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- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.analyze_helper</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">from matplotlib import pyplot as plt
- import os
- from os.path import join as oj
- import numpy as np
- import pandas as pd
- import data
- from sklearn.model_selection import KFold
- from colorama import Fore
- import pickle as pkl
- import config
- import viz
- from config import *
- def load_results(out_dir):
- r = []
- for fname in os.listdir(out_dir):
- d = pkl.load(open(oj(out_dir, fname), 'rb'))
- metrics = {k: d['cv'][k] for k in d['cv'].keys() if not 'curve' in k}
- num_pts_by_fold_cv = d['num_pts_by_fold_cv']
- out = {k: np.average(metrics[k], weights=num_pts_by_fold_cv) for k in metrics}
- out.update({k + '_std': np.std(metrics[k]) for k in metrics})
- out['model_type'] = fname.replace('.pkl', '') # d['model_type']
- imp_mat = np.array(d['imps']['imps'])
- imp_mu = imp_mat.mean(axis=0)
- imp_sd = imp_mat.std(axis=0)
- feat_names = d['feat_names_selected']
- out.update({feat_names[i] + '_f': imp_mu[i] for i in range(len(feat_names))})
- out.update({feat_names[i] + '_std_f': imp_sd[i] for i in range(len(feat_names))})
- r.append(pd.Series(out))
- r = pd.concat(r, axis=1, sort=False).T.infer_objects()
- r = r.reindex(sorted(r.columns), axis=1) # sort the column names
- r = r.round(3)
- r = r.set_index('model_type')
- return r
- def get_data_over_folds(model_names: list, out_dir: str, cell_nums: pd.Series, X, y, outcome_def='y_consec_sig', dset='clath_aux+gak_a7d2'):
- '''Returns predictions/labels over folds in the dataset
- Params
- ------
- cell_nums: pd.Series
- equivalent to df.cell_num
-
- Returns
- -------
- d_full_cv: pd.DataFrame
- n rows, one for each data point in the training set (over all folds)
- 2 columns for each model, one for predictions, and one for predicted probabilities
- idxs_cv: np.array
- indexes corresponding locations of the validation set
- for example, df.y_thresh.iloc[idxs_cv] would yield all the labels corresponding to the cv preds
- '''
- # split testing data based on cell num
- d = {}
- cell_nums_train = config.DSETS[dset]['train']
- kf = KFold(n_splits=len(cell_nums_train))
- idxs_cv = []
- # get predictions over all folds and save into a dict
- if not type(model_names) == 'list' and not 'ndarray' in str(type(model_names)):
- model_names = [model_names]
- for i, model_name in enumerate(model_names):
- results_individual = pkl.load(open(f'{out_dir}/{model_name}.pkl', 'rb'))
- fold_num = 0
- for cv_idx, cv_val_idx in kf.split(cell_nums_train):
- # get sample indices
- idxs_val_cv = cell_nums.isin(cell_nums_train[np.array(cv_val_idx)])
- X_val_cv, Y_val_cv = X[idxs_val_cv], y[idxs_val_cv]
- # get predictions
- preds, preds_proba = analyze_individual_results(results_individual, X_val_cv, Y_val_cv,
- print_results=False, plot_results=False,
- model_cv_fold=fold_num)
- d[f'{model_name}_{fold_num}'] = preds
- d[f'{model_name}_{fold_num}_proba'] = preds_proba
- if i == 0:
- idxs_cv.append(np.arange(X.shape[0])[idxs_val_cv])
- fold_num += 1
- # concatenate over folds
- d2 = {}
- for model_name in model_names:
- d2[model_name] = np.hstack([d[k] for k in d.keys() if model_name in k and not 'proba' in k])
- d2[model_name + '_proba'] = np.hstack([d[k] for k in d.keys() if model_name in k and 'proba' in k])
- return pd.DataFrame.from_dict(d2), np.hstack(idxs_cv)
- def analyze_individual_results(results, X_test, Y_test, print_results=False, plot_results=False, model_cv_fold=0):
