From f836441e483dd5e586c50e45f9010a01f5e4fac6 Mon Sep 17 00:00:00 2001 From: Alex-Lekov <45808195+itLek@users.noreply.github.com> Date: Sun, 4 Oct 2020 21:09:17 +0300 Subject: [PATCH 1/7] fix verbose --- CHANGELOG.md | 6 +++++- automl_alex/__version__.py | 2 +- automl_alex/models/sklearn_models.py | 4 ++-- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ae731d8..668abfa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,8 +4,12 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). +## [0.10.04] -## [0.07.26] +### Fix +- dell verbose in LinearRegression + +## [0.08.05] ### Fix - if y_train is not pd.DataFrame diff --git a/automl_alex/__version__.py b/automl_alex/__version__.py index 26a8e39..839cc9a 100644 --- a/automl_alex/__version__.py +++ b/automl_alex/__version__.py @@ -1 +1 @@ -__version__ = "0.08.05" \ No newline at end of file +__version__ = "0.10.04" \ No newline at end of file diff --git a/automl_alex/models/sklearn_models.py b/automl_alex/models/sklearn_models.py index a51129d..9e9f6f2 100644 --- a/automl_alex/models/sklearn_models.py +++ b/automl_alex/models/sklearn_models.py @@ -8,7 +8,7 @@ filterwarnings("ignore", category=ConvergenceWarning, message="^Liblinear failed to converge") -################################## LogRegClassifier ########################################################## +################################## LinearModel ########################################################## class LinearModel(ModelBase): """ @@ -32,7 +32,7 @@ def _init_model(self, model_param=None): params: : parameters for model. """ if self.type_of_estimator == 'classifier': - model = linear_model.LogisticRegression(**model_param) + model = linear_model.LogisticRegression(**model_param, verbose=0,) elif self.type_of_estimator == 'regression': model = linear_model.LinearRegression(**model_param) return(model) From 23460f809fb2510ed5ab20c5c91a54a526997a99 Mon Sep 17 00:00:00 2001 From: Alex-Lekov <45808195+itLek@users.noreply.github.com> Date: Sun, 4 Oct 2020 21:20:03 +0300 Subject: [PATCH 2/7] fix 2 --- CHANGELOG.md | 2 +- automl_alex/models/sklearn_models.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 668abfa..ae253fa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). ## [0.10.04] ### Fix -- dell verbose in LinearRegression +- verbose in LinearRegression ## [0.08.05] diff --git a/automl_alex/models/sklearn_models.py b/automl_alex/models/sklearn_models.py index 9e9f6f2..871b047 100644 --- a/automl_alex/models/sklearn_models.py +++ b/automl_alex/models/sklearn_models.py @@ -22,7 +22,7 @@ def _init_default_model_param(self,): """ Default model_param """ - model_param = {'verbose':0,} + model_param = {} return(model_param) def _init_model(self, model_param=None): @@ -32,7 +32,7 @@ def _init_model(self, model_param=None): params: : parameters for model. """ if self.type_of_estimator == 'classifier': - model = linear_model.LogisticRegression(**model_param, verbose=0,) + model = linear_model.LogisticRegression(**model_param) elif self.type_of_estimator == 'regression': model = linear_model.LinearRegression(**model_param) return(model) From 104d46dfd780ccb7069ed985c63b433b7242cca6 Mon Sep 17 00:00:00 2001 From: Alex-Lekov <45808195+itLek@users.noreply.github.com> Date: Sun, 4 Oct 2020 22:59:30 +0300 Subject: [PATCH 3/7] fix DataConversionWarning --- CHANGELOG.md | 5 +++++ automl_alex/__version__.py | 2 +- automl_alex/models/sklearn_models.py | 2 +- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ae253fa..01e55ad 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,11 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). +## [0.10.05] + +### Fix +- DataConversionWarning in sklearn_models model.fit(X_train, y_train,) + ## [0.10.04] ### Fix diff --git a/automl_alex/__version__.py b/automl_alex/__version__.py index 839cc9a..f90bceb 100644 --- a/automl_alex/__version__.py +++ b/automl_alex/__version__.py @@ -1 +1 @@ -__version__ = "0.10.04" \ No newline at end of file +__version__ = "0.10.05" \ No newline at end of file diff --git a/automl_alex/models/sklearn_models.py b/automl_alex/models/sklearn_models.py index 871b047..acbd504 100644 --- a/automl_alex/models/sklearn_models.py +++ b/automl_alex/models/sklearn_models.py @@ -80,7 +80,7 @@ def _fit(self, model=None, X_train=None, y_train=None, X_test=None, y_test=None, model = self if (X_train is None) or (y_train is None): X_train = model._data.X_train - y_train = model._data.y_train + y_train = model._data.y_train.values.ravel() model.model = model._init_model(model_param=model.model_param) model.model.fit(X_train, y_train,) From 2be2704a5c47c21a856f57d68607312a9f525730 Mon Sep 17 00:00:00 2001 From: Alex-Lekov <45808195+itLek@users.noreply.github.com> Date: Tue, 6 Oct 2020 17:10:41 +0300 Subject: [PATCH 4/7] v 0.10.07 --- CHANGELOG.md | 2 +- automl_alex/__version__.py | 2 +- automl_alex/models/sklearn_models.py | 6 ++++-- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 01e55ad..c739f4f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). -## [0.10.05] +## [0.10.07] ### Fix - DataConversionWarning in sklearn_models model.fit(X_train, y_train,) diff --git a/automl_alex/__version__.py b/automl_alex/__version__.py index f90bceb..eb92fc7 100644 --- a/automl_alex/__version__.py +++ b/automl_alex/__version__.py @@ -1 +1 @@ -__version__ = "0.10.05" \ No newline at end of file +__version__ = "0.10.07" \ No newline at end of file diff --git a/automl_alex/models/sklearn_models.py b/automl_alex/models/sklearn_models.py index acbd504..25600ea 100644 --- a/automl_alex/models/sklearn_models.py +++ b/automl_alex/models/sklearn_models.py @@ -1,11 +1,13 @@ from sklearn import ensemble, neural_network, linear_model, svm, neighbors from .base import * +import numpy as np from warnings import simplefilter, filterwarnings -from sklearn.exceptions import ConvergenceWarning +from sklearn.exceptions import ConvergenceWarning, DataConversionWarning simplefilter("ignore", category=ConvergenceWarning) filterwarnings("ignore", category=ConvergenceWarning, message="^Maximum number of iteration reached") filterwarnings("ignore", category=ConvergenceWarning, message="^Liblinear failed to converge") +simplefilter("ignore", category=DataConversionWarning) ################################## LinearModel ########################################################## @@ -80,7 +82,7 @@ def _fit(self, model=None, X_train=None, y_train=None, X_test=None, y_test=None, model = self if (X_train is None) or (y_train is None): X_train = model._data.X_train - y_train = model._data.y_train.values.ravel() + y_train = np.array(model._data.y_train.values.ravel()) model.model = model._init_model(model_param=model.model_param) model.model.fit(X_train, y_train,) From 0914661970500b40e246677ab96aa6731547853c Mon Sep 17 00:00:00 2001 From: Alex-Lekov <61644712+Alex-Lekov@users.noreply.github.com> Date: Fri, 16 Oct 2020 21:59:25 +0300 Subject: [PATCH 5/7] devcontainer --- .devcontainer/devcontainer.json | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 .devcontainer/devcontainer.json diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000..e3e3bd9 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,32 @@ +{ + "name": "Existing Dockerfile", + + // Sets the run context to one level up instead of the .devcontainer folder. + "context": "..", + + // Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename. + "dockerFile": "../Dockerfile", + + // Set *default* container specific settings.json values on container create. + "settings": { + "terminal.integrated.shell.linux": null + }, + + // Add the IDs of extensions you want installed when the container is created. + "extensions": [] + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [], + + // Uncomment the next line to run commands after the container is created - for example installing curl. + // "postCreateCommand": "apt-get update && apt-get install -y curl", + + // Uncomment when using a ptrace-based debugger like C++, Go, and Rust + // "runArgs": [ "--cap-add=SYS_PTRACE", "--security-opt", "seccomp=unconfined" ], + + // Uncomment to use the Docker CLI from inside the container. See https://aka.ms/vscode-remote/samples/docker-from-docker. + // "mounts": [ "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind" ], + + // Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root. + // "remoteUser": "vscode" +} From dd4403dcd86574ee1951d83ba222b2dc194d380d Mon Sep 17 00:00:00 2001 From: Alex-Lekov Date: Mon, 23 Nov 2020 16:00:52 +0000 Subject: [PATCH 6/7] v 0.11.22 --- CHANGELOG.md | 12 + automl_alex/__version__.py | 2 +- automl_alex/automl_alex.py | 173 +++-- automl_alex/models/base.py | 17 +- examples/01_Quick_Start.ipynb | 493 +++++++++++- examples/02_Models.ipynb | 379 ++++++++-- ...ta_Cleaning_and_Encoding_(DataBunch).ipynb | 709 ++++++++++++++++-- examples/04_ModelsReview.ipynb | 239 +++++- examples/05_BestSingleModel.ipynb | 136 +++- tests/test_alexautoml.py | 2 +- 10 files changed, 1880 insertions(+), 282 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c739f4f..9152fba 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,21 +4,33 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). +## [0.11.22] + +### ADD +- multivariate TPE sampler. This algorithm captures dependencies among hyperparameters better than the previous algorithm + +### Fix +- "ValueError non-broadcastable output operand..." in AutoMLRegressor + + ## [0.10.07] ### Fix - DataConversionWarning in sklearn_models model.fit(X_train, y_train,) + ## [0.10.04] ### Fix - verbose in LinearRegression + ## [0.08.05] ### Fix - if y_train is not pd.DataFrame + ## [0.07.26] ### Add diff --git a/automl_alex/__version__.py b/automl_alex/__version__.py index eb92fc7..2dfc1ce 100644 --- a/automl_alex/__version__.py +++ b/automl_alex/__version__.py @@ -1 +1 @@ -__version__ = "0.10.07" \ No newline at end of file +__version__ = "0.11.24" \ No newline at end of file diff --git a/automl_alex/automl_alex.py b/automl_alex/automl_alex.py index 5650420..395a9af 100644 --- a/automl_alex/automl_alex.py +++ b/automl_alex/automl_alex.py @@ -5,6 +5,7 @@ from .models import * from .databunch import DataBunch from .encoders import * +from sklearn.preprocessing import StandardScaler ##################################### BestSingleModel ################################################ @@ -477,95 +478,107 @@ def opt(self, ############################################################### # STEP 3 - self.stack_models_predicts = pd.concat([predicts_1, predicts_2], ignore_index=True, sort=False) - self.stack_models_cfgs = pd.concat([stack_models_1_cfgs, stack_model_2_cfgs], ignore_index=True, sort=False) - - score_mean_stack_models = self.metric(self._data.y_train, self.stack_models_predicts['predict_train'].mean()) - if verbose > 0: - print(f'\n Mean Models {self.metric.__name__} Score Train: ', \ - round(score_mean_stack_models, self._metric_round)) - print('_'*50) - print('Step 2: Stacking') - print('_'*50) - time.sleep(0.1) # clean print + self.stack_models_predicts = pd.concat([predicts_1, predicts_2], ignore_index=True, sort=False) + self.stack_models_cfgs = pd.concat([stack_models_1_cfgs, stack_model_2_cfgs], ignore_index=True, sort=False) - # Stacking - X_train_predicts = pd.DataFrame([*self.stack_models_predicts['predict_train']]).T - X_train_predicts.columns = self.stack_models_predicts.model_name.values - X_test_predicts = pd.DataFrame([*self.stack_models_predicts['predict_test']],).T - X_test_predicts.columns = self.stack_models_predicts.model_name.values - - self._data.X_train = X_train_predicts.reset_index(drop=True) - self._data.X_test = X_test_predicts.reset_index(drop=True) - self._data.y_train = self._data.y_train.reset_index(drop=True) - - print('New X_train: ', self._data.X_train.shape, - ' y_train: ', self._data.y_train.shape, - '| X_test shape: ', self._data.X_test.shape) + score_mean_stack_models = self.metric(self._data.y_train, self.stack_models_predicts['predict_train'].mean()) + if verbose > 0: + print('-'*50) + print(f'\n Blend Models {self.metric.__name__} Score Train: ', \ + round(score_mean_stack_models, self._metric_round)) + print('_'*50) + print('Step 2: Stacking') + print('_'*50) + time.sleep(0.1) # clean print + + if self.type_of_estimator == 'regression': + pred_test = (self.stack_models_predicts['predict_test'].mean() * 0.7) + \ + (self.predicts_model_1_full_x['predict_test'].mean() * 0.2) + \ + (self.predicts_model_0_full_x['predict_test'].mean() * 0.1) + + pred_train = self.stack_models_predicts['predict_train'].mean() - self.history_trials = [] - self.history_trials_dataframe = pd.DataFrame() - - stack_model_1 = BestSingleModel(databunch=self._data, - cv=10, - score_cv_folds = 10, - metric=self.metric, - direction=self.direction, - metric_round=self._metric_round, - combined_score_opt=False, - gpu=self._gpu, - random_state=self._random_state, - type_of_estimator=self.type_of_estimator,) + else: + # Stacking + X_train_predicts = pd.DataFrame([*self.stack_models_predicts['predict_train']]).T + X_train_predicts.columns = self.stack_models_predicts.model_name.values + X_test_predicts = pd.DataFrame([*self.stack_models_predicts['predict_test']],).T + X_test_predicts.columns = self.stack_models_predicts.model_name.values - # Opt - history_stack_model = stack_model_1.opt( - iterations=150, - #timeout=100, - opt_lvl=3, - auto_parameters=False, - cold_start=25, - feature_selection=True, - models_names=['LinearModel',], - verbose= (lambda x: 0 if x <= 1 else 1)(verbose), ) + scaler = StandardScaler() + + self._data.X_train = pd.DataFrame(scaler.fit_transform(X_train_predicts.reset_index(drop=True))) + self._data.X_test = pd.DataFrame(scaler.transform(X_test_predicts.reset_index(drop=True))) + self._data.y_train = self._data.y_train.reset_index(drop=True) + + print('New X_train: ', self._data.X_train.shape, + ' y_train: ', self._data.y_train.shape, + '| X_test shape: ', self._data.X_test.shape) - # Predict - history_stack_model = history_stack_model.drop_duplicates(subset=['model_score', 'score_std'], keep='last') - predict_stack_model = stack_model_1.predict(models_cfgs=history_stack_model.head(2)) - - # Score: - score_final_stack_model = self.metric(self._data.y_train, predict_stack_model['predict_train'].mean()) - if verbose > 0: - print(f'\n Stacking model {self.metric.__name__} Score Train: ', - round(score_final_stack_model, self._metric_round)) - time.sleep(0.1) # clean print + self.history_trials = [] + self.history_trials_dataframe = pd.DataFrame() - pred_test = predict_stack_model['predict_test'].mean() - pred_train = predict_stack_model['predict_train'].mean() - - if self.direction == 'maximize': - if score_mean_stack_models >= score_final_stack_model and score_mean_stack_models >= score_mean_models_1: - pred_test = self.stack_models_predicts['predict_test'].mean() - pred_train = self.stack_models_predicts['predict_train'].mean() - if score_mean_models_1 >= score_final_stack_model and score_mean_models_1 >= score_mean_stack_models: - pred_test = predicts_1['predict_test'].mean() - pred_train = predicts_1['predict_train'].mean() - else: - if score_mean_stack_models <= score_final_stack_model and score_mean_stack_models <= score_mean_models_1: - pred_test = self.stack_models_predicts['predict_test'].mean() - pred_train = self.stack_models_predicts['predict_train'].mean() - if score_mean_models_1 <= score_final_stack_model and score_mean_models_1 <= score_mean_stack_models: - pred_test = predicts_1['predict_test'].mean() - pred_train = predicts_1['predict_train'].mean() + stack_model_1 = BestSingleModel(databunch=self._data, + cv=10, + score_cv_folds = 10, + metric=self.metric, + direction=self.direction, + metric_round=self._metric_round, + combined_score_opt=False, + gpu=self._gpu, + random_state=self._random_state, + type_of_estimator=self.type_of_estimator, + clean_and_encod_data=False,) + + # Opt + history_stack_model = stack_model_1.opt( + iterations=150, + #timeout=100, + opt_lvl=3, + auto_parameters=False, + cold_start=25, + feature_selection=False, + models_names=['LinearModel', 'MLP',], + verbose= (lambda x: 0 if x <= 1 else 1)(verbose), ) + + # Predict + history_stack_model = history_stack_model.drop_duplicates(subset=['model_score', 'score_std'], keep='last') + predict_stack_model = stack_model_1.predict(models_cfgs=history_stack_model.head(2), databunch=self._data,) + + # Score: + score_final_stack_model = self.metric(self._data.y_train, predict_stack_model['predict_train'].mean()) + if verbose > 0: + print(f'\n Stacking model {self.metric.__name__} Score Train: ', + round(score_final_stack_model, self._metric_round)) + time.sleep(0.1) # clean print + + pred_test = predict_stack_model['predict_test'].mean() + pred_train = predict_stack_model['predict_train'].mean() + + if self.direction == 'maximize': + if score_mean_stack_models >= score_final_stack_model and score_mean_stack_models >= score_mean_models_1: + pred_test = self.stack_models_predicts['predict_test'].mean() + pred_train = self.stack_models_predicts['predict_train'].mean() + if score_mean_models_1 >= score_final_stack_model and score_mean_models_1 >= score_mean_stack_models: + pred_test = predicts_1['predict_test'].mean() + pred_train = predicts_1['predict_train'].mean() + else: + if score_mean_stack_models <= score_final_stack_model and score_mean_stack_models <= score_mean_models_1: + pred_test = self.stack_models_predicts['predict_test'].mean() + pred_train = self.stack_models_predicts['predict_train'].mean() + if score_mean_models_1 <= score_final_stack_model and score_mean_models_1 <= score_mean_stack_models: + pred_test = predicts_1['predict_test'].mean() + pred_train = predicts_1['predict_train'].mean() + + pred_test = (pred_test * 0.7) + \ + (self.predicts_model_1_full_x['predict_test'].mean() * 0.2) + \ + (self.predicts_model_0_full_x['predict_test'].mean() * 0.1) - final_score_model = self.metric(self._data.y_train, pred_train) + # print score if verbose > 0: + final_score_model = self.metric(self._data.y_train, pred_train) print(f'\n Final Model {self.metric.__name__} Score Train: ', \ round(final_score_model, self._metric_round)) - time.sleep(0.1) # clean print - - pred_test = (pred_test * 0.7) + \ - (self.predicts_model_1_full_x['predict_test'].mean() * 0.2) + \ - (self.predicts_model_0_full_x['predict_test'].mean() * 0.1) return (pred_test, pred_train) diff --git a/automl_alex/models/base.py b/automl_alex/models/base.py index 01d455b..b6617b3 100644 --- a/automl_alex/models/base.py +++ b/automl_alex/models/base.py @@ -445,13 +445,14 @@ def objective(trial, fast_check=True): self._tqdm_opt_print(pbar) return score_opt - sampler=optuna.samplers.TPESampler(consider_prior=True, - prior_weight=1.0, - consider_magic_clip=True, - consider_endpoints=False, + sampler=optuna.samplers.TPESampler(#consider_prior=True, + #prior_weight=1.0, + #consider_magic_clip=True, + #consider_endpoints=False, n_startup_trials=self._cold_start, - n_ei_candidates=50, - seed=self._random_state) + #n_ei_candidates=50, + seed=self._random_state, + multivariate=True,) if self.study is None: self.study = optuna.create_study(direction=self.direction, sampler=sampler,) @@ -680,8 +681,8 @@ def cross_val(self, ) folds_scores = [] - stacking_y_pred_train = np.zeros(X.shape[0]) - stacking_y_pred_test = np.zeros(X_test.shape[0]) + stacking_y_pred_train = np.zeros(len(X)) + stacking_y_pred_test = np.zeros(len(X_test)) feature_importance_df = pd.DataFrame(np.zeros(len(X.columns)), index=X.columns) for i, (train_idx, valid_idx) in enumerate(skf.split(X, y)): diff --git a/examples/01_Quick_Start.ipynb b/examples/01_Quick_Start.ipynb index 9049db9..b623c9e 100644 --- a/examples/01_Quick_Start.ipynb +++ b/examples/01_Quick_Start.ipynb @@ -12,12 +12,12 @@ "outputs": [], "source": [ "# If you run this notebook on Google Colaboratory, uncomment the below to install automl_alex.\n", - "#!pip install --quiet -U automl_alex" + "#!pip install -q -U automl_alex" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T01:05:50.878720Z", @@ -29,20 +29,23 @@ { "output_type": "stream", "name": "stdout", - "text": "0.07.26\n" + "text": [ + "AutoML-Alex version: 0.11.24\n" + ] } ], "source": [ "import automl_alex\n", "import sklearn\n", + "import pandas as pd\n", "import time\n", - "from automl_alex import AutoML, AutoMLClassifier\n", - "print(automl_alex.__version__)" + "from automl_alex import AutoML, AutoMLClassifier, AutoMLRegressor\n", + "print('AutoML-Alex version:', automl_alex.__version__)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T01:05:52.553896Z", @@ -70,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T01:05:55.127924Z", @@ -81,11 +84,46 @@ { "output_type": "execute_result", "data": { - "text/plain": " checking_status duration credit_history \\\n0 <0 6.0 critical/other existing credit \n1 0<=X<200 48.0 existing paid \n2 no checking 12.0 critical/other existing credit \n3 <0 42.0 existing paid \n4 <0 24.0 delayed previously \n\n purpose credit_amount savings_status employment \\\n0 radio/tv 1169.0 no known savings >=7 \n1 radio/tv 5951.0 <100 1<=X<4 \n2 education 2096.0 <100 4<=X<7 \n3 furniture/equipment 7882.0 <100 4<=X<7 \n4 new car 4870.0 <100 1<=X<4 \n\n installment_commitment personal_status other_parties residence_since \\\n0 4.0 male single none 4.0 \n1 2.0 female div/dep/mar none 2.0 \n2 2.0 male single none 3.0 \n3 2.0 male single guarantor 4.0 \n4 3.0 male single none 4.0 \n\n property_magnitude age other_payment_plans housing existing_credits \\\n0 real estate 67.0 none own 2.0 \n1 real estate 22.0 none own 1.0 \n2 real estate 49.0 none own 1.0 \n3 life insurance 45.0 none for free 1.0 \n4 no known property 53.0 none for free 2.0 \n\n job num_dependents own_telephone foreign_worker \n0 skilled 1.0 yes yes \n1 skilled 1.0 none yes \n2 unskilled resident 2.0 none yes \n3 skilled 2.0 none yes \n4 skilled 2.0 none yes ", + "text/plain": [ + " checking_status duration credit_history \\\n", + "0 <0 6.0 critical/other existing credit \n", + "1 0<=X<200 48.0 existing paid \n", + "2 no checking 12.0 critical/other existing credit \n", + "3 <0 42.0 existing paid \n", + "4 <0 24.0 delayed previously \n", + "\n", + " purpose credit_amount savings_status employment \\\n", + "0 radio/tv 1169.0 no known savings >=7 \n", + "1 radio/tv 5951.0 <100 1<=X<4 \n", + "2 education 2096.0 <100 4<=X<7 \n", + "3 furniture/equipment 7882.0 <100 4<=X<7 \n", + "4 new car 4870.0 <100 1<=X<4 \n", + "\n", + " installment_commitment personal_status other_parties residence_since \\\n", + "0 4.0 male single none 4.0 \n", + "1 2.0 female div/dep/mar none 2.0 \n", + "2 2.0 male single none 3.0 \n", + "3 2.0 male single guarantor 4.0 \n", + "4 3.0 male single none 4.0 \n", + "\n", + " property_magnitude age other_payment_plans housing existing_credits \\\n", + "0 real estate 67.0 none own 2.0 \n", + "1 real estate 22.0 none own 1.0 \n", + "2 real estate 49.0 none own 1.0 \n", + "3 life insurance 45.0 none for free 1.0 \n", + "4 no known property 53.0 none for free 2.0 \n", + "\n", + " job num_dependents own_telephone foreign_worker \n", + "0 skilled 1.0 yes yes \n", + "1 skilled 1.0 none yes \n", + "2 unskilled resident 2.0 none yes \n", + "3 skilled 2.0 none yes \n", + "4 skilled 2.0 none yes " + ], "text/html": "
