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prepare optimization study
This commit is contained in:
Hannes Signer
parent
019a426af4
commit
b58e8869ad
3206
src/POET_Training.ipynb
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3206
src/POET_Training.ipynb
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File diff suppressed because one or more lines are too long
60
src/convert_data.jl
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src/convert_data.jl
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using HDF5
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using RData
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using DataFrames
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# Load Training Data
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# train_data = load("Barite_50_Data.rds")
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# training_h5_name = "Barite_50_Data.h5"
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# h5open(training_h5_name, "w") do fid
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# for key in keys(train_data)
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# group = create_group(fid, key)
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# group["names"] = names(train_data[key])
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# group["data", compress=3] = Matrix(train_data[key])
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# # group = create_group(fid, key)
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# # grou["names"] = coln
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# end
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# end
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# List all .rds files starting with "iter" in a given directory
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rds_files = filter(x -> startswith(x, "iter"), readdir("barite_out/"))
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# remove "iter_0.rds" from the list
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rds_files = rds_files[2:end]
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big_df_in = DataFrame()
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big_df_out = DataFrame()
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for rds_file in rds_files
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# Load the RDS file
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data = load("barite_out/$rds_file")
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# Convert the data to a DataFrame
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df_T = DataFrame(data["T"])
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df_C = DataFrame(data["C"])
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# Append the DataFrame to the big DataFrame
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append!(big_df_in, df_T)
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append!(big_df_out, df_C)
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end
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# remove ID, Barite_p1, Celestite_p1 columns
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big_df_in = big_df_in[:, Not([:ID, :Barite_p1, :Celestite_p1])]
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big_df_out = big_df_out[:, Not([:ID, :Barite_p1, :Celestite_p1])]
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inference_h5_name = "Barite_50_Data_inference.h5"
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h5open(inference_h5_name, "w") do fid
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fid["names"] = names(big_df_in)
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fid["data", compress=9] = Matrix(big_df_in)
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end
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training_h5_name = "Barite_50_Data_training.h5"
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h5open(training_h5_name, "w") do fid
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group_in = create_group(fid, "design")
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group_out = create_group(fid, "result")
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group_in["names"] = names(big_df_in)
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group_in["data", compress=9] = Matrix(big_df_in)
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group_out["names"] = names(big_df_out)
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group_out["data", compress=9] = Matrix(big_df_out)
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end
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src/optuna_runs.py
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src/optuna_runs.py
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import keras
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from keras.layers import Dense, Dropout, Input,BatchNormalization
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import tensorflow as tf
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import h5py
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import numpy as np
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import pandas as pd
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import time
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import sklearn.model_selection as sk
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from imblearn.over_sampling import SMOTE
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import RandomOverSampler
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from collections import Counter
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import os
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from preprocessing import *
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from sklearn import set_config
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from importlib import reload
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set_config(transform_output = "pandas")