- scores_cv = results['cv']
- imps = results['imps']
- m = imps['model'][model_cv_fold]
- preds = m.predict(X_test[results['feat_names_selected']])
- try:
- preds_proba = m.predict_proba(X_test[results['feat_names_selected']])[:, 1]
- except:
- preds_proba = preds
- if print_results:
- print(Fore.CYAN + f'{"metric":<25}\tvalidation') # \ttest')
- for s in results['metrics']:
- if not 'curve' in s:
- print(Fore.WHITE + f'{s:<25}\t{np.mean(scores_cv[s]):.3f} ~ {np.std(scores_cv[s]):.3f}')
- # print(Fore.WHITE + f'{s:<25}\t{np.mean(scores_cv[s]):.3f} ~ {np.std(scores_cv[s]):.3f}\t{np.mean(scores_test[s]):.3f} ~ {np.std(scores_test[s]):.3f}')
- print(Fore.CYAN + '\nfeature importances')
- imp_mat = np.array(imps['imps'])
- imp_mu = imp_mat.mean(axis=0)
- imp_sd = imp_mat.std(axis=0)
- for i, feat_name in enumerate(results['feat_names_selected']):
- print(Fore.WHITE + f'{feat_name:<25}\t{imp_mu[i]:.3f} ~ {imp_sd[i]:.3f}')
- if plot_results:
- # print(m.coef_)
- plt.figure(figsize=(10, 3), dpi=140)
- R, C = 1, 3
- plt.subplot(R, C, 1)
- # print(X_test.shape, results['feat_names'])
- viz.plot_confusion_matrix(Y_test, preds, classes=np.array(['Failure', 'Success']))
- plt.subplot(R, C, 2)
- prec, rec, thresh = scores_test['precision_recall_curve'][0]
- plt.plot(rec, prec)
- plt.xlim((-0.1, 1.1))
- plt.ylim((-0.1, 1.1))
- plt.ylabel('Precision')
- plt.xlabel('Recall')
- plt.subplot(R, C, 3)
- plt.hist(preds_proba[Y_test == 0], alpha=0.5, label='Failure')
- plt.hist(preds_proba[Y_test == 1], alpha=0.5, label='Success')
- plt.xlabel('Predicted probability')
- plt.ylabel('Count')
- plt.legend()
- plt.tight_layout()
- plt.show()
- return preds, preds_proba
- def load_results_many_models(out_dir, model_names, X_test, Y_test):
- d = {}
- for i, model_name in enumerate(model_names):
- results_individual = pkl.load(open(oj(out_dir, f'{model_name}.pkl'), 'rb'))
- preds, preds_proba = analyze_individual_results(results_individual, X_test, Y_test,
- print_results=False, plot_results=False)
- d[model_name] = preds
- d[model_name + '_proba'] = preds_proba
- d[model_name + '_errs'] = preds != Y_test
- df_preds = pd.DataFrame.from_dict(d)
- return df_preds
- # normalize and store
- def normalize(df, outcome_def):
- X = df[data.get_feature_names(df)]
- X_mean = X.mean()
- X_std = X.std()
- ks = list(X.keys())
-
- norms = {ks[i]: {'mu': X_mean[i], 'std': X_std[i]}
- for i in range(len(ks))}
- X = (X - X_mean) / X_std
- y = df[outcome_def].values
- return X, y, norms
- def normalize_and_predict(m0, feat_names_selected, dset_name, normalize_by_train,
- exclude_easy_tracks=False, outcome_def='y_consec_thresh'):
- df_new = data.get_data(dset=dset_name, use_processed=True,
- use_processed_dicts=True, outcome_def=outcome_def,
- previous_meta_file=oj(DIR_PROCESSED,
- 'metadata_clath_aux+gak_a7d2.pkl'))
- if exclude_easy_tracks:
- df_new = df_new[df_new['valid']] # exclude test cells, short/long tracks, hotspots
-
- # impute (only does anything for dynamin data)
- df_new = df_new.fillna(df_new.median())
-
- X_new = df_new[data.get_feature_names(df_new)]
- if normalize_by_train:
- X_new = (X_new - X_mean_train) / X_std_train
- else:
- X_new = (X_new - X_new.mean()) / X_new.std()
- y_new = df_new[outcome_def].values
- preds_new = m0.predict(X_new[feat_names_selected])
- preds_proba_new = m0.predict_proba(X_new[feat_names_selected])[:, 1]
- Y_maxes = df_new['Y_max']
- return df_new, y_new, preds_new, preds_proba_new, Y_maxes
- def calc_errs(preds, y_full_cv):
- tp = np.logical_and(preds == 1, y_full_cv == 1)
