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- <article id="content">
- <header>
- <h1 class="title">Module <code>src.train_reg</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import os
- import pickle as pkl
- from os.path import join as oj
- import numpy as np
- import pandas as pd
- from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
- from sklearn.linear_model import LinearRegression, RidgeCV
- from sklearn.metrics import r2_score
- from sklearn.model_selection import KFold
- from sklearn.neural_network import MLPRegressor
- from sklearn.svm import SVR
- from sklearn.tree import DecisionTreeRegressor
- from statsmodels import robust
- import features
- import data
- import config
- from tqdm import tqdm
- from scipy.stats import pearsonr, kendalltau
- from neural_networks import neural_net_sklearn
- #cell_nums_train = np.array([1, 2, 3, 4, 5])
- #cell_nums_test = np.array([6])
- def add_robust_features(df):
- df['X_95_quantile'] = np.array([np.quantile(df.iloc[i].X, 0.95) for i in range(len(df))])
- df['X_mad'] = np.array([robust.mad(df.iloc[i].X) for i in range(len(df))])
- return df
- def log_transforms(df):
-
- df = add_robust_features(df)
- df['X_max_log'] = np.log(df['X_max'])
- df['X_95_quantile_log'] = np.log(df['X_95_quantile'] + 1)
- df['Y_max_log'] = np.log(df['Y_max'])
- df['X_mad_log'] = np.log(df['X_mad'])
- def calc_rise_log(x):
- idx_max = np.argmax(x)
- val_max = x[idx_max]
- rise = np.log(val_max) - np.log(abs(np.min(x[:idx_max + 1])) + 1) # max change before peak
- return rise
- def calc_fall_log(x):
- idx_max = np.argmax(x)
- val_max = x[idx_max]
- fall = np.log(val_max) - np.log(abs(np.min(x[idx_max:])) + 1) # drop after peak
- return fall
- df['rise_log'] = np.array([calc_rise_log(df.iloc[i].X) for i in range(len(df))])
- df['fall_log'] = np.array([calc_fall_log(df.iloc[i].X) for i in range(len(df))])
- num = 3
- df['rise_local_3_log'] = df.apply(lambda row:
- calc_rise_log(np.array(row['X'][max(0, row['X_peak_idx'] - num):
- row['X_peak_idx'] + num + 1])),
- axis=1)
- df['fall_local_3_log'] = df.apply(lambda row:
- calc_fall_log(np.array(row['X'][max(0, row['X_peak_idx'] - num):
- row['X_peak_idx'] + num + 1])),
- axis=1)
- num2 = 11
- df['rise_local_11_log'] = df.apply(lambda row:
- calc_rise_log(np.array(row['X'][max(0, row['X_peak_idx'] - num2):
- row['X_peak_idx'] + num2 + 1])),
- axis=1)
- df['fall_local_11_log'] = df.apply(lambda row:
- calc_fall_log(np.array(row['X'][max(0, row['X_peak_idx'] - num2):
- row['X_peak_idx'] + num2 + 1])),
- axis=1)
- return df
- def train_reg(df,
- feat_names,
- model_type='rf',
- outcome_def='Y_max_log',
- out_name='results/regression/test.pkl',
- seed=42,
- **kwargs):
- '''
- train regression model
-
- hyperparameters of model can be specified using **kwargs
- '''
- np.random.seed(seed)
- X = df[feat_names]
- # X = (X - X.mean()) / X.std() # normalize the data
- y = df[outcome_def].values
- if model_type == 'rf':
- m = RandomForestRegressor(n_estimators=100)
- elif model_type == 'dt':
- m = DecisionTreeRegressor()
- elif model_type == 'linear':
- m = LinearRegression()
- elif model_type == 'ridge':
- m = RidgeCV()
- elif model_type == 'svm':
- m = SVR(gamma='scale')
- elif model_type == 'gb':
- m = GradientBoostingRegressor()
- elif model_type == 'irf':
- m = irf.ensemble.wrf()
- elif 'nn' in model_type: # neural nets
-
- """
- train fully connected neural network
- """
-
- H = kwargs['fcnn_hidden_neurons'] if 'fcnn_hidden_neurons' in kwargs else 40
- epochs = kwargs['fcnn_epochs'] if 'fcnn_epochs' in kwargs else 1000
- batch_size = kwargs['fcnn_batch_size'] if 'fcnn_batch_size' in kwargs else 1000
- track_name = kwargs['track_name'] if 'track_name' in kwargs else 'X_same_length'
- D_in = len(df[track_name].iloc[0])
-
- m = neural_net_sklearn(D_in=D_in,
- H=H,
- p=len(feat_names)-1,
- epochs=epochs,
- batch_size=batch_size,
- track_name=track_name,
- arch=model_type)
- # scores_cv = {s: [] for s in scorers.keys()}
- # scores_test = {s: [] for s in scorers.keys()}
- imps = {'model': [], 'imps': []}
- cell_nums_train = np.array(list(set(df.cell_num.values)))
- kf = KFold(n_splits=len(cell_nums_train))
- # split testing data based on cell num
- #idxs_test = df.cell_num.isin(cell_nums_test)
- #idxs_train = df.cell_num.isin(cell_nums_train)
- #X_test, Y_test = X[idxs_test], y[idxs_test]
- num_pts_by_fold_cv = []
- y_preds = {}
- cv_score = []
- cv_pearsonr = []
-
- print("Looping over cv...")
