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https://github.com/Doctorado-ML/benchmark.git
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102 lines
3.2 KiB
Python
Executable File
102 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python
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import os
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import subprocess
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import json
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from stree import Stree
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from graphviz import Source
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from benchmark.Experiments import Datasets
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from benchmark.Utils import Files, Folders
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from Arguments import Arguments
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def compute_stree(X, y, random_state):
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clf = Stree(random_state=random_state)
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clf.fit(X, y)
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return clf
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def load_hyperparams(score_name, model_name):
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grid_file = os.path.join(
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Folders.results, Files.grid_output(score_name, model_name)
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)
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with open(grid_file) as f:
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return json.load(f)
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def hyperparam_filter(hyperparams):
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res = {}
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for key, value in hyperparams.items():
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if key.startswith("base_estimator"):
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newkey = key.split("__")[1]
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res[newkey] = value
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return res
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def build_title(dataset, accuracy, n_samples, n_features, n_classes, nodes):
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dataset_chars = f"-{dataset}- f={n_features} s={n_samples} c={n_classes}"
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return (
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f'<font point-size="25" color="brown">{dataset_chars}<BR/></font>'
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f'<font point-size="20" color="red">accuracy: {accuracy:.6f} / '
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f"{nodes} nodes</font>"
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)
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def add_color(source):
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return (
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source.replace( # Background and title font color
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"fontcolor=blue", "fontcolor=white\nbgcolor=darkslateblue"
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)
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.replace("brown", "cyan") # subtitle font color
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.replace( # Fill leaves
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"style=filled", 'style="filled" fillcolor="/blues5/1:/blues5/4"'
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)
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.replace( # Fill nodes
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"fontcolor=black",
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'style=radial fillcolor="orange:white" gradientangle=60',
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)
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.replace("color=black", "color=white") # arrow color
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.replace( # accuracy / # nodes
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'color="red"', 'color="darkolivegreen1"'
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)
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)
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def print_stree(clf, dataset, X, y, color, quiet):
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output_folder = "img"
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samples, features = X.shape
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classes = max(y) + 1
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accuracy = clf.score(X, y)
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nodes, _ = clf.nodes_leaves()
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title = build_title(dataset, accuracy, samples, features, classes, nodes)
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dot_source = clf.graph(title)
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if color:
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dot_source = add_color(dot_source)
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grp = Source(dot_source)
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file_name = os.path.join(output_folder, f"stree_{dataset}")
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grp.render(format="png", filename=f"{file_name}")
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os.remove(f"{file_name}")
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print(f"File {file_name}.png generated")
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if not quiet:
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cmd_open = "/usr/bin/open"
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if os.path.isfile(cmd_open) and os.access(cmd_open, os.X_OK):
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subprocess.run([cmd_open, f"{file_name}.png"])
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def main():
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arguments = Arguments()
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arguments.xset("color").xset("dataset", default="all").xset("quiet")
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args = arguments.parse()
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hyperparameters = load_hyperparams("accuracy", "ODTE")
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random_state = 57
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dt = Datasets()
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for dataset in dt:
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if dataset == args.dataset or args.dataset == "all":
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X, y = dt.load(dataset)
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clf = Stree(random_state=random_state)
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hyperparams_dataset = hyperparam_filter(
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hyperparameters[dataset][1]
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)
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clf.set_params(**hyperparams_dataset)
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clf.fit(X, y)
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print_stree(clf, dataset, X, y, args.color, args.quiet)
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