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import optuna
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import pickle
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def objective(trial, X, y, species_columns):
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model_type = trial.suggest_categorical("model", ["small", "large", "paper"])
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scaler_type = trial.suggest_categorical("scaler", ["standard", "minmax"])
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sampling_type = trial.suggest_categorical("sampling", ["over", "off"])
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loss_variant = trial.suggest_categorical("loss", ["huber", "huber_mass_balance"])
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delta = trial.suggest_float("delta", 0.5, 5.0)
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preprocess = preprocessing()
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X, y = preprocess.cluster(df_design[species_columns], df_results[species_columns])
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X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio = 0.2)
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X_train, y_train = preprocess.balancer(X_train, y_train, strategy = sampling_type)
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preprocess.scale_fit(X_train, y_train, scaling = "global", type=scaler_type)
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X_train, X_test, y_train, y_test = preprocess.scale_transform(X_train, X_test, y_train, y_test)
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X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio = 0.1)
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column_dict = {"Ba": X.columns.get_loc("Ba"), "Barite": X.columns.get_loc("Barite"), "Sr": X.columns.get_loc(
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"Sr"), "Celestite": X.columns.get_loc("Celestite"), "H": X.columns.get_loc("H"), "H": X.columns.get_loc("H"), "O": X.columns.get_loc("O")}
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h1 = trial.suggest_float("h1", 0.1, 1.0)
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h2 = trial.suggest_float("h2", 0.1, 1.0)
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h3 = trial.suggest_float("h3", 0.1, 1.0)
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model = model_definition(model_type)
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lr_schedule = keras.optimizers.schedules.ExponentialDecay(
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initial_learning_rate=0.001,
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decay_steps=2000,
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decay_rate=0.9,
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staircase=True
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)
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optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)
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model.compile(optimizer=optimizer, loss=custom_loss(preprocess, column_dict, h1, h2, h3, scaler_type, loss_variant, delta),
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metrics=[huber_metric(preprocess, scaler_type, delta), mass_balance_metric(preprocess, column_dict, scaler_type)])
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callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3)
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history = model.fit(X_train.loc[:, X_train.columns != "Class"],
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y_train.loc[:, y_train.columns != "Class"],
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batch_size=512,
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epochs=100,
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validation_data=(X_val.loc[:, X_val.columns != "Class"], y_val.loc[:, y_val.columns != "Class"]),
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callbacks=[callback])
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prediction_huber_overall = model.evaluate(
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X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])[1]
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prediction_huber_non_reactive = model.evaluate(
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X_test[X_test['Class'] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test['Class'] == 0].iloc[:, y_test.columns != "Class"])[1]
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prediction_huber_reactive = model.evaluate(
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X_test[X_test['Class'] == 1].iloc[:, X_test.columns != "Class"], y_test[X_test['Class'] == 1].iloc[:, y_test.columns != "Class"])[1]
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prediction_mass_balance_overall = model.evaluate(
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X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])[2]
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prediction_mass_balance_non_reactive = model.evaluate(
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X_test[X_test['Class'] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test['Class'] == 0].iloc[:, y_test.columns != "Class"])[2]
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prediction_mass_balance_reactive = model.evaluate(
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X_test[X_test['Class'] == 1].iloc[:, X_test.columns != "Class"], y_test[X_test['Class'] == 1].iloc[:, y_test.columns != "Class"])[2]
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mass_balance_results = mass_balance_evaluation(model, X_test, preprocess)
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mass_balance_ratio = len(mass_balance_results[mass_balance_results < 1e-5]) / len(mass_balance_results)
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results_save_path = os.path.join("./results/", "results.csv")
