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- <article id="content">
- <header>
- <h1 class="title">Module <code>src.data</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import os
- import sys
- from copy import deepcopy
- from os.path import join as oj
- import mat4py
- import numpy as np
- import pandas as pd
- from matplotlib import pyplot as plt
- try:
- from skimage.external.tifffile import imread
- except:
- from skimage.io import imread
- pd.options.mode.chained_assignment = None # default='warn' - caution: this turns off setting with copy warning
- import pickle as pkl
- from viz import *
- import math
- import config
- import features
- import outcomes
- import load_tracking
- from tqdm import tqdm
- import train_reg
- def load_dfs_for_lstm(dsets=['clath_aux+gak_new'],
- splits=['test'],
- meta=None,
- length=40,
- normalize=True,
- lifetime_threshold=15,
- filter_short=True,
- padding='end'):
- '''Loads dataframes preprocessed ready for LSTM
- '''
- dfs = {}
- for dset in tqdm(dsets):
- for split in splits:
- df = get_data(dset=dset)
- df = df[~df.hotspots]
- if filter_short and lifetime_threshold == 15:
- # df = df[~(df.short | df.long)]
- # df = df[df.valid]
- df = df[df.lifetime > 15] # only keep hard tracks
- elif not lifetime_threshold == 15:
- df = df[df.lifetime > lifetime_threshold] # only keep hard tracks
- else:
- df = df[~df.hotspots]
- df = df[df.cell_num.isin(config.DSETS[dset][split])] # select train/test etc.
- feat_names = ['X_same_length_normalized'] + select_final_feats(get_feature_names(df))
- # downsample tracks
- df['X_same_length'] = [features.downsample(df.iloc[i]['X'],length, padding=padding)
- for i in range(len(df))] # downsampling
- df['X_same_length_extended'] = [features.downsample(df.iloc[i]['X_extended'], length, padding=padding)
- for i in range(len(df))] # downsampling
- # normalize tracks
- df = features.normalize_track(df, track='X_same_length', by_time_point=False)
- df = features.normalize_track(df, track='X_same_length_extended', by_time_point=False)
- # regression response
- df = outcomes.add_sig_mean(df, resp_tracks=['Y'])
- df = outcomes.add_aux_dyn_outcome(df)
- df['X_max_orig'] = deepcopy(df['X_max'].values)
- # remove extraneous feats
- # df = df[feat_names + meta]
- # df = df.dropna()
- # normalize features
- if normalize:
- for feat in feat_names:
- if 'X_same_length' not in feat:
- df = features.normalize_feature(df, feat)
- dfs[(dset, split)] = deepcopy(df)
- return dfs, feat_names
-
- def get_data(dset='clath_aux+gak_a7d2', use_processed=True, save_processed=True,
- processed_file=oj(config.DIR_PROCESSED, 'df.pkl'),
- metadata_file=oj(config.DIR_PROCESSED, 'metadata.pkl'),
- use_processed_dicts=True,
- compute_dictionary_learning=False,
- outcome_def='y_consec_thresh',
- pixel_data: bool=False,
- video_data: bool=False,
- acc_thresh=0.95,
- previous_meta_file: str=None):
- '''
- Params
- ------
- use_processed: bool, optional
- determines whether to load df from cached pkl
- save_processed: bool, optional
- if not using processed, determines whether to save the df
- use_processed_dicts: bool, optional
- if False, recalculate the dictionary features
- previous_meta_file: str, optional
- filename for metadata.pkl file saved by previous preprocessing
- the thresholds for lifetime are taken from this file
- '''
- # get things based onn dset
- DSET = config.DSETS[dset]
- LABELS = config.LABELS[dset]
- processed_file = processed_file[:-4] + '_' + dset + '.pkl'
- metadata_file = metadata_file[:-4] + '_' + dset + '.pkl'
- if use_processed and os.path.exists(processed_file):
- return pd.read_pickle(processed_file)
- else:
- print('loading + preprocessing data...')
- metadata = {}
-
-
- # load tracks
- print('\tloading tracks...')