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checking_statusdurationcredit_historypurposecredit_amountsavings_statusemploymentinstallment_commitmentpersonal_statusother_partiesresidence_sinceproperty_magnitudeageother_payment_planshousingexisting_creditsjobnum_dependentsown_telephoneforeign_worker
0<06.0critical/other existing creditradio/tv1169.0no known savings>=74.0male singlenone4.0real estate67.0noneown2.0skilled1.0yesyes
10<=X<20048.0existing paidradio/tv5951.0<1001<=X<42.0female div/dep/marnone2.0real estate22.0noneown1.0skilled1.0noneyes
2no checking12.0critical/other existing crediteducation2096.0<1004<=X<72.0male singlenone3.0real estate49.0noneown1.0unskilled resident2.0noneyes
3<042.0existing paidfurniture/equipment7882.0<1004<=X<72.0male singleguarantor4.0life insurance45.0nonefor free1.0skilled2.0noneyes
4<024.0delayed previouslynew car4870.0<1001<=X<43.0male singlenone4.0no known property53.0nonefor free2.0skilled2.0noneyes
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" }, "metadata": {}, - "execution_count": 4 + "execution_count": 3 } ], "source": [ @@ -98,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T01:05:56.756465Z", @@ -109,10 +147,12 @@ { "output_type": "execute_result", "data": { - "text/plain": "((750, 20), (250, 20))" + "text/plain": [ + "((750, 20), (250, 20))" + ] }, "metadata": {}, - "execution_count": 5 + "execution_count": 7 } ], "source": [ @@ -144,7 +184,27 @@ { "output_type": "stream", "name": "stdout", - "text": "Source X_train shape:(750, 20)| X_test shape:(250, 20)\n##################################################\nAuto detect cat features:13\n> Start preprocessing Data\n> Generate cat encodet features\n +55 Features fromOneHotEncoder\n +44 Features fromHelmertEncoder\n +54 Features fromHashingEncoder\n +16 Features fromFrequencyEncoder\n> Generate Frequency Encode num features\n +4 Frequency Encode Num Features\n> Clean Nans in num features\n> Generate interaction Num Features\n +24 Interaction Features\n> Normalization Features\n##################################################\n> Total Features:201\n##################################################\nNew X_train shape:(750, 201)| X_test shape:(250, 201)\n" + "text": [ + "Source X_train shape: (750, 20) | X_test shape: (250, 20)\n", + "##################################################\n", + "Auto detect cat features: 13\n", + "> Start preprocessing Data\n", + "> Generate cat encodet features\n", + " + 55 Features from OneHotEncoder\n", + " + 44 Features from HelmertEncoder\n", + " + 54 Features from HashingEncoder\n", + " + 16 Features from FrequencyEncoder\n", + "> Generate Frequency Encode num features\n", + " + 4 Frequency Encode Num Features \n", + "> Clean Nans in num features\n", + "> Generate interaction Num Features\n", + " + 24 Interaction Features\n", + "> Normalization Features\n", + "##################################################\n", + "> Total Features: 201\n", + "##################################################\n", + "New X_train shape: (750, 201) | X_test shape: (250, 201)\n" + ] } ], "source": [ @@ -156,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-05-06T17:01:04.947762Z", @@ -168,7 +228,103 @@ { "output_type": "stream", "name": "stdout", - "text": "__________________________________________________\nStep 1: Model 0\n__________________________________________________\n100%|██████████| 1/1 [00:05<00:00, 5.52s/it]\n--------------------------------------------------\nModel 1\nOne iteration takes ~ 1.4 sec\n> Start Auto calibration parameters\n> Start optimization with the parameters:\nCV_Folds =10\nScore_CV_Folds =3\nFeature_Selection =True\nOpt_lvl =2\nCold_start =43.0\nEarly_stoping =100\nMetric =roc_auc_score\nDirection =maximize\n##################################################\nDefault model OptScore = 0.7087\nOptimize: : 255it [09:01, 1.66s/it, | Model: RandomForest | OptScore: 0.7619 | Best roc_auc_score: 0.8087 +- 0.046755]\n EarlyStopping Exceeded: Best Score: 0.7619roc_auc_score\nOptimize: : 255it [09:01, 2.12s/it, | Model: RandomForest | OptScore: 0.7619 | Best roc_auc_score: 0.8087 +- 0.046755]\n\n Predict from Models_1\n100%|██████████| 3/3 [00:01<00:00, 1.64it/s]\n\n > Calc predict policy on Models_1:\n | posible_repeats:89 | stack_top:10 | n_repeats:3\n 0%| | 0/10 [00:00 Start Auto calibration parameters\n> Start optimization with the parameters:\nCV_Folds =10\nScore_CV_Folds =3\nFeature_Selection =True\nOpt_lvl =2\nCold_start =44.0\nEarly_stoping =100\nMetric =roc_auc_score\nDirection =maximize\n##################################################\nDefault model OptScore = 0.7087\nOptimize: : 258it [02:00, 1.97it/s, | Model: MLP | OptScore: 0.7666 | Best roc_auc_score: 0.7914 +- 0.024789]\n EarlyStopping Exceeded: Best Score: 0.7666roc_auc_score\nOptimize: : 258it [02:01, 2.13it/s, | Model: MLP | OptScore: 0.7666 | Best roc_auc_score: 0.7914 +- 0.024789]\n\n Predict from Models_2\n 0%| | 0/5 [00:00 Start optimization with the parameters:\nCV_Folds =10\nScore_CV_Folds =10\nFeature_Selection =True\nOpt_lvl =3\nCold_start =25\nEarly_stoping =100\nMetric =roc_auc_score\nDirection =maximize\n##################################################\nDefault model OptScore = 0.734\nOptimize: : 150it [00:22, 6.70it/s, | Model: LinearModel | OptScore: 0.7957 | Best roc_auc_score: 0.7957 +- 0.045896]\n 0%| | 0/2 [00:00 Start Auto calibration parameters\n", + "\u001b[32m[I 2020-11-22 17:40:49,380]\u001b[0m A new study created in memory with name: no-name-bc95aced-4166-4a42-b077-31385c3f2e68\u001b[0m\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 10\n", + "Score_CV_Folds = 3\n", + "Feature_Selection = True\n", + "Opt_lvl = 2\n", + "Cold_start = 55.0\n", + "Early_stoping = 100\n", + "Metric = roc_auc_score\n", + "Direction = maximize\n", + "##################################################\n", + "Default model OptScore = 0.6906\n", + "Optimize: : 184it [14:57, 4.88s/it, | Model: RandomForest | OptScore: 0.7704 | Best roc_auc_score: 0.8097 +- 0.039278]\n", + "\n", + " Predict from Models_1\n", + "100%|██████████| 3/3 [00:06<00:00, 2.23s/it]\n", + " 0%| | 0/4 [00:00 Calc predict policy on Models_1:\n", + " | posible_repeats: 8 | stack_top: 4 | n_repeats: 2\n", + " 25%|██▌ | 1/4 [00:35<01:45, 35.00s/it]\n", + " Mean Score roc_auc_score on 20 Folds: 0.7838 std: 0.038969\n", + " 50%|█████ | 2/4 [01:14<01:12, 36.36s/it]\n", + " Mean Score roc_auc_score on 20 Folds: 0.7843 std: 0.039836\n", + " 75%|███████▌ | 3/4 [02:02<00:39, 39.78s/it]\n", + " Mean Score roc_auc_score on 20 Folds: 0.7851 std: 0.03985\n", + "100%|██████████| 4/4 [02:38<00:00, 39.54s/it]\n", + " Mean Score roc_auc_score on 20 Folds: 0.7835 std: 0.038533\n", + "\n", + " Models_1 Mean roc_auc_score Score Train: 0.7833\n", + "--------------------------------------------------\n", + "Model 2\n", + "\n", + "One iteration takes ~ 1.6 sec\n", + "> Start Auto calibration parameters\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 5\n", + "Score_CV_Folds = 2\n", + "Feature_Selection = True\n", + "Opt_lvl = 1\n", + "Cold_start = 63.0\n", + "Early_stoping = 100\n", + "Metric = roc_auc_score\n", + "Direction = maximize\n", + "##################################################\n", + "Default model OptScore = 0.7672\n", + "Optimize: : 174it [01:29, 2.28it/s, | Model: LinearModel | OptScore: 0.7626 | Best roc_auc_score: 0.7689 +- 0.006349]\n", + " EarlyStopping Exceeded: Best Score: 0.7626 roc_auc_score\n", + "Optimize: : 174it [01:29, 1.95it/s, | Model: LinearModel | OptScore: 0.7626 | Best roc_auc_score: 0.7689 +- 0.006349]\n", + "\n", + " Predict from Models_2\n", + " 50%|█████ | 1/2 [00:00<00:00, 1.42it/s]\n", + " Mean Score roc_auc_score on 5 Folds: 0.7618 std: 0.016025\n", + "100%|██████████| 2/2 [00:01<00:00, 1.10it/s]\n", + " Mean Score roc_auc_score on 5 Folds: 0.7617 std: 0.016081\n", + "\n", + " Models_2 Mean roc_auc_score Score Train: 0.7604\n", + "\n", + " Mean Models roc_auc_score Score Train: 0.791\n", + "__________________________________________________\n", + "Step 2: Stacking\n", + "__________________________________________________\n", + "\n", + "New X_train: (750, 6) y_train: (750, 1) | X_test shape: (250, 6)\n", + "One iteration takes ~ 0.6 sec\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 10\n", + "Score_CV_Folds = 10\n", + "Feature_Selection = True\n", + "Opt_lvl = 3\n", + "Cold_start = 25\n", + "Early_stoping = 100\n", + "Metric = roc_auc_score\n", + "Direction = maximize\n", + "##################################################\n", + "Default model OptScore = 0.7254\n", + " 0%| | 0/2 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
00.76190.80870.046755RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
10.76020.80720.046989RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
20.75880.80260.043793RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
30.75590.80020.044282RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
40.75450.80140.046876RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
50.75400.80330.049344RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
60.75340.80620.052843RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
70.75290.80440.051466RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
80.75250.80450.052013RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
90.75170.79850.046768RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
100.76660.79140.024789MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
110.75900.76420.005185MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
120.75860.76340.004806MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
130.75830.76310.004828MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
140.75800.76250.004505MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, OneHotEncoder_other_payment_pl...10
\n" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "0 0.7704 0.8097 0.039278 RandomForest \n", + "1 0.7703 0.8105 0.040204 RandomForest \n", + "2 0.7699 0.8097 0.039843 RandomForest \n", + "3 0.7695 0.8088 0.039275 RandomForest \n", + "4 0.7626 0.7688 0.006243 LinearModel \n", + "5 0.7626 0.7689 0.006349 LinearModel \n", + "\n", + " model_param wrapper_params \\\n", + "0 {'verbose': 0, 'random_state': 42, 'n_jobs': -... {} \n", + "1 {'verbose': 0, 'random_state': 42, 'n_jobs': -... {} \n", + "2 {'verbose': 0, 'random_state': 42, 'n_jobs': -... {} \n", + "3 {'verbose': 0, 'random_state': 42, 'n_jobs': -... {} \n", + "4 {'fit_intercept': False, 'C': 72.39799446110504} {} \n", + "5 {'fit_intercept': False, 'C': 88.24233739381233} {} \n", + "\n", + " cat_encoders \\\n", + "0 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "1 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "2 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "3 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "4 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "5 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "0 (credit_amount, OneHotEncoder_personal_status_... 10 \n", + "1 (credit_amount, OneHotEncoder_personal_status_... 10 \n", + "2 (credit_amount, OneHotEncoder_personal_status_... 10 \n", + "3 (credit_amount, OneHotEncoder_personal_status_... 10 \n", + "4 (duration, age, num_dependents, OneHotEncoder_... 5 \n", + "5 (duration, age, num_dependents, OneHotEncoder_... 5 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
00.77040.80970.039278RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_personal_status_...10
10.77030.81050.040204RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_personal_status_...10
20.76990.80970.039843RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_personal_status_...10
30.76950.80880.039275RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_personal_status_...10
40.76260.76880.006243LinearModel{'fit_intercept': False, 'C': 72.39799446110504}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...5
50.76260.76890.006349LinearModel{'fit_intercept': False, 'C': 88.24233739381233}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...5
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" }, "metadata": {}, - "execution_count": 11 + "execution_count": 10 } ], "source": [ "model.stack_models_cfgs" ] }, + { + "source": [ + "# Regression" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Data" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((430, 19), (76, 19))" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "# https://www.openml.org/d/543\n", + "dataset = fetch_openml(data_id=543, as_frame=True)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(pd.DataFrame(dataset.data), \n", + " pd.DataFrame(dataset.target), \n", + " test_size=0.15, \n", + " random_state=RANDOM_SEED,)\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " TOWN TOWN_ID TRACT LON LAT MEDV CMEDV \\\n", + "104 Medford 24.0 3395.0 -71.0690 42.2480 20.1 20.1 \n", + "203 Weston 37.0 3671.0 -71.1990 42.2320 48.5 48.5 \n", + "381 Boston_East_Boston 79.0 407.0 -71.0410 42.2290 10.9 10.9 \n", + "489 Chelsea 89.0 1602.0 -71.0228 42.2335 7.0 7.0 \n", + "69 Wilmington 16.0 3313.0 -71.1110 42.3270 20.9 20.9 \n", + ".. ... ... ... ... ... ... ... \n", + "106 Medford 24.0 3397.0 -71.0622 42.2431 19.5 19.5 \n", + "270 Dedham 46.0 4022.0 -71.0870 42.1410 21.1 21.1 \n", + "348 Norwell 69.0 5041.0 -70.9200 42.1016 24.5 24.5 \n", + "435 Boston_Savin_Hill 83.0 903.0 -71.0460 42.1867 13.4 13.4 \n", + "102 Medford 24.0 3393.0 -71.0812 42.2515 18.6 18.6 \n", + "\n", + " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", + "104 0.13960 0.0 8.56 0 0.5200 6.167 90.0 2.4210 5 384.0 \n", + "203 0.03510 95.0 2.68 0 0.4161 7.853 33.2 5.1180 4 224.0 \n", + "381 15.87440 0.0 18.10 0 0.6710 6.545 99.1 1.5192 24 666.0 \n", + "489 0.18337 0.0 27.74 0 0.6090 5.414 98.3 1.7554 4 711.0 \n", + "69 0.12816 12.5 6.07 0 0.4090 5.885 33.0 6.4980 4 345.0 \n", + ".. ... ... ... ... ... ... ... ... .. ... \n", + "106 0.17120 0.0 8.56 0 0.5200 5.836 91.9 2.2110 5 384.0 \n", + "270 0.29916 20.0 6.96 0 0.4640 5.856 42.1 4.4290 3 223.0 \n", + "348 0.01501 80.0 2.01 0 0.4350 6.635 29.7 8.3440 4 280.0 \n", + "435 11.16040 0.0 18.10 0 0.7400 6.629 94.6 2.1247 24 666.0 \n", + "102 0.22876 0.0 8.56 0 0.5200 6.405 85.4 2.7147 5 384.0 \n", + "\n", + " PTRATIO B \n", + "104 20.9 392.69 \n", + "203 14.7 392.78 \n", + "381 20.2 396.90 \n", + "489 20.1 344.05 \n", + "69 18.9 396.90 \n", + ".. ... ... \n", + "106 20.9 395.67 \n", + "270 18.6 388.65 \n", + "348 17.0 390.94 \n", + "435 20.2 109.85 \n", + "102 20.9 70.80 \n", + "\n", + "[430 rows x 19 columns]" + ], + "text/html": "
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TOWNTOWN_IDTRACTLONLATMEDVCMEDVCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOB
104Medford24.03395.0-71.069042.248020.120.10.139600.08.5600.52006.16790.02.42105384.020.9392.69
203Weston37.03671.0-71.199042.232048.548.50.0351095.02.6800.41617.85333.25.11804224.014.7392.78
381Boston_East_Boston79.0407.0-71.041042.229010.910.915.874400.018.1000.67106.54599.11.519224666.020.2396.90
489Chelsea89.01602.0-71.022842.23357.07.00.183370.027.7400.60905.41498.31.75544711.020.1344.05
69Wilmington16.03313.0-71.111042.327020.920.90.1281612.56.0700.40905.88533.06.49804345.018.9396.90
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106Medford24.03397.0-71.062242.243119.519.50.171200.08.5600.52005.83691.92.21105384.020.9395.67
270Dedham46.04022.0-71.087042.141021.121.10.2991620.06.9600.46405.85642.14.42903223.018.6388.65
348Norwell69.05041.0-70.920042.101624.524.50.0150180.02.0100.43506.63529.78.34404280.017.0390.94
435Boston_Savin_Hill83.0903.0-71.046042.186713.413.411.160400.018.1000.74006.62994.62.124724666.020.2109.85
102Medford24.03393.0-71.081242.251518.618.60.228760.08.5600.52006.40585.42.71475384.020.970.80
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430 rows × 19 columns

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" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Source X_train shape: (430, 19) | X_test shape: (76, 19)\n", + "##################################################\n", + "Auto detect cat features: 0\n", + "> Start preprocessing Data\n", + "> Generate cat encodet features\n", + " + 102 Features from OneHotEncoder\n", + " + 100 Features from HelmertEncoder\n", + " + 100 Features from HashingEncoder\n", + " + 3 Features from FrequencyEncoder\n", + "> Generate Frequency Encode num features\n", + " + 16 Frequency Encode Num Features \n", + "> Clean Nans in num features\n", + "> Generate interaction Num Features\n", + " + 480 Interaction Features\n", + "> Normalization Features\n", + "##################################################\n", + "> Total Features: 817\n", + "##################################################\n", + "New X_train shape: (430, 817) | X_test shape: (76, 817)\n" + ] + } + ], + "source": [ + "model = AutoMLRegressor(X_train, y_train, X_test, random_state=RANDOM_SEED, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "__________________________________________________\n", + "Step 1: Model 0\n", + "__________________________________________________\n", + "100%|██████████| 1/1 [00:53<00:00, 53.13s/it]\n", + "--------------------------------------------------\n", + "Model 1\n", + "One iteration takes ~ 7.6 sec\n", + "> Start Auto calibration parameters\n", + "\u001b[32m[I 2020-11-23 09:53:46,319]\u001b[0m A new study created in memory with name: no-name-8d331de2-19cd-4819-aaf2-353b151e5845\u001b[0m\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 5\n", + "Score_CV_Folds = 2\n", + "Feature_Selection = True\n", + "Opt_lvl = 1\n", + "Cold_start = 55.0\n", + "Early_stoping = 100\n", + "Metric = mean_squared_error\n", + "Direction = minimize\n", + "##################################################\n", + "Default model OptScore = 15.8712\n", + "Optimize: : 113it [14:08, 7.51s/it, | Model: LightGBM | OptScore: 11.4918 | Best mean_squared_error: 10.2078 +- 1.284002]\n", + "\n", + " Predict from Models_1\n", + "100%|██████████| 3/3 [00:21<00:00, 7.04s/it]\n", + " 0%| | 0/4 [00:00 Calc predict policy on Models_1:\n", + " | posible_repeats: 4 | stack_top: 4 | n_repeats: 1\n", + " 25%|██▌ | 1/4 [00:31<01:33, 31.17s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 10.9596 std: 2.033629\n", + " 50%|█████ | 2/4 [00:58<00:59, 29.92s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 10.4869 std: 2.909482\n", + " 75%|███████▌ | 3/4 [01:29<00:30, 30.43s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 10.4684 std: 2.321321\n", + "100%|██████████| 4/4 [02:28<00:00, 37.12s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 10.9694 std: 2.527378\n", + "\n", + " Models_1 Mean mean_squared_error Score Train: 10.29\n", + "--------------------------------------------------\n", + "Model 2\n", + "\n", + "One iteration takes ~ 8.3 sec\n", + "> Start Auto calibration parameters\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 5\n", + "Score_CV_Folds = 1\n", + "Feature_Selection = True\n", + "Opt_lvl = 1\n", + "Cold_start = 10\n", + "Early_stoping = 50\n", + "Metric = mean_squared_error\n", + "Direction = minimize\n", + "##################################################\n", + "Default model OptScore = 15.8712\n", + "Optimize: : 98it [03:21, 2.05s/it, | Model: MLP | OptScore: 11.7292 | Best mean_squared_error: 11.7292 ]\n", + "\n", + " Predict from Models_2\n", + " 50%|█████ | 1/2 [00:11<00:11, 11.43s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 11.0699 std: 1.158163\n", + "100%|██████████| 2/2 [00:21<00:00, 10.65s/it]\n", + " Mean Score mean_squared_error on 5 Folds: 11.796 std: 2.594957\n", + "\n", + " Models_2 Mean mean_squared_error Score Train: 10.8199\n", + "--------------------------------------------------\n", + "\n", + " Blend Models mean_squared_error Score Train: 9.4778\n", + "__________________________________________________\n", + "Step 2: Stacking\n", + "__________________________________________________\n", + "\n", + "\n", + " Final Model mean_squared_error Score Train: 9.4778\n" + ] + } + ], + "source": [ + "predict_test, predict_train = model.fit_predict(timeout=1500, verbose=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test MSE: 8.6016\n" + ] + } + ], + "source": [ + "print('Test MSE: ', round(sklearn.metrics.mean_squared_error(y_test, predict_test),4))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -245,9 +678,13 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.7.6 64-bit ('ds': conda)", - "language": "python", - "name": "python37664bitdscondaeb5aeb426ade4b82a5cf907714e87c5f" + "name": "python3", + "display_name": "Python 3.8.6 64-bit", + "metadata": { + "interpreter": { + "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462" + } + } }, "language_info": { "codemirror_mode": { @@ -259,7 +696,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7-final" + "version": "3.8.6-final" }, "toc": { "base_numbering": 1, diff --git a/examples/02_Models.ipynb b/examples/02_Models.ipynb index bda495f..1468cfe 100644 --- a/examples/02_Models.ipynb +++ b/examples/02_Models.ipynb @@ -17,7 +17,7 @@ }, "source": [ "# If you run this notebook on Google Colaboratory, uncomment the below to install automl_alex.\n", - "#!pip install -U automl-alex" + "#!pip install -U -q automl-alex" ], "execution_count": 1, "outputs": [] @@ -51,7 +51,9 @@ { "output_type": "stream", "name": "stdout", - "text": "0.07.26\n" + "text": [ + "0.11.24\n" + ] } ] }, @@ -122,7 +124,9 @@ { "output_type": "execute_result", "data": { - "text/plain": "((430, 13), (76, 13))" + "text/plain": [ + "((430, 13), (76, 13))" + ] }, "metadata": {}, "execution_count": 3 @@ -163,7 +167,18 @@ { "output_type": "execute_result", "data": { - "text/plain": "{'LightGBM': automl_alex.models.model_lightgbm.LightGBM,\n 'KNeighbors': automl_alex.models.sklearn_models.KNeighbors,\n 'LinearSVM': automl_alex.models.sklearn_models.LinearSVM,\n 'LinearModel': automl_alex.models.sklearn_models.LinearModel,\n 'SGD': automl_alex.models.sklearn_models.SGD,\n 'RandomForest': automl_alex.models.sklearn_models.RandomForest,\n 'ExtraTrees': automl_alex.models.sklearn_models.ExtraTrees,\n 'XGBoost': automl_alex.models.model_xgboost.XGBoost,\n 'CatBoost': automl_alex.models.model_catboost.CatBoost,\n 'MLP': automl_alex.models.sklearn_models.MLP}" + "text/plain": [ + "{'LightGBM': automl_alex.models.model_lightgbm.LightGBM,\n", + " 'KNeighbors': automl_alex.models.sklearn_models.KNeighbors,\n", + " 'LinearSVM': automl_alex.models.sklearn_models.LinearSVM,\n", + " 'LinearModel': automl_alex.models.sklearn_models.LinearModel,\n", + " 'SGD': automl_alex.models.sklearn_models.SGD,\n", + " 'RandomForest': automl_alex.models.sklearn_models.RandomForest,\n", + " 'ExtraTrees': automl_alex.models.sklearn_models.ExtraTrees,\n", + " 'XGBoost': automl_alex.models.model_xgboost.XGBoost,\n", + " 'CatBoost': automl_alex.models.model_catboost.CatBoost,\n", + " 'MLP': automl_alex.models.sklearn_models.MLP}" + ] }, "metadata": {}, "execution_count": 4 @@ -211,7 +226,13 @@ { "output_type": "execute_result", "data": { - "text/plain": "{'random_seed': 42,\n 'early_stopping_rounds': 50,\n 'num_iterations': 200,\n 'verbose': -1,\n 'device_type': 'cpu'}" + "text/plain": [ + "{'random_seed': 42,\n", + " 'early_stopping_rounds': 50,\n", + " 'num_iterations': 200,\n", + " 'verbose': -1,\n", + " 'device_type': 'cpu'}" + ] }, "metadata": {}, "execution_count": 6 @@ -231,12 +252,22 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
model_namepredict_testpredict_train
00_LightGBM[25.624219445377463, 32.65324971423799, 15.085...[20.21678999475246, 47.5198209009377, 9.199600...
