comparison plot_ml_performance.py @ 4:f234e2e59d76 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit daa111fcd8391d451aab39110251864fd120edf0

3:1c5dcef5ce0f

import argparse

import pandas as pd
import plotly
import plotly.graph_objs as go
from galaxy_ml.model_persist import load_model_from_h5
from galaxy_ml.utils import clean_params
from sklearn.metrics import (auc, confusion_matrix,
                             precision_recall_fscore_support, roc_curve)
from sklearn.preprocessing import label_binarize


def main(infile_input, infile_output, infile_trained_model):
    """
    Produce an interactive confusion matrix (heatmap), precision, recall, fscore and auc plots
    Args:
        infile_input: str, input tabular file with true labels
        infile_output: str, input tabular file with predicted labels
        infile_trained_model: str, input trained model file (zip)
    """

    df_input = pd.read_csv(infile_input, sep="\t", parse_dates=True)
    df_output = pd.read_csv(infile_output, sep="\t", parse_dates=True)
    true_labels = df_input.iloc[:, -1].copy()
    predicted_labels = df_output.iloc[:, -1].copy()
    axis_labels = list(set(true_labels))
    c_matrix = confusion_matrix(true_labels, predicted_labels)
    data = [
        go.Heatmap(
            z=c_matrix,
            x=axis_labels,
            y=axis_labels,
            colorscale="Portland",
        )
    ]

    layout = go.Layout(
        title="Confusion Matrix between true and predicted class labels",
        xaxis=dict(title="Predicted class labels"),
        yaxis=dict(title="True class labels"),
    )

    fig = go.Figure(data=data, layout=layout)
    plotly.offline.plot(fig, filename="output_confusion.html", auto_open=False)

    # plot precision, recall and f_score for each class label
    precision, recall, f_score, _ = precision_recall_fscore_support(
        true_labels, predicted_labels
    )

4:f234e2e59d76

import argparse

import matplotlib.pyplot as plt
import pandas as pd
import plotly
import plotly.graph_objs as go
from galaxy_ml.model_persist import load_model_from_h5
from galaxy_ml.utils import clean_params
from sklearn.metrics import (
    auc,
    confusion_matrix,
    precision_recall_fscore_support,
    roc_curve,
)
from sklearn.preprocessing import label_binarize


def main(infile_input, infile_output, infile_trained_model):
    """
    Produce an interactive confusion matrix (heatmap), precision, recall, fscore and auc plots
    Args:
        infile_input: str, input tabular file with true labels
        infile_output: str, input tabular file with predicted labels
        infile_trained_model: str, input trained model file (h5mlm)
    """

    df_input = pd.read_csv(infile_input, sep="\t", parse_dates=True)
    df_output = pd.read_csv(infile_output, sep="\t", parse_dates=True)
    true_labels = df_input.iloc[:, -1].copy()
    predicted_labels = df_output.iloc[:, -1].copy()
    axis_labels = list(set(true_labels))
    c_matrix = confusion_matrix(true_labels, predicted_labels)
    fig, ax = plt.subplots(figsize=(7, 7))
    im = plt.imshow(c_matrix, cmap="viridis")
    # add number of samples to each cell of confusion matrix plot
    for i in range(len(c_matrix)):
        for j in range(len(c_matrix)):
            ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k")
    ax.set_ylabel("True class labels")
    ax.set_xlabel("Predicted class labels")
    ax.set_title("Confusion Matrix between true and predicted class labels")
    ax.set_xticks(axis_labels)
    ax.set_yticks(axis_labels)
    fig.colorbar(im, ax=ax)
    fig.tight_layout()
    plt.savefig("output_confusion.png", dpi=120)

    # plot precision, recall and f_score for each class label
    precision, recall, f_score, _ = precision_recall_fscore_support(
        true_labels, predicted_labels
    )