- df = load_tracking.get_tracks(data_dir=DSET['data_dir'],
- split=DSET,
- pixel_data=pixel_data,
- video_data=video_data,
- dset=dset) # note: different Xs can be different shapes
- # df = df.fillna(df.median()) # this only does anything for the dynamin tracks, where x_pos is sometimes NaN
- # print('num nans', df.isna().sum())
- df['pid'] = np.arange(df.shape[0]) # assign each track a unique id
- df['valid'] = True # all tracks start as valid
-
- # set testing tracks to not valid
- if DSET['test'] is not None:
- df['valid'][df.cell_num.isin(DSET['test'])] = False
- metadata['num_tracks'] = df.valid.sum()
- # print('training', df.valid.sum())
-
-
- # preprocess data
- print('\tpreprocessing data...')
- df = remove_invalid_tracks(df) # use catIdx
- # print('valid', df.valid.sum())
- df = features.add_basic_features(df)
- df = outcomes.add_outcomes(df, LABELS=LABELS)
- metadata['num_tracks_valid'] = df.valid.sum()
- metadata['num_aux_pos_valid'] = df[df.valid][outcome_def].sum()
- metadata['num_hotspots_valid'] = df[df.valid]['hotspots'].sum()
- df['valid'][df.hotspots] = False
- df, meta_lifetime = process_tracks_by_lifetime(df, outcome_def=outcome_def,
- plot=False, acc_thresh=acc_thresh,
- previous_meta_file=previous_meta_file)
- df['valid'][df.short] = False
- df['valid'][df.long] = False
- metadata.update(meta_lifetime)
- metadata['num_tracks_hard'] = df['valid'].sum()
- metadata['num_aux_pos_hard'] = int(df[df.valid == 1][outcome_def].sum())
-
- # add features
- print('\tadding features...')
- df = features.add_dasc_features(df)
- if compute_dictionary_learning:
- df = features.add_dict_features(df, use_processed=use_processed_dicts)
- # df = features.add_smoothed_tracks(df)
- # df = features.add_pcs(df)
- # df = features.add_trend_filtering(df)
- # df = features.add_binary_features(df, outcome_def=outcome_def)
- if save_processed:
- print('\tsaving...')
- pkl.dump(metadata, open(metadata_file, 'wb'))
- df.to_pickle(processed_file)
- return df
- def remove_invalid_tracks(df, keep=[1, 2]):
- '''Remove certain types of tracks based on cat_idx.
- Only keep cat_idx = 1 and 2
- 1-4 (non-complex trajectory - no merges and splits)
- 1 - valid
- 2 - signal occasionally drops out
- 3 - cut - starts / ends
- 4 - multiple - at the same place (continues throughout)
- 5-8 (there is merging or splitting)
- '''
- return df[df.catIdx.isin(keep)]
- def process_tracks_by_lifetime(df: pd.DataFrame, outcome_def: str,
- plot=False, acc_thresh=0.95, previous_meta_file=None):
- '''Calculate accuracy you can get by just predicting max class
- as a func of lifetime and return points within proper lifetime (only looks at training cells)
- '''
- vals = df[df.valid == 1][['lifetime', outcome_def]]
- R, C = 1, 3
- lifetimes = np.unique(vals['lifetime'])
- # cumulative accuracy for different thresholds
- accs_cum_lower = np.array([1 - np.mean(vals[outcome_def][vals['lifetime'] <= l]) for l in lifetimes])
- accs_cum_higher = np.array([np.mean(vals[outcome_def][vals['lifetime'] >= l]) for l in lifetimes]).flatten()
- if previous_meta_file is None:
- try:
- idx_thresh = np.nonzero(accs_cum_lower >= acc_thresh)[0][-1] # last nonzero index
- thresh_lower = lifetimes[idx_thresh]
- except:
- idx_thresh = 0
- thresh_lower = lifetimes[idx_thresh] - 1
- try:
- idx_thresh_2 = np.nonzero(accs_cum_higher >= acc_thresh)[0][0]
- thresh_higher = lifetimes[idx_thresh_2]
- except:
- idx_thresh_2 = lifetimes.size - 1
- thresh_higher = lifetimes[idx_thresh_2] + 1
- else:
- previous_meta = pkl.load(open(previous_meta_file, 'rb'))
- thresh_lower = previous_meta['thresh_short']
- thresh_higher = previous_meta['thresh_long']