\n" }, "metadata": {}, @@ -272,7 +303,9 @@ { "output_type": "stream", "name": "stdout", - "text": "Test MSE:6.5977882213630155\n" + "text": [ + "Test MSE: 6.5977882213630155\n" + ] } ] }, @@ -286,12 +319,17 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00 Start Auto calibration parameters\n> Start optimization with the parameters:\nCV_Folds =5\nScore_CV_Folds =2\nFeature_Selection =True\nOpt_lvl =2\nCold_start =38.0\nEarly_stoping =100\nMetric =mean_squared_error\nDirection =minimize\n##################################################\nDefault model OptScore = 9.0804\nOptimize: : 372it [07:16, 1.51s/it, | Model: LightGBM | OptScore: 7.0101 | Best mean_squared_error: 6.4673 +- 0.542798]\n EarlyStopping Exceeded: Best Score: 7.0101mean_squared_error\nOptimize: : 372it [07:16, 1.17s/it, | Model: LightGBM | OptScore: 7.0101 | Best mean_squared_error: 6.4673 +- 0.542798]\n" + "text": [ + "One iteration takes ~ 4.8 sec\n", + "> Start Auto calibration parameters\n", + "\u001b[32m[I 2020-11-23 10:54:45,950]\u001b[0m A new study created in memory with name: no-name-01162c7a-80c9-453c-b203-bcdaf5a74919\u001b[0m\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 5\n", + "Score_CV_Folds = 2\n", + "Feature_Selection = True\n", + "Opt_lvl = 1\n", + "Cold_start = 61.0\n", + "Early_stoping = 100\n", + "Metric = mean_squared_error\n", + "Direction = minimize\n", + "##################################################\n", + "Default model OptScore = 9.0804\n", + "Optimize: : 94it [10:01, 6.40s/it, | Model: LightGBM | OptScore: 8.1097 | Best mean_squared_error: 7.7077 +- 0.401974]\n" + ] } ] }, @@ -380,7 +434,13 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
2707.01016.46730.542798LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 4, 5, 7, 8, 11, 12, FrequencyEncoder...5
2557.01276.43680.575914LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 4, 5, 7, 8, 11, 12, FrequencyEncoder...5
2157.01276.43680.575914LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 4, 5, 7, 8, 11, 12, FrequencyEncoder...5
3297.01276.43680.575914LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 4, 5, 7, 8, 11, 12, FrequencyEncoder...5
2247.01276.43680.575914LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 4, 5, 7, 8, 11, 12, FrequencyEncoder...5
\n" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "67 8.1097 7.7077 0.401974 LightGBM \n", + "66 8.1570 8.1213 0.035725 LightGBM \n", + "61 8.1787 8.1121 0.066600 LightGBM \n", + "59 8.1805 7.7678 0.412739 LightGBM \n", + "1 8.3097 8.1299 0.179841 LightGBM \n", + "\n", + " model_param \\\n", + "67 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "66 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "61 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "59 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "1 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "\n", + " wrapper_params \\\n", + "67 {'early_stopping': False} \n", + "66 {'early_stopping': False} \n", + "61 {'early_stopping': False} \n", + "59 {'early_stopping': False} \n", + "1 {'early_stopping': False} \n", + "\n", + " cat_encoders \\\n", + "67 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "66 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "61 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "59 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "1 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "67 (0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode... 5 \n", + "66 (0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode... 5 \n", + "61 (0, 1, 3, 5, 7, 9, 11, 12, FrequencyEncoder_5,... 5 \n", + "59 (0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode... 5 \n", + "1 (1, 2, 3, 6, 8, 9, 12, FrequencyEncoder_2, Fre... 5 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
678.10977.70770.401974LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode...5
668.15708.12130.035725LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode...5
618.17878.11210.066600LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(0, 1, 3, 5, 7, 9, 11, 12, FrequencyEncoder_5,...5
598.18057.76780.412739LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(0, 1, 3, 5, 7, 9, 10, 11, 12, FrequencyEncode...5
18.30978.12990.179841LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(1, 2, 3, 6, 8, 9, 12, FrequencyEncoder_2, Fre...5
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" }, "metadata": {}, "execution_count": 12 @@ -439,8 +534,8 @@ "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": {} } @@ -492,7 +587,42 @@ { "output_type": "execute_result", "data": { - "text/plain": " checking_status duration credit_history \\\n0 <0 6.0 critical/other existing credit \n1 0<=X<200 48.0 existing paid \n2 no checking 12.0 critical/other existing credit \n3 <0 42.0 existing paid \n4 <0 24.0 delayed previously \n\n purpose credit_amount savings_status employment \\\n0 radio/tv 1169.0 no known savings >=7 \n1 radio/tv 5951.0 <100 1<=X<4 \n2 education 2096.0 <100 4<=X<7 \n3 furniture/equipment 7882.0 <100 4<=X<7 \n4 new car 4870.0 <100 1<=X<4 \n\n installment_commitment personal_status other_parties residence_since \\\n0 4.0 male single none 4.0 \n1 2.0 female div/dep/mar none 2.0 \n2 2.0 male single none 3.0 \n3 2.0 male single guarantor 4.0 \n4 3.0 male single none 4.0 \n\n property_magnitude age other_payment_plans housing existing_credits \\\n0 real estate 67.0 none own 2.0 \n1 real estate 22.0 none own 1.0 \n2 real estate 49.0 none own 1.0 \n3 life insurance 45.0 none for free 1.0 \n4 no known property 53.0 none for free 2.0 \n\n job num_dependents own_telephone foreign_worker \n0 skilled 1.0 yes yes \n1 skilled 1.0 none yes \n2 unskilled resident 2.0 none yes \n3 skilled 2.0 none yes \n4 skilled 2.0 none yes ", + "text/plain": [ + " checking_status duration credit_history \\\n", + "0 <0 6.0 critical/other existing credit \n", + "1 0<=X<200 48.0 existing paid \n", + "2 no checking 12.0 critical/other existing credit \n", + "3 <0 42.0 existing paid \n", + "4 <0 24.0 delayed previously \n", + "\n", + " purpose credit_amount savings_status employment \\\n", + "0 radio/tv 1169.0 no known savings >=7 \n", + "1 radio/tv 5951.0 <100 1<=X<4 \n", + "2 education 2096.0 <100 4<=X<7 \n", + "3 furniture/equipment 7882.0 <100 4<=X<7 \n", + "4 new car 4870.0 <100 1<=X<4 \n", + "\n", + " installment_commitment personal_status other_parties residence_since \\\n", + "0 4.0 male single none 4.0 \n", + "1 2.0 female div/dep/mar none 2.0 \n", + "2 2.0 male single none 3.0 \n", + "3 2.0 male single guarantor 4.0 \n", + "4 3.0 male single none 4.0 \n", + "\n", + " property_magnitude age other_payment_plans housing existing_credits \\\n", + "0 real estate 67.0 none own 2.0 \n", + "1 real estate 22.0 none own 1.0 \n", + "2 real estate 49.0 none own 1.0 \n", + "3 life insurance 45.0 none for free 1.0 \n", + "4 no known property 53.0 none for free 2.0 \n", + "\n", + " job num_dependents own_telephone foreign_worker \n", + "0 skilled 1.0 yes yes \n", + "1 skilled 1.0 none yes \n", + "2 unskilled resident 2.0 none yes \n", + "3 skilled 2.0 none yes \n", + "4 skilled 2.0 none yes " + ], "text/html": "
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0<06.0critical/other existing creditradio/tv1169.0no known savings>=74.0male singlenone4.0real estate67.0noneown2.0skilled1.0yesyes
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" }, "metadata": {}, @@ -527,7 +657,9 @@ { "output_type": "execute_result", "data": { - "text/plain": "((750, 20), (250, 20))" + "text/plain": [ + "((750, 20), (250, 20))" + ] }, "metadata": {}, "execution_count": 15 @@ -588,7 +720,12 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00 Start Auto calibration parameters\n> Start optimization with the parameters:\nCV_Folds =10\nScore_CV_Folds =3\nFeature_Selection =True\nOpt_lvl =2\nCold_start =54.0\nEarly_stoping =100\nMetric =roc_auc_score\nDirection =maximize\n##################################################\nDefault model OptScore = 0.7045\nOptimize: : 288it [03:19, 1.42it/s, | Model: LightGBM | OptScore: 0.7684 | Best roc_auc_score: 0.8062 +- 0.037773]\n EarlyStopping Exceeded: Best Score: 0.7684roc_auc_score\nOptimize: : 288it [03:19, 1.44it/s, | Model: LightGBM | OptScore: 0.7684 | Best roc_auc_score: 0.8062 +- 0.037773]\n" + "text": [ + "One iteration takes ~ 1.9 sec\n", + "> Start Auto calibration parameters\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 10\n", + "Score_CV_Folds = 3\n", + "Feature_Selection = True\n", + "Opt_lvl = 2\n", + "Cold_start = 34.0\n", + "Early_stoping = 100\n", + "Metric = roc_auc_score\n", + "Direction = maximize\n", + "##################################################\n", + "Default model OptScore = 0.6767\n", + "Optimize: : 198it [12:06, 4.56s/it, | Model: LightGBM | OptScore: 0.7638 | Best roc_auc_score: 0.7994 +- 0.035588]\n", + " EarlyStopping Exceeded: Best Score: 0.7638 roc_auc_score\n", + "Optimize: : 198it [12:06, 3.67s/it, | Model: LightGBM | OptScore: 0.7638 | Best roc_auc_score: 0.7994 +- 0.035588]\n" + ] } ] }, @@ -640,7 +794,12 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00", - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" }, "metadata": {} } @@ -747,7 +906,20 @@ { "output_type": "execute_result", "data": { - "text/plain": "{'random_seed': 42,\n 'early_stopping_rounds': 50,\n 'num_iterations': 100,\n 'verbose': -1,\n 'device_type': 'cpu',\n 'objective': 'binary',\n 'min_child_samples': 6,\n 'learning_rate': 0.02,\n 'num_leaves': 16,\n 'bagging_fraction': 0.4,\n 'feature_fraction': 0.7,\n 'bagging_freq': 3}" + "text/plain": [ + "{'random_seed': 42,\n", + " 'early_stopping_rounds': 50,\n", + " 'num_iterations': 300,\n", + " 'verbose': -1,\n", + " 'device_type': 'cpu',\n", + " 'objective': 'binary',\n", + " 'min_child_samples': 4,\n", + " 'learning_rate': 0.03,\n", + " 'num_leaves': 48,\n", + " 'bagging_fraction': 0.5,\n", + " 'feature_fraction': 0.5,\n", + " 'bagging_freq': 10}" + ] }, "metadata": {}, "execution_count": 21 @@ -793,7 +965,9 @@ { "output_type": "execute_result", "data": { - "text/plain": "{'early_stopping': False}" + "text/plain": [ + "{'early_stopping': False}" + ] }, "metadata": {}, "execution_count": 22 @@ -1008,7 +1182,12 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00 Start optimization with the parameters:\nCV_Folds = 10\nScore_CV_Folds = 3\nFeature_Selection = True\nOpt_lvl = 3\nCold_start = 50\nEarly_stoping = 100\nMetric = log_loss\nDirection = minimize\n##################################################\nDefault model OptScore = 0.9486\nOptimize: : 267it [09:37, 1.87s/it, | Model: LightGBM | OptScore: 0.5169 | Best log_loss: 0.4856 +- 0.031257]\n EarlyStopping Exceeded: Best Score: 0.5169 log_loss\nOptimize: : 267it [09:37, 2.16s/it, | Model: LightGBM | OptScore: 0.5169 | Best log_loss: 0.4856 +- 0.031257]\n" + "text": [ + "One iteration takes ~ 6.3 sec\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 10\n", + "Score_CV_Folds = 3\n", + "Feature_Selection = True\n", + "Opt_lvl = 3\n", + "Cold_start = 50\n", + "Early_stoping = 100\n", + "Metric = log_loss\n", + "Direction = minimize\n", + "##################################################\n", + "Default model OptScore = 0.7764\n", + "Optimize: : 105it [16:52, 9.65s/it, | Model: LightGBM | OptScore: 0.5382 | Best log_loss: 0.4922 +- 0.046016]\n" + ] } ] }, @@ -1075,12 +1268,17 @@ "predicts = model.predict()\n", "print('Test metric: ', round(sklearn.metrics.log_loss(y_test, predicts['predict_test'][0]),4))" ], - "execution_count": 44, + "execution_count": 34, "outputs": [ { "output_type": "stream", "name": "stderr", - "text": "100%|██████████| 1/1 [00:16<00:00, 16.19s/it]\n Mean Score log_loss on 30 Folds: 0.5131 std: 0.036688\nTest metric: 0.4798\n\n" + "text": [ + "100%|██████████| 1/1 [00:29<00:00, 29.80s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5071 std: 0.039572\n", + "Test metric: 0.4851\n", + "\n" + ] } ] }, @@ -1098,14 +1296,14 @@ "source": [ "model.plot_opt_history()" ], - "execution_count": 45, + "execution_count": 35, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": {} } @@ -1120,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1130,23 +1328,25 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "metadata": {}, - "execution_count": 26 + "execution_count": 37 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" 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onsl7773H0qVLgcJjvMeMGVPDvSsfpVJJr169yjwLpIjEpouKkvh0IcomSyF1QF5e5Q72+eqrrzA3Nyc1NbVYJsezbu/evdy7d6+muyGEEHWSzFhUkSlTpnD37l2ys7Px8fFh1KhR7Nq1i/Xr12NmZoadnR2GhobMnz+f5ORkAgMDuX37NlB44NNrr71W6XuXlkR65MgRjhw5QkZGBgUFBaxbt45PP/2UK1eukJeXh5+fHy4uLmUmkzo7O7N7926WLl3KjRs38PT0pGfPnnzyyScl+pCens6UKVNITU0lLy+P6dOn4+Liwq1bt5gwYQIODg6cP3+e9u3bM3z4cFauXElycjJhYWHY29tz//595syZw82bNzE2NiYoKAg7O7sS54AMHjyYL7/8EoD33nuP1157jfPnz2NjY8OaNWs4ceIEcXFx+Pv7Y2RkxI4dOzAyMqp0bYUQQlSMDCyqSEhICJaWlmRlZTFixAj69etHREQESqUSExMTxo4di52dHQDBwcGMHTsWR0dHbt++ja+vL4cOHarUfXNyckpNIoXC5NL9+/djaWnJsmXL6N69O6GhoaSmpuLl5UXPnj3Vv3gfTyZ91EcffcSVK1eKhbI9rn79+qxevRpTU1OSk5MZNWoU/fv3B+DGjRusWLGCkJAQRowYQVRUFNu2beP777/nyy+/ZM2aNYSHh9O2bVvWrFnDzz//zCeffFLm/QCuX7/OsmXLWLRoEdOnT+fw4cN4enqydetW9XKOENWhtFjr2hB3Xd2kRtrVhhrJwKKKbNmyhe+++w6AO3fusG/fPrp06YKlpSVQmDx67do1AKKjo/njjz/Ur3348CHp6emYmJhU+L5Xr14tNYkUoFevXur7nzp1imPHjvHPf/4TgOzsbO7cuaMxmbSiVCoVy5Yt4+zZs+jr65OUlMRff/0FFMbOF7XbqlUrevTogZ6eHq+88gqJiYlAYXJqeHg4AD169OD+/fs8fFj2SnazZs149dVXAWjXrp26LSGqW2nnDOjK+QM1SWqkna7USM6xqGYxMTFER0ezY8cOjI2N8fb2xtbWloSEhFKvLygoYOfOndSvX19r276+vvz111+0b9+e4ODgCvXr8fTQlStXYmtrW6E2yisqKork5GSUSiUGBgY4OzuTnZ0NFE9Q1dfXV3+tp6enNU1VoVBQUFCg/rqozcfbVSgUxZ4TQghRM2RgUQXS0tKwsLDA2NiYhIQELly4gJeXF2fPnuXBgweYmJhw5MgR2rRpAxSGZW3ZsoUJEyYAEB8fr/7L+3EbNmwo896akkgf5+TkxL/+9S/mzZuHnp4ely5dom3bthqTSR9lYmKiDgArqwYNGzbEwMCA06dPV3j2wNHRkf379zN16lRiYmKwsrLC1NSUF154gRMnTgCFR3nfunVLa1vl6W8RSTcVFSUpp0KUTQYWVaBPnz5s376dgQMH0qJFCxwcHLCxsWHixIl4eXlhYWGBra0tZmaFU0dz584lKCgIDw8P8vPzcXR0JCgoqFL31pRE+rgpU6YQEhLCm2++SUFBAc2aNWPt2rUak0kfZWVlRefOnRk8eDC9e/cudfOmh4cHkydPxsPDg/bt21d4ZsTPz485c+bg4eGBsbExn332GQADBgxg3759DBo0CHt7e15++WWtbQ0dOpTAwMByb96UdNOy6crUrBDi2SBZIdWoaN9E0acwhg8fjqura013SzxCskK0k4FF+UidtJMaaacrNZI9FjVk1apVREdHk52djZOTEy4uLjXdJSGEEKJaycCiGpW2ZKDJnj172Lx5c7HHOnfuXCyga+rUqSX2GPj7+9O7d+8n62gFXL58mVmzZhV7zNDQkF27dj21PgghhHh2yVKIqNNkKUQ7XZmarWlSJ+2kRtrpSo3KWgqRI72FEEIIUWVkKUTUeZLcqZ3USDNJOxWiOBlYiDpN0k3Fk5K0UyGK07oU0qlTp2JfK5VKrWculOeaylIqlSQlJam/9vb2ZsCAAXh6euLp6cm0adOq5b4At27dYvDgwdXWfkBAAN9++221tTN37txiR4lXl6KQsKq6rrxUKhWLFi3C1dUVDw8Pfv311yptXwghhHY6NWORn5/P3r17ad26NTY2NurHw8LCnsnAqby8POrVe3ZKXNEjwStr7dq1TJo0qcquK68ffviBa9euceTIEX755RcWLFggn1YRQoin7Il+65Un/jsgIID69esTHx/P33//TUhICJGRkVy4cIGOHTuqT1g8deoU4eHh5OTk0Lx5c0JDQzExMcHZ2ZmBAwcSHR3NuHHjSkRiaxIQEICpqSlxcXH8+eeffPzxx7i5uQGwbt06oqKi0NPTo0+fPvj7+xMfH09gYCCZmZm8+OKLhISEYGFhQVxcHHPmzAEKQ72K5OfnExYWxpkzZ8jJyWHMmDG89dZbxMTEsGLFCszNzbl69SqHDx8utX+RkZFs2LBBHcb1+eefA3Du3Dk2bdpUos/r16/n0KFD5OTk4Orqqp6Z0dROkS+++IK7d+8SHBzMuHHj1KmfnTp1wsfHh+PHj2NkZMSaNWt47rnnuHHjBv7+/mRmZuLs7MzmzZs5f/58qe/h3r17zJw5k4cPH5Kfn8+CBQs4ceIEWVlZeHp60qpVK5YuXVpqpHxYWFix62bOnMmkSZM4cOAAUHiUeUZGBh988AGbN29m+/btKBQKWrVqxfLly0vtz/fff8+QIUPQ09PDwcGB1NRU7t27R6NGjTT+nAhRFSwtG9SKVMrqJjXSrjbUSOvAouj//Is8ePAAZ2dnoPzx36mpqezYsYPvv/+eyZMns23bNlq3bs2IESOIj4/HxsaGiIgINm7cSIMGDVi3bh0bN27Ez88PAEtLS/bu3QvA7t27S0RiFw00AHr27Kk+P+LevXv83//9H//973+ZPHkybm5unDx5kmPHjrFz506MjY25f/8+ALNmzWLevHl07dqVFStWsGrVKubOncvs2bOZP38+Xbp0YfHixep77t69GzMzM/bs2UNOTg5vvfWWeuBx6dIloqKiaN68eak1vXLlChEREWzbtg1ra2t1HzT1+dSpU1y/fp3du3ejUqmYPHkyZ8+