RandomForestRegressor: вход содержит NaN, бесконечность или значение, слишком большое для dtype ('float32') при изучении kaggle - PullRequest
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RandomForestRegressor: вход содержит NaN, бесконечность или значение, слишком большое для dtype ('float32') при изучении kaggle

0 голосов
/ 01 февраля 2020

Выполняя шаг 5 из Упражнение: категориальные переменные в Kaggle Learn, я получил ValueError: Input contains NaN, infinity or a value too large for dtype('float32') во время фазы прогнозирования с набором тестов.

Доступен полный блокнот Jupyter здесь . Полный использованный код отображается в конце сообщения.

Код предназначен для подготовки набора данных для "Конкурса цен на жилье для пользователей Kaggle Learn" .

. проблема заключается в предварительной обработке набора данных X_test, который содержит набор тестов. Сначала я использовал SimpleImputer со стратегией most_frequent. Затем выполнил горячее кодирование для категориальных переменных набора данных.

Я обнаружил, что между X_train (и X_valid) наборами данных и X_test, некоторые функции имеют разные типы данных . В частности, столбцы ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageCars', 'GarageArea'] имеют тип int64 в обучающих данных (X_train и X_valid), в то время как они имеют значение «float64» в тестовых данных (X_test). Я думаю, что проблема может быть здесь, но я не могу ее решить. Пробовал путем приведения значений с помощью следующего фрагмента 

# normalize datatypes columns
#for colName in  ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageCars', 'GarageArea']:
#    OH_X_train[colName] = OH_X_train[colName].astype('float64')
#    OH_X_valid[colName] = OH_X_train[colName].astype('float64')

, но это не сработало. Есть предложения? 

#### DATASETS LOAD ####
import pandas as pd
from sklearn.model_selection import train_test_split

# Read the data
X = pd.read_csv('../input/train.csv', index_col='Id') 
X_test = pd.read_csv('../input/test.csv', index_col='Id')

# Remove rows with missing target, separate target from predictors
X.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X.SalePrice
X.drop(['SalePrice'], axis=1, inplace=True)

# To keep things simple, we'll drop columns with missing values
cols_with_missing = [col for col in X.columns if X[col].isnull().any()] 
X.drop(cols_with_missing, axis=1, inplace=True)
X_test.drop(cols_with_missing, axis=1, inplace=True)

# Break off validation set from training data
X_train, X_valid, y_train, y_valid = train_test_split(X, y,
                                                      train_size=0.8, test_size=0.2,
                                                      random_state=0)

#### IMPUTATION OF MISSING VALUES FOR X_TEST ####
from sklearn.impute import SimpleImputer

# All categorical columns
object_cols = [col for col in X_train.columns if X_train[col].dtype == "object"]

# Columns that will be one-hot encoded
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]

# Fill in the lines below: imputation
my_imputer = SimpleImputer(strategy='most_frequent')
imputed_X_test = pd.DataFrame(my_imputer.fit_transform(X_test))

# Fill in the lines below: imputation removed column names; put them back
imputed_X_test.columns = X_test.columns

#### ONEHOT ENCODING FOR DATA #####
from sklearn.preprocessing import OneHotEncoder

# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(imputed_X_test[low_cardinality_cols]))

# One-hot encoding removed index; put it back
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
OH_cols_test.index = X_test.index

# Remove categorical columns (will replace with one-hot encoding)
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
num_X_test = X_test.drop(object_cols, axis=1)

# Add one-hot encoded columns to numerical features
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
OH_X_test = pd.concat([num_X_test, OH_cols_test], axis=1)

##### BUILD MODEL AND CREATE SUBMISSION ####
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

# normalize datatypes columns
#for colName in  ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageCars', 'GarageArea']:
#    OH_X_train[colName] = OH_X_train[colName].astype('float64')
#    OH_X_valid[colName] = OH_X_train[colName].astype('float64')

# Build model
model = RandomForestRegressor(n_estimators=100, random_state=0)
model.fit(OH_X_train, y_train)
preds_test = model.predict(OH_X_test)

# Save test predictions to file
#output = pd.DataFrame({'Id': OH_X_test.index,
#                       'SalePrice': preds_test})
#output.to_csv('submission.csv', index=False)

А вот полный журнал ошибок: 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-2d85be0f6b26> in <module>
     74 model = RandomForestRegressor(n_estimators=100, random_state=0)
     75 model.fit(OH_X_train, y_train)
---> 76 preds_test = model.predict(OH_X_test)
     77 
     78 # Save test predictions to file