- # only df with lifetimes in proper range
- df['short'] = df['lifetime'] <= thresh_lower
- df['long'] = df['lifetime'] >= thresh_higher
- n = vals.shape[0]
- n_short = np.sum(df['short'])
- n_long = np.sum(df['long'])
- acc_short = 1 - np.mean(vals[outcome_def][vals['lifetime'] <= thresh_lower])
- acc_long = np.mean(vals[outcome_def][vals['lifetime'] >= thresh_higher])
- metadata = {'num_short': n_short, 'num_long': n_long, 'acc_short': acc_short,
- 'acc_long': acc_long, 'thresh_short': thresh_lower, 'thresh_long': thresh_higher}
- if plot:
- plt.figure(figsize=(12, 4), dpi=200)
- plt.subplot(R, C, 1)
- outcome = df[outcome_def]
- plt.hist(df['lifetime'][outcome == 1], label='aux+', alpha=1, color=cb, bins=25)
- plt.hist(df['lifetime'][outcome == 0], label='aux-', alpha=0.7, color=cr, bins=25)
- plt.xlabel('lifetime')
- plt.ylabel('count')
- plt.legend()
- plt.subplot(R, C, 2)
- plt.plot(lifetimes, accs_cum_lower, color=cr)
- # plt.axvline(thresh_lower)
- plt.axvspan(0, thresh_lower, alpha=0.2, color=cr)
- plt.ylabel('fraction of negative events')
- plt.xlabel(f'lifetime <= value\nshaded includes {n_short / n * 100:0.0f}% of pts')
- plt.subplot(R, C, 3)
- plt.plot(lifetimes, accs_cum_higher, cb)
- plt.axvspan(thresh_higher, max(lifetimes), alpha=0.2, color=cb)
- plt.ylabel('fraction of positive events')
- plt.xlabel(f'lifetime >= value\nshaded includes {n_long / n * 100:0.0f}% of pts')
- plt.tight_layout()
- return df, metadata
- def get_feature_names(df):
- '''Returns features (all of which are scalar)
- Removes metadata + time-series columns + outcomes
- '''
- ks = list(df.keys())
- feat_names = [
- k for k in ks
- if not k.startswith('y')
- and not k.startswith('Y')
- and not k.startswith('Z')
- and not k.startswith('pixel')
- # and not k.startswith('pc_')
- and not k in ['catIdx', 'cell_num', 'pid', 'valid', # metadata
- 'X', 'X_pvals', 'x_pos', 'X_starts', 'X_ends', 'X_extended', # curves
- 'short', 'long', 'hotspots', 'sig_idxs', # should be weeded out
- 'X_max_around_Y_peak', 'X_max_after_Y_peak', # redudant with X_max / fall
- 'X_max_diff', 'X_peak_idx', # unlikely to be useful
- 't', 'x_pos_seq', 'y_pos_seq', # curves
- 'X_smooth_spl', 'X_smooth_spl_dx', 'X_smooth_spl_d2x', # curves
- 'X_quantiles',
- ]
- ]
- return feat_names
- def select_final_feats(feat_names, binarize=False):
- feat_names = [x for x in feat_names
- if not x.startswith('sc_') # sparse coding
- and not x.startswith('nmf_') # nmf
- and not x in ['center_max', 'left_max', 'right_max', 'up_max', 'down_max',
- 'X_max_around_Y_peak', 'X_max_after_Y_peak', 'X_max_diff_after_Y_peak']
- and not x.startswith('pc_')
- and not 'extended' in x
- and not x == 'slope_end'
- and not '_tf_smooth' in x
- and not 'local' in x
- and not 'last' in x
- and not 'video' in x
- and not x == 'X_quantiles'
- # and not 'X_peak' in x
- # and not 'slope' in x
- # and not x in ['fall_final', 'fall_slope', 'fall_imp', 'fall']
- ]
- if binarize:
- feat_names = [x for x in feat_names if 'binary' in x]
- else:
- feat_names = [x for x in feat_names if not 'binary' in x]
- return feat_names
- if __name__ == '__main__':
-
- # process original data (and save out lifetime thresholds)
- dset_orig = 'clath_aux+gak_a7d2'
- df = get_data(dset=dset_orig) # save out orig
-
- # process new data (using lifetime thresholds from original data)
- outcome_def = 'y_consec_sig'
- # for dset in ['clath_aux_dynamin']:
- for dset in config.DSETS.keys():
- df = get_data(dset=dset, previous_meta_file=None)
- # df = get_data(dset=dset, previous_meta_file=f'{config.DIR_PROCESSED}/metadata_{dset_orig}.pkl')
- print(dset, 'num cells', len(df['cell_num'].unique()), 'num tracks', df.shape[0], 'num aux+',