- tn = np.logical_and(preds == 0, y_full_cv == 0)
- fp = preds > y_full_cv
- fn = preds < y_full_cv
- return tp, tn, fp, fn</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.analyze_helper.analyze_individual_results"><code class="name flex">
- <span>def <span class="ident">analyze_individual_results</span></span>(<span>results, X_test, Y_test, print_results=False, plot_results=False, model_cv_fold=0)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def analyze_individual_results(results, X_test, Y_test, print_results=False, plot_results=False, model_cv_fold=0):
- scores_cv = results['cv']
- imps = results['imps']
- m = imps['model'][model_cv_fold]
- preds = m.predict(X_test[results['feat_names_selected']])
- try:
- preds_proba = m.predict_proba(X_test[results['feat_names_selected']])[:, 1]
- except:
- preds_proba = preds
- if print_results:
- print(Fore.CYAN + f'{"metric":<25}\tvalidation') # \ttest')
- for s in results['metrics']:
- if not 'curve' in s:
- print(Fore.WHITE + f'{s:<25}\t{np.mean(scores_cv[s]):.3f} ~ {np.std(scores_cv[s]):.3f}')
- # print(Fore.WHITE + f'{s:<25}\t{np.mean(scores_cv[s]):.3f} ~ {np.std(scores_cv[s]):.3f}\t{np.mean(scores_test[s]):.3f} ~ {np.std(scores_test[s]):.3f}')
- print(Fore.CYAN + '\nfeature importances')
- imp_mat = np.array(imps['imps'])
- imp_mu = imp_mat.mean(axis=0)
- imp_sd = imp_mat.std(axis=0)
- for i, feat_name in enumerate(results['feat_names_selected']):
- print(Fore.WHITE + f'{feat_name:<25}\t{imp_mu[i]:.3f} ~ {imp_sd[i]:.3f}')
- if plot_results:
- # print(m.coef_)
- plt.figure(figsize=(10, 3), dpi=140)
- R, C = 1, 3
- plt.subplot(R, C, 1)
- # print(X_test.shape, results['feat_names'])
- viz.plot_confusion_matrix(Y_test, preds, classes=np.array(['Failure', 'Success']))
- plt.subplot(R, C, 2)
- prec, rec, thresh = scores_test['precision_recall_curve'][0]
- plt.plot(rec, prec)
- plt.xlim((-0.1, 1.1))
- plt.ylim((-0.1, 1.1))
- plt.ylabel('Precision')
- plt.xlabel('Recall')
- plt.subplot(R, C, 3)
- plt.hist(preds_proba[Y_test == 0], alpha=0.5, label='Failure')
- plt.hist(preds_proba[Y_test == 1], alpha=0.5, label='Success')
- plt.xlabel('Predicted probability')
- plt.ylabel('Count')
- plt.legend()
- plt.tight_layout()
- plt.show()
- return preds, preds_proba</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.calc_errs"><code class="name flex">
- <span>def <span class="ident">calc_errs</span></span>(<span>preds, y_full_cv)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def calc_errs(preds, y_full_cv):
- tp = np.logical_and(preds == 1, y_full_cv == 1)
- tn = np.logical_and(preds == 0, y_full_cv == 0)
- fp = preds > y_full_cv
- fn = preds < y_full_cv
- return tp, tn, fp, fn</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.get_data_over_folds"><code class="name flex">
- <span>def <span class="ident">get_data_over_folds</span></span>(<span>model_names, out_dir, cell_nums, X, y, outcome_def='y_consec_sig', dset='clath_aux+gak_a7d2')</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Returns predictions/labels over folds in the dataset
- Params</p>
- <hr>
- <dl>
- <dt><strong><code>cell_nums</code></strong> : <code>pd.Series</code></dt>
- <dd>equivalent to df.cell_num</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>d_full_cv</code></strong> : <code>pd.DataFrame</code></dt>
- <dd>n rows, one for each data point in the training set (over all folds)
- 2 columns for each model, one for predictions, and one for predicted probabilities</dd>
- <dt><strong><code>idxs_cv</code></strong> : <code>np.array</code></dt>
