"Dense_0": { 'units': 64, 'activation': 'relu' }, "Flatten": {}, "Dense_3": { 'units': 1 }, } } df = arg_beach() input_features = list(df.columns)[0:-1] # column in dataframe to bse used as output/target outputs = list(df.columns)[-1] model = Model(data=df, batch_size=16, lookback=1, model=mlp_model, inputs=input_features, outputs=[outputs], lr=0.0001) history = model.fit(indices='random') y, obs = model.predict() model.view_model(st=0)
#How to use AI4Water for classification problems import pandas as pd import numpy as np from sklearn.datasets import load_breast_cancer from AI4Water import Model data_class = load_breast_cancer() cols = data_class['feature_names'].tolist() + ['target'] df = pd.DataFrame(np.concatenate( [data_class['data'], data_class['target'].reshape(-1, 1)], axis=1), columns=cols) model = Model( data=df, inputs=data_class['feature_names'].tolist(), outputs=['target'], val_fraction=0.0, model={"DecisionTreeClassifier": { "max_depth": 4, "random_state": 313 }}, transformation=None, problem="classification") h = model.fit() model.view_model()