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results_df = pd.DataFrame({
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"trial": [trial.number],
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"prediction_huber_overall": [prediction_huber_overall],
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"prediction_huber_non_reactive": [prediction_huber_non_reactive],
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"prediction_huber_reactive": [prediction_huber_reactive],
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"prediction_mass_balance_overall": [prediction_mass_balance_overall],
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"prediction_mass_balance_non_reactive": [prediction_mass_balance_non_reactive],
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"prediction_mass_balance_reactive": [prediction_mass_balance_reactive]
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})
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if not os.path.isfile(results_save_path):
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results_df.to_csv(results_save_path, index=False)
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else:
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results_df.to_csv(results_save_path, mode='a', header=False, index=False)
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model_save_path_trial = os.path.join("./results/models/", f"model_trial_{trial.number}.keras")
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history_save_path_trial = os.path.join("./results/history/", f"history_trial_{trial.number}.pkl")
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model.save(model_save_path_trial)
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with open(history_save_path_trial, 'wb') as f:
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pickle.dump(history.history, f)
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return prediction_huber_overall, mass_balance_ratio
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if __name__ == "__main__":
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print(os.path.abspath("./datasets/barite_50_4_corner.h5"))
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data_file = h5py.File("./datasets/barite_50_4_corner.h5")
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design = data_file["design"]
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results = data_file["result"]
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df_design = pd.DataFrame(np.array(design["data"]).transpose(), columns = np.array(design["names"].asstr()))
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df_results = pd.DataFrame(np.array(results["data"]).transpose(), columns = np.array(results["names"].asstr()))
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data_file.close()
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species_columns = ['H', 'O', 'Ba', 'Cl', 'S', 'Sr', 'Barite', 'Celestite']
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study = optuna.create_study(storage="sqlite:///model_large_optimization.db", study_name="model_optimization", directions=["minimize", "maximize"])
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study.optimize(lambda trial: objective(trial, df_design, df_results, species_columns), n_trials=1000)
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print("Number of finished trials: ", len(study.trials))
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print("Best trial:")
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trial = study.best_trial
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print(" Value: ", trial.value)
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print(" Params: ")
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for key, value in trial.params.items():
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print(" {}: {}".format(key, value))
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384
src/preprocessing.py
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src/preprocessing.py
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import keras
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from keras.layers import Dense, Dropout, Input,BatchNormalization, LeakyReLU
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import tensorflow as tf
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import h5py
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import numpy as np
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import pandas as pd
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import time
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import sklearn.model_selection as sk
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from imblearn.over_sampling import SMOTE
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import RandomOverSampler
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from collections import Counter
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import os
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from preprocessing import *
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from sklearn import set_config
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from importlib import reload
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set_config(transform_output = "pandas")
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# preprocessing pipeline
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#
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def Safelog(val):
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# get range of vector
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if val > 0:
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return np.log10(val)
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elif val < 0:
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return -np.log10(-val)
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else:
