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model_metrics.py
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import pandas as pd
import numpy as np
import pickle, sys
from sklearn.model_selection import cross_validate
from sklearn.preprocessing import LabelBinarizer
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import make_scorer, log_loss, roc_auc_score
from xgboost import XGBClassifier
from fancyimpute import MatrixFactorization, SimpleFill
from impute_transform import ImputeTransform
def get_metrics(X, y, clf_dict,
scoring, metric_df_cols,
X_test, y_test,
cv,
n_folds=10):
"""Runs cross validation to obtain error metrics for several classifiers.
Outputs a formatted dataframe.
INPUTS
------
- X: dataframe representing feature matrix for training data
- y: series representing target for training data
- clf_dict: dict of list of objects and characteristics of prepared classifiers
- scoring: dict of strings and objects for sklearn scoring
- metric_df_cols: dict of strings for desired columns of output DataFrame
- n_folds: int, number of folds for k-fold cross validation
- multiclass: bool, whether target has multiple classes
- cv: bool, whether to run cross validation
OUTPUTS
-------
"""
if cv == True:
clf_metrics = _run_clfs(clf_dict,
X, y, scoring,
n_folds)
else:
clf_metrics = _run_train_test(clf_dict,
X, y,
X_test, y_test,
scoring)
return clf_metrics
def _run_clfs(clf_dict,
X, y, scoring,
n_folds):
"""Runs cross validation on classifiers"""
for name in clf_dict.keys():
clf = clf_dict[name]['clf']
scores = cross_validate(clf, X, y,
scoring=scoring, cv=n_folds,
return_train_score=True)
clf_dict[name]['metrics'] = scores
return clf_dict
def _run_train_test(clf_dict,
X_train, y_train,
X_test, y_test,
scoring):
for name in clf_dict.keys():
clf_metric_dict = {}
clf = clf_dict[name]['clf']
clf.fit(X_train, y_train)
y_pred_proba = clf.predict_proba(X_test)
clf_metric_dict['test_neg_log_loss'] = log_loss(y_test, y_pred_proba)
clf_metric_dict['test_roc_auc'] = roc_auc_score(pd.get_dummies(y_test), y_pred_proba)
clf_dict[name]['metrics'] = clf_metric_dict
return clf_dict
def multiclass_roc_auc_score(truth, pred, average=None):
"""Returns multiclass roc auc score"""
lb = LabelBinarizer()
lb.fit(truth)
truth = lb.transform(truth)
pred = lb.transform(pred)
# with open('multiclass.txt', 'a') as f:
# data = list(roc_auc_score(truth, pred, average=None))
# data.append(truth.shape[0])
# f.write(str(data))
# f.write('\n')
return roc_auc_score(truth, pred, average='macro')
def prep_x_y(df, target, feature):
if target == 'DX':
y = df[target].map({3:1, 1:0})
else:
y = df[target]
if feature == 'tmcq':
cols = ['Y1_P_TMCQ_ACTIVCONT', 'Y1_P_TMCQ_ACTIVITY', 'Y1_P_TMCQ_AFFIL',
'Y1_P_TMCQ_ANGER', 'Y1_P_TMCQ_FEAR', 'Y1_P_TMCQ_HIP',
'Y1_P_TMCQ_IMPULS', 'Y1_P_TMCQ_INHIBIT', 'Y1_P_TMCQ_SAD',