exdLSUmM7AIsXLyY9PZ3Q0FD09PSKPZeRkUHHjh2ZOXMmS5YsYefOnUyZMoXg4GB8fHwYPHgw27ZtK/2H4v87cOAATk5OTJ48mfz8fDIzM3F0dGTr1q3FIs8fj5R/44038Pf3L3ZdWRkg69at49ixYxgaGpKamqrxuqSkJBo3bqz+unHjxiQlJcnAQlS7+/czdOZjgjVJaqSdrtToiU7eNDIyKvZLQqlUEhcXB2iO/37c66+/rv6L+rnnnisWoZ2YmMjdu3f5448/GD16NAC5ubk4ODioX+/u7l5mHzUthbi4uKCvr0+rVq3UEd4///wzw4YNUyd/WlpakpaWRlpaGl27dgUKsyamT59OamoqaWlpdOnSBQBPT09+/PFHAH766ScuX76snpFIS0vj+vXrGBgY0KFDB42DCoDTp0/j5uaGtbW1ug9l9fmnn37ip59+YsiQIUDhoODatWtkZWVpbGfNmjV07NiRTz/9tNQ+GBgY8PrrrwPQvn17fvrpJwAuXLjA6tWrgcL8jyVLlmh8Hx06dGDOnDnk5eXh4uKiMUjt8Uj569evY2VlpbHdx73yyiv4+/vTv39/Ob1UCCGecU+0FFLe+O9HY7Ifj9DOy8tDX1+fXr16sWzZslJf/3j8d3k9eq+qplKp+Mc//lHi1MuYmBgaNKj8NFZpfVapVLz//vu89dZbxR7fsmWLxnY6dOjAr7/+yv3794sNOIoYGBioZzH09fW1xpeXpkuXLvzrX//i5MmTBAQE8O6776oHP0VKi5QvLd68Xr16GuPR161bx9mzZzl+/DhffvklUVFRpe5dsbGx4e7du+qv7969W2wvjhBCiOr3RAOLisR/l8XBwYGgoCCuX7/OSy+9REZGBklJSbRo0aLEtRWJxC5Nz549WbNmjTpFs+gXr7m5OefOncPR0ZF9+/bRpUsXzM3NMTMzUz8eFRVV7L1v27aN7t27Y2BgwNWrV8v9S6x79+74+fkxbtw4rKysNP7yf/ReK1aswMPDAxMTE5KSkqhXr16Z7fTu3RsnJycmTpzIhg0bMDU1LVffOnbsyJEjR3B3d+fgwbI/hpmYmEjjxo0ZOXIkOTk5/PrrrwwZMoR69eqRm5uLgYFBqZHyRR69rmHDhvz999+kpKRgYmLCiRMn6N27NwUFBdy5c4fu3bvz2muvcfDgQTIyMjA3Ny/RH2dnZ/71r38xaNAgfvnlF8zMzLQug0hsunhSEqMuRHFPNLCoqvhva2trQkND+fDDD8nJyQFgxowZpQ4sHo/EhuJ7LKysrNi0aZPGe/Xp04fffvuN4cOHY2BgQN++ffnwww9ZvHixevNm0eZRgNDQUObMmYOenl6xzZteXl4kJiYybNgwVCoVVlZWrFmzplzvt3Xr1kyaNAlvb2/09fVp27atehNraZycnEhISFDPWDRo0IDPP/9cazsDBw4kPT2dyZMn89VXX5Wrb3PmzOHjjz8mIiKC3r17lzkgOXPmDBs2bKBevXo0aNBAvQdl5MiRvPnmm7Rt25bQ0NASkfJFHr1u6dKlTJ06FS8vL2xsbNSx6/n5+Xz88cc8fPgQlUqFj49PqYMKgL59+3Ly5ElcXV0xNjYmJCSkXO9ZYtPLpitrvkKIZ4NkhYhiMjMzMTIyQk9Pj4MHD3LgwAEiIiJqulvVRrJCtJOBRflInbSTGmmnKzWS2HRRbr/++itBQUGoVCrMzc3L/Ve/EEIIATKwqFYpKSmMGzeuxOObNm2q0KciniZHR0f2799f7LFnLSq9PBHzQgghaoYshYg6TZZCtNOVqdmaJnXSTmqkna7USGLThRBCCPFUyFKIqPMkElw7qVH5VKZOErsuahsZWIgqEx4eToMGDfD19X3itlJTU4mKimLMmDFA4XHdwcHBrFy58onbfpTEpouaJrHroraRpRBRQkBAADExMdV+n7w8zQcLpaamFssqsbGxqfJBhRBCiKonMxY6prSk0F27drF+/XrMzMyws7PD0NCQ+fPnlyt99klFREQQGRmJtbU1TZo0oV27dgB4e3urw+KSk5MZMWIEx44dQ6lUcuTIETIyMigoKGDt2rVMmTKF1NRU8vLymD59Oi4uLixdupQbN27g6elJz549GTNmjDr9NDs7mwULFhAXF4dCoSAgIIDu3bujVCo5duwYmZmZ3Lx5ExcXlxKfZhFCCFG9ZGChYx5PCu3Xrx8REREolUpMTEwYO3YsdnZ2QPnTZysrLi6Ob775hsjISPLz8xk6dKh6YFGWS5cusX//fiwtLcnLy2P16tWYmpqSnJzMqFGj6N+/Px999BFXrlwpNf1069atAERFRZGQkICvr686DC4+Pp7IyEgMDQ1xc3PD29ubJk2aVNl7FqI66HpMdnnVhkjw6lYbaiQDCx3zeFJoUa5JUUaIm5sb165dAzSnz5qYmJRo98cffyQsLEzdbmxsLA0aNCjzvIpz587h4uKiDolzdnYu13vo1auXur8qlYply5Zx9uxZ9PX1SUpKUqe6ahIbG8s777wDQMuWLWnatClXr14FoEePHpiZmamfS0xMlIGFeObpwscLq4KufJSyJulKjeTkzVqitKRQW1tbEhISSr2+vOmzUBhaVpTUGhAQwNChQ+nWrVul+6pQKCg6IqUo/6XIo2m1UVFRJCcno1QqMTAwwNnZudT00/J6NB1WoVBUKrVVCCFE5cnAQoeUlhTq5eXF2bNnefDgASYmJhw5coQ2bdoAVZc+q0mXLl0ICAhg4sSJ5OXlcfz4cUaNGgXACy+8QFxcHPb29nz77bdlvqeGDRtiYGDA6dOnSUxMBMpOsS1Kmu3RowdXr17lzp072NracunSpQq/B0k3FTVN0lFFbSMDCx3Sp0+fEkmhNjY2TJw4ES8vLywsLLC1tVUvBVRV+qwm7dq1w93dHU9PT6ytrenQoYP6ufHjxzNjxgx27txJ3759Nbbh4eHB5MmT8fDwoH379upUUysrKzp37szgwYPp3bu3+mOnAG+//TYLFizAw8MDhUJBaGhosZmKipJ007LpytRsTZM6CVFIjvSuBYr2TeTl5eHn58fw4cNxdXWt6W7pBDnSWzv5hVk+UiftpEba6UqNZI9FLbdq1Sqio6PJzs7GyckJFxeXmu6SEEKIOkoGFrXAJ598Uu5rK5MMqosprUIIIWqGLIWIOk2WQrTTlanZmiZ10k5qpJ2u1EjSTYUQQgjxVMhSiKjzJLlTO6lR+VR1nST5VOgiGViIOk3STcWzTJJPhS6SpRAhhBBCVBmZsRDPtGctzVUIIUTZZGAhnmnPUpqrEDVB15MuH1UbkjurW22okQwsxDOtutJchdAVuvDRw/LSlY9S1iRdqZGcvCl0UnWmuQohhKgesnlTPLNKS3PNyMhQp7nm5eVx5MgR9fVFaa5F4uPja6LbQghRp8nJm+KZlZOTw5QpU0hMTKRFixakpaXh5+fHtWvX2LBhgzrNtXHjxsycOZPk5GSCgoJISEgod5prQYEKfX29p/SOhKiY2naOha5M89ckXalRWUshMrAQOqcq01zlSG/tdOX/6Gqa1Ek7qZF2ulIj2WMhahVJcxVCiGeXDCyEzqlImqsQQoinSzZvCiGEEKLKyMBCCCGEEFVGBhZCCCGEqDKyx0LUeRIJrp3UqHx0tU617WOtomZVaGBx9+5dFi5cSEJCAgUFBfTr149Zs2ZhaGhY4Rt7e3sza9YsOnToAMCtW7eYNGkSBw4c0Pia+Ph47t27R9++fQFQKpUsWbIEGxsb9TVLly6lVatWFe5PZfpclWJiYvjnP//J2rVrq6Wd77//noSEBN5///0nal+bo0eP8vLLL2v9HpT3uoqYPXs2J06coGHDhmX+HD1KYtOFkHh2UbXKvRSiUqnw8/PDxcWFI0eOcPjwYTIyMli+fHl19q+Y+Ph4Tp48Wewxd3d39u3bp/5fdQ0qKiM/P7+mu6DWv3//ah9UQOGA4dG8jie9riKGDRvG+vXrq7RNIYQQFVPuGYvTp09Tv359hg8fDoBCoWDOnDn079+fZs2a8fPPP5OZmcnNmzdxcXFh1qxZAJw6dYrw8HBycnJo3rw5oaGhWkOhsrOzWbBgAXFxcSgUCgICAujcuTMrV64kKyuL2NhYJk6cqPH1MTExrFq1CisrK37//XfatWtHWFgYenp6XLx4kZCQEDIyMjA0NGTTpk0YGBiUuF/37t3Jyspi9uzZ/Pbbb9ja2pKVlaW+h6b35ezszMCBA4mOjmbChAkMGjSoRP+uX79OYGAgycnJKBQKVqxYAUBGRgbTpk0r0ee4uDg+++wzMjIysLKyIjQ0lEaNGmlsp8jFixeZP38+K1eu5Ny5c8TFxTF//nwCAgIwNTUlLi6OP//8k48//hg3NzcKCgoICgri9OnTNGnShHr16jF8+HDc3NxKrXNYWBjHjh1DoVDg5OSEq6srx44d48yZM0RERBAeHs7p06fZsWMHubm5vPTSSyxZsoT4+PgS182dO1c9G5ScnMyIESM4duwYV65cYfbs2eTm5lJQUEB4eDgvv/xyqf3p0qULt27dKvNnSwghRPUq98DiypUrtGvXrthjpqamNGnShPz8fOLj44mMjMTQ0BA3Nze8vb2pX78+ERERbNy4kQYNGrBu3To2btyIn58fAP7+/hgZGQGQm5uLvn7hBMrWrVsBiIqKIiEhAV9fXw4fPsy0adPUvxyhcCnkm2++ITY2Vt2nHTt2AHDp0iUOHjxIo0aNGD16NLGxsdjb2zNz5kyWL1+Ovb09Dx8+xMjIiM2bN5d6v23btmFkZMShQ4f47bffGDZsGADJycllvi9LS0v27t2rsZb+/v68//77uLq6kp2dTUFBAXfu3Cm1zx07dmTRokWsWbMGa2trvvnmG5YvX05oaKjGdgD+/e9/q1/XtGlTzp07V6wP9+7d4//+7//473//y+TJk3Fzc+PIkSMkJibyzTff8Pfff+Pu7q4eSD4uJSWF7777jm+//RY9PT1SU1MxNzfH2dmZfv36qQcjZmZmjBw5EoDly5eze/duvL29S1ynyfbt2/Hx8eHNN98kJyeHgoKCMq8XQlTO04jqrg2R4NWtNtSoyjZv9ujRAzOzwo1LLVu2JDExkbS0NP744w9Gjx4NFA4eHBwc1K8JCwsrsccCIDY2lnfeeUfdVtOmTbl69Wqp93V3d1cPNB5lb29P48aNAbCzsyMxMREzMzOef/557O3tgcKBUVn3O3v2LN7e3uo2XnnlFQB++eWXMt+Xu7u7xjo9fPiQpKQk9RHUjyZxltZnc3Nzfv/9d959912gMMHz+eefL7OdhIQE5s+fz4YNG4rtP3mUi4sL+vr6tGrVir/++ktdBzc3N/T19Xn++efp1q2bxvdhZmZG/fr1mTNnDq+//jr9+vUr9borV67wxRdfkJaWRnp6Ok5OThrbLI2DgwNffvkld+/e5Y033tA4WyGEeDJP4xhpXTmuuibpSo2q5EjvVq1acfjw4WKPPXz4kDt37qBQKIpt4FQoFOTn56NSqejVqxfLli2rRLefTGn9qSra3pexsXGl2tVUw9atW6tnYoo8fKh5q9Xzzz9PdnY28fHxGgcWldlw+6h69eqxe/dufv75Z7799lv+9a9/qWd+HhUQEMCaNWuws7NDqVRy5syZUttTKBQUxdbk5OSoH/fw8KBjx46cOHGC999/n4ULF9KjR48n6rsQQojqU+6BRY8ePQgLCyMyMpIhQ4aQn5/PZ599xtChQzX+InVwcCAoKIjr16/z0ksvkZGRQVJSEi1atCjzXo6OjkRFRdGjRw+uXr3KnTt3sLW15fr166Snp1fsHT6iRYsW/Pnnn1y8eLHYUoim+3Xp0oUDBw7Qo0cPfv/9dy5fvvxE7wsKZ0kaN27M0aNHcXFxIScnp8xBT4sWLUhOTub8+fN06tSJ3Nxcrl27RuvWrTW2Y25uTnBwMO+++y7GxsZlzjw8qnPnzkRGRjJ06FCSk5M5c+YMgwcPLvXa9PR0srKy6Nu3L507d1bndZiYmBT7HqWnp/P888+Tm5tLVFSUeqDz+HUvvPACcXFx2Nvb8+2336ofv3nzJs2bN8fHx4c7d+5w+fLlKh1YFBSouPZZyX0wQtQlmdl5Nd0FUYuUe2Chp6fH6tWrWbhwIWvWrKGgoIC+ffvy4Ycfavxon7W1NaGhoXz44Yfqv0JnzJih9Rfw22+/zYIFC/Dw8EChUBAaGoqhoSHdunVj3bp1eHp6qjdvPr7HIjAwUGO7hoaGLF++nEWLFpGVlYWRkREbN27UeL/Ro0cze/ZsBg4cSMuWLdV7TCr7voosWbKE+fPns2LFCgwMDEpsuny8zytXrmTRokWkpaWRn5/P2LFjad26dZntPPfcc6xdu5b33nuPkJCQcvVrwIAB/Pzzz7i7u9OkSRPatm2rXt56XHp6OlOmTCE7OxsonJmAwmWgefPmsWXLFlauXMn06dPx8vLC2tqajh07qgcTj183fvx4ZsyYwc6dO9UfJwY4dOgQ+/bto169ejz33HNlbtr98MMPOXPmDCkpKfTp04cPPvgALy8vre/7zz/TylWfukpXpmZrmtRJiEISmy6KKYokT0lJwcvLi23btvH888/XdLeqjcSmaye/MMtH6qSd1Eg7XamRxKaLcps0aRKpqank5uYyZcqUWj2oEEIIUfVkYFGNFi5cyL///e9ij/n4+Gj8COezYMuWLSUemzp1aonzIfz9/endu/fT6pZaSkoK48aNK/H4pk2bsLKyeur9EUIIUZwshYg6TZZCtNOVqdmaJnXSTmqkna7UqKylEEk3FUIIIUSVkaUQUefpaiLl0yQ1Kh9drpMknIqqIgMLUadJuqkQhSThVFQVWQoRJahUKm7duoVSqazQ65ydnUlOTq6SPsTExBTb+Lpt2zYiIyOrpG0hhBDVRwYWooTAwEBiY2O5ffs2c+bMISkpqVruk5en+bS/M2fOcP78efXXo0ePZsiQIdXSDyGEEFVHlkJ02JQpU7h79y7Z2dn4+PgwatQodu3axfr16zEzM8POzg5DQ0Pmz59PcnIygYGB3L59G4A5c+bw2muvldpuYGAgkydP5o8//mDXrl00bNiw1OtSUlL46KOPSEpKwsHBQZ31URQoV3Qi64YNG8jIyOCDDz7A29sbOzs7YmNjGTx4MC+//DIRERHk5uZiaWlJWFgYWVlZbN++HX19ffbv38+8efP4+eefadCgAb6+vsTHxxMYGEhmZiYvvvgiISEhWFhY4O3tjb29PTExMaSlpREcHIyjo2M1VF6I2qm6UzVrQ3JndasNNZKBhQ4LCQnB0tKSrKwsRowYQb9+/YiIiECpVGJiYsLYsWOxs7MDIDg4mLFjx+Lo6Mjt27fx9fXl0KFDpbYbFBTEoEGDuHnzJsuXL+eDDz4oNcxs9erVdO7cGT8/P06cOMHu3bvL1e/c3Fz1MsuDBw/YuXMnenp66kFRQEAAb731lnogAfDzzz+rXz9r1izmzZtH165dWbFiBatWrWLu3LkA5Ofns3v3bk6ePMmqVavYtGlTuespRF1X3R9z1JWPUtYkXamRnLxZS23ZsoXvvvsOgDt37rBv3z66dOmCpaUlAG5ubly7dg2A6Oho/vjjD/VrHz58qD6++3ELFiwgMTGR/Px8/Pz8NN7/7NmzrFq1CoB+/fphYWFRrn4/Git/9+5dZs6cyZ9//klOTg7NmjUr87VpaWmkpaXRtWtXAIYOHcr06dPVzxfFyLdr147ExMRy9UcIIUTVkYGFjoqJiSE6OpodO3ZgbGyMt7c3tra2JCQklHp9QUEBO3fupH79+lrb1tPTo1mzZlp/yWtSr149CgoK1F8XBZUVeTQNd9GiRYwbN47+/fsTExOjHqhUVlEcvL6+fpmpsUIIIaqHDCx0VFpaGhYWFhgbG5OQkMCFCxfw8vLi7NmzPHjwABMTE44cOUKbNm0AcHJyYsuWLUyYMAGA+Ph4Xn311SfqQ5cuXYiKimLKlCmcPHmSBw8eANCwYUP+/vtvUlJSMDEx4cSJExqP/05LS1Mvszz6qQ8TExMePiz54TczMzPMzc05d+4cjo6O6lmaypLYdCEKSXS6qCoysNBRffr0Yfv27QwcOJAWLVrg4OCAjY0NEydOxMvLCwsLC2xtbdWx53PnziUoKAgPDw/y8/NxdHQkKCjoifowdepUPvroIwYNGkSnTp1o2rQpAAYGBkydOhUvLy9sbGywtbXV2Iafnx/Tp0/HwsKCbt26qTNJXn/9daZNm8b333/PvHnzir1m8eLF6s2bzZs3JzQ09Ineh8Sml01X1nxrmtRJiEKSFVLLFO2byMvLw8/Pj+HDh6v3HYiSJCtEO/mFWT5SJ+2kRtrpSo1k82YdsmrVKqKjo8nOzsbJyQkXF5ea7pIQQog6RAYWtcwnn3xS7mv37NnD5s2biz3WuXNnAgMDK3WdEEIIIUshok6TpRDtdGVqtqZJnbSTGmmnKzWS2HQhhBBCPBWyFCLqPF2Oun5apEblU9frJNHrAmRgIapITEwM//znP1m7dm2VtLdp0yZGjRqlPkzrvffeY+nSpZibm1dJ+0UkNl2IqiPR6wJkKUTUEJVKVex0zsdt3ryZzMz//eXz1VdfVfmgQgghRNWTGQsdV10Jp+Xxww8/EBISgrGxcbF2wsPDiwWIDR48mC+//BIAX19fOnbsyK+//sq6detYt24d//nPf8jOzmbAgAFMmzaNzZs3c+/ePcaOHYulpSVbtmzB2dmZ3bt3Y21tzcaNG9mzZw8AI0aMYNy4cdy6dYv33nuP1157jfPnz2NjY8OaNWswMjKq9PsTQghRcTKw0HHVlXCqTXZ2NvPmzePrr7/mpZdeYsaMGeV63fXr11m8eDEODg4AzJw5E0tLS/Lz8xk3bhy//fYbPj4+bNq0ia+//hpra+tir4+Li0OpVLJz505UKhUjR46ka9eumJubc/36dZYtW8aiRYuYPn06hw8fxtPTs1LvTwhROWVFfteGSPDqVhtqJAMLHVddCafa/Pe//6VZs2a8/PLLALz55pvs3LlT6+uaNm2qHlQAHDp0iJ07d5KXl8eff/5JQkKCeiBUmtjYWFxcXGjQoPAfnqurK+fOncPZ2ZlmzZqp808k3VSImlHWRyV15aOUNUlXaiQnb9ZS1Zlw6uvry19//UX79u0JDg6uUL8UCoXGdNOiAQHAzZs3+ec//8nu3buxsLAgICCgRBJqRRQlmxb14UnaEkIIUTkysNBh1ZlwumHDhjLvbWtrS2JiIjdu3ODFF1/k4MH/fbLihRde4MSJEwD8+uuv6mCxx6Wnp2NsbIyZmRl//fUXP/zwA127dgUK003T09NLLIU4OjoSEBDA+++/j0ql4ujRoyxZskR7sTSQdFMhqo4kpAqQgYVOq8mE0/r16xMUFMT777+v3ryZnp4OwIABA9i3bx+DBg3C3t5evVzyODs7O9q2bcvAgQNp3LgxnTt3Vj83cuRIJkyYQKNGjdiyZYv68Xbt2jFs2DC8vLyAws2bbdu21Th4KQ9JNy2brkzN1jSpkxCF5EjvWkgSTstPjvTWTn5hlo/USTupkXa6UiPZY1HHSMKpEEKImiIDi1qoqhNOp06dWmKpwd/fn969ez9ZR4UQQtQ6shQi6jRZCtFOV6Zma5rUSTupkXa6UiNJNxVCCCHEUyEzFqJOKyhQoa+vV9PdEKJOkjTUkmrDjIXssRB1mqSbClFzJA21dpKlEFFCp06dAEhKSmLatGlA4WFaJ0+erMluVUhR6JkQQoinSwYWdUReXsVPxLOxsWHlypWA7g0s1q5dW9NdEEKIOkmWQmqRyMhINmzYgJ6eHq+88goKhQJDQ0Pi4+Pp3LkzY8aMYeHChaSkpGBkZMSnn35Ky5YtuXnzJv7+/mRkZODs7Kxu79atW0yaNAmlUsnKlSvJysoiNjaWiRMn4u7uXuL+Fy9eJDg4mOzsbIyMjAgJCcHW1halUsnRo0fJzMzk+vXrjB8/ntzcXPbt24ehoSHr1q3D0tKS+Ph4AgMDyczM5MUXXyQkJAQLCwu8vb2ZNWsWHTp0IDk5mREjRnDs2DGUSiXHjh0jMzOTmzdv4uLiwqxZswgLCyMrKwtPT09atWrF0qVLn+a3QQhRAbqe5FnVJN1UPDOuXLlCREQE27Ztw9ramvv37/PZZ5+RlJTE9u3bUSgUjB07loULF/Lyyy/zyy+/sHDhQjZv3kxwcDCjR49myJAhbN26tUTbhoaGTJs2jbi4OObPn6+xD7a2tmzdupV69eoRHR3N8uXLCQ8PV/dv79695OTk4Orqir+/P5GRkYSEhBAZGcm4ceOYNWsW8+bNo2vXrqxYsYJVq1Yxd+7cMt93fHw8kZGRGBoa4ubmhre3N/7+/mzdupV9+/Y9WVGFENVOFzYqPk2yeVM8M06fPo2bm5s6tOvR2HSFQkF6ejrnz59n+vTp6tfk5OQAcP78efUAwNPTk7CwsEr1IS0tjU8++YTr16+jp6dHbm6u+rlu3bphamoKgJmZmXpmpE2bNly+fJm0tDTS0tLUIWRDhw4t1ldNevTooc5CadmyJYmJiTRp0qRS/RdCCPHkZGBRyxkbGwOgUqkwNzfX+Fe8nt6Tf+RyxYoVdOvWjdWrV3Pr1i18fHzUzz0aaa6vr4+BgYH6v/Pz88tsV6FQUPSp6KLBUGntKhQKrW0JIYSoXjKwqCW6d++On58f48aNw8rKivv37xd73tTUlGbNmnHo0CEGDhyISqXi8uXL2NnZ0alTJw4ePIinpyf79+8vtf2iGPOypKWlYWNjA8DevXsr1H8zMzPMzc05d+4cjo6O7Nu3jy5dugCFMexxcXHY29vz7bfflqu9evXqkZubqx7AaCKx6ULUHIlZr51kYFFLtG7dmkmTJuHt7Y2+vj5t27Ytcc3nn3/OggULiIiIIC8vD3d3d+zs7Jg7dy7+/v6sX7++2ObNR3Xr1o1169bh6empcfPmhAkTCAgIICIigr59+1b4PSxevFi9ebN58+aEhoYCMH78eGbMmMHOnTvL3e7IkSN58803adu2rdbNmxKbXjZdWfOtaVIn7aRGdYOcvCnqNMkK0U5+GZSP1Ek7qZF2ulIjyQoRQgghxFMhSyGiwsoTtS6EEKJukqUQUafJUoh2ujI1W9OkTtpJjbTTlRrJUogQQgghngqZsRB1msSmC/FsqetR6rVhxkL2WFRAp06dOH/+PElJSQQHB7Ny5Uri4+O5d+9epT5eWRO+/PJLJk2apPF5lUqFnp4e4eHhfPDBB+qvtQkPD6dBgwb4+vo+cR9TU1OJiopizJgxAMXqXdUkNl2IZ4tEqeu+Or8UIqmfxe3fv5/169eTnZ3NV199pfHArCdVVt1TU1PZtm2b+utH6y2EEOLZVidmLCT1s/ypn56enhw4cIClS5cSFhbGoEGaT6WMiIggMjISa2trmjRpQrt27QDK7NeRI0fIyMigoKCAtWvXMmXKFFJTU8nLy2P69Om4uLiwdOlSbty4gaenJz179mTMmDFMmjSJAwcOkJ2dzYIFC4iLi0OhUBAQEED37t01vmchhBBPV60fWEjqZ8VSP6OiokhKSsLX15fbt28TFRWFh4dHievi4uL45ptviIyMJD8/n6FDh6oHFmW5dOkS+/fvx9LSkry8PFavXo2pqSnJycmMGjWK/v3789FHH3HlyhV1P2/duqV+fdH3ISoqioSEBHx9fTl8+LDG9yyBZELoHl2PDX8SEpuuAyT1s2Kpn4MHD1bvsXjvvffQtLf33LlzuLi4qEPONB0F/rhevXqpvwcqlYply5Zx9uxZ9PX1SUpK4q+//irz9bGxsbzzzjvq99W0aVOuXr36RO9ZCPFs0YXNi9VFNm/qMEn9LF3R+/3ggw+KfV0RZfWrqO5QOOuQnJyMUqnEwMAAZ2dnsrOzK3y/IpJ0KoQQNa/WDywk9bO48qZ+atOlSxcCAgKYOHEieXl5HD9+nFGjRlWoX2lpaTRs2BADAwNOnz5NYmIiUHZNHR0diYqKokePHly9epU7d+5ga2vLpUuXKvU+JN1UiGeLJJ7qvlo/sJDUz+IqkvpZlnbt2uHu7o6npyfW1tZ06NBB/Vx5++Xh4cHkyZPx8PCgffv22NraAmBlZUXnzp0ZPHgwvXv3Vn/sFODtt99mwYIFeHh4oFAoCA0NLTZTURmSblo2XZmarWlSJ+2kRnWDHJAl6jQ50ls7+WVQPlIn7aRG2ulKjeRIbyGEEEI8FbV+KeRp0qXUz5SUFMaNG1fi8U2bNmFlZVXh64QQQgiQpRBRx8lSiHa6MjVb06RO2kmNtNOVGslSiBBCCCGeCpmxEHWapJsKoZtqawpqbZixkD0Wok6TdFMhdJOkoD67ZClER+Tm5jJ06NCncq9OnToBhXHl06ZNA3QvxfXLL7+s6S4IIUSdJAMLHREbG0vnzp0r/XqJhxdCCPE0yFJIFZoyZQp3794lOzsbHx8fRo0axa5du1i/fj1mZmbY2dlhaGjI/PnzSU5OJjAwkNu3bwMwZ84cXnvtNY1t//jjj/Tp00fj849Hw3/++ecEBARIPLyGeHghhO7T9RTQ0ki6qSgmJCQES0tLsrKyGDFiBP369SMiIgKlUomJiQljx47Fzs4OgODgYMaOHYujoyO3b9/G19eXQ4cOaWw7JiYGPz+/Up8rLRq+iMTDCyFqK13Y5FhRsnlTFLNlyxa+++47AO7cuaMODHs0qv3atWsAREdH88cff6hf+/DhQ9LT0zExMSnRblJSEhYWFsWSQR+lKRq+6J4SDy+EEOJpkYFFFYmJiSE6OpodO3ZgbGyMt7c3tra2JCQklHp9QUEBO3fupH79+lrb/uGHH+jdu3el+iXx8EIIIZ4mGVhUkbS0NPWsQkJCAhcuXMDLy4uzZ8/y4MEDTExMOHLkCG3atAHAycmJLVu2MGHCBKBwSv/VV18tte0ff/yxzL/eS4uGf3TWAiQeXhOJTRdCN0m8+rNLBhZVpE+fPmzfvp2BAwfSokULHBwcsLGxYeLEiXh5eWFhYYGtra162n7u3LkEBQXh4eFBfn4+jo6OBAUFlWg3Pz+fGzdu0LJlS433Li0a/rPPPitxncTDl05i08umK2u+NU3qpJ3UqG6QkzerWdG+iby8PPz8/Bg+fDiurq7lfv25c+fYv39/qYMO8eQkK0Q7+WVQPlIn7aRG2ulKjWTzZg1atWoV0dHRZGdn4+TkhIuLS4Ve7+joiKOjYzX1TgghhKhaMrCoZp988km5r9UWu/6sRJjrUjy8EEKIp0uWQkSdJksh2unK1GxNkzppJzXSTldqJLHpQgghhHgqZMZC1GkSmy6E7qtNEeq1YcZC9lg8w1QqFYmJiZw5c4Zhw4Y9lXsqlUr10d3btm3D2NiYIUOGoFQq6dWrl/qcimdZfHw89+7dK9dHUyU2XQjdJxHqzxZZCnmGBQYGEhsby+3bt5kzZw5JSUmVbqsyJ1IW5YdA4YFX9+7dq/T9nyZdS2IVQojaRGYsqkB1pZoGBgYyefJk/vjjD3bt2kXDhg1LvS49PZ1FixYRFxcHgJ+fHwMGDKBTp06MGjWK6Oho5s+fT2JiIlu2bCE3N5eOHTsSGBiIQqFgz549rFu3rlhfAcLDw2nQoIH65Et/f3+MjIzYsWMHRkZGJfqxatUqjh8/TnZ2Np06dSIoKAg9PT28vb159dVXOXfuHJmZmSxevJh169bx+++/M3DgQGbOnAnAxo0b2bNnDwAjRoxg3Lhx6oTVAwcOALBhwwYyMjL44IMP8Pb2xt7enpiYGNLS0ggODsbe3r5cSaxCCCGqhwwsqkB1pZoGBQUxaNAgbt68yfLly/nggw9KXYpYs2YNpqamREVFAfDgwQMAMjIysLe3JyAggISEBNavX8+2bdswMDBgwYIFREVF0bNnT8LDw1EqlZiamuLj40Pbtm2Lte/m5sbWrVvV0eWavPPOO+oE1o8//pjjx4+rT/I0MDBAqVTy9ddfM2XKFJRKJZaWlri4uDBu3DgSExNRKpXs3LkTlUrFyJEj6dq1K+bm5mXWPj8/n927d3Py5ElWrVrFpk2bypXEKoSoXXQ9aryIxKYLoPpSTRcsWEBiYiL5+fkaI9MBfv75Z5YtW6b+2sLCAigM5RowYID6mri4OEaMGAFAVlYWDRs25OLFi3Tt2lWdjOru7q7ua0XFxMSwfv16srKyuH//Pq1bt1YPLB5NM23dujWNGjUCoHnz5ty9e5fY2FhcXFxo0KDwH5Srqyvnzp3TeMR4kaJTTNu1a0diYmKl+i2E0H26sOGxPGTzpqjWVFM9PT2aNWtGs2bNKtW3+vXro1AogMKNoEOHDuWjjz4qds3Ro0cr1fbjsrOzWbhwIXv27KFJkyaEh4eTnZ2tfr5oeUVfX79E0mlenuYwoXr16lFQUFDsPo96tF1JNhVCiJonA4snVJ2ppuXVs2dPtm7dyty5c4HCpZCiWYsiPXr0YMqUKYwbN46GDRty//590tPTsbe3Jzg4mJSUFExNTfn222/VyzaP0pZuWvQL38rKivT0dA4fPqyeLSkPR0dHAgICeP/991GpVBw9epQlS5bQsGFD/v77b1JSUjAxMeHEiRNaI+TLk8RaRNJNhdB9knT6bJGBxROqrlTTipg8eTJBQUEMHjwYfX19/Pz8eOONN4pd06pVK2bMmMH48eMpKCjAwMCA+fPn4+DggJ+fH2+99RZmZmYaBzlDhw4lMDBQ4+ZNc3NzvLy8GDx4MM8991yZezFK065dO4YNG4aXlxdQuHmzaK/H1KlT8fLywsbGBltbW61tlSeJ9VGSblo2XZmarWlSJ+2kRnWDHJBVTZ401VQ8HXKkt3byy6B8pE7aSY2005UayR6LGvCkqaZCCCGELpKBRTWpylTTil5X3aZOncqtW7eKPebv769174MQQojaT5ZCRJ0mSyHa6crUbE2TOmknNdJOV2ok6aZCCCGEeCpkxkLUaZJuKoSoTZ5W0qts3hRCA0k3FULUJs9C0qsshVRCp06dAEhKSmLatGmA7iVqfvnll+W6bv78+cTGxmq97tatWwwePPhJu6WmVCqLpbnOnTu32FHoQgghnk0ysPj/yjpWWhMbGxtWrlwJ6N7AYu3ateW67pdffsHBwaFa+lDWEdyPx7QHBwfTqlWraumHEEKIqlOnlkIiIyPZsGEDenp6vPLKKygUCgwNDYmPj6dz586MGTOGhQsXkpKSgpGREZ9++iktW7bk5s2b+Pv7k5GRUSwUqyjSW6lUliuq++LFiwQHB5OdnY2RkREhISHY2tqiVCo5evQomZmZXL9+nfHjx5Obm8u+ffswNDRk3bp1WFpaEh8fT2BgIJmZmbz44ouEhIRgYWGBt7e3Onk0OTmZESNGcOzYMZRKJceOHSMzM5ObN2/i4uLCrFmzCAsLIysrC09PT1q1asXSpUtLrVdCQgIvv/yyOm/kcXFxccyZMweAXr16