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- </head>
- <body>
- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.train</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import sys
- from sklearn.linear_model import LogisticRegression, Lasso
- from sklearn.neural_network import MLPClassifier
- from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
- from sklearn.tree import DecisionTreeClassifier
- from sklearn.svm import SVC
- from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
- import eli5
- import numpy as np
- from copy import deepcopy
- from sklearn import metrics
- from sklearn.feature_selection import SelectFromModel
- from sklearn.calibration import CalibratedClassifierCV
- from imblearn.over_sampling import RandomOverSampler, SMOTE
- from sklearn.model_selection import KFold
- import pickle as pkl
- sys.path.append('lib')
- from sklearn.neighbors import KNeighborsClassifier as KNN
- scorers = {'balanced_accuracy': metrics.balanced_accuracy_score, 'accuracy': metrics.accuracy_score,
- 'precision': metrics.precision_score, 'recall': metrics.recall_score, 'f1': metrics.f1_score,
- 'roc_auc': metrics.roc_auc_score,
- 'precision_recall_curve': metrics.precision_recall_curve, 'roc_curve': metrics.roc_curve}
- def get_feature_importance(model, model_type, X_val, Y_val):
- if 'Calibrated' in str(type(model)):
- perm = eli5.sklearn.permutation_importance.PermutationImportance(model).fit(X_val, Y_val)
- imps = perm.feature_importances_
- elif model_type in ['dt']:
- imps = model.feature_importances_
- elif model_type in ['rf', 'irf']:
- # imps, _ = feature_importance(model, np.array(X_val), np.transpose(np.vstack((Y_val, 1-Y_val))))
- imps = model.feature_importances_
- elif model_type == 'logistic':
- imps = model.coef_
- else:
- perm = eli5.sklearn.permutation_importance.PermutationImportance(model).fit(X_val, Y_val)
- imps = perm.feature_importances_
- return imps.squeeze()
- def balance(X, y, balancing='ros', balancing_ratio=1):
- '''Balance classes in y using strategy specified by balancing
- Params
- -----
- balancing_ratio: float
- ratio of pos: neg samples
- '''
- class0 = np.sum(y == 0)
- class1 = np.sum(y == 1)
- class_max = max(class0, class1)
- if balancing_ratio >= 1:
- sample_nums = {0: int(class_max), 1: int(class_max * balancing_ratio)}
- else:
- sample_nums = {0: int(class_max / balancing_ratio), 1: int(class_max)}
- if balancing == 'none':
- return X, y
- if balancing == 'ros':
- sampler = RandomOverSampler(sampling_strategy=sample_nums, random_state=42)
- elif balancing == 'smote':
- sampler = SMOTE(sampling_strategy=sample_nums, random_state=42)
- X_r, Y_r = sampler.fit_resample(X, y)
- return X_r, Y_r
- def train(df, feat_names,
- cell_nums_feature_selection, cell_nums_train,
- model_type='rf', outcome_def='y_thresh',
- balancing='ros', balancing_ratio=1, out_name='results/classify/test.pkl',
- calibrated=False, feature_selection=None,
- feature_selection_num=3, hyperparam=0, seed=42):
- '''Run training and fit models
- This will balance the data
- This will normalize the features before fitting
-
- Params
- ------
- normalize: bool
- if True, will normalize features before fitting
- cell_nums_feature_selection: list[str]
- cell names to use for feature selection
-
- '''
- 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 = RandomForestClassifier(n_estimators=100)
- elif model_type == 'dt':
- m = DecisionTreeClassifier()
- elif model_type == 'logistic':
- m = LogisticRegression(solver='lbfgs')
- elif model_type == 'svm':
- h = {
- -1: 0.5,
- 0: 1,
- 1: 5
- }[hyperparam]
- m = SVC(C=h, gamma='scale')
- elif model_type == 'mlp2':
- h = {
- -1: (50,),
- 0: (100,),
- 1: (50, 50,)
- }[hyperparam]
- m = MLPClassifier(hidden_layer_sizes=h)
- elif model_type == 'gb':
- m = GradientBoostingClassifier()
- elif model_type == 'qda':
- m = QDA()
- elif model_type == 'KNN':
- m = KNN()
- elif model_type == 'irf':
- import irf
- m = irf.ensemble.wrf()
- elif model_type == 'voting_mlp+svm+rf':
- models_list = [('mlp', MLPClassifier()),
- ('svm', SVC(gamma='scale')),
- ('rf', RandomForestClassifier(n_estimators=100))]
- m = VotingClassifier(estimators=models_list, voting='hard')
- if calibrated:
- m = CalibratedClassifierCV(m)
- scores_cv = {s: [] for s in scorers.keys()}
- imps = {'model': [], 'imps': []}
- kf = KFold(n_splits=len(cell_nums_train))
- # feature selection on cell num 1
- feature_selector = None
- if feature_selection is not None:
- if feature_selection == 'select_lasso':
- feature_selector_model = Lasso()
- elif feature_selection == 'select_rf':
- feature_selector_model = RandomForestClassifier()
