文章MSM_metagenomics（八）：机器学习分析

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* #!/usr/bin/env python​"""NAME: evaluation_kfold.pyDESCRIPTION: This script is to evaluate a model's prediction power using tenfold method."""​from sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn import metricsfrom collections import namedtuplefrom sklearn.calibration import CalibratedClassifierCVfrom sklearn.feature_selection import SelectFromModelimport pandas as pdimport itertools import sysimport argparseimport textwrapimport subprocessimport randomimport numpy as npfrom sklearn.model_selection import StratifiedKFoldfrom sklearn.metrics import roc_curvefrom sklearn.metrics import roc_auc_scoreimport matplotlib.pyplot as pltfrom sklearn.metrics import aucfrom sklearn.metrics import RocCurveDisplayimport matplotlib​matplotlib.rcParams['font.family'] = 'sans-serif'matplotlib.rcParams['font.sans-serif'] = 'Arial'​def read_args(args):    # This function is to parse arguments​    parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,                                    description = textwrap.dedent('''\                                    This script is to estimate ROC AUC based on metaphlan-style table with metadata being inserted.                                    '''),                                    epilog = textwrap.dedent('''\                                    examples: evaluation_kfold.py --mpa_df <mpa_df.tsv> --md_rows 0,1,2,3,4 --target_row 3 --pos_feature <CRC> --neg_feature <Healthy> --fold_number 10 --repeat_time 20 --output ROC_AUC.svg                                      '''))​    parser.add_argument('--mpa_df',                        nargs = '?',                        help = 'Input a mpa-style table with metadata being inserted.',                        type = str,                        default = None)​    parser.add_argument('--md_rows',                        nargs = '?',                        help = 'Input row numbers for specifying metadata without considering header row, zero-based, comma delimited. for example, 0,1,2,3,4.',                        default = None)​    parser.add_argument('--target_row',                        nargs = '?',                        help = 'Specify the row number for indicating target metadata to examine, zero-based without considering header row.',                        type = int,                        default = None)​    parser.add_argument('--pos_feature',                        nargs = '?',                        help = 'Specify the feature name to be labeled as posive, e.g. 1.',                        type = str,                        default = None)​    parser.add_argument('--neg_feature',                        nargs = '?',                        help = 'Specify the feature name to be labeld as negative, e.g. 0.',                        type = str,                        default = None)​    parser.add_argument('--fold_number',                        nargs = '?',                        help = 'Specify the fold number you want split the whole dataset.',                        type = int,                        default = None)​    parser.add_argument('--repeat_time',                        nargs = '?',                        help = 'Specify the repeat time you want to split the dataset.',                        type = int,                        default = None)​    parser.add_argument('--output',                        nargs = '?',                        help = 'Specify the output figure name.',                        type = str,                        default = None)​    parser.add_argument('--output_values',                        nargs = '?',                        help = 'Specify the output file name for storing ROC-AUC values.',                        type = str,                        default = None)​    parser.add_argument('--nproc',                        nargs = '?',                        help = 'Specify the number of processors you want to use. 4 by default.',                        type = int,                        default = 4)​    parser.add_argument('--transform',                        nargs = '?',                        help = 'Transform values in the matrix, [arcsin_sqrt] or [binary] or [None]. [None] by default',                        type = str,                        default = None)​    return vars(parser.parse_args())​​​def get_df_dropping_metadata(mpa4_df, row_number_list):    # row_number_list: a list of row numbers in integer.    # this function is to drop rows containing metadata.​    df_ = df_.drop(row_number_list)​    return df_​def get_target_metadata(mpa4_df, row_number):    # row_number: the integer indicating the row which contains the metadata one wants to examine.    # this function is to get a list of binary metadata for establishing ML model.        features = mpa4_df.iloc[row_number].to_list()​    return features​def prepare_dataset(mpa4_style_md_df, pos_neg_dict, row_number_list, target_row, transform):    # mpa4_style_md_df: the merged metaphlan4 table with metadata being inseted.    # pos_neg_dict: the dictionary which maps examine value to 1 or 0.    # This function is to prepare dataset for downstream analysis.​    df_no_md = mpa4_style_md_df.drop(row_number_list)    sample_names = df_no_md.columns[1:].to_list()    matrix = []    for s in sample_names:        values = [float(i) for i in df_no_md[s].to_list()]        matrix.append(values)​    matrix = np.array(matrix)        if transform == 'arcsin_sqrt':        matrix = matrix/100        print(matrix)        matrix = np.arcsin(np.sqrt(matrix))​    elif transform == 'binary':        matrix[np.where(matrix > 0)] = 1    else:        matrix = matrix​    features = get_target_metadata(mpa4_style_md_df, target_row)[1:]​    X = np.asarray(matrix)    y = [pos_neg_dict[i] for i in features]    y = np.asarray(y)​    return X, y​​def roc_auc_curve(model, X, y, fold, repeat, output_name, output_values):    # model: machine learning model to use.    # X: the value matrix    # y: the list of features, 1 and 0    # fold: the number of fold to split the dataset.    # repeat: the repeat number for splitting the dataset.    # output_name: specify the output figure name.    # outout_values: specify the output file name for storing estimated roc-auc values.​    cv = StratifiedKFold(n_splits=fold, shuffle=True)    classifier = model​    tprs = []    aucs = []    mean_fpr = np.linspace(0, 1, 100)        rocauc_opt = open(output_values, "w")​    rocauc_opt.write("repeat"+ "\t" + "fold" + "\t" + "roc_auc" + "\n")    while repeat > 0:        repeat -= 1        for i, (train, test) in enumerate(cv.split(X, y)):            classifier.fit(X[train], y[train])            viz = RocCurveDisplay.from_estimator(                classifier,                X[test],                y[test],                name="ROC fold {}".format(i),                alpha=0.3,                lw=1,            )            interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)            interp_tpr[0] = 0.0            tprs.append(interp_tpr)            aucs.append(viz.roc_auc)            rocauc_opt.write(str(repeat) + "\t" + str(i) + "\t" + str(viz.roc_auc) + "\n")​    fig, ax = plt.subplots()    ax.plot([0, 1], [0, 1], linestyle="--", lw=2, color="r", label="Chance", alpha=0.8)    mean_tpr = np.mean(tprs, axis=0)    mean_tpr[-1] = 1.0    mean_auc = auc(mean_fpr, mean_tpr)    std_auc = np.std(aucs)    ax.plot(        mean_fpr,        mean_tpr,        color="b",        label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc),        lw=2,        alpha=0.8,    )    std_tpr = np.std(tprs, axis=0)    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)    ax.fill_between(    mean_fpr,    tprs_lower,    tprs_upper,    color="grey",    alpha=0.2,    label=r"$\pm$ 1 std. dev.",    )    ax.set(        xlim=[-0.05, 1.05],        ylim=[-0.05, 1.05],    )    ax.set_xlabel('False Positive Rate')    ax.set_ylabel('True Positive Rate')    ax.legend(loc="lower right")    plt.savefig(output_name)    rocauc_opt.close()​​​if __name__ == "__main__":​    pars = read_args(sys.argv)    df = pd.read_csv(pars["mpa_df"], sep = "\t", index_col = False)    row_number_list = [int(i) for i in pars["md_rows"].split(",")]    pos_neg_dict = {pars["pos_feature"]:1, pars["neg_feature"]:0}    X, y = prepare_dataset(df, pos_neg_dict, row_number_list, pars["target_row"], pars["transform"])    model = RandomForestClassifier(n_estimators = 1000,                                   criterion = 'entropy',                                   min_samples_leaf = 1,                                   max_features = 'sqrt',                                   n_jobs = 4) # initiating a RF classifier    roc_auc_curve(model, X, y, pars["fold_number"], pars["repeat_time"], pars["output"], pars["output_values"])
*/

