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""" |
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X. |
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""" |
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from __future__ import annotations |
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from dataclasses import dataclass |
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from pathlib import Path |
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from typing import TypeVar, Mapping, Sequence, List |
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import numpy as np |
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import pandas as pd |
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from typeddfs import BaseDf |
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from typeddfs.df_errors import UnsupportedOperationError |
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from mandos import logger |
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from mandos.analysis.io_defns import SimilarityDfLongForm, SimilarityDfShortForm |
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from mandos.entries.searcher import InputFrame |
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from mandos.model.rdkit_utils import RdkitUtils |
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T = TypeVar("T", bound=BaseDf) |
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@dataclass(frozen=True, repr=True) |
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class MatrixPrep: |
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kind: str |
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normalize: bool |
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log: bool |
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invert: bool |
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def from_files(self, paths: Sequence[Path]) -> SimilarityDfLongForm: |
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dct = {} |
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for p in paths: |
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key = p.with_suffix("").name |
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try: |
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mx = SimilarityDfShortForm.read_file(p) |
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dct[key] = mx |
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except (OSError, UnsupportedOperationError, ValueError): |
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logger.error(f"Failed to load matrix at {str(p)}") |
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raise |
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return self.create(dct) |
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def create(self, key_to_mx: Mapping[str, SimilarityDfShortForm]) -> SimilarityDfLongForm: |
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df = SimilarityDfLongForm( |
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pd.concat([mx.to_long_form(self.kind, key) for key, mx in key_to_mx.items()]) |
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) |
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vals = df["value"] |
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if self.invert: |
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vals = -vals |
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if self.normalize: |
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mn, mx = vals.min(), vals.max() |
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vals = (vals - mn) / (mn - mx) |
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if self.log: |
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# this is a bit stupid, but calc the log then normalize again |
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# we can't take the log before normalization because we might have negative values |
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vals = vals.map(np.log10) |
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mn, mx = vals.min(), vals.max() |
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vals = (vals - mn) / (mn - mx) |
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df["value"] = vals |
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return SimilarityDfLongForm.convert(df) |
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@classmethod |
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def ecfp_matrix(cls, df: InputFrame, radius: int, n_bits: int) -> SimilarityDfShortForm: |
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# TODO: This is inefficient and long |
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indices = range(len(df)) |
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keys = df["inchikey"] |
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on_bits = [ |
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RdkitUtils.ecfp(c, radius=radius, n_bits=n_bits).list_on for c in df.get_structures() |
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] |
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the_rows: List[List[float]] = [] |
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for i, row_key, row_print in zip(indices, keys, on_bits): |
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for j, col_key, col_print in zip(indices, keys, on_bits): |
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the_row = [] |
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if i < j: |
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jaccard = len(row_print.intersection(col_print)) / len( |
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row_print.union(col_print) |
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) |
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the_row.append(jaccard) |
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the_rows.append(the_row) |
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short = SimilarityDfShortForm(the_rows) |
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short["inchikey"] = keys |
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short = short.set_index("inchikey") |
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short.columns = keys |
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return SimilarityDfShortForm.convert(short) |
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84
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85
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__all__ = ["MatrixPrep"] |
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dmyersturnbull /
mandos
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