- # loops over cv, where test set order is cell_nums_train[0], ..., cell_nums_train[-1]
- for cv_idx, cv_val_idx in tqdm(kf.split(cell_nums_train)):
- # get sample indices
-
-
- idxs_cv = df.cell_num.isin(cell_nums_train[np.array(cv_idx)])
- idxs_val_cv = df.cell_num.isin(cell_nums_train[np.array(cv_val_idx)])
- X_train_cv, Y_train_cv = X[idxs_cv], y[idxs_cv]
- X_val_cv, Y_val_cv = X[idxs_val_cv], y[idxs_val_cv]
- num_pts_by_fold_cv.append(X_val_cv.shape[0])
- # resample training data
- # fit
- m.fit(X_train_cv, Y_train_cv)
- # get preds
- preds = m.predict(X_val_cv)
- y_preds[cell_nums_train[np.array(cv_val_idx)][0]] = preds
- if 'log' in outcome_def:
- cv_score.append(r2_score(np.exp(Y_val_cv), np.exp(preds)))
- cv_pearsonr.append(pearsonr(np.exp(Y_val_cv), np.exp(preds))[0])
- else:
- print(r2_score(Y_val_cv, preds))
- cv_score.append(r2_score(Y_val_cv, preds))
- cv_pearsonr.append(pearsonr(Y_val_cv, preds)[0])
-
- print("Training with full data...")
- # cv_score = cv_score/len(cell_nums_train)
- m.fit(X, y)
- #print(cv_score)
- #test_preds = m.predict(X_test)
- results = {'y_preds': y_preds,
- 'y': y,
- 'model_state_dict': m.model.state_dict(),
- #'test_preds': test_preds,
- 'cv': {'r2': cv_score, 'pearsonr': cv_pearsonr},
- 'model_type': model_type,
- #'model': m,
- 'num_pts_by_fold_cv': np.array(num_pts_by_fold_cv),
- }
- if model_type in ['rf', 'linear', 'ridge', 'gb', 'svm', 'irf']:
- results['model'] = m
- # save results
- # os.makedirs(out_dir, exist_ok=True)
- pkl.dump(results, open(out_name, 'wb'))
- def load_results(out_dir, by_cell=True):
- r = []
- for fname in os.listdir(out_dir):
- if os.path.isdir(oj(out_dir, fname)):
- continue
- 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']
- print(metrics)
- out = {k: np.average(metrics[k], weights=num_pts_by_fold_cv) for k in metrics}
- if by_cell:
- out.update({'cv_accuracy_by_cell': metrics['r2']})
- out.update({k + '_std': np.std(metrics[k]) for k in metrics})
- out['model_type'] = fname.replace('.pkl', '') # d['model_type']
- print(d['cv'].keys())
- # 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 load_and_train(dset, outcome_def, out_dir, feat_names=None, use_processed=True):
-
- df = pd.read_pickle(f'../data/tracks/tracks_{dset}.pkl')
- if dset == 'clath_aux_dynamin':
- df = df[df.catIdx.isin([1, 2])]
- df = df[df.lifetime > 15]
- else:
- df = df[df['valid'] == 1]
- df = features.add_basic_features(df)
- df = log_transforms(df)
- df = add_sig_mean(df)
- df_train = df[df.cell_num.isin(config.DSETS[dset]['train'])]
- df_test = df[df.cell_num.isin(config.DSETS[dset]['test'])]
- df_train = df_train.dropna()
-
- #outcome_def = 'Z_sig_mean'
- #out_dir = 'results/regression/Sep15'
- os.makedirs(out_dir, exist_ok=True)
- if not feat_names:
- feat_names = data.get_feature_names(df_train)
- feat_names = [x for x in feat_names
- if not x.startswith('sc_')
- and not x.startswith('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']
- and not x.startswith('pc_')
- and not 'log' in x
- and not 'binary' in x
- # and not 'slope' in x
- ]
- for model_type in tqdm(['linear', 'gb', 'rf', 'svm', 'ridge']):
- out_name = f'{model_type}'
- #print(out_name)
- if use_processed and os.path.exists(f'{out_dir}/{out_name}.pkl'):
- continue
- train_reg(df_train, feat_names=feat_names, model_type=model_type,
- outcome_def=outcome_def,
- out_name=f'{out_dir}/{out_name}.pkl')
-
- def test_reg(df,
- model,
- feat_names=None,
- outcome_def='Y_max_log',
- out_name='results/regression/test.pkl',
- seed=42):
- np.random.seed(seed)
- if not feat_names:
- feat_names = data.get_feature_names(df)