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return 0
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def Safeexp(val):
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if val > 0:
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return -10 ** -val
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elif val < 0:
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return 10 ** val
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else:
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return 0
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def model_definition(architecture):
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dtype = "float32"
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if architecture == "small":
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model = keras.Sequential(
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[
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keras.Input(shape=(8,), dtype="float32"),
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keras.layers.Dense(units=128, dtype="float32"),
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LeakyReLU(negative_slope=0.01),
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# Dropout(0.2),
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keras.layers.Dense(units=128, dtype="float32"),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(units=8, dtype="float32")
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]
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)
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elif architecture == "large":
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model = keras.Sequential(
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[
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keras.layers.Input(shape=(8,), dtype=dtype),
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keras.layers.Dense(512, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(1024, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(512, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(8, dtype=dtype)
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]
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)
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elif architecture == "paper":
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model = keras.Sequential(
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[keras.layers.Input(shape=(8,), dtype=dtype),
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keras.layers.Dense(128, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(256, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(512, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(256, dtype=dtype),
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LeakyReLU(negative_slope=0.01),
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keras.layers.Dense(8, dtype=dtype)
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])
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return model
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def custom_loss(preprocess, column_dict, h1, h2, h3, scaler_type="minmax", loss_variant="huber", delta=1.0):
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# extract the scaling parameters
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if scaler_type == "minmax":
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scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
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min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)
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scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
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min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)
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elif scaler_type == "standard":
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scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
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mean_X = tf.convert_to_tensor(preprocess.scaler_X.mean_, dtype=tf.float32)
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scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
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mean_y = tf.convert_to_tensor(preprocess.scaler_y.mean_, dtype=tf.float32)
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def loss(results, predicted):
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# inverse min/max scaling
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if scaler_type == "minmax":
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predicted_inverse = predicted * scale_y + min_y
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results_inverse = results * scale_X + min_X
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elif scaler_type == "standard":
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predicted_inverse = predicted * scale_y + mean_y
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results_inverse = results * scale_X + mean_X
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# mass balance
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dBa = tf.keras.backend.abs(
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(predicted_inverse[:, column_dict["Ba"]] + predicted_inverse[:, column_dict["Barite"]]) -
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(results_inverse[:, column_dict["Ba"]] + results_inverse[:, column_dict["Barite"]])
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)
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dSr = tf.keras.backend.abs(
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(predicted_inverse[:, column_dict["Sr"]] + predicted_inverse[:, column_dict["Celestite"]]) -
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(results_inverse[:, column_dict["Sr"]] + results_inverse[:, column_dict["Celestite"]])
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)
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# H/O ratio has to be 2