- df[outcome_def].sum(), 'aux+ fraction', (df[outcome_def].sum() / df.shape[0]).round(3),
- 'valid', df.valid.sum(), 'valid aux+', df[df.valid][outcome_def].sum(), 'valid aux+ fraction',
- (df[df.valid][outcome_def].sum() / df.valid.sum()).round(3))</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.data.get_data"><code class="name flex">
- <span>def <span class="ident">get_data</span></span>(<span>dset='clath_aux+gak_a7d2', use_processed=True, save_processed=True, processed_file='/accounts/projects/vision/chandan/auxilin-prediction/src/../data/processed/df.pkl', metadata_file='/accounts/projects/vision/chandan/auxilin-prediction/src/../data/processed/metadata.pkl', use_processed_dicts=True, compute_dictionary_learning=False, outcome_def='y_consec_thresh', pixel_data=False, video_data=False, acc_thresh=0.95, previous_meta_file=None)</span>
- </code></dt>
- <dd>
- <section class="desc"><h2 id="params">Params</h2>
- <dl>
- <dt><strong><code>use_processed</code></strong> : <code>bool</code>, optional</dt>
- <dd>determines whether to load df from cached pkl</dd>
- <dt><strong><code>save_processed</code></strong> : <code>bool</code>, optional</dt>
- <dd>if not using processed, determines whether to save the df</dd>
- <dt><strong><code>use_processed_dicts</code></strong> : <code>bool</code>, optional</dt>
- <dd>if False, recalculate the dictionary features</dd>
- <dt><strong><code>previous_meta_file</code></strong> : <code>str</code>, optional</dt>
- <dd>filename for metadata.pkl file saved by previous preprocessing
- the thresholds for lifetime are taken from this file</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_data(dset='clath_aux+gak_a7d2', use_processed=True, save_processed=True,
- processed_file=oj(config.DIR_PROCESSED, 'df.pkl'),
- metadata_file=oj(config.DIR_PROCESSED, 'metadata.pkl'),
- use_processed_dicts=True,
- compute_dictionary_learning=False,
- outcome_def='y_consec_thresh',
- pixel_data: bool=False,
- video_data: bool=False,
- acc_thresh=0.95,
- previous_meta_file: str=None):
- '''
- Params
- ------
- use_processed: bool, optional
- determines whether to load df from cached pkl
- save_processed: bool, optional
- if not using processed, determines whether to save the df
- use_processed_dicts: bool, optional
- if False, recalculate the dictionary features
- previous_meta_file: str, optional
- filename for metadata.pkl file saved by previous preprocessing
- the thresholds for lifetime are taken from this file
- '''
- # get things based onn dset
- DSET = config.DSETS[dset]
- LABELS = config.LABELS[dset]
- processed_file = processed_file[:-4] + '_' + dset + '.pkl'
- metadata_file = metadata_file[:-4] + '_' + dset + '.pkl'
- if use_processed and os.path.exists(processed_file):
- return pd.read_pickle(processed_file)
- else:
- print('loading + preprocessing data...')
- metadata = {}
-
-
- # load tracks
- print('\tloading tracks...')
- df = load_tracking.get_tracks(data_dir=DSET['data_dir'],
- split=DSET,
- pixel_data=pixel_data,
- video_data=video_data,
- dset=dset) # note: different Xs can be different shapes
- # df = df.fillna(df.median()) # this only does anything for the dynamin tracks, where x_pos is sometimes NaN
- # print('num nans', df.isna().sum())
- df['pid'] = np.arange(df.shape[0]) # assign each track a unique id
- df['valid'] = True # all tracks start as valid
-
- # set testing tracks to not valid
- if DSET['test'] is not None:
- df['valid'][df.cell_num.isin(DSET['test'])] = False
- metadata['num_tracks'] = df.valid.sum()
- # print('training', df.valid.sum())
-
-
- # preprocess data
- print('\tpreprocessing data...')