- feat_names = [x for x in feat_names
- if not x.startswith('sc_')
- and not x.startswith('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']
- and not x.startswith('pc_')
- and not 'log' in x
- and not 'binary' in x
- # and not 'slope' in x
- ]
- X = df[feat_names]
- # X = (X - X.mean()) / X.std() # normalize the data
- test_preds = model.predict(X)
- results = {'preds': test_preds}
- if outcome_def in df.keys():
- y = df[outcome_def].values
- results['r2'] = r2_score(y, test_preds)
- results['pearsonr'] = pearsonr(y, test_preds)
- results['kendalltau'] = kendalltau(y, test_preds)
-
- return results</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.train_reg.add_robust_features"><code class="name flex">
- <span>def <span class="ident">add_robust_features</span></span>(<span>df)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def add_robust_features(df):
- df['X_95_quantile'] = np.array([np.quantile(df.iloc[i].X, 0.95) for i in range(len(df))])
- df['X_mad'] = np.array([robust.mad(df.iloc[i].X) for i in range(len(df))])
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.train_reg.load_and_train"><code class="name flex">
- <span>def <span class="ident">load_and_train</span></span>(<span>dset, outcome_def, out_dir, feat_names=None, use_processed=True)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def load_and_train(dset, outcome_def, out_dir, feat_names=None, use_processed=True):
-
- df = pd.read_pickle(f'../data/tracks/tracks_{dset}.pkl')
- if dset == 'clath_aux_dynamin':
- df = df[df.catIdx.isin([1, 2])]
- df = df[df.lifetime > 15]
- else:
- df = df[df['valid'] == 1]
- df = features.add_basic_features(df)
- df = log_transforms(df)
- df = add_sig_mean(df)
- df_train = df[df.cell_num.isin(config.DSETS[dset]['train'])]
- df_test = df[df.cell_num.isin(config.DSETS[dset]['test'])]
- df_train = df_train.dropna()
-
- #outcome_def = 'Z_sig_mean'
- #out_dir = 'results/regression/Sep15'
- os.makedirs(out_dir, exist_ok=True)
- if not feat_names:
- feat_names = data.get_feature_names(df_train)
- feat_names = [x for x in feat_names
- if not x.startswith('sc_')
- and not x.startswith('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']
- and not x.startswith('pc_')
- and not 'log' in x
- and not 'binary' in x
- # and not 'slope' in x
- ]
- for model_type in tqdm(['linear', 'gb', 'rf', 'svm', 'ridge']):
- out_name = f'{model_type}'
- #print(out_name)
- if use_processed and os.path.exists(f'{out_dir}/{out_name}.pkl'):
- continue
- train_reg(df_train, feat_names=feat_names, model_type=model_type,
- outcome_def=outcome_def,
- out_name=f'{out_dir}/{out_name}.pkl') </code></pre>
- </details>
- </dd>
- <dt id="src.train_reg.load_results"><code class="name flex">
- <span>def <span class="ident">load_results</span></span>(<span>out_dir, by_cell=True)</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, by_cell=True):
- r = []
- for fname in os.listdir(out_dir):
- if os.path.isdir(oj(out_dir, fname)):
- continue
- 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']
- print(metrics)
- out = {k: np.average(metrics[k], weights=num_pts_by_fold_cv) for k in metrics}
- if by_cell:
- out.update({'cv_accuracy_by_cell': metrics['r2']})
- out.update({k + '_std': np.std(metrics[k]) for k in metrics})
- out['model_type'] = fname.replace('.pkl', '') # d['model_type']
- print(d['cv'].keys())
- # 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.train_reg.log_transforms"><code class="name flex">
- <span>def <span class="ident">log_transforms</span></span>(<span>df)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def log_transforms(df):
-
- df = add_robust_features(df)
