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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import os |
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import json |
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import shutil |
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import hashlib |
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import inspect |
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current_path = os.path.realpath(__file__) |
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meta_learn_path, _ = current_path.rsplit("/", 1) |
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meta_path = meta_learn_path + "/meta_data/" |
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""" |
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def get_best_models(X, y): |
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# TODO: model_dict key:model value:score |
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return model_dict |
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def get_model_search_config(model): |
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# TODO |
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return search_config |
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def get_model_init_config(model): |
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# TODO |
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return init_config |
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""" |
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def delete_model(model): |
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model_hash = _get_model_hash(model) |
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path = meta_path + str(model_hash) |
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if os.path.exists(path) and os.path.isdir(path): |
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shutil.rmtree(meta_path + str(model_hash)) |
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print("Model data successfully removed") |
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else: |
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print("Model data not found in memory") |
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def delete_model_dataset(model, X, y): |
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csv_file = _get_file_path(model, X, y) |
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if os.path.exists(csv_file): |
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os.remove(csv_file) |
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print("Model data successfully removed") |
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else: |
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print("Model data not found in memory") |
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def connect_model_IDs(model1, model2): |
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# do checks if search space has same dim |
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with open(meta_path + "model_connections.json") as f: |
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data = json.load(f) |
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model1_hash = _get_model_hash(model1) |
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model2_hash = _get_model_hash(model2) |
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if model1_hash in data: |
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key_model = model1_hash |
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value_model = model2_hash |
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data = _connect_key2value(data, key_model, value_model) |
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else: |
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data[model1_hash] = [model2_hash] |
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print("IDs successfully connected") |
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if model2_hash in data: |
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key_model = model2_hash |
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value_model = model1_hash |
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data = _connect_key2value(data, key_model, value_model) |
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else: |
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data[model2_hash] = [model1_hash] |
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print("IDs successfully connected") |
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with open(meta_path + "model_connections.json", "w") as f: |
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json.dump(data, f, indent=4) |
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def _connect_key2value(data, key_model, value_model): |
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if value_model in data[key_model]: |
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print("IDs of models are already connected") |
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else: |
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data[key_model].append(value_model) |
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print("IDs successfully connected") |
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return data |
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def _split_key_value(data, key_model, value_model): |
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if value_model in data[key_model]: |
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data[key_model].remove(value_model) |
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if len(data[key_model]) == 0: |
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del data[key_model] |
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print("ID connection successfully deleted") |
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else: |
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print("IDs of models are already connected") |
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return data |
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def split_model_IDs(model1, model2): |
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# TODO: do checks if search space has same dim |
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with open(meta_path + "model_connections.json") as f: |
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data = json.load(f) |
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model1_hash = _get_model_hash(model1) |
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model2_hash = _get_model_hash(model2) |
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if model1_hash in data: |
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key_model = model1_hash |
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value_model = model2_hash |
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data = _split_key_value(data, key_model, value_model) |
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else: |
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print("IDs of models are not connected") |
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if model2_hash in data: |
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key_model = model2_hash |
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value_model = model1_hash |
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data = _split_key_value(data, key_model, value_model) |
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else: |
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print("IDs of models are not connected") |
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with open(meta_path + "model_connections.json", "w") as f: |
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json.dump(data, f, indent=4) |
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def _get_file_path(model, X, y): |
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func_path_ = _get_model_hash(model) + "/" |
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func_path = meta_path + func_path_ |
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feature_hash = _get_hash(X) |
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label_hash = _get_hash(y) |
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return func_path + (feature_hash + "_" + label_hash + "_.csv") |
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def _get_model_hash(model): |
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return str(_get_hash(_get_func_str(model).encode("utf-8"))) |
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def _get_func_str(func): |
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return inspect.getsource(func) |
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def _get_hash(object): |
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return hashlib.sha1(object).hexdigest() |
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SimonBlanke /
Hyperactive
