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import numpy as np |
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from functools import partial |
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import pandas as pd |
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def predict_memory_states(my_collection, group): |
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states = my_collection.states(*group.name) |
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group['stability'] = float(states[0]) |
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group['difficulty'] = float(states[1]) |
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group['count'] = len(group) |
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return pd.DataFrame({ |
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'r_history': [group.name[1]], |
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't_history': [group.name[0]], |
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'stability': [round(float(states[0]), 2)], |
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'difficulty': [round(float(states[1]), 2)], |
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'count': [len(group)] |
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}) |
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def get_my_memory_states(proj_dir, dataset, my_collection): |
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prediction = dataset.groupby(by=['t_history', 'r_history']).progress_apply( |
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partial(predict_memory_states, my_collection)) |
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prediction.reset_index(drop=True, inplace=True) |
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prediction.sort_values(by=['r_history'], inplace=True) |
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prediction.to_csv(proj_dir / "prediction.tsv", sep='\t', index=None) |
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prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x))) |
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difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum() |
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difficulty_distribution_padding = np.zeros(10) |
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for i in range(10): |
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if i + 1 in difficulty_distribution.index: |
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difficulty_distribution_padding[i] = difficulty_distribution.loc[i + 1] |
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return difficulty_distribution_padding, difficulty_distribution |
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