我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用sklearn.preprocessing.MaxAbsScaler()。
def train_model(self, train_file_path, model_path): print("==> Load the data ...") X_train, Y_train = self.load_file(train_file_path) print(train_file_path, shape(X_train)) print("==> Train the model ...") min_max_scaler = preprocessing.MaxAbsScaler() X_train_minmax = min_max_scaler.fit_transform(X_train) clf = RandomForestRegressor(n_estimators=self.n_estimators) clf.fit(X_train_minmax.toarray(), Y_train) print("==> Save the model ...") pickle.dump(clf, open(model_path, 'wb')) scaler_path = model_path.replace('.pkl', '.scaler.pkl') pickle.dump(min_max_scaler, open(scaler_path, 'wb')) return clf
def train_model(self, train_file_path, model_path): print("==> Load the data ...") X_train, Y_train = self.load_file(train_file_path) print(train_file_path, shape(X_train)) print("==> Train the model ...") min_max_scaler = preprocessing.MaxAbsScaler() X_train_minmax = min_max_scaler.fit_transform(X_train) clf = GradientBoostingRegressor(n_estimators=self.n_estimators) clf.fit(X_train_minmax.toarray(), Y_train) print("==> Save the model ...") pickle.dump(clf, open(model_path, 'wb')) scaler_path = model_path.replace('.pkl', '.scaler.pkl') pickle.dump(min_max_scaler, open(scaler_path, 'wb')) return clf
def scale_features(features, train): """Scale features, using test set to learn parameters. Returns: Scaled copy of features. """ if FLAGS.scaling is None: return features logging.info('Scaling features with %s', FLAGS.scaling) if FLAGS.scaling == 'max_abs': scaler = preprocessing.MaxAbsScaler() elif FLAGS.scaling == 'standard': scaler = preprocessing.StandardScaler() else: raise ValueError('Unrecognized scaling %s' % FLAGS.scaling) scaler.fit(features[train]) return scaler.transform(features)
def test_MaxAbsScaler(): ''' test the method of MaxAbs Scaler :return: None ''' X=[ [1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1] ] print("before transform:",X) scaler=MaxAbsScaler() scaler.fit(X) print("scale_ is :",scaler.scale_) print("max_abs_ is :",scaler.max_abs_) print("after transform:",scaler.transform(X))
def scale(df, scaling=None): """Scale data included in pandas dataframe. Parameters ---------- df : pandas dataframe dataframe to scale scaling : 'maxabs', 'minmax', 'std', or None, optional (default 'std') type of scaling to apply """ if scaling is None: return df df = df.dropna(axis=1, how='any') # Scaling data if scaling == 'maxabs': # Normalizing -1 to 1 scaler = MaxAbsScaler() elif scaling == 'minmax': # Scaling to [0,1] scaler = MinMaxScaler() else: # Standard normalization scaler = StandardScaler() mat = df.as_matrix() mat = scaler.fit_transform(mat) # print(mat.shape) df = pd.DataFrame(mat, columns=df.columns) return df
def impute_and_scale(df, scaling=None): """Impute missing values with mean and scale data included in pandas dataframe. Parameters ---------- df : pandas dataframe dataframe to impute and scale scaling : 'maxabs' [-1,1], 'minmax' [0,1], 'std', or None, optional (default 'std') type of scaling to apply """ df = df.dropna(axis=1, how='all') imputer = Imputer(strategy='mean', axis=0) mat = imputer.fit_transform(df) # print(mat.shape) if scaling is None: return pd.DataFrame(mat, columns=df.columns) # Scaling data if scaling == 'maxabs': # Normalizing -1 to 1 scaler = MaxAbsScaler() elif scaling == 'minmax': # Scaling to [0,1] scaler = MinMaxScaler() else: # Standard normalization scaler = StandardScaler() mat = scaler.fit_transform(mat) # print(mat.shape) df = pd.DataFrame(mat, columns=df.columns) return df
def scale(df, scaling=None): """Scale data included in pandas dataframe. Parameters ---------- df : pandas dataframe dataframe to scale scaling : 'maxabs', 'minmax', 'std', or None, optional (default 'std') type of scaling to apply """ if scaling is None or scaling.lower() == 'none': return df df = df.dropna(axis=1, how='any') # Scaling data if scaling == 'maxabs': # Normalizing -1 to 1 scaler = MaxAbsScaler() elif scaling == 'minmax': # Scaling to [0,1] scaler = MinMaxScaler() else: # Standard normalization scaler = StandardScaler() mat = df.as_matrix() mat = scaler.fit_transform(mat) df = pd.DataFrame(mat, columns=df.columns) return df
def load_data(shuffle=True, n_cols=None): train_path = get_p1_file('http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P1B1/P1B1.train.csv') test_path = get_p1_file('http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P1B1/P1B1.test.csv') usecols = list(range(n_cols)) if n_cols else None df_train = pd.read_csv(train_path, engine='c', usecols=usecols) df_test = pd.read_csv(test_path, engine='c', usecols=usecols) df_train = df_train.drop('case_id', 1).astype(np.float32) df_test = df_test.drop('case_id', 1).astype(np.float32) if shuffle: df_train = df_train.sample(frac=1, random_state=seed) df_test = df_test.sample(frac=1, random_state=seed) X_train = df_train.as_matrix() X_test = df_test.as_matrix() scaler = MaxAbsScaler() mat = np.concatenate((X_train, X_test), axis=0) mat = scaler.fit_transform(mat) X_train = mat[:X_train.shape[0], :] X_test = mat[X_train.shape[0]:, :] return X_train, X_test
def scale_by_max_value(X): """ Scale each feature by its abs maximum value. Keyword arguments: X -- The feature vectors """ if verbose: print '\nScaling to the range [-1,1] ...' max_abs_scaler = preprocessing.MaxAbsScaler() return max_abs_scaler.fit_transform(X)