- df = remove_invalid_tracks(df) # use catIdx
- # print('valid', df.valid.sum())
- df = features.add_basic_features(df)
- df = outcomes.add_outcomes(df, LABELS=LABELS)
- metadata['num_tracks_valid'] = df.valid.sum()
- metadata['num_aux_pos_valid'] = df[df.valid][outcome_def].sum()
- metadata['num_hotspots_valid'] = df[df.valid]['hotspots'].sum()
- df['valid'][df.hotspots] = False
- df, meta_lifetime = process_tracks_by_lifetime(df, outcome_def=outcome_def,
- plot=False, acc_thresh=acc_thresh,
- previous_meta_file=previous_meta_file)
- df['valid'][df.short] = False
- df['valid'][df.long] = False
- metadata.update(meta_lifetime)
- metadata['num_tracks_hard'] = df['valid'].sum()
- metadata['num_aux_pos_hard'] = int(df[df.valid == 1][outcome_def].sum())
-
- # add features
- print('\tadding features...')
- df = features.add_dasc_features(df)
- if compute_dictionary_learning:
- df = features.add_dict_features(df, use_processed=use_processed_dicts)
- # df = features.add_smoothed_tracks(df)
- # df = features.add_pcs(df)
- # df = features.add_trend_filtering(df)
- # df = features.add_binary_features(df, outcome_def=outcome_def)
- if save_processed:
- print('\tsaving...')
- pkl.dump(metadata, open(metadata_file, 'wb'))
- df.to_pickle(processed_file)
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.data.get_feature_names"><code class="name flex">
- <span>def <span class="ident">get_feature_names</span></span>(<span>df)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Returns features (all of which are scalar)
- Removes metadata + time-series columns + outcomes</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_feature_names(df):
- '''Returns features (all of which are scalar)
- Removes metadata + time-series columns + outcomes
- '''
- ks = list(df.keys())
- feat_names = [
- k for k in ks
- if not k.startswith('y')
- and not k.startswith('Y')
- and not k.startswith('Z')
- and not k.startswith('pixel')
- # and not k.startswith('pc_')
- and not k in ['catIdx', 'cell_num', 'pid', 'valid', # metadata
- 'X', 'X_pvals', 'x_pos', 'X_starts', 'X_ends', 'X_extended', # curves
- 'short', 'long', 'hotspots', 'sig_idxs', # should be weeded out
- 'X_max_around_Y_peak', 'X_max_after_Y_peak', # redudant with X_max / fall
- 'X_max_diff', 'X_peak_idx', # unlikely to be useful
- 't', 'x_pos_seq', 'y_pos_seq', # curves
- 'X_smooth_spl', 'X_smooth_spl_dx', 'X_smooth_spl_d2x', # curves
- 'X_quantiles',
- ]
- ]
- return feat_names</code></pre>
- </details>
- </dd>
- <dt id="src.data.load_dfs_for_lstm"><code class="name flex">
- <span>def <span class="ident">load_dfs_for_lstm</span></span>(<span>dsets=['clath_aux+gak_new'], splits=['test'], meta=None, length=40, normalize=True, lifetime_threshold=15, filter_short=True, padding='end')</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Loads dataframes preprocessed ready for LSTM</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def load_dfs_for_lstm(dsets=['clath_aux+gak_new'],
- splits=['test'],
- meta=None,
- length=40,
- normalize=True,
- lifetime_threshold=15,
- filter_short=True,
- padding='end'):
- '''Loads dataframes preprocessed ready for LSTM
- '''
- dfs = {}
- for dset in tqdm(dsets):
- for split in splits:
- df = get_data(dset=dset)
- df = df[~df.hotspots]
- if filter_short and lifetime_threshold == 15:
- # df = df[~(df.short | df.long)]
- # df = df[df.valid]
- df = df[df.lifetime > 15] # only keep hard tracks
- elif not lifetime_threshold == 15:
- df = df[df.lifetime > lifetime_threshold] # only keep hard tracks
- else:
- df = df[~df.hotspots]
- df = df[df.cell_num.isin(config.DSETS[dset][split])] # select train/test etc.
