comparison ml_visualization_ex.py @ 13:ebd3bd2f2985 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a

12:0460590afd6e

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
from galaxy_ml.utils import load_model, read_columns, SafeEval
from keras.models import model_from_json
from keras.utils import plot_model
from sklearn.feature_selection.base import SelectorMixin
from sklearn.metrics import (auc, average_precision_score, confusion_matrix,
                             precision_recall_curve, roc_curve)
from sklearn.pipeline import Pipeline

safe_eval = SafeEval()

# plotly default colors
default_colors = [

    title = params["plotting_selection"]["title"].strip()
    plot_type = params["plotting_selection"]["plot_type"]
    plot_format = params["plotting_selection"]["plot_format"]

    if plot_type == "feature_importances":
        with open(infile_estimator, "rb") as estimator_handler:
            estimator = load_model(estimator_handler)

        column_option = params["plotting_selection"]["column_selector_options"][
            "selected_column_selector_option"
        ]
        if column_option in [

13:ebd3bd2f2985

import matplotlib.pyplot as plt
import numpy as np
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 read_columns, SafeEval
from sklearn.feature_selection._base import SelectorMixin
from sklearn.metrics import (
    auc,
    average_precision_score,
    confusion_matrix,
    precision_recall_curve,
    roc_curve,
)
from sklearn.pipeline import Pipeline
from tensorflow.keras.models import model_from_json
from tensorflow.keras.utils import plot_model

safe_eval = SafeEval()

# plotly default colors
default_colors = [

    title = params["plotting_selection"]["title"].strip()
    plot_type = params["plotting_selection"]["plot_type"]
    plot_format = params["plotting_selection"]["plot_format"]

    if plot_type == "feature_importances":
        estimator = load_model_from_h5(infile_estimator)

        column_option = params["plotting_selection"]["column_selector_options"][
            "selected_column_selector_option"
        ]
        if column_option in [