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提交 362c229d 编写于 作者: P PyCaret
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updated pycaret-nightly==0.13

上级 ea358d65
隐藏空白更改
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Showing with 218 addition and 44 deletion
pycaret/classification.py
浏览文件 @ 362c229d
......@@ -48,6 +48,8 @@ def setup(data,
feature_interaction = False,
feature_ratio = False,
interaction_threshold = 0.01,
fix_imbalance = False, #added in pycaret==2.0.0
fix_imbalance_method = None, #added in pycaret==2.0.0
data_split_shuffle = True, #added in pycaret==2.0.0
folds_shuffle = False, #added in pycaret==2.0.0
n_jobs = -1, #added in pycaret==2.0.0
......@@ -358,6 +360,16 @@ def setup(data,
percentile of the defined threshold are kept in the dataset. Remaining features
are dropped before further processing.
fix_imbalance: bool, default = False
When dataset has unequal distribution of target class it can be fixed using
fix_imbalance parameter. When set to True, SMOTE (Synthetic Minority Over-sampling
Technique) is applied by default to create synthetic datapoints for minority class.
fix_imbalance_method: obj, default = None
When fix_imbalance is set to True and fix_imbalance_method is None, 'smote' is applied
by default to oversample minority class during cross validation. This parameter
accepts any module from 'imblearn' that supports 'fit_resample' method.
data_split_shuffle: bool, default = True
If set to False, prevents shuffling of rows when splitting data
......@@ -781,7 +793,8 @@ def setup(data,
#declaring global variables to be accessed by other functions
global X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, experiment__,\
folds_shuffle_param, n_jobs_param, create_model_container, master_model_container,\
display_container, exp_name_log, logging_param, log_plots_param, USI
display_container, exp_name_log, logging_param, log_plots_param, USI,\
fix_imbalance_param, fix_imbalance_method_param
#generate seed to be used globally
if session_id is None:
......@@ -1192,6 +1205,15 @@ def setup(data,
else:
log_plots_param = False
#create a fix_imbalance_param and fix_imbalance_method_param
fix_imbalance_param = fix_imbalance
fix_imbalance_method_param = fix_imbalance_method
if fix_imbalance_method_param is None:
fix_imbalance_model_name = 'SMOTE'
else:
fix_imbalance_model_name = str(fix_imbalance_method_param).split("(")[0]
#sample estimator
if sample_estimator is None:
model = LogisticRegression()
......@@ -1392,9 +1414,9 @@ def setup(data,
['Missing Values ', missing_flag],
['Numeric Features ', str(float_type) ],
['Categorical Features ', str(cat_type) ],
['Ordinal Features ', ordinal_features_grid], #new
['High Cardinality Features ', high_cardinality_features_grid], #latest
['High Cardinality Method ', high_cardinality_method_grid], #latest
['Ordinal Features ', ordinal_features_grid],
['High Cardinality Features ', high_cardinality_features_grid],
['High Cardinality Method ', high_cardinality_method_grid],
['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ],
['Transformed Train Set', X_train.shape ],
['Transformed Test Set',X_test.shape ],
......@@ -1417,16 +1439,18 @@ def setup(data,
['Multicollinearity Threshold ', multicollinearity_threshold_grid],
['Clustering ', create_clusters],
['Clustering Iteration ', cluster_iter_grid],
['Polynomial Features ', polynomial_features], #new
['Polynomial Degree ', polynomial_degree_grid], #new
['Trignometry Features ', trigonometry_features], #new
['Polynomial Threshold ', polynomial_threshold_grid], #new
['Group Features ', group_features_grid], #new
['Feature Selection ', feature_selection], #new
['Features Selection Threshold ', feature_selection_threshold_grid], #new
['Feature Interaction ', feature_interaction], #new
['Feature Ratio ', feature_ratio], #new
['Interaction Threshold ', interaction_threshold_grid], #new
['Polynomial Features ', polynomial_features],
['Polynomial Degree ', polynomial_degree_grid],
['Trignometry Features ', trigonometry_features],
['Polynomial Threshold ', polynomial_threshold_grid],
['Group Features ', group_features_grid],
['Feature Selection ', feature_selection],
['Features Selection Threshold ', feature_selection_threshold_grid],
['Feature Interaction ', feature_interaction],
['Feature Ratio ', feature_ratio],
['Interaction Threshold ', interaction_threshold_grid],
['Fix Imbalance', fix_imbalance_param],
['Fix Imbalance Method', fix_imbalance_model_name]
], columns = ['Description', 'Value'] )
functions_ = functions.style.apply(highlight_max)
