sklearn_feature_selection: ml_visualization

comparison ml_visualization_ex.py @ 35:61edd9e5c17f draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

author	bgruening
date	Wed, 09 Aug 2023 13:10:57 +0000
parents	5773e98921fc
children	5276bdb49315

comparison

equal deleted inserted replaced

-:4483b84310ec
+:61edd9e5c17f
 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 galaxy_ml.model_persist import load_model_from_h5
-from keras.models import model_from_json
+from galaxy_ml.utils import read_columns, SafeEval
-from keras.utils import plot_model
+from sklearn.feature_selection._base import SelectorMixin
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.metrics import (
-from sklearn.metrics import (auc, average_precision_score, confusion_matrix,
+auc,
-precision_recall_curve, roc_curve)
+average_precision_score,
+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 = [
 folder = os.getcwd()
 plt.savefig(os.path.join(folder, "output.svg"), format="svg")
 os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output"))
-def get_dataframe(file_path, plot_selection, header_name, column_name):
-header = "infer" if plot_selection[header_name] else None
-column_option = plot_selection[column_name]["selected_column_selector_option"]
-if column_option in [
-"by_index_number",
-"all_but_by_index_number",
-"by_header_name",
-"all_but_by_header_name",
-]:
-col = plot_selection[column_name]["col1"]
-else:
-col = None
-_, input_df = read_columns(
-file_path,
-c=col,
-c_option=column_option,
-return_df=True,
-sep="\t",
-header=header,
-parse_dates=True,
-)
-return input_df
 def main(
 inputs,
 infile_estimator=None,
 infile1=None,
 infile2=None,
 ref_seq=None,
 intervals=None,
 targets=None,
 fasta_path=None,
 model_config=None,
-true_labels=None,
-predicted_labels=None,
-plot_color=None,
-title=None,
 ):
 """
 Parameter
 ---------
 inputs : str
 fasta_path : str, default is None
 File path to dataset containing fasta file
 model_config : str, default is None
 File path to dataset containing JSON config for neural networks
-true_labels : str, default is None
-File path to dataset containing true labels
-predicted_labels : str, default is None
-File path to dataset containing true predicted labels
-plot_color : str, default is None
-Color of the confusion matrix heatmap
-title : str, default is None
-Title of the confusion matrix heatmap
 """
 warnings.simplefilter("ignore")
 with open(inputs, "r") as param_handler:
 params = json.load(param_handler)
 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_from_h5(infile_estimator)
-estimator = load_model(estimator_handler)
 column_option = params["plotting_selection"]["column_selector_options"][
 "selected_column_selector_option"
 ]
 if column_option in [
 plot_model(model, to_file="output.png")
 os.rename("output.png", "output")
 return 0
-elif plot_type == "classification_confusion_matrix":
-plot_selection = params["plotting_selection"]
-input_true = get_dataframe(
-true_labels, plot_selection, "header_true", "column_selector_options_true"
-)
-header_predicted = "infer" if plot_selection["header_predicted"] else None
-input_predicted = pd.read_csv(
-predicted_labels, sep="\t", parse_dates=True, header=header_predicted
-)
-true_classes = input_true.iloc[:, -1].copy()
-predicted_classes = input_predicted.iloc[:, -1].copy()
-axis_labels = list(set(true_classes))
-c_matrix = confusion_matrix(true_classes, predicted_classes)
-fig, ax = plt.subplots(figsize=(7, 7))
-im = plt.imshow(c_matrix, cmap=plot_color)
-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(title)
-ax.set_xticks(axis_labels)
-ax.set_yticks(axis_labels)
-fig.colorbar(im, ax=ax)
-fig.tight_layout()
-plt.savefig("output.png", dpi=125)
-os.rename("output.png", "output")
-return 0
 # save pdf file to disk
 # fig.write_image("image.pdf", format='pdf')
 # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
 aparser.add_argument("-b", "--intervals", dest="intervals")
 aparser.add_argument("-t", "--targets", dest="targets")
 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
 aparser.add_argument("-c", "--model_config", dest="model_config")
-aparser.add_argument("-tl", "--true_labels", dest="true_labels")
-aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels")
-aparser.add_argument("-pc", "--plot_color", dest="plot_color")
-aparser.add_argument("-pt", "--title", dest="title")
 args = aparser.parse_args()
 main(
 args.inputs,
 args.infile_estimator,
 ref_seq=args.ref_seq,
 intervals=args.intervals,
 targets=args.targets,
 fasta_path=args.fasta_path,
 model_config=args.model_config,
-true_labels=args.true_labels,
-predicted_labels=args.predicted_labels,
-plot_color=args.plot_color,
-title=args.title,
 )

Mercurial > repos > bgruening > sklearn_feature_selection

comparison ml_visualization_ex.py @ 35:61edd9e5c17f draft