Python TPOTClassifier.score方法代碼示例

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
def test_score():
    """Assert that the TPOT score function raises a ValueError when no optimized pipeline exists"""

    tpot_obj = TPOTClassifier()

    try:
        tpot_obj.score(testing_features, testing_classes)
        assert False  # Should be unreachable
    except ValueError:
        pass

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
def test_score_2():
    """Assert that the TPOTClassifier score function outputs a known score for a fix pipeline"""

    tpot_obj = TPOTClassifier()
    known_score = 0.977777777778  # Assumes use of the TPOT balanced_accuracy function

    # Reify pipeline with known score
    pipeline_string= ('KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=10, '
    'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform)')
    tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
    tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
    tpot_obj._fitted_pipeline.fit(training_features, training_classes)
    # Get score from TPOT
    score = tpot_obj.score(testing_features, testing_classes)

    # http://stackoverflow.com/questions/5595425/
    def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
        return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)

    assert isclose(known_score, score)

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
def test_score_2():
    """Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline"""

    tpot_obj = TPOTClassifier()
    tpot_obj._pbar = tqdm(total=1, disable=True)
    known_score = 0.986318199045  # Assumes use of the TPOT balanced_accuracy function

    # Reify pipeline with known score
    tpot_obj._optimized_pipeline = creator.Individual.\
        from_string('RandomForestClassifier(input_matrix)', tpot_obj._pset)
    tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
    tpot_obj._fitted_pipeline.fit(training_features, training_classes)

    # Get score from TPOT
    score = tpot_obj.score(testing_features, testing_classes)

    # http://stackoverflow.com/questions/5595425/
    def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
        return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)

    assert isclose(known_score, score)

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()

X_train, X_test, y_train,  y_test = train_test_split(digits.data, digits.target,
													train_size = 0.75, test_size = 0.25)

tpot = TPOTClassifier(generations = 5, population_size = 20, verbosity = 2)

tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_mnist_pipeline.py')

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
def main():

  # set up the path to the data sets and the data were are going to experiment 
  # with 
  base_path = '/scratch/ditzler/Git/ClassificationDatasets/csv/'
  data_setz = [#'bank',
    'blood',
    'breast-cancer-wisc-diag',
    'breast-cancer-wisc-prog',
    'breast-cancer-wisc',
    'breast-cancer',
    'congressional-voting',
    'conn-bench-sonar-mines-rocks',
    'credit-approval',
    'cylinder-bands',
    'echocardiogram',
    #'fertility',
    'haberman-survival',
    'heart-hungarian',
    'hepatitis',
    'ionosphere',
    'mammographic',
    'molec-biol-promoter',
    'musk-1',
    'oocytes_merluccius_nucleus_4d',
    'oocytes_trisopterus_nucleus_2f',
    'ozone',
    'parkinsons',
    'pima',
    #'pittsburg-bridges-T-OR-D';
    'planning',
    'ringnorm',
    #'spambase',
    'spectf_train',
    'statlog-australian-credit',
    'statlog-german-credit',
    'statlog-heart',
    'titanic',
    #'twonorm',
    'vertebral-column-2clases']

  # nsplits is like the number of cv (its bootstraps here) then set up some variales
  # to save the results to. 
  n_splitz = 10
  errors = np.zeros((len(data_setz),))
  fms = np.zeros((len(data_setz),))
  times = np.zeros((len(data_setz),))
  m = 0

  for n in range(n_splitz):
    print 'Spilt ' + str(n) + ' of ' + str(n_splitz)
    for i in range(len(data_setz)):
      print '    ' + data_setz[i]
      df = pd.read_csv(base_path + data_setz[i] + '.csv', sep=',')
      data = df.as_matrix()
      X = data[:, :-1]
      y = data[:, -1]
      X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25, random_state=m)
      m += 1
      
      ts = time.time()
      tpot = TPOTClassifier(generations=10, population_size=25, verbosity=1)
      tpot.fit(X_train, y_train)
      times[i] += (time.time() - ts)

      errors[i] += (1-tpot.score(X_test, y_test))
      yhat = tpot.predict(X_test)
      fms[i] += f1_score(y_test, yhat, average='macro')
  
  errors /= n_splitz
  fms /= n_splitz
  times /= n_splitz

  df = pd.DataFrame({'errors': errors, 'fms': fms, 'times': times})
  df.to_csv(path_or_buf='tpot-results2.csv', sep=',')

  return None 

# 需要導入模塊: from tpot import TPOTClassifier [as 別名]
# 或者: from tpot.TPOTClassifier import score [as 別名]
#data,label = data[idx_row,:],label[idx_row]
#features = data
#tpot_data=pd.DataFrame({'class':label},columns=['class'])
#training_features, testing_features, training_classes, testing_classes = \
#    train_test_split(features, tpot_data['class'], random_state=42)
data,label,idx_row = np.concatenate(samples),np.concatenate(label),np.arange(0,len(label),1)
print('shuffle')
for ii in range(100):
    shuffle(idx_row)
data,label = data[idx_row,:],label[idx_row]
X_train, X_test, y_train, y_test = train_test_split(data,label,train_size=0.80)
print('model selection')
tpot = TPOTClassifier(generations=10, population_size=25,
                      verbosity=2,random_state=373849,num_cv_folds=5,scoring='roc_auc' )
tpot.fit(X_train,y_train)
tpot.score(X_test,y_test)
tpot.export('%s%s_tpot_exported_pipeline.py'%(folder,type_) )  
print('finished model selection')
"""
from sklearn.ensemble import VotingClassifier
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
from sklearn.model_selection import KFold
exported_pipeline = make_pipeline(
    make_union(
        FunctionTransformer(lambda X: X),
        FunctionTransformer(lambda X: X)
    ),