qR9XKpXF0kUnTpzI+PHj6datW4ko99OnT5eIWj98+HCJmPb33ntP/R4PHDjA2rVrUalU9O3bl48//hgonEny8fHh+PHjGBkZsWbNGp577rnK/bAIIYSOqul01DozY3HlyhUiIiL4+uuv2b9/vzpXIykpie3btzN79mzmzZvHvHnzUCqVfPLJJyxcuBAo/Gt59OjRREVFqVM5H2VoaMi0adNwd3dn3759Go+QtrW1ZevWrURGRjJt2jSWL19erH/h4eHs3r2b5cuXY2RkRGRkJA4ODkRGRgIwa9Ys/P39iYqKok2bNqxatUrr+46Pj+eLL74gKiqKQ4cOcefOHfUv7H379mkcVAD88MMPZZ5NUVSz/fv3a+1HkaIo9/379+Po6Mg777zDnj17OHDgAFlZWRw/fhw3Nzfat29PWFgY+/btK3Z8eFJSEmFhYXz99ddERkbyn//8Rx2klpGRQceOHdVt79y5s9z9EkKI2uL+/Yxq/19Z6szA4vTp07i5uanjwR+NNFcoFKSnp3P+/HmmT5+Op6cn8+fP588//wTg/PnzDBpUGFTl6elZ6T6kpaUxffp0Bg8eTGhoKFeuXFE/161bN0xNTbG2tsbMzKxYzHhiYiJpaWmkpaXRtWtXoDC749y5c1rv2aNHD8zMzKhfvz4tW7asULT4qVOnNA4sUlNTSUtLo0uXLkD56/JolDsUpsN6eXnh4eHB6dOnte6j+M9//qOOea9Xrx4eHh6cPXsWAAMDA15//XUA2rdvLzHqQghRA+rUUkhpjI2NgcJYcXNzc/bt21fqdXp6T/6RxBUrVtCtWzdWr17NrVu38PHxUT/3eJS4gYGB+r+1xYErFAqKPjWck5NT7LlH21UoFOWOFs/MzCQ1NRUbG5tyXf94fzRFnT8a5a4tar2iDAwM1N8niVEXQoiaUWcGFt27d8fPz49x48ZhZWXF/fv3iz1vampKs2bNOHToEAMHDkSlUnH58mXs7Ozo1KkTBw8exNPTU+O0f3miutPS0tS/qPfu3Vuh/puZmWFubs65c+dwdHRk37596tmCF154gbi4OOzt7fn222/L1V69evXIzc1VD2AeFxMTQ7du3TS+3tzcHDMzM3V/oqKi1M+98MILbNu2jYKCApKSkrh48WKpbZQVta6pnkUx78nJyVhYWHDw4EHeeeedcr3n0khsuhCiNnkWIuTrzMCidevWTJo0CW9vb/T19dWR3I/6/PPPWbBgAREREeTl5eHu7o6dnR1z587F39+f9evXF9u8+ajyRHVPmDCBgIAAIiIi6Nu3b4Xfw+LFi9WbN5s3b05oaCgA48ePZ8aMGezcubPc7Y4cOZI333yTtm3blrrP4ocffii2ZFGa0NBQ5syZg56eXrHNm6+99hovvPAC7u7utGzZknbt2pX6+rKi1h+PaS/SqFEjPvroI8aOHavevPmkAW8Sm142XTliuKZJnbSTGmlXG2okJ2+KUg0dOpSdO3dqnNGoLSQrRLva8H90T4PUSTupkXa6UiM5eVNUWEWXaoQQQgiQgUW1eFbizcsjJSWFcePGlXh806ZNWFlZAbBw4UL+/e9/F3vex8eH4cOHP40uCiGE0CGyFCLqNFkK0U5XpmZrmtRJO6mRdrpSI4lNF0IIIcRTITMWok6T2HQhRF30pPHqsnlTVFpubi4jR44s12bO8PBwGjRogK+v7xPfNzU1laioKMaMGQMUHuUdHBysDn2rKhKbLoSoi6ozXl2WQkSZYmNj6dy5c7W0XVaibGpqKtu2bVN//WiSrBBCiGeXzFjUAlOmTOHu3btkZ2fj4+PDqFGj2LVrF+vXr8fMzAw7OzsMDQ2ZP38+ycnJBAYGcvv2bQDmzJnDa6+9prHtH3/8kT59+mh8PiIigsjISKytrWnSpIn6MKyyElePHDlCRkYGBQUFrF27lilTppCamkpeXh7Tp0/HxcWFpUuXcuPGDTw9PenZsydjxoxh0qRJHDhwgOzsbBYsWEBcXBwKhYKAgAC6d++uMc1VCCHE0yMDi1ogJCQES0tLsrKyGDFiBP369SMiIgKlUomJiQljx47Fzs4OKExqHTt2LI6Ojty+fRtfX18OHTqkse2YmBj8/PxKfS4uLo5vvvmGyMhI8vPzGTp0qMZTNh916dIl9u/fj6WlJXl5eaxevRpTU1OSk5MZNWoU/fv356OPPuLKlSvq7JZbt26pX79161YAoqKiSEhIwNfXl8OHDwOFaa6RkZEYGhri5uaGt7c3TZo0KV8hhRCiDqmueHUZWNQCW7Zs4bvvvgPgzp076hyRRxNcr127BkB0dHSxBNGHDx+Snp6OiYlJiXaTkpKwsLBQB7U97ty5c7i4uKif13Tc+eN69eql7ptKpWLZsmWcPXsWfX19kpKS+Ouvv8p8fWxsrDofpGXLljRt2pSrV68C/0tzLXouMTFRBhZCCFGKJ/lYq2zerMViYmKIjo5mx44dGBsb4+3tja2tLQkJCaVeX1BQwM6dO6lfv77Wtn/44QeNsenalJW4+uhAJSoqiuTkZJRKJQYGBjg7Oz9Rwmll01yFEEJUDRlY6Li0tDT1rEJCQgIXLlzAy8uLs2fP8uDBA0xMTDhy5Aht2rQBwMnJiS1btjBhwgSgcOng1VdfLbXtH3/8kenTp2u8d5cuXQgICGDixInk5eVx/PhxRo0aBZQ/cTUtLY2GDRtiYGDA6dOnSUxMBMpOiy1KU+3RowdXr17lzp072NracunSJe0Fe4ykmwoh6qLqTEGVgYWO69OnD9u3b2fgwIG0aNECBwcHbGxsmDhxIl5eXlhYWGBra6teHpg7dy5BQUF4eHiQn5+Po6MjQUFBJdrNz8/nxo0btGzZUuO927Vrh7u7O56enlhbWxdLJy1v4qqHhweTJ0/Gw8OD9u3bY2trCxRGqXfu3JnBgwfTu3dv9cdOAd5++20WLFiAh4cHCoWC0NDQYjMVFSXppmXTlZMAa5rUSTupkXa1oUZyQFYtVbRvIi8vDz8/P4YPH46rq2u5X3/u3Dn2799f6qCjNpEjvbWrDf9H9zRInbSTGmmnKzWSPRZ10KpVq4iOjiY7OxsnJydcXFwq9HpHR0ccHR2rqXdCCCFqK5mxEFrTWMuTgCqEEEKADCyEEEIIUYXkSG8hhBBCVBkZWAghhBCiysjAQgghhBBVRgYWQgghhKgyMrAQQgghRJWRgYUQQgghqowMLESd9cMPPzBgwABcXV1Zt25dTXfnqXN2dsbDwwNPT0+GDRsGwP3793n33Xd54403ePfdd3nw4AFQmEK7aNEiXF1d8fDw4Ndff1W3s3fvXt544w3eeOMN9u7dWyPvparMnj2bHj16MHjwYPVjVVmTuLg4PDw8cHV1ZdGiRejip/1Lq1F4eDi9e/fG09MTT09PTp48qX5u7dq1uLq6MmDAAH788Uf145r+/d28eRMvLy9cXV2ZMWNGiRBDXXDnzh28vb1xd3dn0KBBfP3110Ad+llSCVEH5eXlqfr376+6ceOGKjs7W+Xh4aG6cuVKTXfrqXr99ddVf//9d7HHFi9erFq7dq1KpVKp1q5dq1qyZIlKpVKpTpw4ofL19VUVFBSozp8/rxoxYoRKpVKpUlJSVM7OzqqUlBTV/fv3Vc7Ozqr79+8/3TdShc6cOaOKi4tTDRo0SP1YVdZk+PDhqvPnz6sKCgpUvr6+qhMnTjzld/jkSqvRypUrVevXry9x7ZUrV1QeHh6q7Oxs1Y0bN1T9+/dX5eXllfnvb9q0aaoDBw6oVCqVat68eaqtW7c+nTdWhZKSklRxcXEqlUqlSktLU73xxhuqK1eu1JmfJZmxEHXSxYsXeemll2jevDmGhoYMGjSI77//vqa7VeO+//57hgwZAsCQIUM4evRoscf19PRwcHAgNTWVe/fucerUKXr16oWlpSUWFhb06tWr2F+luqZLly5YWFgUe6yqanLv3j0ePnyIg4MDenp6DBkyRCd/5kqrkSbff/89gwYNwtDQkObNm/PSSy9x8eJFjf/+VCoVp0+fZsCAAQAMHTpUJ2vUqFEj2rVrB4CpqSm2trYkJSXVmZ8lGViIOikpKYnGjRurv7axsSEpKakGe1QzfH19GTZsGDt27ADg77//plGjRgA8//zz/P3330DJejVu3JikpKQ6Uceqqomm62uLrVu34uHhwezZs9VT/OWtRdHjKSkpmJubU69eYYxVbajRrVu3iI+Pp2PHjnXmZ0kGFkLUUdu2bWPv3r189dVXbN26lbNnzxZ7Xk9PDz09vRrq3bNJalK60aNH891337Fv3z4aNWrEZ599VtNdeiakp6czbdo05syZg6mpabHnavPPkgwsRJ1kY2PD3bt31V8nJSVhY2NTgz16+oreb8OGDXF1deXixYs0bNiQe/fuAXDv3j2sra3V1z5ar7t372JjY1Mn6lhVNdF0fW3w3HPPoVAo0NfXx8vLi//85z+A5n9nmh63srIiNTWVvLw8QLdrlJuby7Rp0/Dw8OCNN94A6s7PkgwsRJ3UoUMHrl27xs2bN8nJyeHgwYM4OzvXdLeemoyMDB4+fKj+759++onWrVvj7OxMZGQkAJGRkfTv3x9A/bhKpeLChQuYmZnRqFEjnJycOHXqFA8ePODBgwecOnUKJyenmnpb1aKqatKoUSNMTU25cOECKpWqWFu6ruiXJcDRo0dp3bo1UFijgwcPkpOTw82bN7l27Rr29vYa//3p6enRrVs3Dh8+DBR+IkIX/12qVCrmzp2Lra0t7777rvrxuvKzJOmmos46efIkISEh5OfnM3z4cCZPnlzTXXpqbt68ydSpUwHIz89n8ODBTJ48mZSUFGbMmMGdO3do2rQpX3zxBZaWlqhUKoKCgvjxxx8xNjYmJCSEDh06ALB7927Wrl0LwKRJkxg+fHiNva8n9eGHH3LmzBlSUlJo2LAhH3zwAS4uLlVWk//85z/Mnj2brKws+vTpw7x583RuOry0Gp05c4bffvsNgBdeeIGgoCD1XoKIiAj27NmDQqFgzpw59O3bF9D87+/mzZvMnDmTBw8e8OqrrxIWFoahoWHNvNlKOnfuHGPGjKFNmzbo6xf+/f7hhx9ib29fJ36WZGAhhBBCiCojSyFCCCGEqDIysBBCCCFElZGBhRBCCCGqjAwshBBCCFFlZGAhhBBCiCojAwshhBBCVBkZWAghhBCiyvw/4SbSgEvR6O0AAAAASUVORK5CYII=\n" }, "metadata": {} } @@ -1168,17 +1368,52 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 38, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { - "text/plain": " score_opt model_score score_std model_name \\\n234 0.5169 0.4856 0.031257 LightGBM \n193 0.5172 0.4857 0.031492 LightGBM \n214 0.5185 0.4913 0.027242 LightGBM \n246 0.5188 0.4909 0.027940 LightGBM \n196 0.5189 0.4865 0.032421 LightGBM \n\n model_param \\\n234 {'random_seed': 42, 'early_stopping_rounds': 5... \n193 {'random_seed': 42, 'early_stopping_rounds': 5... \n214 {'random_seed': 42, 'early_stopping_rounds': 5... \n246 {'random_seed': 42, 'early_stopping_rounds': 5... \n196 {'random_seed': 42, 'early_stopping_rounds': 5... \n\n wrapper_params \\\n234 {'early_stopping': False} \n193 {'early_stopping': False} \n214 {'early_stopping': False} \n246 {'early_stopping': False} \n196 {'early_stopping': False} \n\n cat_encoders \\\n234 [HelmertEncoder, HashingEncoder, FrequencyEnco... \n193 [HelmertEncoder, HashingEncoder, FrequencyEnco... \n214 [HelmertEncoder, HashingEncoder, FrequencyEnco... \n246 [HelmertEncoder, HashingEncoder, FrequencyEnco... \n196 [HelmertEncoder, HashingEncoder, FrequencyEnco... \n\n columns cv_folds \n234 [HelmertEncoder_credit_history_0, HelmertEncod... 10 \n193 [HelmertEncoder_credit_history_0, HelmertEncod... 10 \n214 [HelmertEncoder_credit_history_0, HelmertEncod... 10 \n246 [HelmertEncoder_credit_history_0, HelmertEncod... 10 \n196 [HelmertEncoder_credit_history_0, HelmertEncod... 10 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
2340.51690.48560.031257LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[HelmertEncoder, HashingEncoder, FrequencyEnco...[HelmertEncoder_credit_history_0, HelmertEncod...10
1930.51720.48570.031492LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[HelmertEncoder, HashingEncoder, FrequencyEnco...[HelmertEncoder_credit_history_0, HelmertEncod...10
2140.51850.49130.027242LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[HelmertEncoder, HashingEncoder, FrequencyEnco...[HelmertEncoder_credit_history_0, HelmertEncod...10
2460.51880.49090.027940LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[HelmertEncoder, HashingEncoder, FrequencyEnco...[HelmertEncoder_credit_history_0, HelmertEncod...10
1960.51890.48650.032421LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[HelmertEncoder, HashingEncoder, FrequencyEnco...[HelmertEncoder_credit_history_0, HelmertEncod...10
\n
" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "92 0.5382 0.4922 0.046016 LightGBM \n", + "90 0.5425 0.5030 0.039452 LightGBM \n", + "64 0.5449 0.4846 0.060256 LightGBM \n", + "80 0.5504 0.5054 0.044980 LightGBM \n", + "62 0.5506 0.4993 0.051347 LightGBM \n", + "\n", + " model_param \\\n", + "92 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "90 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "64 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "80 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "62 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "\n", + " wrapper_params \\\n", + "92 {'early_stopping': False} \n", + "90 {'early_stopping': False} \n", + "64 {'early_stopping': False} \n", + "80 {'early_stopping': False} \n", + "62 {'early_stopping': False} \n", + "\n", + " cat_encoders \\\n", + "92 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "90 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "64 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "80 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "62 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "92 (OneHotEncoder_other_parties_3, OneHotEncoder_... 10 \n", + "90 (num_dependents, OneHotEncoder_other_parties_3... 10 \n", + "64 (num_dependents, OneHotEncoder_own_telephone, ... 10 \n", + "80 (num_dependents, OneHotEncoder_own_telephone, ... 10 \n", + "62 (duration, num_dependents, OneHotEncoder_other... 10 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
920.53820.49220.046016LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_other_parties_3, OneHotEncoder_...10
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640.54490.48460.060256LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(num_dependents, OneHotEncoder_own_telephone, ...10
800.55040.50540.044980LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(num_dependents, OneHotEncoder_own_telephone, ...10
620.55060.49930.051347LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, num_dependents, OneHotEncoder_other...10
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" }, "metadata": {}, - "execution_count": 48 + "execution_count": 38 } ], "source": [ @@ -1188,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 39, "metadata": { "tags": [] }, @@ -1196,16 +1431,42 @@ { "output_type": "stream", "name": "stderr", - "text": "20%|██ | 1/5 [00:13<00:54, 13.67s/it]\n Mean Score log_loss on 30 Folds: 0.5131 std: 0.036688\n 40%|████ | 2/5 [00:28<00:42, 14.14s/it]\n Mean Score log_loss on 30 Folds: 0.5131 std: 0.035375\n 60%|██████ | 3/5 [00:47<00:30, 15.40s/it]\n Mean Score log_loss on 30 Folds: 0.5146 std: 0.032718\n 80%|████████ | 4/5 [01:06<00:16, 16.50s/it]\n Mean Score log_loss on 30 Folds: 0.5146 std: 0.033139\n100%|██████████| 5/5 [01:44<00:00, 20.96s/it]\n Mean Score log_loss on 30 Folds: 0.5129 std: 0.036481\n\n" + "text": [ + " 20%|██ | 1/5 [00:30<02:01, 30.32s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5071 std: 0.039572\n", + " 40%|████ | 2/5 [01:03<01:33, 31.27s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5151 std: 0.028694\n", + " 60%|██████ | 3/5 [01:30<00:59, 29.77s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5063 std: 0.044698\n", + " 80%|████████ | 4/5 [01:45<00:25, 25.54s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5224 std: 0.027288\n", + "100%|██████████| 5/5 [02:24<00:00, 28.99s/it]\n", + " Mean Score log_loss on 20 Folds: 0.5059 std: 0.044746\n", + "\n" + ] }, { "output_type": "execute_result", "data": { - "text/plain": " model_name predict_test \\\n0 234_LightGBM [0.2676726365102943, 0.32764189369411517, 0.65... \n1 193_LightGBM [0.2732019034669371, 0.3263156968428175, 0.649... \n2 214_LightGBM [0.27469530508062867, 0.3350693986834506, 0.63... \n3 246_LightGBM [0.27087622571272385, 0.33683594113402854, 0.6... \n4 196_LightGBM [0.2747192963109885, 0.32548602182313097, 0.65... \n\n predict_train \n0 [0.25025976714384707, 0.2127160735659406, 0.41... \n1 [0.24269228064244477, 0.21841960948537156, 0.4... \n2 [0.2740846458562222, 0.21655982296749077, 0.39... \n3 [0.2662580756156583, 0.21390391010295007, 0.38... \n4 [0.25376919387751234, 0.21131499888561237, 0.4... ", - "text/html": "
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model_namepredict_testpredict_train
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model_namepredict_testpredict_train
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" }, "metadata": {}, - "execution_count": 49 + "execution_count": 39 } ], "source": [ @@ -1215,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 40, "metadata": { "tags": [] }, @@ -1223,7 +1484,9 @@ { "output_type": "stream", "name": "stdout", - "text": "Test metric: 0.4809\n" + "text": [ + "Test metric: 0.4865\n" + ] } ], "source": [ @@ -1247,9 +1510,13 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.7.6 64-bit ('ds': conda)", - "language": "python", - "name": "python37664bitdscondaeb5aeb426ade4b82a5cf907714e87c5f" + "name": "python3", + "display_name": "Python 3.8.6 64-bit", + "metadata": { + "interpreter": { + "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462" + } + } }, "language_info": { "codemirror_mode": { @@ -1261,7 +1528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7-final" + "version": "3.8.6-final" }, "toc": { "base_numbering": 1, diff --git a/examples/03_Data_Cleaning_and_Encoding_(DataBunch).ipynb b/examples/03_Data_Cleaning_and_Encoding_(DataBunch).ipynb index 3b25377..4967c13 100644 --- a/examples/03_Data_Cleaning_and_Encoding_(DataBunch).ipynb +++ b/examples/03_Data_Cleaning_and_Encoding_(DataBunch).ipynb @@ -12,7 +12,7 @@ "outputs": [], "source": [ "# If you run this notebook on Google Colaboratory, uncomment the below to install automl_alex.\n", - "#!pip install automl-alex" + "#!pip install -U -q automl-alex" ] }, { @@ -29,7 +29,9 @@ { "output_type": "stream", "name": "stdout", - "text": "0.07.17\n" + "text": [ + "0.11.24\n" + ] } ], "source": [ @@ -66,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:23:54.266953Z", @@ -77,11 +79,32 @@ { "output_type": "execute_result", "data": { - "text/plain": " age workclass fnlwgt education education-num \\\n0 2 State-gov 77516.0 Bachelors 13.0 \n1 3 Self-emp-not-inc 83311.0 Bachelors 13.0 \n2 2 Private 215646.0 HS-grad 9.0 \n3 3 Private 234721.0 11th 7.0 \n4 1 Private 338409.0 Bachelors 13.0 \n\n marital-status occupation relationship race sex \\\n0 Never-married Adm-clerical Not-in-family White Male \n1 Married-civ-spouse Exec-managerial Husband White Male \n2 Divorced Handlers-cleaners Not-in-family White Male \n3 Married-civ-spouse Handlers-cleaners Husband Black Male \n4 Married-civ-spouse Prof-specialty Wife Black Female \n\n capitalgain capitalloss hoursperweek native-country \n0 1 0 2 United-States \n1 0 0 0 United-States \n2 0 0 2 United-States \n3 0 0 2 United-States \n4 0 0 2 Cuba ", + "text/plain": [ + " age workclass fnlwgt education education-num \\\n", + "0 2 State-gov 77516.0 Bachelors 13.0 \n", + "1 3 Self-emp-not-inc 83311.0 Bachelors 13.0 \n", + "2 2 Private 215646.0 HS-grad 9.0 \n", + "3 3 Private 234721.0 11th 7.0 \n", + "4 1 Private 338409.0 Bachelors 13.0 \n", + "\n", + " marital-status occupation relationship race sex \\\n", + "0 Never-married Adm-clerical Not-in-family White Male \n", + "1 Married-civ-spouse Exec-managerial Husband White Male \n", + "2 Divorced Handlers-cleaners Not-in-family White Male \n", + "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", + "4 Married-civ-spouse Prof-specialty Wife Black Female \n", + "\n", + " capitalgain capitalloss hoursperweek native-country \n", + "0 1 0 2 United-States \n", + "1 0 0 0 United-States \n", + "2 0 0 2 United-States \n", + "3 0 0 2 United-States \n", + "4 0 0 2 Cuba " + ], "text/html": "