......@@ -1486,9 +1510,9 @@ def setup(data,
['Missing Values ', missing_flag],
['Numeric Features ', str(float_type) ],
['Categorical Features ', str(cat_type) ],
['Ordinal Features ', ordinal_features_grid], #new
['Ordinal Features ', ordinal_features_grid],
['High Cardinality Features ', high_cardinality_features_grid],
['High Cardinality Method ', high_cardinality_method_grid], #latest
['High Cardinality Method ', high_cardinality_method_grid],
['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ],
['Transformed Train Set', X_train.shape ],
['Transformed Test Set',X_test.shape ],
......@@ -1511,16 +1535,18 @@ def setup(data,
['Multicollinearity Threshold ', multicollinearity_threshold_grid],
['Clustering ', create_clusters],
['Clustering Iteration ', cluster_iter_grid],
['Polynomial Features ', polynomial_features], #new
['Polynomial Degree ', polynomial_degree_grid], #new
['Trignometry Features ', trigonometry_features], #new
['Polynomial Threshold ', polynomial_threshold_grid], #new
['Group Features ', group_features_grid], #new
['Feature Selection ', feature_selection], #new
['Features Selection Threshold ', feature_selection_threshold_grid], #new
['Feature Interaction ', feature_interaction], #new
['Feature Ratio ', feature_ratio], #new
['Interaction Threshold ', interaction_threshold_grid], #new
['Polynomial Features ', polynomial_features],
['Polynomial Degree ', polynomial_degree_grid],
['Trignometry Features ', trigonometry_features],
['Polynomial Threshold ', polynomial_threshold_grid],
['Group Features ', group_features_grid],
['Feature Selection ', feature_selection],
['Features Selection Threshold ', feature_selection_threshold_grid],
['Feature Interaction ', feature_interaction],
['Feature Ratio ', feature_ratio],
['Interaction Threshold ', interaction_threshold_grid],
['Fix Imbalance', fix_imbalance_param],
['Fix Imbalance Method', fix_imbalance_model_name]
], columns = ['Description', 'Value'] )
#functions_ = functions.style.hide_index()
......@@ -1579,9 +1605,9 @@ def setup(data,
['Missing Values ', missing_flag],
['Numeric Features ', str(float_type) ],
['Categorical Features ', str(cat_type) ],
['Ordinal Features ', ordinal_features_grid], #new
['Ordinal Features ', ordinal_features_grid],
['High Cardinality Features ', high_cardinality_features_grid],
['High Cardinality Method ', high_cardinality_method_grid], #latest
['High Cardinality Method ', high_cardinality_method_grid],
['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ],
['Transformed Train Set', X_train.shape ],
['Transformed Test Set',X_test.shape ],
......@@ -1604,16 +1630,18 @@ def setup(data,
['Multicollinearity Threshold ', multicollinearity_threshold_grid],
['Clustering ', create_clusters],
['Clustering Iteration ', cluster_iter_grid],
['Polynomial Features ', polynomial_features], #new
['Polynomial Degree ', polynomial_degree_grid], #new
['Trignometry Features ', trigonometry_features], #new
['Polynomial Threshold ', polynomial_threshold_grid], #new
['Group Features ', group_features_grid], #new
['Feature Selection ', feature_selection], #new
['Features Selection Threshold ', feature_selection_threshold_grid], #new
['Feature Interaction ', feature_interaction], #new
['Feature Ratio ', feature_ratio], #new
['Interaction Threshold ', interaction_threshold_grid], #new
['Polynomial Features ', polynomial_features],
['Polynomial Degree ', polynomial_degree_grid],
['Trignometry Features ', trigonometry_features],
['Polynomial Threshold ', polynomial_threshold_grid],
['Group Features ', group_features_grid],
['Feature Selection ', feature_selection],
['Features Selection Threshold ', feature_selection_threshold_grid],
['Feature Interaction ', feature_interaction],
['Feature Ratio ', feature_ratio],
['Interaction Threshold ', interaction_threshold_grid],
['Fix Imbalance', fix_imbalance_param],
['Fix Imbalance Method', fix_imbalance_model_name]
], columns = ['Description', 'Value'] )
functions_ = functions.style.apply(highlight_max)
......@@ -1739,14 +1767,15 @@ def setup(data,
return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, experiment__,\
folds_shuffle_param, n_jobs_param, html_param, create_model_container, master_model_container,\
display_container, exp_name_log, logging_param, log_plots_param, USI
display_container, exp_name_log, logging_param, log_plots_param, USI,\
fix_imbalance_param, fix_imbalance_method_param
def create_model(estimator = None,
ensemble = False,
method = None,
fold = 10,
round = 4,
round = 4,
verbose = True,
system = True, #added in pycaret==2.0.0