- feat_names = ['X_same_length_normalized'] + select_final_feats(get_feature_names(df))
- # downsample tracks
- df['X_same_length'] = [features.downsample(df.iloc[i]['X'],length, padding=padding)
- for i in range(len(df))] # downsampling
- df['X_same_length_extended'] = [features.downsample(df.iloc[i]['X_extended'], length, padding=padding)
- for i in range(len(df))] # downsampling
- # normalize tracks
- df = features.normalize_track(df, track='X_same_length', by_time_point=False)
- df = features.normalize_track(df, track='X_same_length_extended', by_time_point=False)
- # regression response
- df = outcomes.add_sig_mean(df, resp_tracks=['Y'])
- df = outcomes.add_aux_dyn_outcome(df)
- df['X_max_orig'] = deepcopy(df['X_max'].values)
- # remove extraneous feats
- # df = df[feat_names + meta]
- # df = df.dropna()
- # normalize features
- if normalize:
- for feat in feat_names:
- if 'X_same_length' not in feat:
- df = features.normalize_feature(df, feat)
- dfs[(dset, split)] = deepcopy(df)
- return dfs, feat_names</code></pre>
- </details>
- </dd>
- <dt id="src.data.process_tracks_by_lifetime"><code class="name flex">
- <span>def <span class="ident">process_tracks_by_lifetime</span></span>(<span>df, outcome_def, plot=False, acc_thresh=0.95, previous_meta_file=None)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Calculate accuracy you can get by just predicting max class
- as a func of lifetime and return points within proper lifetime (only looks at training cells)</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def process_tracks_by_lifetime(df: pd.DataFrame, outcome_def: str,
- plot=False, acc_thresh=0.95, previous_meta_file=None):
- '''Calculate accuracy you can get by just predicting max class
- as a func of lifetime and return points within proper lifetime (only looks at training cells)
- '''
- vals = df[df.valid == 1][['lifetime', outcome_def]]
- R, C = 1, 3
- lifetimes = np.unique(vals['lifetime'])
- # cumulative accuracy for different thresholds
- accs_cum_lower = np.array([1 - np.mean(vals[outcome_def][vals['lifetime'] <= l]) for l in lifetimes])
- accs_cum_higher = np.array([np.mean(vals[outcome_def][vals['lifetime'] >= l]) for l in lifetimes]).flatten()
- if previous_meta_file is None:
- try:
- idx_thresh = np.nonzero(accs_cum_lower >= acc_thresh)[0][-1] # last nonzero index
- thresh_lower = lifetimes[idx_thresh]
- except:
- idx_thresh = 0
- thresh_lower = lifetimes[idx_thresh] - 1
- try:
- idx_thresh_2 = np.nonzero(accs_cum_higher >= acc_thresh)[0][0]
- thresh_higher = lifetimes[idx_thresh_2]
- except:
- idx_thresh_2 = lifetimes.size - 1
- thresh_higher = lifetimes[idx_thresh_2] + 1
- else:
- previous_meta = pkl.load(open(previous_meta_file, 'rb'))
- thresh_lower = previous_meta['thresh_short']
- thresh_higher = previous_meta['thresh_long']
- # only df with lifetimes in proper range
- df['short'] = df['lifetime'] <= thresh_lower
- df['long'] = df['lifetime'] >= thresh_higher
- n = vals.shape[0]
- n_short = np.sum(df['short'])
- n_long = np.sum(df['long'])
- acc_short = 1 - np.mean(vals[outcome_def][vals['lifetime'] <= thresh_lower])
- acc_long = np.mean(vals[outcome_def][vals['lifetime'] >= thresh_higher])
- metadata = {'num_short': n_short, 'num_long': n_long, 'acc_short': acc_short,
- 'acc_long': acc_long, 'thresh_short': thresh_lower, 'thresh_long': thresh_higher}
- if plot:
- plt.figure(figsize=(12, 4), dpi=200)
- plt.subplot(R, C, 1)
- outcome = df[outcome_def]
- plt.hist(df['lifetime'][outcome == 1], label='aux+', alpha=1, color=cb, bins=25)
- plt.hist(df['lifetime'][outcome == 0], label='aux-', alpha=0.7, color=cr, bins=25)
- plt.xlabel('lifetime')
- plt.ylabel('count')
- plt.legend()
- plt.subplot(R, C, 2)
- plt.plot(lifetimes, accs_cum_lower, color=cr)
- # plt.axvline(thresh_lower)
- plt.axvspan(0, thresh_lower, alpha=0.2, color=cr)
- plt.ylabel('fraction of negative events')
- plt.xlabel(f'lifetime <= value\nshaded includes {n_short / n * 100:0.0f}% of pts')
- plt.subplot(R, C, 3)
- plt.plot(lifetimes, accs_cum_higher, cb)
- plt.axvspan(thresh_higher, max(lifetimes), alpha=0.2, color=cb)
- plt.ylabel('fraction of positive events')
- plt.xlabel(f'lifetime >= value\nshaded includes {n_long / n * 100:0.0f}% of pts')
- plt.tight_layout()
- return df, metadata</code></pre>
- </details>
- </dd>
- <dt id="src.data.remove_invalid_tracks"><code class="name flex">
- <span>def <span class="ident">remove_invalid_tracks</span></span>(<span>df, keep=[1, 2])</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Remove certain types of tracks based on cat_idx.