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# h2o_ratio = tf.keras.backend.abs(
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# (predicted_inverse[:, column_dict["H"]] / predicted_inverse[:, column_dict["O"]]) - 2
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# )
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# huber loss
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huber_loss = tf.keras.losses.Huber(delta)(results, predicted)
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# total loss
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if loss_variant == "huber":
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total_loss = huber_loss
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elif loss_variant == "huber_mass_balance":
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total_loss = h1 * huber_loss + h2 * dBa + h3 * dSr
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return total_loss
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return loss
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def mass_balance_metric(preprocess, column_dict, scaler_type="minmax"):
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if scaler_type == "minmax":
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scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
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min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)
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scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
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min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)
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elif scaler_type == "standard":
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scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
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mean_X = tf.convert_to_tensor(preprocess.scaler_X.mean_, dtype=tf.float32)
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scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
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mean_y = tf.convert_to_tensor(preprocess.scaler_y.mean_, dtype=tf.float32)
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def mass_balance(results, predicted):
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# inverse min/max scaling
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if scaler_type == "minmax":
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predicted_inverse = predicted * scale_y + min_y
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results_inverse = results * scale_X + min_X
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elif scaler_type == "standard":
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predicted_inverse = predicted * scale_y + mean_y
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results_inverse = results * scale_X + mean_X
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# mass balance
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dBa = tf.keras.backend.abs(
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(predicted_inverse[:, column_dict["Ba"]] + predicted_inverse[:, column_dict["Barite"]]) -
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(results_inverse[:, column_dict["Ba"]] + results_inverse[:, column_dict["Barite"]])
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)
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dSr = tf.keras.backend.abs(
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(predicted_inverse[:, column_dict["Sr"]] + predicted_inverse[:, column_dict["Celestite"]]) -
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(results_inverse[:, column_dict["Sr"]] + results_inverse[:, column_dict["Celestite"]])
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)
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return tf.reduce_mean(dBa + dSr)
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return mass_balance
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def huber_metric(preprocess, scaler_type="minmax", delta=1.0):
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if scaler_type == "minmax":
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scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
|
||||
min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)
|
||||
scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
|
||||
min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)
|
||||
|
||||
elif scaler_type == "standard":
|
||||
scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)
|
||||
mean_X = tf.convert_to_tensor(preprocess.scaler_X.mean_, dtype=tf.float32)
|
||||
scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)
|
||||
mean_y = tf.convert_to_tensor(preprocess.scaler_y.mean_, dtype=tf.float32)
|
||||
|
||||
|
||||
def huber(results, predicted):
|
||||
|
||||
if scaler_type == "minmax":
|
||||
predicted_inverse = predicted * scale_y + min_y
|
||||
results_inverse = results * scale_X + min_X
|
||||
|
||||
elif scaler_type == "standard":
|
||||
predicted_inverse = predicted * scale_y + mean_y
|
||||
results_inverse = results * scale_X + mean_X
|
||||
|
||||
huber_loss = tf.keras.losses.Huber(delta)(results, predicted)
|
||||
|
||||
return huber_loss
|
||||
|
||||
return huber
|
||||
|
||||
def mass_balance_evaluation(model, X, preprocess):
|
||||
|
||||
# predict the chemistry
|
||||
columns = X.iloc[:, X.columns != "Class"].columns
|
||||
prediction = pd.DataFrame(model.predict(X[columns]), columns=columns)
|
||||
|
||||
# backtransform min/max or standard scaler
|
||||
X = pd.DataFrame(preprocess.scaler_X.inverse_transform(X.iloc[:, X.columns != "Class"]), columns=columns)
|
||||
prediction = pd.DataFrame(preprocess.scaler_y.inverse_transform(prediction), columns=columns)
|
||||
|
||||
# calculate mass balance
|
||||
dBa = np.abs((prediction["Ba"] + prediction["Barite"]) - (X["Ba"] + X["Barite"]))
|
||||
