,6,8,8,1
    sample_05,benign,4,1,1,3
    sample_06,malign,8,10,10
    ...[many more rows]
    """
    
    df = pd.read_csv(csv_file_path).dropna(how="all", axis=1)

    # get a Numpy array of the feature names 
    col_names = list(df.columns)
    feature_names = col_names[2:]

    # get a Pandas dataframe of the feature values (e.g. gene expression values)
    features = df.drop(columns=["sample_id","sample_class"], axis=1)

    # get a Numpy array of the sample classes
    labels = df.loc[:,"sample_class"]
    
    return features, labels, feature_names



class RandomForestAnalysis:
    def __init__(self, csv_file_path="data/breast-cancer.csv"):
        self.csv_file_path = csv_file_path
        self.features, self.labels, self.feature_names = import_data_and_separate_features_from_labels(self.csv_file_path)