'Y1_P_TMCQ_SHY', 'Y1_P_TMCQ_SOOTHE', 'Y1_P_TMCQ_ASSERT',
'Y1_P_TMCQ_ATTFOCUS', 'Y1_P_TMCQ_LIP', 'Y1_P_TMCQ_PERCEPT',
'Y1_P_TMCQ_DISCOMF', 'Y1_P_TMCQ_OPENNESS', 'Y1_P_TMCQ_SURGENCY',
'Y1_P_TMCQ_EFFCONT', 'Y1_P_TMCQ_NEGAFFECT']
elif feature == 'neuro':
cols = ['STOP_SSRTAVE_Y1', 'DPRIME1_Y1', 'DPRIME2_Y1', 'SSBK_NUMCOMPLETE_Y1',
'SSFD_NUMCOMPLETE_Y1', 'V_Y1', 'Y1_CLWRD_COND1', 'Y1_CLWRD_COND2',
'Y1_DIGITS_BKWD_RS', 'Y1_DIGITS_FRWD_RS', 'Y1_TRAILS_COND2',
'Y1_TRAILS_COND3', 'CW_RES', 'TR_RES', 'Y1_TAP_SD_TOT_CLOCK']
elif feature == 'all':
cols = ['Y1_P_TMCQ_ACTIVCONT', 'Y1_P_TMCQ_ACTIVITY', 'Y1_P_TMCQ_AFFIL',
'Y1_P_TMCQ_ANGER', 'Y1_P_TMCQ_FEAR', 'Y1_P_TMCQ_HIP',
'Y1_P_TMCQ_IMPULS', 'Y1_P_TMCQ_INHIBIT', 'Y1_P_TMCQ_SAD',
'Y1_P_TMCQ_SHY', 'Y1_P_TMCQ_SOOTHE', 'Y1_P_TMCQ_ASSERT',
'Y1_P_TMCQ_ATTFOCUS', 'Y1_P_TMCQ_LIP', 'Y1_P_TMCQ_PERCEPT',
'Y1_P_TMCQ_DISCOMF', 'Y1_P_TMCQ_OPENNESS', 'Y1_P_TMCQ_SURGENCY',
'Y1_P_TMCQ_EFFCONT', 'Y1_P_TMCQ_NEGAFFECT',
'STOP_SSRTAVE_Y1', 'DPRIME1_Y1', 'DPRIME2_Y1', 'SSBK_NUMCOMPLETE_Y1',
'SSFD_NUMCOMPLETE_Y1', 'V_Y1', 'Y1_CLWRD_COND1', 'Y1_CLWRD_COND2',
'Y1_DIGITS_BKWD_RS', 'Y1_DIGITS_FRWD_RS', 'Y1_TRAILS_COND2',
'Y1_TRAILS_COND3', 'CW_RES', 'TR_RES', 'Y1_TAP_SD_TOT_CLOCK']
X = df[cols]
if feature == 'tmcq':
X_no_null = X[X.isnull().sum(axis=1) == 0]
y_no_null = y[X.isnull().sum(axis=1) == 0]
else:
X_no_null = X[X.isnull().sum(axis=1) != X.shape[1]]
y_no_null = y[X.isnull().sum(axis=1) != X.shape[1]]
return X_no_null, y_no_null
def prep_clfs(feature):
if feature == 'tmcq':
log_reg_clf = make_pipeline(LogisticRegression(random_state=56))
rf_clf = make_pipeline(RandomForestClassifier(n_jobs=-1, random_state=56))
gb_clf = make_pipeline(GradientBoostingClassifier(random_state=56))
xgb_clf = make_pipeline(XGBClassifier(max_depth=3, learning_rate=0.1,
random_state=56, n_jobs=-1))
else:
log_reg_clf = make_pipeline(ImputeTransform(strategy=MatrixFactorization()),
LogisticRegression(random_state=56))
rf_clf = make_pipeline(ImputeTransform(strategy=MatrixFactorization()),
RandomForestClassifier(n_jobs=-1, random_state=56))
gb_clf = make_pipeline(ImputeTransform(strategy=MatrixFactorization()),
GradientBoostingClassifier(random_state=56))
xgb_clf = make_pipeline(ImputeTransform(strategy=MatrixFactorization()),
XGBClassifier(max_depth=3, learning_rate=0.1,
random_state=56, n_jobs=-1))
classifier_dict = {'LogReg':
{'clf': log_reg_clf},
'RandomForest':
{'clf': rf_clf},
'GradientBoosting':
{'clf': gb_clf},
'XGB':
{'clf': xgb_clf}}
return classifier_dict
def prep_scoring(target):
scoring_dict = {'accuracy': 'accuracy',
'neg_log_loss': 'neg_log_loss'}
if target == 'DXSUB':
multiclass_roc = make_scorer(multiclass_roc_auc_score,
greater_is_better=True)
scoring_dict['roc_auc'] = multiclass_roc
else:
scoring_dict['roc_auc'] = 'roc_auc'
return scoring_dict