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How to use helper1 method in green
Best Python code snippet using green
parameters_finding.py
Source:parameters_finding.py 
1import numpy as np2import pandas as pd3from sklearn.ensemble import AdaBoostClassifier4from sklearn.ensemble import GradientBoostingClassifier5from sklearn.model_selection import GridSearchCV6from sklearn.neighbors import KNeighborsClassifier7from sklearn.neural_network import MLPClassifier8from sklearn.svm import SVC9from sklearn.tree import DecisionTreeClassifier10from helpers import get_wine_data11from helpers import get_abalone_data12class EstimatorSelectionHelper:13 def __init__(self, models, params):14 if not set(models.keys()).issubset(set(params.keys())):15 missing_params = list(set(models.keys()) - set(params.keys()))16 raise ValueError("Some estimators are missing parameters: %s" % missing_params)17 self.models = models18 self.params = params19 self.keys = models.keys()20 self.grid_searches = {}21 def fit(self, X, y, cv=3, n_jobs=3, verbose=1, scoring=None, refit=False):22 for key in self.keys:23 print("Running GridSearchCV for %s." % key)24 model = self.models[key]25 params = self.params[key]26 gs = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs,27 verbose=verbose, scoring=scoring, refit=refit,28 return_train_score=True)29 gs.fit(X,y)30 self.grid_searches[key] = gs31 def score_summary(self, sort_by='mean_score'):32 def row(key, scores, params):33 d = {34 'estimator': key,35 'min_score': min(scores),36 'max_score': max(scores),37 'mean_score': np.mean(scores),38 'std_score': np.std(scores),39 }40 return pd.Series({**params,**d})41 rows = []42 for k in self.grid_searches:43 print(k)44 params = self.grid_searches[k].cv_results_['params']45 scores = []46 for i in range(self.grid_searches[k].cv):47 key = "split{}_test_score".format(i)48 r = self.grid_searches[k].cv_results_[key]49 scores.append(r.reshape(len(params),1))50 all_scores = np.hstack(scores)51 for p, s in zip(params,all_scores):52 rows.append((row(k, s, p)))53 df = pd.concat(rows, axis=1).T.sort_values([sort_by], ascending=False)54 columns = ['estimator', 'mean_score', 'max_score', 'std_score']55 columns = columns + [c for c in df.columns if c not in columns]56 return df[columns]57models1 = {58 'AdaBoostClassifier': AdaBoostClassifier(base_estimator=DecisionTreeClassifier()),59 'DecisionTreeClassifier': DecisionTreeClassifier(),60 'KNeighborsClassifier': KNeighborsClassifier()61}62params1 = {63 'AdaBoostClassifier': {64 'n_estimators': [1, 3, 5, 7, 9, 11, 13, 15],65 "base_estimator__criterion": ["gini"],66 "base_estimator__splitter": ["best", "random"],67 'base_estimator__max_depth': [None, 1, 2, 3],68 'base_estimator__min_samples_leaf': [1, 2, 3, 4, 5]69 },70 'DecisionTreeClassifier': {'max_depth': [None, 1, 2, 3], 'min_samples_leaf': [1, 2, 3, 4, 5]},71 'KNeighborsClassifier': {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9]}72}73models2 = {74 'SVC': SVC()75}76params2 = {77 'SVC': [78 {'kernel': ['linear'], 'C': [1, 10]},79 {'kernel': ['rbf'], 'C': [1, 10], 'gamma': [0.001, 0.0001]},80 ]81}82models3 = {83 'MLPClassifier': MLPClassifier()84}85params3 = {86 'MLPClassifier': {87 'solver': ['lbfgs'],88 'max_iter': [1, 3, 5, 7, 9],89 'alpha': 10.0 ** -np.arange(1, 10),90 'hidden_layer_sizes': np.arange(10, 15),91 'random_state': [0,1]92 }93}94if __name__ == "__main__":95 X, y = get_wine_data()96 X1, y1 = get_abalone_data()97 helper1 = EstimatorSelectionHelper(models1, params1)98 helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)99 results = (helper1.score_summary(sort_by='max_score'))100 results.to_csv("out/wine_params_4.csv")101 helper1 = EstimatorSelectionHelper(models1, params1)102 helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)103 results = (helper1.score_summary(sort_by='max_score'))104 results.to_csv("out/abalone_params_4.csv")105 # helper1 = EstimatorSelectionHelper(models2, params2)106 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)107 # results = (helper1.score_summary(sort_by='max_score'))108 # results.to_csv("out/wine_params_2.csv")109 # helper1 = EstimatorSelectionHelper(models2, params2)110 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)111 # results = (helper1.score_summary(sort_by='max_score'))112 # results.to_csv("out/abalone_params_2.csv")113 # helper1 = EstimatorSelectionHelper(models3, params3)114 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)115 # results = (helper1.score_summary(sort_by='max_score'))116 # results.to_csv("out/wine_params_3.csv")117 # helper1 = EstimatorSelectionHelper(models3, params3)118 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)119 # results = (helper1.score_summary(sort_by='max_score'))...