**kwargs): #added in pycaret==2.0.0
......@@ -2209,8 +2238,19 @@ def create_model(estimator = None,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if hasattr(model, 'predict_proba'):
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state=seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if hasattr(model, 'predict_proba'):
model.fit(Xtrain,ytrain)
pred_prob = model.predict_proba(Xtest)
pred_prob = pred_prob[:,1]
......@@ -2877,6 +2917,17 @@ def ensemble_model(estimator,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state=seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if hasattr(model, 'predict_proba'):
model.fit(Xtrain,ytrain)
......@@ -4360,6 +4411,16 @@ def compare_models(blacklist = None,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if hasattr(model, 'predict_proba'):
time_start=time.time()
model.fit(Xtrain,ytrain)
......@@ -5463,6 +5524,17 @@ def tune_model(estimator = None,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if hasattr(model, 'predict_proba'):
model.fit(Xtrain,ytrain)
......@@ -6289,6 +6361,17 @@ def blend_models(estimator_list = 'All',
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if voting == 'hard':
model.fit(Xtrain,ytrain)
......@@ -7035,6 +7118,16 @@ def stack_models(estimator_list,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
time_start=time.time()
model.fit(Xtrain,ytrain)
......@@ -7860,6 +7953,17 @@ def create_stacknet(estimator_list,
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
model.fit(Xtrain,ytrain)
try:
pred_prob = model.predict_proba(Xtest)
......@@ -8664,6 +8768,17 @@ def calibrate_model(estimator,
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
if fix_imbalance_param:
if fix_imbalance_method_param is None:
from imblearn.over_sampling import SMOTE
resampler = SMOTE(random_state = seed)
else:
resampler = fix_imbalance_method_param
Xtrain,ytrain = resampler.fit_sample(Xtrain, ytrain)
if hasattr(model, 'predict_proba'):
model.fit(Xtrain,ytrain)
......@@ -10654,3 +10769,61 @@ def automl(optimize='Accuracy', use_holdout=False):
def pull():
return display_container[-1]
def models():
"""
returns table of models available in model zoo
"""
import pandas as pd
model_id = ['lr', 'knn', 'nb', 'dt', 'svm', 'rbfsvm', 'gpc', 'mlp', 'ridge', 'rf', 'qda', 'ada', 'gbc', 'lda', 'et', 'xgboost', 'lightgbm', 'catboost']
model_name = ['Logistic Regression',
'K Neighbors Classifier',
'Naive Bayes',
'Decision Tree Classifier',
'SVM - Linear Kernel',
'SVM - Radial Kernel',
'Gaussian Process Classifier',
'MLP Classifier',
'Ridge Classifier',
'Random Forest Classifier',
'Quadratic Discriminant Analysis',
'Ada Boost Classifier',
'Gradient Boosting Classifier',
'Linear Discriminant Analysis',
'Extra Trees Classifier',
'Extreme Gradient Boosting',
'Light Gradient Boosting Machine',
'CatBoost Classifier']
model_ref = ['sklearn.linear_model.LogisticRegression',
'sklearn.neighbors.KNeighborsClassifier',
'sklearn.naive_bayes.GaussianNB',
'sklearn.tree.DecisionTreeClassifier',
'sklearn.linear_model.SGDClassifier',
'sklearn.svm.SVC',
'sklearn.gaussian_process.GPC',
'sklearn.neural_network.MLPClassifier',
'sklearn.linear_model.RidgeClassifier',
'sklearn.ensemble.RandomForestClassifier',
'sklearn.discriminant_analysis.QDA',
'sklearn.ensemble.AdaBoostClassifier',
'sklearn.ensemble.GradientBoostingClassifier',
'sklearn.discriminant_analysis.LDA',
'sklearn.ensemble.ExtraTreesClassifier',
'xgboost.readthedocs.io',
'github.com/microsoft/LightGBM',
'catboost.ai']
model_turbo = [True, True, True, True, True, False, False, False, True,
True, True, True, True, True, True, True, True, True]
df = pd.DataFrame({'ID' : model_id,
'Name' : model_name,
'Reference' : model_ref,
'Turbo' : model_turbo})
df.set_index('ID', inplace=True)
return df
\ No newline at end of file
pycaret/utils.py
浏览文件 @ 362c229d
......@@ -3,7 +3,7 @@
# License: MIT
def version():
print("pycaret-nightly-0.12")
print("pycaret-nightly-0.13")
def check_metric(actual, prediction, metric, round=4):
......
requirements.txt
浏览文件 @ 362c229d
......@@ -27,4 +27,5 @@ kmodes>=0.10.1
datefinder>=0.7.0
DateTime>=4.3
awscli
mlflow
\ No newline at end of file
mlflow
imbalanced-learn
\ No newline at end of file
setup.py
浏览文件 @ 362c229d
......@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
setup(
name="pycaret-nightly",
version="0.12",
version="0.13",
description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
long_description=readme(),
long_description_content_type="text/markdown",
......
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