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" }, "metadata": {}, - "execution_count": 4 + "execution_count": 3 } ], "source": [ @@ -93,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:23:54.292030Z", @@ -104,10 +127,12 @@ { "output_type": "execute_result", "data": { - "text/plain": "((39073, 14), (9769, 14))" + "text/plain": [ + "((39073, 14), (9769, 14))" + ] }, "metadata": {}, - "execution_count": 5 + "execution_count": 4 } ], "source": [ @@ -127,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:23:54.610535Z", @@ -138,11 +163,32 @@ { "output_type": "execute_result", "data": { - "text/plain": " age workclass fnlwgt education education-num \\\n37193 1 Private 50753.0 HS-grad 9.0 \n31093 2 State-gov 144351.0 Masters 14.0 \n33814 1 Local-gov 252217.0 12th 8.0 \n14500 4 Private 69525.0 HS-grad 9.0 \n23399 4 Self-emp-not-inc 28612.0 HS-grad 9.0 \n\n marital-status occupation relationship race sex \\\n37193 Married-civ-spouse Transport-moving Husband White Male \n31093 Married-civ-spouse Prof-specialty Husband White Male \n33814 Married-civ-spouse Farming-fishing Husband White Male \n14500 Divorced Craft-repair Unmarried White Male \n23399 Widowed Sales Not-in-family White Male \n\n capitalgain capitalloss hoursperweek native-country \n37193 0 0 2 United-States \n31093 0 0 2 United-States \n33814 0 0 2 United-States \n14500 0 0 0 United-States \n23399 0 0 4 United-States ", + "text/plain": [ + " age workclass fnlwgt education education-num \\\n", + "37193 1 Private 50753.0 HS-grad 9.0 \n", + "31093 2 State-gov 144351.0 Masters 14.0 \n", + "33814 1 Local-gov 252217.0 12th 8.0 \n", + "14500 4 Private 69525.0 HS-grad 9.0 \n", + "23399 4 Self-emp-not-inc 28612.0 HS-grad 9.0 \n", + "\n", + " marital-status occupation relationship race sex \\\n", + "37193 Married-civ-spouse Transport-moving Husband White Male \n", + "31093 Married-civ-spouse Prof-specialty Husband White Male \n", + "33814 Married-civ-spouse Farming-fishing Husband White Male \n", + "14500 Divorced Craft-repair Unmarried White Male \n", + "23399 Widowed Sales Not-in-family White Male \n", + "\n", + " capitalgain capitalloss hoursperweek native-country \n", + "37193 0 0 2 United-States \n", + "31093 0 0 2 United-States \n", + "33814 0 0 2 United-States \n", + "14500 0 0 0 United-States \n", + "23399 0 0 4 United-States " + ], "text/html": "
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" }, "metadata": {}, - "execution_count": 8 + "execution_count": 5 } ], "source": [ @@ -151,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:23:55.231008Z", @@ -163,7 +209,9 @@ { "output_type": "stream", "name": "stdout", - "text": "\nInt64Index: 39073 entries, 37193 to 15795\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 age 39073 non-null category\n 1 workclass 36851 non-null category\n 2 fnlwgt 39073 non-null float64 \n 3 education 39073 non-null category\n 4 education-num 39073 non-null float64 \n 5 marital-status 39073 non-null category\n 6 occupation 36842 non-null category\n 7 relationship 39073 non-null category\n 8 race 39073 non-null category\n 9 sex 39073 non-null category\n 10 capitalgain 39073 non-null category\n 11 capitalloss 39073 non-null category\n 12 hoursperweek 39073 non-null category\n 13 native-country 38396 non-null category\ndtypes: category(12), float64(2)\nmemory usage: 1.3 MB\n" + "text": [ + "\nInt64Index: 39073 entries, 37193 to 15795\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 age 39073 non-null category\n 1 workclass 36851 non-null category\n 2 fnlwgt 39073 non-null float64 \n 3 education 39073 non-null category\n 4 education-num 39073 non-null float64 \n 5 marital-status 39073 non-null category\n 6 occupation 36842 non-null category\n 7 relationship 39073 non-null category\n 8 race 39073 non-null category\n 9 sex 39073 non-null category\n 10 capitalgain 39073 non-null category\n 11 capitalloss 39073 non-null category\n 12 hoursperweek 39073 non-null category\n 13 native-country 38396 non-null category\ndtypes: category(12), float64(2)\nmemory usage: 1.3 MB\n" + ] } ], "source": [ @@ -191,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:23:58.470851Z", @@ -205,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:24:10.501288Z", @@ -217,7 +265,12 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00 Start preprocessing Data\n> Generate cat encodet features\n +121 Features fromOneHotEncoder\n> Clean Nans in num features\n##################################################\n> Total Generated Features:109\n##################################################\nNew X_train shape:(39073, 122)| X_test shape:(9769, 122)\n" + "text": [ + "Source X_train shape: (39073, 14) | X_test shape: (9769, 14)\n", + "##################################################\n", + "Auto detect cat features: 12\n", + "> Start preprocessing Data\n", + "> Generate cat encodet features\n", + " + 121 Features from OneHotEncoder\n", + "> Clean Nans in num features\n", + "##################################################\n", + "> Total Features: 122\n", + "##################################################\n", + "New X_train shape: (39073, 122) | X_test shape: (9769, 122)\n" + ] } ], "source": [ @@ -289,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:24:11.943189Z", @@ -300,11 +365,76 @@ { "output_type": "execute_result", "data": { - "text/plain": " fnlwgt OneHotEncoder_occupation_1 OneHotEncoder_occupation_2 \\\n0 50753.0 1 0 \n1 144351.0 0 1 \n2 252217.0 0 0 \n3 69525.0 0 0 \n4 28612.0 0 0 \n\n OneHotEncoder_occupation_3 OneHotEncoder_occupation_4 \\\n0 0 0 \n1 0 0 \n2 1 0 \n3 0 1 \n4 0 0 \n\n OneHotEncoder_occupation_5 OneHotEncoder_occupation_6 \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 1 0 \n\n OneHotEncoder_occupation_7 OneHotEncoder_occupation_8 \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n OneHotEncoder_occupation_9 ... OneHotEncoder_age_2 OneHotEncoder_age_3 \\\n0 0 ... 0 0 \n1 0 ... 1 0 \n2 0 ... 0 0 \n3 0 ... 0 1 \n4 0 ... 0 1 \n\n OneHotEncoder_age_4 OneHotEncoder_age_5 OneHotEncoder_relationship_1 \\\n0 0 0 1 \n1 0 0 1 \n2 0 0 1 \n3 0 0 0 \n4 0 0 0 \n\n OneHotEncoder_relationship_2 OneHotEncoder_relationship_3 \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 1 0 \n4 0 1 \n\n OneHotEncoder_relationship_4 OneHotEncoder_relationship_5 \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n OneHotEncoder_relationship_6 \n0 0 \n1 0 \n2 0 \n3 0 \n4 0 \n\n[5 rows x 122 columns]", - "text/html": "
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" }, "metadata": {}, - "execution_count": 7 + "execution_count": 17 } ], "source": [ @@ -576,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": { "tags": [] }, @@ -584,16 +1009,50 @@ { "output_type": "stream", "name": "stdout", - "text": "Source X_train shape:(800, 20)| X_test shape:(200, 20)\n##################################################\nAuto detect cat features:13\n> Start preprocessing Data\n> Generate Frequency Encode num features\n +4 Frequency Encode Num Features\n> Clean Nans in num features\n> Normalization Features\n##################################################\n> Total Features:8\n##################################################\nNew X_train shape:(800, 8)| X_test shape:(200, 8)\n" + "text": [ + "Source X_train shape: (800, 20) | X_test shape: (200, 20)\n", + "##################################################\n", + "Auto detect cat features: 13\n", + "> Start preprocessing Data\n", + "> Generate Frequency Encode num features\n", + " + 4 Frequency Encode Num Features \n", + "> Clean Nans in num features\n", + "> Normalization Features\n", + "##################################################\n", + "> Total Features: 8\n", + "##################################################\n", + "New X_train shape: (800, 8) | X_test shape: (200, 8)\n" + ] }, { "output_type": "execute_result", "data": { - "text/plain": " duration credit_amount age num_dependents \\\n0 3.297082 1.199912 2.406187 -0.409736 \n1 -0.008051 -0.359630 -0.224364 -0.409736 \n2 -1.279256 -0.733547 1.266282 -0.409736 \n3 -0.008051 0.567050 -0.575104 -0.409736 \n4 -0.770774 -0.854388 -1.276585 -0.409736 \n\n FrequencyEncoder_credit_amount FrequencyEncoder_duration \\\n0 -0.403815 -1.406620 \n1 -0.403815 -1.142860 \n2 2.062233 -0.444670 \n3 -0.403815 -1.142860 \n4 -0.403815 1.168925 \n\n FrequencyEncoder_num_dependents FrequencyEncoder_age \n0 0.409736 -1.603822 \n1 0.409736 0.166108 \n2 0.409736 -1.320634 \n3 0.409736 0.449297 \n4 0.409736 -1.179039 ", - "text/html": "
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" }, "metadata": {}, - "execution_count": 10 + "execution_count": 19 } ], "source": [ @@ -652,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:24:14.648430Z", @@ -664,16 +1144,101 @@ { "output_type": "stream", "name": "stdout", - "text": "Source X_train shape:(800, 20)| X_test shape:(200, 20)\n##################################################\nAuto detect cat features:13\n> Start preprocessing Data\n> Generate cat encodet features\n +55 Features fromOneHotEncoder\n +44 Features fromHelmertEncoder\n +54 Features fromHashingEncoder\n +16 Features fromFrequencyEncoder\n> Generate Frequency Encode num features\n +4 Frequency Encode Num Features\n> Clean Nans in num features\n> Generate interaction Num Features\n +24 Interaction Features\n> Normalization Features\n##################################################\n> Total Features:201\n##################################################\nNew X_train shape:(800, 201)| X_test shape:(200, 201)\n" + "text": [ + "Source X_train shape: (800, 20) | X_test shape: (200, 20)\n", + "##################################################\n", + "Auto detect cat features: 13\n", + "> Start preprocessing Data\n", + "> Generate cat encodet features\n", + " + 55 Features from OneHotEncoder\n", + " + 44 Features from HelmertEncoder\n", + " + 54 Features from HashingEncoder\n", + " + 16 Features from FrequencyEncoder\n", + "> Generate Frequency Encode num features\n", + " + 4 Frequency Encode Num Features \n", + "> Clean Nans in num features\n", + "> Generate interaction Num Features\n", + " + 24 Interaction Features\n", + "> Normalization Features\n", + "##################################################\n", + "> Total Features: 201\n", + "##################################################\n", + "New X_train shape: (800, 201) | X_test shape: (200, 201)\n" + ] }, { "output_type": "execute_result", "data": { - "text/plain": " duration credit_amount age num_dependents \\\n0 3.297082 1.199912 2.406187 -0.409736 \n1 -0.008051 -0.359630 -0.224364 -0.409736 \n2 -1.279256 -0.733547 1.266282 -0.409736 \n3 -0.008051 0.567050 -0.575104 -0.409736 \n4 -0.770774 -0.854388 -1.276585 -0.409736 \n\n OneHotEncoder_checking_status_1 OneHotEncoder_checking_status_2 \\\n0 1.654786 -0.255434 \n1 -0.604308 3.914911 \n2 -0.604308 -0.255434 \n3 -0.604308 -0.255434 \n4 -0.604308 -0.255434 \n\n OneHotEncoder_checking_status_3 OneHotEncoder_checking_status_4 \\\n0 -0.822891 -0.604308 \n1 -0.822891 -0.604308 \n2 1.215228 -0.604308 \n3 1.215228 -0.604308 \n4 1.215228 -0.604308 \n\n OneHotEncoder_housing_1 OneHotEncoder_housing_2 ... \\\n0 0.629413 -0.460566 ... \n1 -1.588782 2.171241 ... \n2 -1.588782 2.171241 ... \n3 0.629413 -0.460566 ... \n4 0.629413 -0.460566 ... \n\n duration_-_num_dependents duration_+_num_dependents duration_/_age \\\n0 3.307063 3.284183 0.719309 \n1 0.004129 -0.020229 -0.037499 \n2 -1.266231 -1.291156 -1.274102 \n3 0.004129 -0.020229 0.172706 \n4 -0.758087 -0.782785 -0.193007 \n\n duration_*_age duration_-_age duration_+_age num_dependents_/_age \\\n0 6.110167 0.682788 4.140952 -0.398178 \n1 -0.102263 0.146742 -0.165632 -0.398178 \n2 -0.893155 -1.759198 -0.040803 -0.398178 \n3 -0.271308 0.384985 -0.415289 -0.398178 \n4 -0.989752 0.325424 -1.476331 -0.398178 \n\n num_dependents_*_age num_dependents_-_age num_dependents_+_age \n0 -0.39846 -2.427029 2.383275 \n1 -0.39846 0.212480 -0.235948 \n2 -0.39846 -1.283241 1.248278 \n3 -0.39846 0.564415 -0.585178 \n4 -0.39846 1.268284 -1.283638 \n\n[5 rows x 201 columns]", - "text/html": "
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" }, "metadata": {}, - "execution_count": 13 + "execution_count": 20 } ], "source": [ @@ -705,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2020-05-07T04:24:37.124919Z", @@ -717,7 +1282,12 @@ { "output_type": "stream", "name": "stderr", - "text": "0%| | 0/1 [00:00=7 \n1 radio/tv 5951.0 <100 1<=X<4 \n2 education 2096.0 <100 4<=X<7 \n3 furniture/equipment 7882.0 <100 4<=X<7 \n4 new car 4870.0 <100 1<=X<4 \n\n installment_commitment personal_status other_parties residence_since \\\n0 4.0 male single none 4.0 \n1 2.0 female div/dep/mar none 2.0 \n2 2.0 male single none 3.0 \n3 2.0 male single guarantor 4.0 \n4 3.0 male single none 4.0 \n\n property_magnitude age other_payment_plans housing existing_credits \\\n0 real estate 67.0 none own 2.0 \n1 real estate 22.0 none own 1.0 \n2 real estate 49.0 none own 1.0 \n3 life insurance 45.0 none for free 1.0 \n4 no known property 53.0 none for free 2.0 \n\n job num_dependents own_telephone foreign_worker \n0 skilled 1.0 yes yes \n1 skilled 1.0 none yes \n2 unskilled resident 2.0 none yes \n3 skilled 2.0 none yes \n4 skilled 2.0 none yes ", + "text/plain": [ + " checking_status duration credit_history \\\n", + "0 <0 6.0 critical/other existing credit \n", + "1 0<=X<200 48.0 existing paid \n", + "2 no checking 12.0 critical/other existing credit \n", + "3 <0 42.0 existing paid \n", + "4 <0 24.0 delayed previously \n", + "\n", + " purpose credit_amount savings_status employment \\\n", + "0 radio/tv 1169.0 no known savings >=7 \n", + "1 radio/tv 5951.0 <100 1<=X<4 \n", + "2 education 2096.0 <100 4<=X<7 \n", + "3 furniture/equipment 7882.0 <100 4<=X<7 \n", + "4 new car 4870.0 <100 1<=X<4 \n", + "\n", + " installment_commitment personal_status other_parties residence_since \\\n", + "0 4.0 male single none 4.0 \n", + "1 2.0 female div/dep/mar none 2.0 \n", + "2 2.0 male single none 3.0 \n", + "3 2.0 male single guarantor 4.0 \n", + "4 3.0 male single none 4.0 \n", + "\n", + " property_magnitude age other_payment_plans housing existing_credits \\\n", + "0 real estate 67.0 none own 2.0 \n", + "1 real estate 22.0 none own 1.0 \n", + "2 real estate 49.0 none own 1.0 \n", + "3 life insurance 45.0 none for free 1.0 \n", + "4 no known property 53.0 none for free 2.0 \n", + "\n", + " job num_dependents own_telephone foreign_worker \n", + "0 skilled 1.0 yes yes \n", + "1 skilled 1.0 none yes \n", + "2 unskilled resident 2.0 none yes \n", + "3 skilled 2.0 none yes \n", + "4 skilled 2.0 none yes " + ], "text/html": "
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checking_statusdurationcredit_historypurposecredit_amountsavings_statusemploymentinstallment_commitmentpersonal_statusother_partiesresidence_sinceproperty_magnitudeageother_payment_planshousingexisting_creditsjobnum_dependentsown_telephoneforeign_worker
0<06.0critical/other existing creditradio/tv1169.0no known savings>=74.0male singlenone4.0real estate67.0noneown2.0skilled1.0yesyes
10<=X<20048.0existing paidradio/tv5951.0<1001<=X<42.0female div/dep/marnone2.0real estate22.0noneown1.0skilled1.0noneyes
2no checking12.0critical/other existing crediteducation2096.0<1004<=X<72.0male singlenone3.0real estate49.0noneown1.0unskilled resident2.0noneyes
3<042.0existing paidfurniture/equipment7882.0<1004<=X<72.0male singleguarantor4.0life insurance45.0nonefor free1.0skilled2.0noneyes
4<024.0delayed previouslynew car4870.0<1001<=X<43.0male singlenone4.0no known property53.0nonefor free2.0skilled2.0noneyes
