(╬▔皿▔)…所以我们先用scikit-learn里面的preprocessing模块对这俩货做一个scaling,所谓scaling,其实就是将一些变化幅度较大的特征化到[-1,1]之内。import sklearn.preprocessing as preprocessing scaler = preprocessing.StandardScaler() age_scale_param = scaler.fit(df['Age']) df['Age_scaled'] = scaler...
其中每个图中的点就是其item_id,特征咱们暂且用其id来表示,之后会做embedding #如何构建数据集fromtorch_geometric.dataimportInMemoryDatasetfromtqdmimporttqdm#进度条classYooChooseBinaryDataset(InMemoryDataset):def__init__(self, root, transform=None, pre_transform=None):#构造函数super(YooChooseBinaryDataset, ...
importmatplotlib.pyplotasplt from pandasimportread_csv from pandasimportDataFrame from pandasimportconcat from sklearn.preprocessingimportMinMaxScaler from tensorflow.keras.modelsimportSequential from tensorflow.keras.layersimportLSTM,Dense,Dropout from numpyimportconcatenate from sklearn.metricsimportmean_squared_er...
This transformation is often used as an alternative to zero mean, unit variance scaling. Read more in the :ref:`User Guide <preprocessing_scaler>`. “”通过将每个特性缩放到给定范围来转换特性。 这个估计量对每个特征进行了缩放和单独转换,使其位于训练集的给定范围内,即在0和1之间。 变换由:: X_s...
Since, sklearn requires all inputs to be numeric, we should convert all our categorical variables into numeric by encoding the categories. This can be done using the following code: from sklearn.preprocessing import LabelEncoder 1. var_mod = ['Gender','Married','Dependents','Education','Self...
#sklearn# from sklearn.preprocessing import MinMaxScaler import pandas as pd #数据归一化 dataset=[[-100,-10],[200,20],[300,30]] dataset=pd.DataFrame(dataset) scaler = MinMaxScaler(feature_range=(...
importnumpyasnpimportpandasaspdimportscipyascpfromscipyimportstatsimportmatplotlib.pyplotaspltimportseabornassnsfromsklearnimportpreprocessingfromsklearn.metricsimportconfusion_matrix,roc_auc_scorefromsklearn.model_selectionimportStratifiedKFold,cross_val_score,KFoldfromxgboostimportXGBClassifierimportxgboostasxgbimport...
from sklearn.preprocessing import MinMaxScaler data = [[0, 0], [0, 78], [80, 1], [100, 89]] scaler = MinMaxScaler() scaler. (data) results=scaler. (data) print (results) A、fit, fit B、transform, transform C、transform, fit ...
from sklearn.metrics import accuracy_score import shap from sklearn.feature_selection import SelectKBest,f_regression from sklearn import preprocessing #画图 import seaborn as sns import matplotlib.pyplot as plt import plotly.graph_objects as go ...
importnumpyasnp importpandasaspd importtensorflowastf fromsklearn.model_selectionimporttrain_test_split fromsklearn.metricsimportaccuracy_score fromtensorflow.keras.preprocessing.textimportTokenizer fromtensorflow.keras.preprocessing.sequenceimportpad_sequences ...