account for mt_pos being a list not an int

[github-actions]

account for mt_pos being a list not an int

Mito_Trace/src/simulations/fullsimulation.py

Line 125 in 1b49ccc

# TODO account for mt_pos being a list not an int

import numpy as np
from numpy import random
import os
import pandas as pd
from tqdm import tqdm
#from src.config import ROOT_DIR
from sklearn.metrics import roc_curve, average_precision_score, confusion_matrix
import matplotlib.pyplot as plt
import pickle
import seaborn as sns
import glob
from sklearn.cluster import KMeans
from sklearn import metrics
from scipy.spatial.distance import cdist
from pandarallel import pandarallel

from mplh.color_utils import get_colors
from mplh.fig_utils import legend_from_color
from mplh import cluster_help as ch
from src.simulations.utils.config import read_config_file, write_config_file

from dynamicTreeCut import cutreeHybrid
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import linkage
from sklearn.model_selection import ParameterGrid
from src.simulations.utils.config import check_required

from .simulation import Simulation


# Does this ruin running the MCMC? I don't think so, b/c that format is going to be put in after anyway
class FullSimulation:
    """
    Class that simulates cell growth for lineage tracing. Reads in a
    parameter file and runs a certain number of iterations based on
    the num_iterations parameter.

    :ivar n_iter: Number of iterations
    :type n_iter: int
    :ivar num_cells: Number of cells to sequence
    :type num_cells: int

    :ivar sim: Each index is a different iteration of the simulation.
    :type sim: pandas Series

    """
    def __init__(self, params_f):
        params = read_config_file(params_f)
        self.n_iter = params['num_iterations']
        self.num_cells = params['num_cells']
        self.params = params
        self.f_save = os.path.join(self.params['local_outdir'], self.params['prefix']+'.p')
        return
        #for i in self.n_iter:

    def run(self):
        """
        Runs the simulation and stores it in sim attr.

        This uses Pandaralel to parallelize the runs.
        :return:
        """
        # Parallelize df
        df = pd.Series(index=range(self.n_iter))
        #df = df.apply(self.run_sim, args=(self.params,))
        pandarallel.initialize(nb_workers=self.params['cpus'])
        df = df.parallel_apply(self.run_sim, args=(self.params,))

        self.sim = df
        #self.cluster_before_after()
        self.sim_performance_dominant(group='both')
        self.stats_before_after()
        return

    @staticmethod
    def run_sim(x, params):
        """
        For a simulation, it will initialize, grow, subsample,
        and merge the before stimulus and after stimulus variables.
        :param x: Placeholder variable
        :param params: The parameter dictionary to use
        :return:
        """
        s = Simulation(params)
        s.initialize()
        s.grow()
        s.subsample_new(to_delete=True)
        s.combine_init_growth()
        return s

    def flatten_sim(self):
        ## TODO
        # This will extract out the classes of df
        return

    def sim_performance_dominant(self, group='both'):
        """
        Will colect metrics that are averaged over the simulations.
        These are specifically for looking at the main, dominant clone,
        and what the allele-frequency of that clone variant
        is for each cell.

        :param group: {'init', 'growth', 'both'} This will indicate to group by
        :ivar dropout: Number of dominant clone cells that have 0 reads
        at the lineage variant position.
        :type dropout: list
        :ivar prec_scores: sklearn average precision score based on
        the allele frequencies seen in the dominant clone cells versus
        the non-clone cells.
        :type prec_scores: list
        :ivar rocs: ROC curves for each iteration based on allele
        frequencies.

        :return:
        """
        dropout = []
        rocs = []
        prec_scores = []


        for iter, s in enumerate(self.sim.values):
            # First get the dominant clone , which is indexed as 1
            mt_pos = s.clone_mt_dict[1]
            # TODO account for mt_pos being a list not an int
            if group == 'init':
                clones = s.clone_cell
                cell_af = s.cell_af.loc[:,mt_pos]
            elif group == 'growth':
                clones = s.new_clone_cell
                cell_af = s.new_cell_af.loc[:,mt_pos]
            elif group == 'both':
                #clones = pd.concat((s.clone_cell, s.subsample_new_clone_cell)).reset_index(drop=True)
                #cell_af = pd.concat((s.cell_af.loc[:,mt_pos], s.subsample_new_cell_af.loc[:,mt_pos])).reset_index(drop=True)
                clones = s.combined_clones
                cell_af = s.combined_cell_af.loc[:,mt_pos]
            else:
                raise ValueError('group variable not properly set.')

            y_true = clones.values.copy()
            y_true[y_true != 1] = 0  # Set nondominant clones to 0
            rocs.append(roc_curve(y_true, cell_af))
            prec_scores.append(average_precision_score(y_true, cell_af))
            dropout.append((cell_af[clones==1]==0).sum()/cell_af.shape[0])

        self.dropout = dropout
        self.prec_scores = prec_scores
        self.rocs = rocs
        return


    def reduce_cells(self, cell_af):
        #self.sim
        return


    def stats_before_after(self, clone_id=1):
        b_a_df = pd.DataFrame(index=np.arange(0,len(self.sim)), columns=["Before", "After", "A/B"], dtype=str)
        for iter, s in enumerate(self.sim.values):
            b_clones = s.clone_cell
            a_clones = s.subsample_new_clone_cell
            b_a_df.at[iter, "Before"] = (b_clones == clone_id).sum()
            b_a_df.at[iter, "After"] = (a_clones==clone_id).sum()
            b_a_df.at[iter,"A/B"] = (b_a_df.at[iter, "After"]/b_a_df.at[iter, "Before"])
        self.b_a_df = b_a_df
        return


    def cluster_before_after(self):
        """
        Loops through the simulations and for each,
        it clusters the cells.

        :ivar cluster_results: Cluster labels for each cell in each
        iteration.
        :type List of tuples, which is a list of
        a tuple, where the tuple is indexed by the cell and the value
        is the cell's cluster label
        """
        cluster_results = []
        print('clustering')
        for s in tqdm(self.sim.values):
            cluster_results.append(s.cluster(s.combined_cell_af))
            print(len(cluster_results[-1]))
        self.cluster_results = cluster_results


    def stats_cluster_before_after(self, clone_id=1):
        """
        Confusion matrix for clustering the proper clone cells together.
        :param clone_id: Which clone to get metrics for
        :return:
        """


        b_a_df = pd.DataFrame(index=len(self.sim),
                              columns=["TN", "FP", "FN", "TP"], dtype=int)
        for ind, s in enumerate(self.sim.values):
            y_true = s.combined_clones
            y_true[y_true!=1] = 0
            y_pred = self.cluster_results[ind]

            # y_true, y_pred
            tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
            b_a_df.loc[ind] = [tn, fp, fn, tp]
        self.b_a_df = b_a_df
        return



    def save(self, f_save=None):
        if f_save is None:
            f_save = self.f_save
        f = open(f_save, 'wb')
        pickle.dump(self.__dict__, f, 2)
        f.close()


    def load(self, f_save=None):
        #filename = self.params['filename']
        if f_save is None:
            f_save = self.f_save
        f = open(f_save, 'rb')
        tmp_dict = pickle.load(f)
        f.close()
        self.__dict__.update(tmp_dict)


def main():
    return


if "__name__" == "__main__":
    main()
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++ b/src/simulations/parametersweep.py

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