- # select only feature_selection_num features
- feature_selector = SelectFromModel(feature_selector_model, threshold=-np.inf,
- max_features=feature_selection_num)
- idxs = df.cell_num.isin(cell_nums_feature_selection)
- feature_selector.fit(X[idxs], y[idxs].reshape(-1, 1))
- X = feature_selector.transform(X)
- support = np.array(feature_selector.get_support())
- else:
- support = np.ones(len(feat_names)).astype(np.bool)
- num_pts_by_fold_cv = []
- # loops over cv, where validation set order is cell_nums_train[0], ..., cell_nums_train[-1]
- for cv_idx, cv_val_idx in 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
- X_train_r_cv, Y_train_r_cv = balance(X_train_cv, Y_train_cv, balancing, balancing_ratio)
- # fit
- m.fit(X_train_r_cv, Y_train_r_cv)
- # get preds
- preds = m.predict(X_val_cv)
- if 'svm' in model_type:
- preds_proba = preds
- else:
- preds_proba = m.predict_proba(X_val_cv)[:, 1]
- # add scores
- for s in scorers.keys():
- scorer = scorers[s]
- if 'roc' in s or 'curve' in s:
- scores_cv[s].append(scorer(Y_val_cv, preds_proba))
- else:
- scores_cv[s].append(scorer(Y_val_cv, preds))
- imps['model'].append(deepcopy(m))
- imps['imps'].append(get_feature_importance(m, model_type, X_val_cv, Y_val_cv))
- # save results
- # os.makedirs(out_dir, exist_ok=True)
- results = {'metrics': list(scorers.keys()),
- 'num_pts_by_fold_cv': np.array(num_pts_by_fold_cv),
- 'cv': scores_cv,
- 'imps': imps, # note this contains the model
- 'feat_names': feat_names,
- 'feature_selector': feature_selector,
- 'feature_selection_num': feature_selection_num,
- 'model_type': model_type,
- 'balancing': balancing,
- 'feat_names_selected': np.array(feat_names)[support],
- 'calibrated': calibrated
- }
- pkl.dump(results, open(out_name, 'wb'))</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.train.balance"><code class="name flex">
- <span>def <span class="ident">balance</span></span>(<span>X, y, balancing='ros', balancing_ratio=1)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Balance classes in y using strategy specified by balancing
- Params</p>
- <hr>
- <dl>
- <dt><strong><code>balancing_ratio</code></strong> : <code>float</code></dt>
- <dd>ratio of pos: neg samples</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def balance(X, y, balancing='ros', balancing_ratio=1):
- '''Balance classes in y using strategy specified by balancing
- Params
- -----
- balancing_ratio: float
- ratio of pos: neg samples
- '''
- class0 = np.sum(y == 0)
- class1 = np.sum(y == 1)
- class_max = max(class0, class1)
- if balancing_ratio >= 1:
- sample_nums = {0: int(class_max), 1: int(class_max * balancing_ratio)}
- else:
- sample_nums = {0: int(class_max / balancing_ratio), 1: int(class_max)}
- if balancing == 'none':
- return X, y
- if balancing == 'ros':
- sampler = RandomOverSampler(sampling_strategy=sample_nums, random_state=42)
- elif balancing == 'smote':
- sampler = SMOTE(sampling_strategy=sample_nums, random_state=42)
- X_r, Y_r = sampler.fit_resample(X, y)
- return X_r, Y_r</code></pre>
- </details>
- </dd>
- <dt id="src.train.get_feature_importance"><code class="name flex">
- <span>def <span class="ident">get_feature_importance</span></span>(<span>model, model_type, X_val, Y_val)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_feature_importance(model, model_type, X_val, Y_val):
- if 'Calibrated' in str(type(model)):
- perm = eli5.sklearn.permutation_importance.PermutationImportance(model).fit(X_val, Y_val)
- imps = perm.feature_importances_
- elif model_type in ['dt']:
- imps = model.feature_importances_
- elif model_type in ['rf', 'irf']:
- # imps, _ = feature_importance(model, np.array(X_val), np.transpose(np.vstack((Y_val, 1-Y_val))))
- imps = model.feature_importances_
- elif model_type == 'logistic':
- imps = model.coef_
- else:
- perm = eli5.sklearn.permutation_importance.PermutationImportance(model).fit(X_val, Y_val)
- imps = perm.feature_importances_
- return imps.squeeze()</code></pre>
- </details>
- </dd>
- <dt id="src.train.train"><code class="name flex">
- <span>def <span class="ident">train</span></span>(<span>df, feat_names, cell_nums_feature_selection, cell_nums_train, model_type='rf', outcome_def='y_thresh', balancing='ros', balancing_ratio=1, out_name='results/classify/test.pkl', calibrated=False, feature_selection=None, feature_selection_num=3, hyperparam=0, seed=42)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Run training and fit models
- This will balance the data
- This will normalize the features before fitting</p>
- <h2 id="params">Params</h2>
- <dl>