parameters_finding_non_svc.py
Source:parameters_finding_non_svc.py
1import numpy as np2import pandas as pd3from sklearn.ensemble import AdaBoostClassifier4from sklearn.ensemble import GradientBoostingClassifier5from sklearn.model_selection import GridSearchCV6from sklearn.neighbors import KNeighborsClassifier7from sklearn.neural_network import MLPClassifier8from sklearn.svm import SVC9from sklearn.tree import DecisionTreeClassifier10from helpers import get_wine_data11from helpers import get_abalone_data12class EstimatorSelectionHelper:13 def __init__(self, models, params):14 if not set(models.keys()).issubset(set(params.keys())):15 missing_params = list(set(models.keys()) - set(params.keys()))16 raise ValueError("Some estimators are missing parameters: %s" % missing_params)17 self.models = models18 self.params = params19 self.keys = models.keys()20 self.grid_searches = {}21 def fit(self, X, y, cv=3, n_jobs=3, verbose=1, scoring=None, refit=False):22 for key in self.keys:23 print("Running GridSearchCV for %s." % key)24 model = self.models[key]25 params = self.params[key]26 gs = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs,27 verbose=verbose, scoring=scoring, refit=refit,28 return_train_score=True)29 gs.fit(X,y)30 self.grid_searches[key] = gs31 def score_summary(self, sort_by='mean_score'):32 def row(key, scores, params):33 d = {34 'estimator': key,35 'min_score': min(scores),36 'max_score': max(scores),37 'mean_score': np.mean(scores),38 'std_score': np.std(scores),39 }40 return pd.Series({**params,**d})41 rows = []42 for k in self.grid_searches:43 print(k)44 params = self.grid_searches[k].cv_results_['params']45 scores = []46 for i in range(self.grid_searches[k].cv):47 key = "split{}_test_score".format(i)48 r = self.grid_searches[k].cv_results_[key]49 scores.append(r.reshape(len(params),1))50 all_scores = np.hstack(scores)51 for p, s in zip(params,all_scores):52 rows.append((row(k, s, p)))53 df = pd.concat(rows, axis=1).T.sort_values([sort_by], ascending=False)54 columns = ['estimator', 'mean_score', 'max_score', 'std_score']55 columns = columns + [c for c in df.columns if c not in columns]56 return df[columns]57models1 = {58 'AdaBoostClassifier': AdaBoostClassifier(),59 'GradientBoostingClassifier': GradientBoostingClassifier(),60 'DecisionTreeClassifier': DecisionTreeClassifier(),61 'KNeighborsClassifier': KNeighborsClassifier()62}63params1 = {64 'AdaBoostClassifier': {'n_estimators': np.arange(1, 20)},65 'GradientBoostingClassifier': {'n_estimators': np.arange(1, 20), 'learning_rate': 0.1 ** np.arange(1, 10)},66 'DecisionTreeClassifier': {'max_depth': np.arange(1, 20), 'min_samples_leaf': np.arange(1, 20)},67 'KNeighborsClassifier': {'n_neighbors': np.arange(1, 10)}68}69models2 = {70 'SVC': SVC()71}72params2 = {73 'SVC': [74 {'kernel': ['linear'], 'C': [1, 10]},75 {'kernel': ['rbf'], 'C': [1, 10], 'gamma': [0.001, 0.0001]},76 ]77}78models3 = {79 'MLPClassifier': MLPClassifier()80}81params3 = {82 'MLPClassifier': {83 'solver': ['lbfgs'],84 'max_iter': [1, 3, 5, 7, 9],85 'alpha': 10.0 ** -np.arange(1, 10),86 'hidden_layer_sizes': np.arange(10, 15),87 'random_state': [0,1]88 }89}90if __name__ == "__main__":91 X, y = get_wine_data()92 X1, y1 = get_abalone_data()93 # helper1 = EstimatorSelectionHelper(models1, params1)94 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)95 # results = (helper1.score_summary(sort_by='max_score'))96 # results.to_csv("out/wine_params_1.csv")97 # helper1 = EstimatorSelectionHelper(models1, params1)98 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)99 # results = (helper1.score_summary(sort_by='max_score'))100 # results.to_csv("out/abalone_params_1.csv")101 # helper1 = EstimatorSelectionHelper(models2, params2)102 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)103 # results = (helper1.score_summary(sort_by='max_score'))104 # results.to_csv("out/wine_params_2.csv")105 # helper1 = EstimatorSelectionHelper(models2, params2)106 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)107 # results = (helper1.score_summary(sort_by='max_score'))108 # results.to_csv("out/abalone_params_2.csv")109 helper1 = EstimatorSelectionHelper(models3, params3)110 helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)111 results = (helper1.score_summary(sort_by='max_score'))112 results.to_csv("out/wine_params_3.csv")113 helper1 = EstimatorSelectionHelper(models3, params3)114 helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)115 results = (helper1.score_summary(sort_by='max_score'))...