evaluation_kfold.py [-h] [--mpa_df [MPA_DF]] [--md_rows [MD_ROWS]] [--target_row [TARGET_ROW]] [--pos_feature [POS_FEATURE]] [--neg_feature [NEG_FEATURE]] [--fold_number [FOLD_NUMBER]] [--repeat_time [REPEAT_TIME]] [--output [OUTPUT]]                           [--output_values [OUTPUT_VALUES]] [--nproc [NPROC]] [--transform [TRANSFORM]]​This script is to estimate ROC AUC based on metaphlan-style table with metadata being inserted.​optional arguments:  -h, --help            show this help message and exit  --mpa_df [MPA_DF]     Input a mpa-style table with metadata being inserted.  --md_rows [MD_ROWS]   Input row numbers for specifying metadata without considering header row, zero-based, comma delimited. for example, 0,1,2,3,4.  --target_row [TARGET_ROW]                        Specify the row number for indicating target metadata to examine, zero-based without considering header row.  --pos_feature [POS_FEATURE]                        Specify the feature name to be labeled as posive, e.g. 1.  --neg_feature [NEG_FEATURE]                        Specify the feature name to be labeld as negative, e.g. 0.  --fold_number [FOLD_NUMBER]                        Specify the fold number you want split the whole dataset.  --repeat_time [REPEAT_TIME]                        Specify the repeat time you want to split the dataset.  --output [OUTPUT]     Specify the output figure name.  --output_values [OUTPUT_VALUES]                        Specify the output file name for storing ROC-AUC values.  --nproc [NPROC]       Specify the number of processors you want to use. 4 by default.  --transform [TRANSFORM]                        Transform values in the matrix, [arcsin_sqrt] or [binary] or [None]. [None] by default​examples: ​python evaluation_kfold.py --mpa_df <mpa_df.tsv> --md_rows 0,1,2,3,4 --target_row 3 --pos_feature <CRC> --neg_feature <Healthy> --fold_number 10 --repeat_time 20 --output ROC_AUC.svg  

