164 lines
5.9 KiB
Python
Executable file
164 lines
5.9 KiB
Python
Executable file
#%% Import libs
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.svm import SVC
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from sklearn.base import BaseEstimator
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.linear_model import SGDClassifier
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from xgboost import XGBClassifier
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#######################################################
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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#, 'jaccard' : make_scorer(jaccard_score)
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})
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#%% Logistic Regression + hyperparam: GridSearch
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# Note: cannot have '___' in estimator names
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# '__' is used only before stating the param names
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# '__' is usef in both places when using clf_switcher
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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# FIXME: solver and penalty conflict, consider using 1
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grid_params_log_reg = [{
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#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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#'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
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}]
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pipe_log_reg = Pipeline([
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('pre', MinMaxScaler())
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,('clf', LogisticRegression(**rs))])
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gs_log_reg = GridSearchCV(pipe_log_reg
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, param_grid = grid_params_log_reg
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, scoring ='f1' , refit = 'f1' # works
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#, scoring = mcc_score_fn, refit = 'mcc'
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#, scoring = scoring_fn, refit = False # problem doesn't predict because doesn't know
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, cv = 10
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, n_jobs = 10# based on /proc/cpuinfo
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, return_train_score = False
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, verbose = 3)
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gs_log_reg.fit(X_train, y_train)
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#gs_log_reg_fit = gs_log_reg.fit(X_train, y_train)
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#gs_log_reg_fit_res = gs_log_reg.cv_results_ # still don't know how to use it
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#pp.pprint(gs_log_reg_fit_res)
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#y_predict = gs_log_reg.predict(X_test)
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#print('Test set accuracy score for best params: %.3f ' % accuracy_score(y_test, y_predict))
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print('Best model:\n', gs_log_reg.best_params_)
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print('Best models score:\n', gs_log_reg.best_score_)
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#GridSearchCV giving score from the best estimator different from the one indicated in refit parameter
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#https://stackoverflow.com/questions/66116996/gridsearchcv-giving-score-from-the-best-estimator-different-from-the-one-indicat
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#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
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class ClfSwitcher(BaseEstimator):
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def __init__(
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self,
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estimator = SGDClassifier(),
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):
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"""
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A Custom BaseEstimator that can switch between classifiers.
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:param estimator: sklearn object - The classifier
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"""
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self.estimator = estimator
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def fit(self, X, y=None, **kwargs):
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self.estimator.fit(X, y)
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return self
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def predict(self, X, y=None):
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return self.estimator.predict(X)
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def predict_proba(self, X):
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return self.estimator.predict_proba(X)
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def score(self, X, y):
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return self.estimator.score(X, y)
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parameters = [
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{
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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#'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
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}
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]
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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gscv = GridSearchCV(pipeline
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, parameters
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, scoring = 'f1', refit = 'f1'
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, cv = 10
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, n_jobs = 10 #based on /proc/cpuinfo
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, return_train_score = False
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, verbose = 3)
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# Fit
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gscv.fit(X_train, y_train)
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print('Best model:\n', gscv.best_params_)
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print('Best models score:\n', gscv.best_score_, ':' ,round(gscv.best_score_, 2))
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# gscv.score(X_test, y_test) # see how it does on test
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# check_score = f1_score(y_train, gscv.predict(X_train))
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# check_score # should be the same as the best score when the same metric used!
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# mod_pred = gscv.predict(X_test)
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# fscore = f1_score(y_test, mod_pred)
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# fscore
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gscv_fit_be = gscv.fit(X_train, y_train)
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gscv_fit_be_res = gscv_fit_be.cv_results_
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print('\nMean test score from fit results:', round(mean(gscv_fit_be_res['mean_test_score']),2))
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best_model = gscv.best_params_
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best_model.keys()
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best_model.values
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cross_val_score(LogisticRegression(random_state=42
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, solver='liblinear'
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, max_iter = 100)
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, X_train
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, y_train
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, cv = 10)
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cval =round(mean(cross_val_score(LogisticRegression(random_state=42
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, solver='liblinear'
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, max_iter = 100)
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, X_train
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, y_train
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, cv = 10)),2)
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########check
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print('Best models score:', round(gscv.best_score_, 2))
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print('Mean test score from fit results:', round(mean(gscv_fit_be_res['mean_test_score']),2))
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print('Best models cval:', cval)
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