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" }, "metadata": {}, @@ -165,7 +202,9 @@ { "output_type": "execute_result", "data": { - "text/plain": "((750, 20), (250, 20))" + "text/plain": [ + "((750, 20), (250, 20))" + ] }, "metadata": {}, "execution_count": 6 @@ -213,7 +252,29 @@ { "output_type": "stream", "name": "stderr", - "text": "11%|█ | 1/9 [00:02<00:18, 2.34s/it]\n Mean Score roc_auc_score on 10 Folds: 0.7528 std: 0.041646\n 22%|██▏ | 2/9 [00:02<00:12, 1.73s/it]\n Mean Score roc_auc_score on 10 Folds: 0.6885 std: 0.082375\n 33%|███▎ | 3/9 [00:05<00:12, 2.00s/it]\n Mean Score roc_auc_score on 10 Folds: 0.6768 std: 0.030046\n 44%|████▍ | 4/9 [00:06<00:08, 1.64s/it]\n Mean Score roc_auc_score on 10 Folds: 0.7604 std: 0.048009\n 56%|█████▌ | 5/9 [00:06<00:05, 1.29s/it]\n Mean Score roc_auc_score on 10 Folds: 0.6058 std: 0.066032\n 67%|██████▋ | 6/9 [00:10<00:06, 2.08s/it]\n Mean Score roc_auc_score on 10 Folds: 0.7803 std: 0.049861\n 78%|███████▊ | 7/9 [00:13<00:04, 2.33s/it]\n Mean Score roc_auc_score on 10 Folds: 0.763 std: 0.045639\n 89%|████████▉ | 8/9 [00:16<00:02, 2.46s/it]\n Mean Score roc_auc_score on 10 Folds: 0.6741 std: 0.055327\n100%|██████████| 9/9 [00:21<00:00, 2.38s/it]\n Mean Score roc_auc_score on 10 Folds: 0.7972 std: 0.053879\n\n" + "text": [ + " 10%|█ | 1/10 [00:28<04:14, 28.31s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7503 std: 0.04765\n", + " 20%|██ | 2/10 [00:28<02:39, 19.93s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.6936 std: 0.078554\n", + " 30%|███ | 3/10 [00:33<01:47, 15.42s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.6678 std: 0.037481\n", + " 40%|████ | 4/10 [00:35<01:08, 11.36s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7592 std: 0.047259\n", + " 50%|█████ | 5/10 [00:36<00:40, 8.12s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.6429 std: 0.050253\n", + " 60%|██████ | 6/10 [00:39<00:26, 6.60s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7661 std: 0.053527\n", + " 70%|███████ | 7/10 [00:41<00:15, 5.32s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7595 std: 0.054004\n", + " 80%|████████ | 8/10 [00:55<00:15, 7.85s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.6543 std: 0.04842\n", + " 90%|█████████ | 9/10 [01:02<00:07, 7.73s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7974 std: 0.048408\n", + "100%|██████████| 10/10 [01:08<00:00, 6.80s/it]\n", + " Mean Score roc_auc_score on 10 Folds: 0.7603 std: 0.059691\n", + "\n" + ] } ], "source": [ @@ -229,8 +290,68 @@ { "output_type": "execute_result", "data": { - "text/plain": " score_opt model_score score_std model_name \\\n0 0.711154 0.7528 0.041646 LightGBM \n1 0.606125 0.6885 0.082375 KNeighbors \n2 0.646754 0.6768 0.030046 LinearSVM \n3 0.712391 0.7604 0.048009 LinearModel \n4 0.539768 0.6058 0.066032 SGD \n5 0.730439 0.7803 0.049861 RandomForest \n6 0.717361 0.7630 0.045639 ExtraTrees \n7 0.618773 0.6741 0.055327 XGBoost \n8 0.743321 0.7972 0.053879 CatBoost \n\n model_param \\\n0 {'random_seed': 42, 'early_stopping_rounds': 5... \n1 {'n_jobs': -1} \n2 {'verbose': 0, 'random_state': 42} \n3 {'verbose': 0} \n4 {'max_iter': 5000, 'verbose': 0, 'fit_intercep... \n5 {'verbose': 0, 'random_state': 42, 'n_jobs': -1} \n6 {'verbose': 0, 'random_state': 42, 'n_jobs': -1} \n7 {'verbosity': 0, 'early_stopping_rounds': 100,... \n8 {'verbose': 0, 'early_stopping_rounds': 50, 't... \n\n wrapper_params \\\n0 {'early_stopping': False} \n1 {} \n2 {} \n3 {} \n4 {} \n5 {} \n6 {} \n7 {'early_stopping': False} \n8 {'early_stopping': True} \n\n cat_encoder \\\n0 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n1 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n2 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n3 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n4 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n5 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n6 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n7 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n8 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n\n columns cv_folds \n0 [duration, credit_amount, age, num_dependents,... 10 \n1 [duration, credit_amount, age, num_dependents,... 10 \n2 [duration, credit_amount, age, num_dependents,... 10 \n3 [duration, credit_amount, age, num_dependents,... 10 \n4 [duration, credit_amount, age, num_dependents,... 10 \n5 [duration, credit_amount, age, num_dependents,... 10 \n6 [duration, credit_amount, age, num_dependents,... 10 \n7 [duration, credit_amount, age, num_dependents,... 10 \n8 [duration, credit_amount, age, num_dependents,... 10 ", - "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encodercolumnscv_folds
00.7111540.75280.041646LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
10.6061250.68850.082375KNeighbors{'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
20.6467540.67680.030046LinearSVM{'verbose': 0, 'random_state': 42}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
30.7123910.76040.048009LinearModel{'verbose': 0}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
40.5397680.60580.066032SGD{'max_iter': 5000, 'verbose': 0, 'fit_intercep...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
50.7304390.78030.049861RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
60.7173610.76300.045639ExtraTrees{'verbose': 0, 'random_state': 42, 'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
70.6187730.67410.055327XGBoost{'verbosity': 0, 'early_stopping_rounds': 100,...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
80.7433210.79720.053879CatBoost{'verbose': 0, 'early_stopping_rounds': 50, 't...{'early_stopping': True}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
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" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "0 0.702650 0.7503 0.047650 LightGBM \n", + "1 0.615046 0.6936 0.078554 KNeighbors \n", + "2 0.630319 0.6678 0.037481 LinearSVM \n", + "3 0.711941 0.7592 0.047259 LinearModel \n", + "4 0.592647 0.6429 0.050253 SGD \n", + "5 0.712573 0.7661 0.053527 RandomForest \n", + "6 0.705496 0.7595 0.054004 ExtraTrees \n", + "7 0.605880 0.6543 0.048420 XGBoost \n", + "8 0.748992 0.7974 0.048408 CatBoost \n", + "9 0.700609 0.7603 0.059691 MLP \n", + "\n", + " model_param \\\n", + "0 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "1 {'n_jobs': -1} \n", + "2 {'verbose': 0, 'random_state': 42} \n", + "3 {} \n", + "4 {'max_iter': 5000, 'verbose': 0, 'fit_intercep... \n", + "5 {'verbose': 0, 'random_state': 42, 'n_jobs': -1} \n", + "6 {'verbose': 0, 'random_state': 42, 'n_jobs': -1} \n", + "7 {'verbosity': 0, 'early_stopping_rounds': 100,... \n", + "8 {'verbose': 0, 'early_stopping_rounds': 50, 't... \n", + "9 {'verbose': 0, 'random_state': 42, 'max_iter':... \n", + "\n", + " wrapper_params \\\n", + "0 {'early_stopping': False} \n", + "1 {} \n", + "2 {} \n", + "3 {} \n", + "4 {} \n", + "5 {} \n", + "6 {} \n", + "7 {'early_stopping': False} \n", + "8 {'early_stopping': True} \n", + "9 {} \n", + "\n", + " cat_encoder \\\n", + "0 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "1 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "2 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "3 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "4 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "5 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "6 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "7 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "8 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "9 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "0 [duration, credit_amount, age, num_dependents,... 10 \n", + "1 [duration, credit_amount, age, num_dependents,... 10 \n", + "2 [duration, credit_amount, age, num_dependents,... 10 \n", + "3 [duration, credit_amount, age, num_dependents,... 10 \n", + "4 [duration, credit_amount, age, num_dependents,... 10 \n", + "5 [duration, credit_amount, age, num_dependents,... 10 \n", + "6 [duration, credit_amount, age, num_dependents,... 10 \n", + "7 [duration, credit_amount, age, num_dependents,... 10 \n", + "8 [duration, credit_amount, age, num_dependents,... 10 \n", + "9 [duration, credit_amount, age, num_dependents,... 10 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encodercolumnscv_folds
00.7026500.75030.047650LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
10.6150460.69360.078554KNeighbors{'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
20.6303190.66780.037481LinearSVM{'verbose': 0, 'random_state': 42}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
30.7119410.75920.047259LinearModel{}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
40.5926470.64290.050253SGD{'max_iter': 5000, 'verbose': 0, 'fit_intercep...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
50.7125730.76610.053527RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
60.7054960.75950.054004ExtraTrees{'verbose': 0, 'random_state': 42, 'n_jobs': -1}{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
70.6058800.65430.048420XGBoost{'verbosity': 0, 'early_stopping_rounds': 100,...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
80.7489920.79740.048408CatBoost{'verbose': 0, 'early_stopping_rounds': 50, 't...{'early_stopping': True}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
90.7006090.76030.059691MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, num_dependents,...10
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" }, "metadata": {}, "execution_count": 9 @@ -260,12 +381,35 @@ "# let's optimize all the models and see what the result will be (it takes a lot of time)\n", "review = model.opt(timeout=1000, verbose=1)" ], - "execution_count": 8, + "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stderr", - "text": "10%|█ | 1/10 [01:43<15:34, 103.83s/it]\n LightGBM Best Score: 0.7907\n 20%|██ | 2/10 [02:31<11:36, 87.05s/it]\n KNeighbors Best Score: 0.8105\n 30%|███ | 3/10 [04:18<10:51, 93.07s/it]\n LinearSVM Best Score: 0.7025\n 40%|████ | 4/10 [06:39<10:44, 107.34s/it]\n LinearModel Best Score: 0.7527\n 50%|█████ | 5/10 [07:32<07:34, 90.96s/it]\n SGD Best Score: 0.7279\n 60%|██████ | 6/10 [11:16<08:43, 130.92s/it]\n RandomForest Best Score: 0.8036\n 70%|███████ | 7/10 [15:40<08:32, 170.84s/it]\n ExtraTrees Best Score: 0.8035\n 80%|████████ | 8/10 [21:06<07:14, 217.34s/it]\n XGBoost Best Score: 0.7136\n 90%|█████████ | 9/10 [26:58<04:17, 257.74s/it]\n CatBoost Best Score: 0.8041\n100%|██████████| 10/10 [31:20<00:00, 188.03s/it]\n MLP Best Score: 0.7854\n\n" + "text": [ + " 0%| | 0/10 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
00.75020.79070.040533LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, age, OneHotEncoder_p...10
10.76290.81050.047637KNeighbors{'n_jobs': -1, 'n_neighbors': 29, 'weights': '...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, credit_amount, num_dependents, OneH...10
20.68940.70250.013116LinearSVM{'verbose': 0, 'random_state': 42, 'tol': 0.00...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[age, num_dependents, OneHotEncoder_property_m...10
30.70580.75270.046933LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 88...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[age, OneHotEncoder_property_magnitude_4, OneH...10
40.67120.72790.056703SGD{'max_iter': 5000, 'verbose': 0, 'fit_intercep...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[age, OneHotEncoder_property_magnitude_1, OneH...10
50.75400.80360.049615RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, age, num_dependents, OneHotEncoder_...10
60.75760.80350.045896ExtraTrees{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, age, num_dependents, OneHotEncoder_...10
70.67240.71360.041203XGBoost{'verbosity': 0, 'early_stopping_rounds': 0, '...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, age, OneHotEncoder_property_magnitu...10
80.79320.80410.010855CatBoost{'verbose': 0, 'early_stopping_rounds': 50, 't...{'early_stopping': True}[OneHotEncoder, HelmertEncoder, HashingEncoder...[duration, num_dependents, OneHotEncoder_prope...10
90.74020.78540.045211MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[age, OneHotEncoder_property_magnitude_1, OneH...10
\n" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "0 0.7261 0.7644 0.038315 LightGBM \n", + "1 0.7968 0.8276 0.030760 KNeighbors \n", + "2 0.6843 0.7076 0.023320 LinearSVM \n", + "3 0.7368 0.7610 0.024152 LinearModel \n", + "4 0.7208 0.7350 0.014166 SGD \n", + "5 0.7535 0.7945 0.041027 RandomForest \n", + "6 0.7508 0.7902 0.039376 ExtraTrees \n", + "7 0.6488 0.6848 0.035964 XGBoost \n", + "8 0.7864 0.8184 0.032003 CatBoost \n", + "9 0.7567 0.7674 0.010686 MLP \n", + "\n", + " model_param \\\n", + "0 {'random_seed': 42, 'early_stopping_rounds': 5... \n", + "1 {'n_jobs': -1, 'n_neighbors': 46, 'weights': '... \n", + "2 {'verbose': 0, 'random_state': 42, 'tol': 0.00... \n", + "3 {'fit_intercept': True, 'C': 57.59620969341546... \n", + "4 {'max_iter': 5000, 'verbose': 0, 'fit_intercep... \n", + "5 {'verbose': 0, 'random_state': 42, 'n_jobs': -... \n", + "6 {'verbose': 0, 'random_state': 42, 'n_jobs': -... \n", + "7 {'verbosity': 0, 'early_stopping_rounds': 0, '... \n", + "8 {'verbose': 0, 'early_stopping_rounds': 50, 't... \n", + "9 {'verbose': 0, 'random_state': 42, 'max_iter':... \n", + "\n", + " wrapper_params \\\n", + "0 {'early_stopping': False} \n", + "1 {} \n", + "2 {} \n", + "3 {} \n", + "4 {} \n", + "5 {} \n", + "6 {} \n", + "7 {'early_stopping': False} \n", + "8 {'early_stopping': True} \n", + "9 {} \n", + "\n", + " cat_encoders \\\n", + "0 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "1 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "2 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "3 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "4 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "5 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "6 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "7 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "8 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "9 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "0 (credit_amount, OneHotEncoder_savings_status_1... 10 \n", + "1 (duration, OneHotEncoder_savings_status_4, One... 10 \n", + "2 (duration, credit_amount, num_dependents, OneH... 10 \n", + "3 (OneHotEncoder_savings_status_2, OneHotEncoder... 10 \n", + "4 (num_dependents, OneHotEncoder_savings_status_... 10 \n", + "5 (duration, age, num_dependents, OneHotEncoder_... 10 \n", + "6 (credit_amount, age, OneHotEncoder_savings_sta... 10 \n", + "7 (duration, credit_amount, age, OneHotEncoder_s... 10 \n", + "8 (duration, credit_amount, OneHotEncoder_saving... 10 \n", + "9 (credit_amount, OneHotEncoder_savings_status_3... 10 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
00.72610.76440.038315LightGBM{'random_seed': 42, 'early_stopping_rounds': 5...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_savings_status_1...10
10.79680.82760.030760KNeighbors{'n_jobs': -1, 'n_neighbors': 46, 'weights': '...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, OneHotEncoder_savings_status_4, One...10
20.68430.70760.023320LinearSVM{'verbose': 0, 'random_state': 42, 'tol': 0.00...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, credit_amount, num_dependents, OneH...10
30.73680.76100.024152LinearModel{'fit_intercept': True, 'C': 57.59620969341546...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_savings_status_2, OneHotEncoder...10