/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py in predict(self, X)
    691         check_is_fitted(self, 'estimators_')
    692         # Check data
--> 693         X = self._validate_X_predict(X)
    694 
    695         # Assign chunk of trees to jobs

/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py in _validate_X_predict(self, X)
    357                                  "call `fit` before exploiting the model.")
    358 
--> 359         return self.estimators_[0]._validate_X_predict(X, check_input=True)
    360 
    361     @property

/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py in _validate_X_predict(self, X, check_input)
    389         """Validate X whenever one tries to predict, apply, predict_proba"""
    390         if check_input:
--> 391             X = check_array(X, dtype=DTYPE, accept_sparse="csr")
    392             if issparse(X) and (X.indices.dtype != np.intc or
    393                                 X.indptr.dtype != np.intc):

/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    540         if force_all_finite:
    541             _assert_all_finite(array,
--> 542                                allow_nan=force_all_finite == 'allow-nan')
    543 
    544     if ensure_min_samples > 0:

/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X, allow_nan)
     54                 not allow_nan and not np.isfinite(X).all()):
     55             type_err = 'infinity' if allow_nan else 'NaN, infinity'
---> 56             raise ValueError(msg_err.format(type_err, X.dtype))
     57     # for object dtype data, we only check for NaNs (GH-13254)
     58     elif X.dtype == np.dtype('object') and not allow_nan:

ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

1 Ответ

0 голосов
/ 02 февраля 2020

Проблема вызвана, как указано в сообщении об ошибке, значением NaN в OH_X_test. Эти значения вводятся в операторе concat, поскольку индексы кадров данных перепутаны.

Поэтому я добавил 3 исправления в приведенном ниже коде: посмотрите на тег ###FIX. 

#### DATASETS LOAD ####
import pandas as pd
from sklearn.model_selection import train_test_split

# Read the data
X = pd.read_csv('../input/train.csv', index_col='Id') 
X_test = pd.read_csv('../input/test.csv', index_col='Id')

# Remove rows with missing target, separate target from predictors
X.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X.SalePrice
X.drop(['SalePrice'], axis=1, inplace=True)

# To keep things simple, we'll drop columns with missing values
cols_with_missing = [col for col in X.columns if X[col].isnull().any()] 
X.drop(cols_with_missing, axis=1, inplace=True)
X_test.drop(cols_with_missing, axis=1, inplace=True)

# Break off validation set from training data
X_train, X_valid, y_train, y_valid = train_test_split(X, y,
                                                      train_size=0.8, test_size=0.2,
                                                      random_state=0)

#### IMPUTATION OF MISSING VALUES FOR X_TEST ####
from sklearn.impute import SimpleImputer

# All categorical columns
object_cols = [col for col in X_train.columns if X_train[col].dtype == "object"]

# Columns that will be one-hot encoded
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]

# Fill in the lines below: imputation
my_imputer = SimpleImputer(strategy='most_frequent')
imputed_X_test = pd.DataFrame(my_imputer.fit_transform(X_test))

# Fill in the lines below: imputation removed column names; put them back
imputed_X_test.columns = X_test.columns 
imputed_X_test.index = X_test.index ###FIX

#### ONEHOT ENCODING FOR DATA #####
from sklearn.preprocessing import OneHotEncoder

# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(imputed_X_test[low_cardinality_cols]))

# One-hot encoding removed index; put it back
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
OH_cols_test.index = imputed_X_test.index ####FIX

# Remove categorical columns (will replace with one-hot encoding)
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
num_X_test = imputed_X_test.drop(object_cols, axis=1) ####FIX

# Add one-hot encoded columns to numerical features
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
OH_X_test = pd.concat([num_X_test, OH_cols_test], axis=1)

##### BUILD MODEL AND CREATE SUBMISSION ####
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

# normalize datatypes columns
#for colName in  ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageCars', 'GarageArea']:
#    OH_X_train[colName] = OH_X_train[colName].astype('float64')
#    OH_X_valid[colName] = OH_X_train[colName].astype('float64')

# Build model
model = RandomForestRegressor(n_estimators=100, random_state=0)
model.fit(OH_X_train, y_train)
preds_test = model.predict(OH_X_test)

# Save test predictions to file
output = pd.DataFrame({'Id': OH_X_test.index,
                       'SalePrice': preds_test})
output.to_csv('submission.csv', index=False)
...