- df['X_max_log'] = np.log(df['X_max'])
- df['X_95_quantile_log'] = np.log(df['X_95_quantile'] + 1)
- df['Y_max_log'] = np.log(df['Y_max'])
- df['X_mad_log'] = np.log(df['X_mad'])
- def calc_rise_log(x):
- idx_max = np.argmax(x)
- val_max = x[idx_max]
- rise = np.log(val_max) - np.log(abs(np.min(x[:idx_max + 1])) + 1) # max change before peak
- return rise
- def calc_fall_log(x):
- idx_max = np.argmax(x)
- val_max = x[idx_max]
- fall = np.log(val_max) - np.log(abs(np.min(x[idx_max:])) + 1) # drop after peak
- return fall
- df['rise_log'] = np.array([calc_rise_log(df.iloc[i].X) for i in range(len(df))])
- df['fall_log'] = np.array([calc_fall_log(df.iloc[i].X) for i in range(len(df))])
- num = 3
- df['rise_local_3_log'] = df.apply(lambda row:
- calc_rise_log(np.array(row['X'][max(0, row['X_peak_idx'] - num):
- row['X_peak_idx'] + num + 1])),
- axis=1)
- df['fall_local_3_log'] = df.apply(lambda row:
- calc_fall_log(np.array(row['X'][max(0, row['X_peak_idx'] - num):
- row['X_peak_idx'] + num + 1])),
- axis=1)
- num2 = 11
- df['rise_local_11_log'] = df.apply(lambda row:
- calc_rise_log(np.array(row['X'][max(0, row['X_peak_idx'] - num2):
- row['X_peak_idx'] + num2 + 1])),
- axis=1)
- df['fall_local_11_log'] = df.apply(lambda row:
- calc_fall_log(np.array(row['X'][max(0, row['X_peak_idx'] - num2):
- row['X_peak_idx'] + num2 + 1])),
- axis=1)
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.train_reg.test_reg"><code class="name flex">
- <span>def <span class="ident">test_reg</span></span>(<span>df, model, feat_names=None, outcome_def='Y_max_log', out_name='results/regression/test.pkl', seed=42)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def test_reg(df,
- model,
- feat_names=None,
- outcome_def='Y_max_log',
- out_name='results/regression/test.pkl',
- seed=42):
- np.random.seed(seed)
- if not feat_names:
- feat_names = data.get_feature_names(df)
- feat_names = [x for x in feat_names
- if not x.startswith('sc_')
- and not x.startswith('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']
- and not x.startswith('pc_')
- and not 'log' in x
- and not 'binary' in x
- # and not 'slope' in x
- ]
- X = df[feat_names]
- # X = (X - X.mean()) / X.std() # normalize the data
- test_preds = model.predict(X)
- results = {'preds': test_preds}
- if outcome_def in df.keys():
- y = df[outcome_def].values
- results['r2'] = r2_score(y, test_preds)
- results['pearsonr'] = pearsonr(y, test_preds)
- results['kendalltau'] = kendalltau(y, test_preds)
-
- return results</code></pre>
- </details>
- </dd>
- <dt id="src.train_reg.train_reg"><code class="name flex">
- <span>def <span class="ident">train_reg</span></span>(<span>df, feat_names, model_type='rf', outcome_def='Y_max_log', out_name='results/regression/test.pkl', seed=42, **kwargs)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>train regression model</p>
- <p>hyperparameters of model can be specified using **kwargs</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def train_reg(df,
- feat_names,
- model_type='rf',
- outcome_def='Y_max_log',
- out_name='results/regression/test.pkl',
- seed=42,
- **kwargs):
- '''
- train regression model
-
- hyperparameters of model can be specified using **kwargs
- '''
- np.random.seed(seed)
- X = df[feat_names]
- # X = (X - X.mean()) / X.std() # normalize the data
- y = df[outcome_def].values
- if model_type == 'rf':
- m = RandomForestRegressor(n_estimators=100)
- elif model_type == 'dt':
- m = DecisionTreeRegressor()
- elif model_type == 'linear':
- m = LinearRegression()
- elif model_type == 'ridge':
- m = RidgeCV()
- elif model_type == 'svm':
- m = SVR(gamma='scale')