- <dd>indexes corresponding locations of the validation set
- for example, df.y_thresh.iloc[idxs_cv] would yield all the labels corresponding to the cv preds</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_data_over_folds(model_names: list, out_dir: str, cell_nums: pd.Series, X, y, outcome_def='y_consec_sig', dset='clath_aux+gak_a7d2'):
- '''Returns predictions/labels over folds in the dataset
- Params
- ------
- cell_nums: pd.Series
- equivalent to df.cell_num
-
- Returns
- -------
- d_full_cv: pd.DataFrame
- n rows, one for each data point in the training set (over all folds)
- 2 columns for each model, one for predictions, and one for predicted probabilities
- idxs_cv: np.array
- indexes corresponding locations of the validation set
- for example, df.y_thresh.iloc[idxs_cv] would yield all the labels corresponding to the cv preds
- '''
- # split testing data based on cell num
- d = {}
- cell_nums_train = config.DSETS[dset]['train']
- kf = KFold(n_splits=len(cell_nums_train))
- idxs_cv = []
- # get predictions over all folds and save into a dict
- if not type(model_names) == 'list' and not 'ndarray' in str(type(model_names)):
- model_names = [model_names]
- for i, model_name in enumerate(model_names):
- results_individual = pkl.load(open(f'{out_dir}/{model_name}.pkl', 'rb'))
- fold_num = 0
- for cv_idx, cv_val_idx in kf.split(cell_nums_train):
- # get sample indices
- idxs_val_cv = cell_nums.isin(cell_nums_train[np.array(cv_val_idx)])
- X_val_cv, Y_val_cv = X[idxs_val_cv], y[idxs_val_cv]
- # get predictions
- preds, preds_proba = analyze_individual_results(results_individual, X_val_cv, Y_val_cv,
- print_results=False, plot_results=False,
- model_cv_fold=fold_num)
- d[f'{model_name}_{fold_num}'] = preds
- d[f'{model_name}_{fold_num}_proba'] = preds_proba
- if i == 0:
- idxs_cv.append(np.arange(X.shape[0])[idxs_val_cv])
- fold_num += 1
- # concatenate over folds
- d2 = {}
- for model_name in model_names:
- d2[model_name] = np.hstack([d[k] for k in d.keys() if model_name in k and not 'proba' in k])
- d2[model_name + '_proba'] = np.hstack([d[k] for k in d.keys() if model_name in k and 'proba' in k])
- return pd.DataFrame.from_dict(d2), np.hstack(idxs_cv)</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.load_results"><code class="name flex">
- <span>def <span class="ident">load_results</span></span>(<span>out_dir)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def load_results(out_dir):
- r = []
- for fname in os.listdir(out_dir):
- d = pkl.load(open(oj(out_dir, fname), 'rb'))
- metrics = {k: d['cv'][k] for k in d['cv'].keys() if not 'curve' in k}
- num_pts_by_fold_cv = d['num_pts_by_fold_cv']
- out = {k: np.average(metrics[k], weights=num_pts_by_fold_cv) for k in metrics}
- out.update({k + '_std': np.std(metrics[k]) for k in metrics})
- out['model_type'] = fname.replace('.pkl', '') # d['model_type']
- imp_mat = np.array(d['imps']['imps'])
- imp_mu = imp_mat.mean(axis=0)
- imp_sd = imp_mat.std(axis=0)
- feat_names = d['feat_names_selected']
- out.update({feat_names[i] + '_f': imp_mu[i] for i in range(len(feat_names))})
- out.update({feat_names[i] + '_std_f': imp_sd[i] for i in range(len(feat_names))})
- r.append(pd.Series(out))
- r = pd.concat(r, axis=1, sort=False).T.infer_objects()
- r = r.reindex(sorted(r.columns), axis=1) # sort the column names
- r = r.round(3)
- r = r.set_index('model_type')
- return r</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.load_results_many_models"><code class="name flex">