parameters_finding_ann.py
Source:parameters_finding_ann.py
1import numpy as np2import pandas as pd3from sklearn.ensemble import AdaBoostClassifier4from sklearn.ensemble import GradientBoostingClassifier5from sklearn.model_selection import GridSearchCV6from sklearn.neighbors import KNeighborsClassifier7from sklearn.neural_network import MLPClassifier8from sklearn.svm import SVC9from sklearn.tree import DecisionTreeClassifier10from helpers import get_wine_data11from helpers import get_abalone_data12class EstimatorSelectionHelper:13 def __init__(self, models, params):14 if not set(models.keys()).issubset(set(params.keys())):15 missing_params = list(set(models.keys()) - set(params.keys()))16 raise ValueError("Some estimators are missing parameters: %s" % missing_params)17 self.models = models18 self.params = params19 self.keys = models.keys()20 self.grid_searches = {}21 def fit(self, X, y, cv=3, n_jobs=3, verbose=1, scoring=None, refit=False):22 for key in self.keys:23 print("Running GridSearchCV for %s." % key)24 model = self.models[key]25 params = self.params[key]26 gs = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs,27 verbose=verbose, scoring=scoring, refit=refit,28 return_train_score=True)29 gs.fit(X,y)30 self.grid_searches[key] = gs31 def score_summary(self, sort_by='mean_score'):32 def row(key, scores, params):33 d = {34 'estimator': key,35 'min_score': min(scores),36 'max_score': max(scores),37 'mean_score': np.mean(scores),38 'std_score': np.std(scores),39 }40 return pd.Series({**params,**d})41 rows = []42 for k in self.grid_searches:43 print(k)44 params = self.grid_searches[k].cv_results_['params']45 scores = []46 for i in range(self.grid_searches[k].cv):47 key = "split{}_test_score".format(i)48 r = self.grid_searches[k].cv_results_[key]49 scores.append(r.reshape(len(params),1))50 all_scores = np.hstack(scores)51 for p, s in zip(params,all_scores):52 rows.append((row(k, s, p)))53 df = pd.concat(rows, axis=1).T.sort_values([sort_by], ascending=False)54 columns = ['estimator', 'mean_score', 'max_score', 'std_score']55 columns = columns + [c for c in df.columns if c not in columns]56 return df[columns]57models1 = {58 'AdaBoostClassifier': AdaBoostClassifier(),59 'GradientBoostingClassifier': GradientBoostingClassifier(),60 'DecisionTreeClassifier': DecisionTreeClassifier(),61 'KNeighborsClassifier': KNeighborsClassifier()62}63params1 = {64 'AdaBoostClassifier': {'n_estimators': np.arange(1, 20)},65 'GradientBoostingClassifier': {'n_estimators': np.arange(1, 20), 'learning_rate': 0.1 ** np.arange(1, 10)},66 'DecisionTreeClassifier': {'max_depth': np.arange(1, 20), 'min_samples_leaf': np.arange(1, 20)},67 'KNeighborsClassifier': {'n_neighbors': np.arange(1, 10)}68}69models2 = {70 'SVC': SVC()71}72params2 = {73 'SVC': [74 {'kernel': ['linear'], 'C': [1, 10]},75 {'kernel': ['rbf'], 'C': [1, 10], 'gamma': [0.001, 0.0001]},76 ]77}78models3 = {79 'MLPClassifier': MLPClassifier()80}81params3 = {82 'MLPClassifier': {83 'solver': ['lbfgs'],84 'max_iter': [1, 3, 5, 7, 9],85 'alpha': 10.0 ** -np.arange(1, 10),86 'hidden_layer_sizes': np.arange(10, 15),87 'random_state': [0,1]88 }89}90if __name__ == "__main__":91 X, y = get_wine_data()92 X1, y1 = get_abalone_data()93 # helper1 = EstimatorSelectionHelper(models1, params1)94 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)95 # results = (helper1.score_summary(sort_by='max_score'))96 # results.to_csv("out/wine_params_1.csv")97 # helper1 = EstimatorSelectionHelper(models1, params1)98 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)99 # results = (helper1.score_summary(sort_by='max_score'))100 # results.to_csv("out/abalone_params_1.csv")101 helper1 = EstimatorSelectionHelper(models2, params2)102 helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)103 results = (helper1.score_summary(sort_by='max_score'))104 results.to_csv("out/wine_params_2.csv")105 helper1 = EstimatorSelectionHelper(models2, params2)106 helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)107 results = (helper1.score_summary(sort_by='max_score'))108 results.to_csv("out/abalone_params_2.csv")109 # helper1 = EstimatorSelectionHelper(models3, params3)110 # helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)111 # results = (helper1.score_summary(sort_by='max_score'))112 # results.to_csv("out/wine_params_3.csv")113 # helper1 = EstimatorSelectionHelper(models3, params3)114 # helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)115 # results = (helper1.score_summary(sort_by='max_score'))...
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