added practice and base_estimator for all the confusion in my head
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base_estimator.py
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base_estimator.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 15 09:50:37 2022
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@author: tanu
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"""
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#https://stackoverflow.com/questions/50272416/gridsearch-on-model-and-classifiers
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#%%
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# https://github.com/davidsbatista/machine-learning-notebooks/blob/master/hyperparameter-across-models.ipynb
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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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#%%
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class EstimatorSelectionHelper:
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def __init__(self, models, params):
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self.models = models
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self.params = params
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self.keys = models.keys()
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self.grid_searches = {}
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def fit(self, X, y, **grid_kwargs):
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for key in self.keys:
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print('Running GridSearchCV for %s.' % key)
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model = self.models[key]
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params = self.params[key]
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grid_search = GridSearchCV(model, params, **grid_kwargs)
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grid_search.fit(X, y)
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self.grid_searches[key] = grid_search
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print('Done.')
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def score_summary(self, sort_by='mean_test_score'):
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frames = []
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for name, grid_search in self.grid_searches.items():
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frame = pd.DataFrame(grid_search.cv_results_)
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frame = frame.filter(regex='^(?!.*param_).*$')
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frame['estimator'] = len(frame)*[name]
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frames.append(frame)
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df = pd.concat(frames)
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df = df.sort_values([sort_by], ascending=False)
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df = df.reset_index()
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df = df.drop(['rank_test_score', 'index'], 1)
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columns = df.columns.tolist()
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columns.remove('estimator')
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columns = ['estimator']+columns
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df = df[columns]
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return df
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#%%
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breast_cancer = datasets.load_breast_cancer()
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X_cancer = breast_cancer.data
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y_cancer = breast_cancer.target
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models1 = {
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'ExtraTreesClassifier': ExtraTreesClassifier(),
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'RandomForestClassifier': RandomForestClassifier(),
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'AdaBoostClassifier': AdaBoostClassifier(),
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'GradientBoostingClassifier': GradientBoostingClassifier()
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}
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params1 = {
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'ExtraTreesClassifier': { 'n_estimators': [16, 32] },
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'RandomForestClassifier': [
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{ 'n_estimators': [16, 32] },
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{'criterion': ['gini', 'entropy'], 'n_estimators': [8, 16]}],
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'AdaBoostClassifier': { 'n_estimators': [16, 32] },
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'GradientBoostingClassifier': { 'n_estimators': [16, 32], 'learning_rate': [0.8, 1.0] }
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}
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helper1 = EstimatorSelectionHelper(models1, params1)
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helper1.fit(X_cancer, y_cancer, scoring='f1', n_jobs=2)
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helper1.score_summary()
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mm_df = helper1.score_summary()
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# COMMENT: Not sure what scores is it mean of and the options available thus
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#%%
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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': [SGDClassifier()], # SVM if hinge loss / logreg if log loss
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#'tfidf__max_df': (0.25, 0.5, 0.75, 1.0),
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#'tfidf__stop_words': ['english', None],
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'clf__estimator__penalty': ('l2', 'elasticnet', 'l1'),
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'clf__estimator__max_iter': [50, 80],
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'clf__estimator__tol': [1e-4],
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'clf__estimator__loss': ['hinge', 'log', 'modified_huber'],
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},
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{
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'clf__estimator': [MultinomialNB()],
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#'tfidf__max_df': (0.25, 0.5, 0.75, 1.0),
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#'tfidf__stop_words': [None],
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'clf__estimator__alpha': (1e-2, 1e-3, 1e-1),
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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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, cv=5
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, n_jobs=12
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, return_train_score=False
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, verbose=3)
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#gscv.fit(train_data, train_labels)
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#%% my numerical data
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X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
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, num_df_wtgt['mutation_class']
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, test_size = 0.33
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, random_state = 2
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, shuffle = True
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, stratify = num_df_wtgt['mutation_class'])
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y_train.to_frame().value_counts().plot(kind = 'bar')
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y_test.to_frame().value_counts().plot(kind = 'bar')
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#%%
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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_)
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gscv.score(X_test, y_test) # see how it does on test
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#===========================================
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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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#%% same as above
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# custom classifier
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class MyClassifier(BaseEstimator):
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def __init__(self, classifier_type: str = 'SGDClassifier'):
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"""
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A Custome BaseEstimator that can switch between classifiers.
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:param classifier_type: string - The switch for different classifiers
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"""
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self.classifier_type = classifier_type
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def fit(self, X, y=None):
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if self.classifier_type == 'SGDClassifier':
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self.classifier_ = SGDClassifier()
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elif self.classifier_type == 'MultinomialNB':
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self.classifier_ = MultinomialNB()
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else:
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raise ValueError('Unkown classifier type.')
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self.classifier_.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.classifier_.predict(X)
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def score(self, X, y):
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return self.estimator.score(X, y)
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pipeline = Pipeline([
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('pre', MinMaxScaler())
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#, ('clf', ClfSwitcher()
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, ('clf', MyClassifier())
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])
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# parameter_space = {
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# 'clf__classifier_type': ['SGDClassifier', 'MultinomialNB']
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# }
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parameter_space = [
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{
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'clf__estimator': [SGDClassifier()],
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'clf__estimator__penalty': ('l2', 'elasticnet', 'l1'),
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'clf__estimator__max_iter': [50, 80],
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'clf__estimator__tol': [1e-4],
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'clf__estimator__loss': ['hinge', 'log', 'modified_huber'],
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},
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{
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'clf__estimator': [MultinomialNB()],
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'clf__estimator__alpha': (1e-2, 1e-3, 1e-1),
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},
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]
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search = GridSearchCV(pipeline , parameter_space, n_jobs=-1, cv=5)
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search.fit(X_train, y_train)
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print('Best model:\n', search.best_params_)
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print('Best models score:\n', gscv.best_score_)
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