- <span>def <span class="ident">load_results_many_models</span></span>(<span>out_dir, model_names, X_test, Y_test)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def load_results_many_models(out_dir, model_names, X_test, Y_test):
- d = {}
- for i, model_name in enumerate(model_names):
- results_individual = pkl.load(open(oj(out_dir, f'{model_name}.pkl'), 'rb'))
- preds, preds_proba = analyze_individual_results(results_individual, X_test, Y_test,
- print_results=False, plot_results=False)
- d[model_name] = preds
- d[model_name + '_proba'] = preds_proba
- d[model_name + '_errs'] = preds != Y_test
- df_preds = pd.DataFrame.from_dict(d)
- return df_preds</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.normalize"><code class="name flex">
- <span>def <span class="ident">normalize</span></span>(<span>df, outcome_def)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def normalize(df, outcome_def):
- X = df[data.get_feature_names(df)]
- X_mean = X.mean()
- X_std = X.std()
- ks = list(X.keys())
-
- norms = {ks[i]: {'mu': X_mean[i], 'std': X_std[i]}
- for i in range(len(ks))}
- X = (X - X_mean) / X_std
- y = df[outcome_def].values
- return X, y, norms</code></pre>
- </details>
- </dd>
- <dt id="src.analyze_helper.normalize_and_predict"><code class="name flex">
- <span>def <span class="ident">normalize_and_predict</span></span>(<span>m0, feat_names_selected, dset_name, normalize_by_train, exclude_easy_tracks=False, outcome_def='y_consec_thresh')</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def normalize_and_predict(m0, feat_names_selected, dset_name, normalize_by_train,
- exclude_easy_tracks=False, outcome_def='y_consec_thresh'):
- df_new = data.get_data(dset=dset_name, use_processed=True,
- use_processed_dicts=True, outcome_def=outcome_def,
- previous_meta_file=oj(DIR_PROCESSED,
- 'metadata_clath_aux+gak_a7d2.pkl'))
- if exclude_easy_tracks:
- df_new = df_new[df_new['valid']] # exclude test cells, short/long tracks, hotspots
-
- # impute (only does anything for dynamin data)
- df_new = df_new.fillna(df_new.median())
-
- X_new = df_new[data.get_feature_names(df_new)]
- if normalize_by_train:
- X_new = (X_new - X_mean_train) / X_std_train
- else:
- X_new = (X_new - X_new.mean()) / X_new.std()
- y_new = df_new[outcome_def].values
- preds_new = m0.predict(X_new[feat_names_selected])
- preds_proba_new = m0.predict_proba(X_new[feat_names_selected])[:, 1]
- Y_maxes = df_new['Y_max']
- return df_new, y_new, preds_new, preds_proba_new, Y_maxes</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.analyze_helper.analyze_individual_results" href="#src.analyze_helper.analyze_individual_results">analyze_individual_results</a></code></li>
- <li><code><a title="src.analyze_helper.calc_errs" href="#src.analyze_helper.calc_errs">calc_errs</a></code></li>
- <li><code><a title="src.analyze_helper.get_data_over_folds" href="#src.analyze_helper.get_data_over_folds">get_data_over_folds</a></code></li>
- <li><code><a title="src.analyze_helper.load_results" href="#src.analyze_helper.load_results">load_results</a></code></li>
- <li><code><a title="src.analyze_helper.load_results_many_models" href="#src.analyze_helper.load_results_many_models">load_results_many_models</a></code></li>
- <li><code><a title="src.analyze_helper.normalize" href="#src.analyze_helper.normalize">normalize</a></code></li>
- <li><code><a title="src.analyze_helper.normalize_and_predict" href="#src.analyze_helper.normalize_and_predict">normalize_and_predict</a></code></li>
- </ul>
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