python evaluation_kfold.py \  --mpa_df machine_learning_input.tsv \  --md_rows 0 \  --target_row 0 \  --pos_feature ">3" \  --neg_feature "0_3" \  --fold_number 3 \  --repeat_time 50 \  --output roc_auc_npartners.png \  --output_values roc_auc_npartners_values.tsv \  --nproc 10

data_summary <- function(data,                         var_estimates,                         groups){  require(plyr)  summary_func <- function(x, col){    c(mean = mean(x[[col]], na.rm=TRUE),      sd = sd(x[[col]], na.rm=TRUE))  }  data_sum<-ddply(data, groups, .fun=summary_func,                  var_estimates)  data_sum <- rename(data_sum, c("mean" = var_estimates))  return(data_sum)}​std_deviation_plot <- function(df,                               x_axis,                               y_axis,                               stdv_column,                               palette = "jco",                               y_label = "AUC-ROC",                               x_label = "",                               order_x_axis = NULL,                               font_size = 11,                               font_family = "Arial"){    stdv_plot <- ggpubr::ggdotplot(data = df, x = x_axis, y = y_axis, color = x_axis, fill = x_axis,                           palette = palette, ylab = y_label, xlab = x_label,                           order = order_x_axis) +                           ggplot2::geom_hline(yintercept = 0.5, linetype = "dotted", col = 'red') +                           ggplot2::theme(text = ggplot2::element_text(size = font_size, family = font_family)) +                           ggplot2::theme(legend.title = ggplot2::element_blank()) +                           ggplot2::geom_errorbar(ggplot2::aes(ymin = eval(parse(text = y_axis)) - eval(parse(text = stdv_column)),                                                    ymax =  eval(parse(text = y_axis)) + eval(parse(text = stdv_column)),                                                    color = eval(parse(text = x_axis))), width = .2,                                                    position = ggplot2::position_dodge(0.7))    stdv_plot}

roc_auc_merged <- data.frame(read.csv("./data/roc_auc_merged.tsv", header = TRUE, sep = "\t"))

roc_auc_std <- data_summary(data = roc_auc_merged,                            var_estimates = "roc.auc",                            groups = "sexual.practice")

rocauc_plot <- std_deviation_plot(df = std,                                  x_axis = "sexual.practice",                                  y_axis = "roc.auc",                                  stdv_column = "sd",                                  order = c("Receptive anal intercourse", "Number of partners",                                            "Oral sex", "Sex transmitted infection", "Condom use"))

rocauc_plot + ggplot2::ylim(0, 1) + ggpubr::rotate_x_text(45)