- Only keep cat_idx
- = 1 and 2
- 1-4 (non-complex trajectory - no merges and splits)
- 1 - valid
- 2 - signal occasionally drops out
- 3 - cut
- - starts / ends
- 4 - multiple - at the same place (continues throughout)
- 5-8 (there is merging or splitting)</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def remove_invalid_tracks(df, keep=[1, 2]):
- '''Remove certain types of tracks based on cat_idx.
- Only keep cat_idx = 1 and 2
- 1-4 (non-complex trajectory - no merges and splits)
- 1 - valid
- 2 - signal occasionally drops out
- 3 - cut - starts / ends
- 4 - multiple - at the same place (continues throughout)
- 5-8 (there is merging or splitting)
- '''
- return df[df.catIdx.isin(keep)]</code></pre>
- </details>
- </dd>
- <dt id="src.data.select_final_feats"><code class="name flex">
- <span>def <span class="ident">select_final_feats</span></span>(<span>feat_names, binarize=False)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def select_final_feats(feat_names, binarize=False):
- feat_names = [x for x in feat_names
- if not x.startswith('sc_') # sparse coding
- and not x.startswith('nmf_') # nmf
- and not x in ['center_max', 'left_max', 'right_max', 'up_max', 'down_max',
- 'X_max_around_Y_peak', 'X_max_after_Y_peak', 'X_max_diff_after_Y_peak']
- and not x.startswith('pc_')
- and not 'extended' in x
- and not x == 'slope_end'
- and not '_tf_smooth' in x
- and not 'local' in x
- and not 'last' in x
- and not 'video' in x
- and not x == 'X_quantiles'
- # and not 'X_peak' in x
- # and not 'slope' in x
- # and not x in ['fall_final', 'fall_slope', 'fall_imp', 'fall']
- ]
- if binarize:
- feat_names = [x for x in feat_names if 'binary' in x]
- else:
- feat_names = [x for x in feat_names if not 'binary' in x]
- return feat_names</code></pre>
- </details>
- </dd>
- </dl>
- </section>
- <section>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="src" href="index.html">src</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-functions">Functions</a></h3>
- <ul class="">
- <li><code><a title="src.data.get_data" href="#src.data.get_data">get_data</a></code></li>
- <li><code><a title="src.data.get_feature_names" href="#src.data.get_feature_names">get_feature_names</a></code></li>
- <li><code><a title="src.data.load_dfs_for_lstm" href="#src.data.load_dfs_for_lstm">load_dfs_for_lstm</a></code></li>
- <li><code><a title="src.data.process_tracks_by_lifetime" href="#src.data.process_tracks_by_lifetime">process_tracks_by_lifetime</a></code></li>
- <li><code><a title="src.data.remove_invalid_tracks" href="#src.data.remove_invalid_tracks">remove_invalid_tracks</a></code></li>
- <li><code><a title="src.data.select_final_feats" href="#src.data.select_final_feats">select_final_feats</a></code></li>
- </ul>
- </li>
- </ul>
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