40.72080.73500.014166SGD{'max_iter': 5000, 'verbose': 0, 'fit_intercep...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(num_dependents, OneHotEncoder_savings_status_...10
50.75350.79450.041027RandomForest{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, age, num_dependents, OneHotEncoder_...10
60.75080.79020.039376ExtraTrees{'verbose': 0, 'random_state': 42, 'n_jobs': -...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, age, OneHotEncoder_savings_sta...10
70.64880.68480.035964XGBoost{'verbosity': 0, 'early_stopping_rounds': 0, '...{'early_stopping': False}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, credit_amount, age, OneHotEncoder_s...10
80.78640.81840.032003CatBoost{'verbose': 0, 'early_stopping_rounds': 50, 't...{'early_stopping': True}[OneHotEncoder, HelmertEncoder, HashingEncoder...(duration, credit_amount, OneHotEncoder_saving...10
90.75670.76740.010686MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(credit_amount, OneHotEncoder_savings_status_3...10
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" }, "metadata": {}, - "execution_count": 9 + "execution_count": 11 } ] }, @@ -316,8 +520,13 @@ ], "metadata": { "kernelspec": { - "name": "python38364bitdscondadc8f49c0681c4f8d9aaeec09a1cf65fe", - "display_name": "Python 3.8.3 64-bit ('ds': conda)" + "name": "python3", + "display_name": "Python 3.8.6 64-bit", + "metadata": { + "interpreter": { + "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462" + } + } }, "toc": { "base_numbering": 1, diff --git a/examples/05_BestSingleModel.ipynb b/examples/05_BestSingleModel.ipynb index 4ca30c6..1380588 100644 --- a/examples/05_BestSingleModel.ipynb +++ b/examples/05_BestSingleModel.ipynb @@ -13,9 +13,9 @@ }, "source": [ "# If you run this notebook on Google Colaboratory, uncomment the below to install automl_alex.\n", - "#!pip install automl-alex" + "#!pip install -U -q automl-alex" ], - "execution_count": 0, + "execution_count": 1, "outputs": [] }, { @@ -64,7 +64,9 @@ { "output_type": "stream", "name": "stdout", - "text": "0.07.17\n" + "text": [ + "0.11.24\n" + ] } ] }, @@ -132,7 +134,42 @@ { "output_type": "execute_result", "data": { - "text/plain": " checking_status duration credit_history \\\n0 <0 6.0 critical/other existing credit \n1 0<=X<200 48.0 existing paid \n2 no checking 12.0 critical/other existing credit \n3 <0 42.0 existing paid \n4 <0 24.0 delayed previously \n\n purpose credit_amount savings_status employment \\\n0 radio/tv 1169.0 no known savings >=7 \n1 radio/tv 5951.0 <100 1<=X<4 \n2 education 2096.0 <100 4<=X<7 \n3 furniture/equipment 7882.0 <100 4<=X<7 \n4 new car 4870.0 <100 1<=X<4 \n\n installment_commitment personal_status other_parties residence_since \\\n0 4.0 male single none 4.0 \n1 2.0 female div/dep/mar none 2.0 \n2 2.0 male single none 3.0 \n3 2.0 male single guarantor 4.0 \n4 3.0 male single none 4.0 \n\n property_magnitude age other_payment_plans housing existing_credits \\\n0 real estate 67.0 none own 2.0 \n1 real estate 22.0 none own 1.0 \n2 real estate 49.0 none own 1.0 \n3 life insurance 45.0 none for free 1.0 \n4 no known property 53.0 none for free 2.0 \n\n job num_dependents own_telephone foreign_worker \n0 skilled 1.0 yes yes \n1 skilled 1.0 none yes \n2 unskilled resident 2.0 none yes \n3 skilled 2.0 none yes \n4 skilled 2.0 none yes ", + "text/plain": [ + " checking_status duration credit_history \\\n", + "0 <0 6.0 critical/other existing credit \n", + "1 0<=X<200 48.0 existing paid \n", + "2 no checking 12.0 critical/other existing credit \n", + "3 <0 42.0 existing paid \n", + "4 <0 24.0 delayed previously \n", + "\n", + " purpose credit_amount savings_status employment \\\n", + "0 radio/tv 1169.0 no known savings >=7 \n", + "1 radio/tv 5951.0 <100 1<=X<4 \n", + "2 education 2096.0 <100 4<=X<7 \n", + "3 furniture/equipment 7882.0 <100 4<=X<7 \n", + "4 new car 4870.0 <100 1<=X<4 \n", + "\n", + " installment_commitment personal_status other_parties residence_since \\\n", + "0 4.0 male single none 4.0 \n", + "1 2.0 female div/dep/mar none 2.0 \n", + "2 2.0 male single none 3.0 \n", + "3 2.0 male single guarantor 4.0 \n", + "4 3.0 male single none 4.0 \n", + "\n", + " property_magnitude age other_payment_plans housing existing_credits \\\n", + "0 real estate 67.0 none own 2.0 \n", + "1 real estate 22.0 none own 1.0 \n", + "2 real estate 49.0 none own 1.0 \n", + "3 life insurance 45.0 none for free 1.0 \n", + "4 no known property 53.0 none for free 2.0 \n", + "\n", + " job num_dependents own_telephone foreign_worker \n", + "0 skilled 1.0 yes yes \n", + "1 skilled 1.0 none yes \n", + "2 unskilled resident 2.0 none yes \n", + "3 skilled 2.0 none yes \n", + "4 skilled 2.0 none yes " + ], "text/html": "
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checking_statusdurationcredit_historypurposecredit_amountsavings_statusemploymentinstallment_commitmentpersonal_statusother_partiesresidence_sinceproperty_magnitudeageother_payment_planshousingexisting_creditsjobnum_dependentsown_telephoneforeign_worker
0<06.0critical/other existing creditradio/tv1169.0no known savings>=74.0male singlenone4.0real estate67.0noneown2.0skilled1.0yesyes
10<=X<20048.0existing paidradio/tv5951.0<1001<=X<42.0female div/dep/marnone2.0real estate22.0noneown1.0skilled1.0noneyes
2no checking12.0critical/other existing crediteducation2096.0<1004<=X<72.0male singlenone3.0real estate49.0noneown1.0unskilled resident2.0noneyes
3<042.0existing paidfurniture/equipment7882.0<1004<=X<72.0male singleguarantor4.0life insurance45.0nonefor free1.0skilled2.0noneyes
4<024.0delayed previouslynew car4870.0<1001<=X<43.0male singlenone4.0no known property53.0nonefor free2.0skilled2.0noneyes
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" }, "metadata": {}, @@ -167,7 +204,9 @@ { "output_type": "execute_result", "data": { - "text/plain": "((800, 20), (200, 20))" + "text/plain": [ + "((800, 20), (200, 20))" + ] }, "metadata": {}, "execution_count": 5 @@ -200,13 +239,7 @@ "model = BestSingleModelClassifier(X_train, y_train, X_test, random_state=RANDOM_SEED)" ], "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Source X_train shape: (800, 20) | X_test shape: (200, 20)\nAuto detect cat features: 13\n> Start preprocessing Data\n> Generate cat encodet features\n + 55 Features from OneHotEncoder\n + 44 Features from HelmertEncoder\n + 54 Features from HashingEncoder\n + 16 Features from FrequencyEncoder\n> Clean Nans in num features\n> Generate interaction Num Features\n + 24 Interaction Features\n##################################################\n> Total Generated Features: 185\nNew X_train shape: (800, 201) | X_test shape: (200, 201)\n##################################################\n" - } - ] + "outputs": [] }, { "cell_type": "code", @@ -232,7 +265,25 @@ { "output_type": "stream", "name": "stdout", - "text": "One iteration takes ~ 0.8 sec\nStart Auto calibration parameters\nStart optimization with the parameters:\nCV_Folds = 10\nScore_CV_Folds = 5\nOpt_lvl = 3\nCold_start = 80.0\nEarly_stoping = 160.0\nMetric = roc_auc_score\nDirection = maximize\nDefault model OptScore = 0.6256\n########################################\nOptimize: : 624it [15:18, 1.44s/it, | Model: LinearModel | OptScore: 0.7454 | Best roc_auc_score: 0.7621 +- 0.016687]\n EarlyStopping Exceeded: Best Score: 0.7454 roc_auc_score\nOptimize: : 624it [15:18, 1.47s/it, | Model: LinearModel | OptScore: 0.7454 | Best roc_auc_score: 0.7621 +- 0.016687]\n" + "text": [ + "One iteration takes ~ 3.2 sec\n", + "> Start Auto calibration parameters\n", + "\u001b[32m[I 2020-11-23 14:38:22,492]\u001b[0m A new study created in memory with name: no-name-45247feb-994a-46a6-ae21-c9604bef0640\u001b[0m\n", + "> Start optimization with the parameters:\n", + "CV_Folds = 10\n", + "Score_CV_Folds = 3\n", + "Feature_Selection = True\n", + "Opt_lvl = 2\n", + "Cold_start = 20.0\n", + "Early_stoping = 100\n", + "Metric = roc_auc_score\n", + "Direction = maximize\n", + "##################################################\n", + "Default model OptScore = 0.6574\n", + "Optimize: : 271it [06:19, 1.14s/it, | Model: LinearModel | OptScore: 0.768 | Best roc_auc_score: 0.7873 +- 0.019269]\n", + " EarlyStopping Exceeded: Best Score: 0.768 roc_auc_score\n", + "Optimize: : 271it [06:19, 1.40s/it, | Model: LinearModel | OptScore: 0.768 | Best roc_auc_score: 0.7873 +- 0.019269]\n" + ] } ] }, @@ -244,8 +295,36 @@ { "output_type": "execute_result", "data": { - "text/plain": " score_opt model_score score_std model_name \\\n462 0.7454 0.7621 0.016687 LinearModel \n582 0.7449 0.7675 0.022554 LinearModel \n607 0.7445 0.7666 0.022069 LinearModel \n568 0.7445 0.7667 0.022182 LinearModel \n545 0.7440 0.7658 0.021751 LinearModel \n\n model_param wrapper_params \\\n462 {'verbose': 0, 'fit_intercept': False, 'C': 95... {} \n582 {'verbose': 0, 'fit_intercept': False, 'C': 47... {} \n607 {'verbose': 0, 'fit_intercept': False, 'C': 30... {} \n568 {'verbose': 0, 'fit_intercept': False, 'C': 46... {} \n545 {'verbose': 0, 'fit_intercept': False, 'C': 91... {} \n\n cat_encoders \\\n462 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n582 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n607 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n568 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n545 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n\n columns cv_folds \n462 [num_dependents, OneHotEncoder_personal_status... 10 \n582 [num_dependents, OneHotEncoder_personal_status... 10 \n607 [num_dependents, OneHotEncoder_personal_status... 10 \n568 [num_dependents, OneHotEncoder_personal_status... 10 \n545 [num_dependents, OneHotEncoder_personal_status... 10 ", - "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
4620.74540.76210.016687LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 95...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[num_dependents, OneHotEncoder_personal_status...10
5820.74490.76750.022554LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 47...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[num_dependents, OneHotEncoder_personal_status...10
6070.74450.76660.022069LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 30...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[num_dependents, OneHotEncoder_personal_status...10
5680.74450.76670.022182LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 46...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[num_dependents, OneHotEncoder_personal_status...10
5450.74400.76580.021751LinearModel{'verbose': 0, 'fit_intercept': False, 'C': 91...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...[num_dependents, OneHotEncoder_personal_status...10
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" + "text/plain": [ + " score_opt model_score score_std model_name \\\n", + "169 0.7680 0.7873 0.019269 LinearModel \n", + "226 0.7664 0.7876 0.021219 LinearModel \n", + "130 0.7662 0.7758 0.009626 MLP \n", + "247 0.7661 0.7878 0.021671 LinearModel \n", + "214 0.7660 0.7871 0.021071 LinearModel \n", + "\n", + " model_param wrapper_params \\\n", + "169 {'fit_intercept': True, 'C': 75.1104945590015,... {} \n", + "226 {'fit_intercept': True, 'C': 13.59023095309521... {} \n", + "130 {'verbose': 0, 'random_state': 42, 'max_iter':... {} \n", + "247 {'fit_intercept': True, 'C': 16.30300999930395... {} \n", + "214 {'fit_intercept': True, 'C': 20.01505637206522... {} \n", + "\n", + " cat_encoders \\\n", + "169 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "226 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "130 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "247 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "214 [OneHotEncoder, HelmertEncoder, HashingEncoder... \n", + "\n", + " columns cv_folds \n", + "169 (OneHotEncoder_housing_3, OneHotEncoder_credit... 10 \n", + "226 (OneHotEncoder_housing_3, OneHotEncoder_credit... 10 \n", + "130 (OneHotEncoder_housing_1, OneHotEncoder_housin... 10 \n", + "247 (OneHotEncoder_housing_3, OneHotEncoder_credit... 10 \n", + "214 (OneHotEncoder_housing_3, OneHotEncoder_credit... 10 " + ], + "text/html": "
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score_optmodel_scorescore_stdmodel_namemodel_paramwrapper_paramscat_encoderscolumnscv_folds
1690.76800.78730.019269LinearModel{'fit_intercept': True, 'C': 75.1104945590015,...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_housing_3, OneHotEncoder_credit...10
2260.76640.78760.021219LinearModel{'fit_intercept': True, 'C': 13.59023095309521...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_housing_3, OneHotEncoder_credit...10
1300.76620.77580.009626MLP{'verbose': 0, 'random_state': 42, 'max_iter':...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_housing_1, OneHotEncoder_housin...10
2470.76610.78780.021671LinearModel{'fit_intercept': True, 'C': 16.30300999930395...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_housing_3, OneHotEncoder_credit...10
2140.76600.78710.021071LinearModel{'fit_intercept': True, 'C': 20.01505637206522...{}[OneHotEncoder, HelmertEncoder, HashingEncoder...(OneHotEncoder_housing_3, OneHotEncoder_credit...10
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" }, "metadata": {}, "execution_count": 8 @@ -264,8 +343,8 @@ "output_type": "display_data", "data": { "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "image/png": 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\n" }, "metadata": {} } @@ -284,7 +363,12 @@ { "output_type": "stream", "name": "stderr", - "text": "100%|██████████| 1/1 [00:01<00:00, 1.16s/it]\n Mean Score roc_auc_score on 30 Folds: 0.7757 std: 0.057386\nTest AUC: 0.8107\n\n" + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 1.13it/s]\n", + " Mean Score roc_auc_score on 20 Folds: 0.7713 std: 0.058559\n", + "Test AUC: 0.8289\n", + "\n" + ] } ], "source": [ @@ -292,13 +376,6 @@ "print('Test AUC: ', round(sklearn.metrics.roc_auc_score(y_test, predicts['predict_test'][0]),4))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -309,8 +386,13 @@ ], "metadata": { "kernelspec": { - "name": "python38364bitdscondadc8f49c0681c4f8d9aaeec09a1cf65fe", - "display_name": "Python 3.8.3 64-bit ('ds': conda)" + "name": "python3", + "display_name": "Python 3.8.6 64-bit", + "metadata": { + "interpreter": { + "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462" + } + } }, "toc": { "base_numbering": 1, diff --git a/tests/test_alexautoml.py b/tests/test_alexautoml.py index 3d064c8..bf6bac4 100644 --- a/tests/test_alexautoml.py +++ b/tests/test_alexautoml.py @@ -80,7 +80,7 @@ def test_BestSingleModelClassifier(get_data): def test_AutoMLClassifier(get_data): data = get_data test_model = AutoMLClassifier(databunch=data, random_state=RANDOM_SEED) - predict_test, predict_train = test_model.opt(timeout=1000, verbose=0,) + predict_test, predict_train = test_model.opt(timeout=1500, verbose=0,) assert predict_test is not None score = sklearn.metrics.roc_auc_score(data.y_test, predict_test) assert score is not None From a0d97a0b7b7fe1b16e4f0a19340013a0e49f11b2 Mon Sep 17 00:00:00 2001 From: Alex-Lekov <61644712+Alex-Lekov@users.noreply.github.com> Date: Mon, 23 Nov 2020 19:18:04 +0300 Subject: [PATCH 7/7] fix version name --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9152fba..9b0a303 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). -## [0.11.22] +## [0.11.24] ### ADD - multivariate TPE sampler. This algorithm captures dependencies among hyperparameters better than the previous algorithm