print(dBa.min())
|
||||
dSr = np.abs((prediction["Sr"] + prediction["Celestite"]) - (X["Sr"] + X["Celestite"]))
|
||||
print(dSr.min())
|
||||
return dBa+dSr
|
||||
|
||||
|
||||
class preprocessing:
|
||||
|
||||
def __init__(self, func_dict_in=None, func_dict_out=None, random_state=42):
|
||||
self.random_state = random_state
|
||||
self.scaler_X = None
|
||||
self.scaler_y = None
|
||||
self.func_dict_in = None
|
||||
self.func_dict_in = func_dict_in if func_dict_in is not None else None
|
||||
self.func_dict_out = func_dict_out if func_dict_out is not None else None
|
||||
self.state = {"cluster": False, "log": False, "balance": False, "scale": False}
|
||||
|
||||
def funcTranform(self, X, y):
|
||||
for key in X.keys():
|
||||
if "Class" not in key:
|
||||
X[key] = X[key].apply(self.func_dict_in[key])
|
||||
y[key] = y[key].apply(self.func_dict_in[key])
|
||||
self.state["log"] = True
|
||||
|
||||
return X, y
|
||||
|
||||
def funcInverse(self, X, y):
|
||||
|
||||
for key in X.keys():
|
||||
if "Class" not in key:
|
||||
X[key] = X[key].apply(self.func_dict_out[key])
|
||||
y[key] = y[key].apply(self.func_dict_out[key])
|
||||
self.state["log"] = False
|
||||
return X, y
|
||||
|
||||
def cluster(self, X, y, species='Barite', n_clusters=2, x_length=50, y_length=50):
|
||||
|
||||
class_labels = np.array([])
|
||||
grid_length = x_length * y_length
|
||||
iterations = int(len(X) / grid_length)
|
||||
|
||||
for i in range(0, iterations):
|
||||
field = np.array(X[species][(i*grid_length):(i*grid_length+grid_length)]
|
||||
).reshape(x_length, y_length)
|
||||
kmeans = KMeans(n_clusters=n_clusters, random_state=self.random_state).fit(field.reshape(-1, 1))
|
||||
class_labels = np.append(class_labels.astype(int), kmeans.labels_)
|
||||
|
||||
if ("Class" in X.columns and "Class" in y.columns):
|
||||
print("Class column already exists")
|
||||
else:
|
||||
class_labels_df = pd.DataFrame(class_labels, columns=['Class'])
|
||||
X = pd.concat([X, class_labels_df], axis=1)
|
||||
y = pd.concat([y, class_labels_df], axis=1)
|
||||
self.state["cluster"] = True
|
||||
|
||||
return X, y
|
||||
|
||||
|
||||
def balancer(self, X, y, strategy, sample_fraction=0.5):
|
||||
|
||||
number_features = (X.columns != "Class").sum()
|
||||
if("Class" not in X.columns):
|
||||
if("Class" in y.columns):
|
||||
classes = y['Class']
|
||||
else:
|
||||
raise Exception("No class column found")
|
||||
else:
|
||||
classes = X['Class']
|
||||
counter = classes.value_counts()
|
||||
print("Amount class 0 before:", counter[0] / (counter[0] + counter[1]) )
|
||||
print("Amount class 1 before:", counter[1] / (counter[0] + counter[1]) )
|
||||
df = pd.concat([X.loc[:,X.columns != "Class"], y.loc[:, y.columns != "Class"], classes], axis=1)
|
||||
|
||||
if strategy == 'smote':
|
||||
print("Using SMOTE strategy")
|
||||
smote = SMOTE(sampling_strategy=sample_fraction)
|
||||
df_resampled, classes_resampled = smote.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df. columns == "Class"])
|
||||
|
||||
elif strategy == 'over':
|
||||
print("Using Oversampling")
|
||||
over = RandomOverSampler()
|
||||
df_resampled, classes_resampled = over.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df. columns == "Class"])
|
||||
|
||||
elif strategy == 'under':
|
||||
print("Using Undersampling")
|
||||
under = RandomUnderSampler()
|
||||
df_resampled, classes_resampled = under.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df. columns == "Class"])
|
||||
|
||||
else:
|
||||
return X, y
|
||||
|
||||
counter = classes_resampled["Class"].value_counts()
|
||||
print("Amount class 0 after:", counter[0] / (counter[0] + counter[1]) )
|
||||
print("Amount class 1 after:", counter[1] / (counter[0] + counter[1]) )
|
||||
|
||||
design_resampled = pd.concat([df_resampled.iloc[:,0:number_features], classes_resampled], axis=1)
|
||||
target_resampled = pd.concat([df_resampled.iloc[:,number_features:], classes_resampled], axis=1)
|
||||
|
||||
self.state['balance'] = True
|
||||
return design_resampled, target_resampled
|
||||
|
||||
|
||||
def scale_fit(self, X, y, scaling, type='Standard'):
|
||||
|
||||
if type == 'minmax':
|
||||
self.scaler_X = MinMaxScaler()
|
||||
self.scaler_y = MinMaxScaler()
|
||||
elif type == 'standard':
|
||||
self.scaler_X = StandardScaler()
|
||||
self.scaler_y = StandardScaler()
|
||||
|
||||
else:
|
||||
raise Exception("No valid scaler type found")
|
||||
|
||||
if scaling == 'individual':
|
||||
self.scaler_X.fit(X.iloc[:, X.columns != "Class"])
|
||||
self.scaler_y.fit(y.iloc[:, y.columns != "Class"])
|
||||
|
||||
elif scaling == 'global':
|
||||
self.scaler_X.fit(pd.concat([X.iloc[:, X.columns != "Class"], y.iloc[:, y.columns != "Class"]], axis=0))
|
||||
self.scaler_y = self.scaler_X
|
||||
|
||||
self.state['scale'] = True
|
||||
|
||||
def scale_transform(self, X_train, X_test, y_train, y_test):
|
||||
X_train = pd.concat([self.scaler_X.transform(X_train.loc[:, X_train.columns != "Class"]), X_train.loc[:, "Class"]], axis=1)
|
||||
|
||||
X_test = pd.concat([self.scaler_X.transform(X_test.loc[:, X_test.columns != "Class"]), X_test.loc[:, "Class"]], axis=1)
|
||||
|
||||
y_train = pd.concat([self.scaler_y.transform(y_train.loc[:, y_train.columns != "Class"]), y_train.loc[:, "Class"]], axis=1)
|
||||
|
||||
y_test = pd.concat([self.scaler_y.transform(y_test.loc[:, y_test.columns != "Class"]), y_test.loc[:, "Class"]], axis=1)
|
||||
|
||||
return X_train, X_test, y_train, y_test
|
||||
|
||||
def scale_inverse(self, X):
|
||||
|
||||
if("Class" in X.columns):
|
||||
X = pd.concat([self.scaler_X.inverse_transform(X.loc[:, X.columns != "Class"]), X.loc[:, "Class"]], axis=1)
|
||||
else:
|
||||
X = self.scaler_X.inverse_transform(X)
|
||||
|
||||
return X
|
||||
|
||||
def split(self, X, y, ratio=0.8):
|
||||
X_train, y_train, X_test, y_test = sk.train_test_split(X, y, test_size = ratio, random_state=self.random_state)
|
||||
|
||||
return X_train, y_train, X_test, y_test
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user