<jupyter_start><jupyter_text>gbr2.fit(X_train2, y_train2)<jupyter_code>gbr.fit(X_train, y_train)<jupyter_output><empty_output><jupyter_text>gbr2.score(X_train2, y_train2)<jupyter_code>gbr.score(X_train, y_train)
import pickle
with open("models/2020-Jul-26-19-06-47.pkl", "rb") as data:
    model = pickle.load(data)
model.n_features_<jupyter_output><empty_output><jupyter_text>Novo método de feat eng<jupyter_code>feat_eng = Enginerring(data)
features = feat_eng.planning(params).\
                convert_missing_to_class().\   # missing_class
                convert_missing_to_number().\  # missing_number_to_inf  # imputation
                convert_binary_to_class().\    # binary_dummies         # one-hot encoding
                convert_number_to_binary().\   # continuous_to_binary   # binning
                combine_classes_to_binary().\  # unify_classes          # binning
                order_class_levels().\         # factor_to_number       #  
                keep_original_feature().\      # identity
                transform_number().\           # scale_adjust
                create_missing_dummy().\       # insert_dummy_faltante
                create_custom_classes()        # dummy_controlada       
                
                
                
                
                
import os
os.chdir("..")
import pandas as pd
from calysto.engineering import Blueprint
bpr = Blueprint(pd.DataFrame())
bpr.create_plan()
bpr.impute_missing_as_category(["asdad"]).\
    impute_missing_as_inf(["asdad"]).\
    impute_missing_as_category(["asdad"]).\
    impute_missing_as_number(["asd", "asdasd"], [4])
 bpr.plan
a = {}
bpr.

ok    convert_missing_to_class().\  # missing_class         # imputation                   # impute_categorical
ok    convert_missing_to_number().\ # missing_number_to_inf # imputation       # impute_
ok    convert_binary_to_class().\   # binary_dummies        # one-hot encoding
ok    convert_number_to_binary().\  # continuous_to_binary  # binning
ok    combine_classes_to_binary().\ # unify_classes         # binning
ok    order_class_levels().\        # factor_to_number      # ordering
ok    keep_original_feature().\     # identity              # NA
ok    transform_number().\          # scale_adjust          # scaling    
ok    create_missing_dummy().\      # insert_dummy_faltante # binning
ok    create_custom_classes()       # dummy_controlada      # binning
bpr.execute()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10069021518473406, 'fraction_numerical': 0.007714169711733658, 'mean_word_length': 2.4269819193324063, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '657205', 'license': 'MIT', 'n_tokens_mistral': 812, 'n_tokens_neox': 778, 'n_words': 114, 'path': 'dev/Untitled.ipynb', 'repo_name': 'adelmofilho/mlworks', 'size': '2463'}