- elif model_type == 'gb':
- m = GradientBoostingRegressor()
- elif model_type == 'irf':
- m = irf.ensemble.wrf()
- elif 'nn' in model_type: # neural nets
-
- """
- train fully connected neural network
- """
-
- H = kwargs['fcnn_hidden_neurons'] if 'fcnn_hidden_neurons' in kwargs else 40
- epochs = kwargs['fcnn_epochs'] if 'fcnn_epochs' in kwargs else 1000
- batch_size = kwargs['fcnn_batch_size'] if 'fcnn_batch_size' in kwargs else 1000
- track_name = kwargs['track_name'] if 'track_name' in kwargs else 'X_same_length'
- D_in = len(df[track_name].iloc[0])
-
- m = neural_net_sklearn(D_in=D_in,
- H=H,
- p=len(feat_names)-1,
- epochs=epochs,
- batch_size=batch_size,
- track_name=track_name,
- arch=model_type)
- # scores_cv = {s: [] for s in scorers.keys()}
- # scores_test = {s: [] for s in scorers.keys()}
- imps = {'model': [], 'imps': []}
- cell_nums_train = np.array(list(set(df.cell_num.values)))
- kf = KFold(n_splits=len(cell_nums_train))
- # split testing data based on cell num
- #idxs_test = df.cell_num.isin(cell_nums_test)
- #idxs_train = df.cell_num.isin(cell_nums_train)
- #X_test, Y_test = X[idxs_test], y[idxs_test]
- num_pts_by_fold_cv = []
- y_preds = {}
- cv_score = []
- cv_pearsonr = []
-
- print("Looping over cv...")
- # loops over cv, where test set order is cell_nums_train[0], ..., cell_nums_train[-1]
- for cv_idx, cv_val_idx in tqdm(kf.split(cell_nums_train)):
- # get sample indices
-
-
- idxs_cv = df.cell_num.isin(cell_nums_train[np.array(cv_idx)])
- idxs_val_cv = df.cell_num.isin(cell_nums_train[np.array(cv_val_idx)])
- X_train_cv, Y_train_cv = X[idxs_cv], y[idxs_cv]
- X_val_cv, Y_val_cv = X[idxs_val_cv], y[idxs_val_cv]
- num_pts_by_fold_cv.append(X_val_cv.shape[0])
- # resample training data
- # fit
- m.fit(X_train_cv, Y_train_cv)
- # get preds
- preds = m.predict(X_val_cv)
- y_preds[cell_nums_train[np.array(cv_val_idx)][0]] = preds
- if 'log' in outcome_def:
- cv_score.append(r2_score(np.exp(Y_val_cv), np.exp(preds)))
- cv_pearsonr.append(pearsonr(np.exp(Y_val_cv), np.exp(preds))[0])
- else:
- print(r2_score(Y_val_cv, preds))
- cv_score.append(r2_score(Y_val_cv, preds))
- cv_pearsonr.append(pearsonr(Y_val_cv, preds)[0])
-
- print("Training with full data...")
- # cv_score = cv_score/len(cell_nums_train)
- m.fit(X, y)
- #print(cv_score)
- #test_preds = m.predict(X_test)
- results = {'y_preds': y_preds,
- 'y': y,
- 'model_state_dict': m.model.state_dict(),
- #'test_preds': test_preds,
- 'cv': {'r2': cv_score, 'pearsonr': cv_pearsonr},
- 'model_type': model_type,
- #'model': m,
- 'num_pts_by_fold_cv': np.array(num_pts_by_fold_cv),
- }
- if model_type in ['rf', 'linear', 'ridge', 'gb', 'svm', 'irf']:
- results['model'] = m
- # save results
- # os.makedirs(out_dir, exist_ok=True)
- pkl.dump(results, open(out_name, 'wb'))</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="two-column">
- <li><code><a title="src.train_reg.add_robust_features" href="#src.train_reg.add_robust_features">add_robust_features</a></code></li>
- <li><code><a title="src.train_reg.load_and_train" href="#src.train_reg.load_and_train">load_and_train</a></code></li>
- <li><code><a title="src.train_reg.load_results" href="#src.train_reg.load_results">load_results</a></code></li>
- <li><code><a title="src.train_reg.log_transforms" href="#src.train_reg.log_transforms">log_transforms</a></code></li>
- <li><code><a title="src.train_reg.test_reg" href="#src.train_reg.test_reg">test_reg</a></code></li>
- <li><code><a title="src.train_reg.train_reg" href="#src.train_reg.train_reg">train_reg</a></code></li>
- </ul>
- </li>
- </ul>
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