- <dt><strong><code>normalize</code></strong> : <code>bool</code></dt>
- <dd>if True, will normalize features before fitting</dd>
- <dt><strong><code>cell_nums_feature_selection</code></strong> : <code>list</code>[<code>str</code>]</dt>
- <dd>cell names to use for feature selection</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def train(df, feat_names,
- cell_nums_feature_selection, cell_nums_train,
- model_type='rf', outcome_def='y_thresh',
- balancing='ros', balancing_ratio=1, out_name='results/classify/test.pkl',
- calibrated=False, feature_selection=None,
- feature_selection_num=3, hyperparam=0, seed=42):
- '''Run training and fit models
- This will balance the data
- This will normalize the features before fitting
-
- Params
- ------
- normalize: bool
- if True, will normalize features before fitting
- cell_nums_feature_selection: list[str]
- cell names to use for feature selection
-
- '''
- 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 = RandomForestClassifier(n_estimators=100)
- elif model_type == 'dt':
- m = DecisionTreeClassifier()
- elif model_type == 'logistic':
- m = LogisticRegression(solver='lbfgs')
- elif model_type == 'svm':
- h = {
- -1: 0.5,
- 0: 1,
- 1: 5
- }[hyperparam]
- m = SVC(C=h, gamma='scale')
- elif model_type == 'mlp2':
- h = {
- -1: (50,),
- 0: (100,),
- 1: (50, 50,)
- }[hyperparam]
- m = MLPClassifier(hidden_layer_sizes=h)
- elif model_type == 'gb':
- m = GradientBoostingClassifier()
- elif model_type == 'qda':
- m = QDA()
- elif model_type == 'KNN':
- m = KNN()
- elif model_type == 'irf':
- import irf
- m = irf.ensemble.wrf()
- elif model_type == 'voting_mlp+svm+rf':
- models_list = [('mlp', MLPClassifier()),
- ('svm', SVC(gamma='scale')),
- ('rf', RandomForestClassifier(n_estimators=100))]
- m = VotingClassifier(estimators=models_list, voting='hard')
- if calibrated:
- m = CalibratedClassifierCV(m)
- scores_cv = {s: [] for s in scorers.keys()}
- imps = {'model': [], 'imps': []}
- kf = KFold(n_splits=len(cell_nums_train))
- # feature selection on cell num 1
- feature_selector = None
- if feature_selection is not None:
- if feature_selection == 'select_lasso':
- feature_selector_model = Lasso()
- elif feature_selection == 'select_rf':
- feature_selector_model = RandomForestClassifier()
- # select only feature_selection_num features
- feature_selector = SelectFromModel(feature_selector_model, threshold=-np.inf,
- max_features=feature_selection_num)
- idxs = df.cell_num.isin(cell_nums_feature_selection)
- feature_selector.fit(X[idxs], y[idxs].reshape(-1, 1))
- X = feature_selector.transform(X)
- support = np.array(feature_selector.get_support())
- else:
- support = np.ones(len(feat_names)).astype(np.bool)
- num_pts_by_fold_cv = []
- # loops over cv, where validation set order is cell_nums_train[0], ..., cell_nums_train[-1]
- for cv_idx, cv_val_idx in 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
- X_train_r_cv, Y_train_r_cv = balance(X_train_cv, Y_train_cv, balancing, balancing_ratio)
- # fit
- m.fit(X_train_r_cv, Y_train_r_cv)
- # get preds
- preds = m.predict(X_val_cv)
- if 'svm' in model_type:
- preds_proba = preds
- else:
- preds_proba = m.predict_proba(X_val_cv)[:, 1]
- # add scores
- for s in scorers.keys():
- scorer = scorers[s]
- if 'roc' in s or 'curve' in s:
- scores_cv[s].append(scorer(Y_val_cv, preds_proba))
- else:
- scores_cv[s].append(scorer(Y_val_cv, preds))
- imps['model'].append(deepcopy(m))
- imps['imps'].append(get_feature_importance(m, model_type, X_val_cv, Y_val_cv))
- # save results
- # os.makedirs(out_dir, exist_ok=True)
- results = {'metrics': list(scorers.keys()),
- 'num_pts_by_fold_cv': np.array(num_pts_by_fold_cv),
- 'cv': scores_cv,
- 'imps': imps, # note this contains the model
- 'feat_names': feat_names,
- 'feature_selector': feature_selector,
- 'feature_selection_num': feature_selection_num,
- 'model_type': model_type,
- 'balancing': balancing,
- 'feat_names_selected': np.array(feat_names)[support],
- 'calibrated': calibrated
- }
- pkl.dump(results, open(out_name, 'wb'))</code></pre>
- </details>
- </dd>
- </dl>
- </section>
- <section>
- </section>
- </article>
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- <div class="toc">
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- </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.train.balance" href="#src.train.balance">balance</a></code></li>
- <li><code><a title="src.train.get_feature_importance" href="#src.train.get_feature_importance">get_feature_importance</a></code></li>
- <li><code><a title="src.train.train" href="#src.train.train">train</a></code></li>
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