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README.md
浏览文件 @ 6fe0a3ec
......@@ -2,10 +2,10 @@
PyCaret is end-to-end open source machine learning library for python programming language. Its primary objective is to reduce the cycle time of hypothesis to insights by providing an easy to use high level unified API. PyCaret's vision is to become defacto standard for teaching machine learning and data science. Our strength is in our easy to use unified interface for both supervised and unsupervised learning. It saves time and effort that citizen data scientists, students and researchers spent on coding or learning to code using different interfaces, so that now they can focus on business problem.
## Current Release
The current release is beta 0.0.28 (as of 29/01/2020). A full release is targetted in the first week of February 2020.
The current release is beta 0.0.29 (as of 30/01/2020). A full release is targetted in the first week of February 2020.
## Features Currently Available
As per beta 0.0.28 following modules are generally available:
As per beta 0.0.29 following modules are generally available:
* pycaret.datasets <br/>
* pycaret.classification (binary and multiclass) <br/>
* pycaret.regression <br/>
......@@ -31,7 +31,7 @@ pip install pycaret
```
## Quick Start
As of beta 0.0.28 classification, regression, nlp, arules, anomaly and clustering modules are available.
As of beta 0.0.29 classification, regression, nlp, arules, anomaly and clustering modules are available.
### Classification / Regression
......
anomaly.py
浏览文件 @ 6fe0a3ec
......@@ -555,7 +555,12 @@ def setup(data,
if supervised is False:
experiment__ = []
else:
pass
try:
experiment__.append('dummy')
experiment__.remove('dummy')
except:
experiment__ = []
progress.value += 1
......
classification.py
浏览文件 @ 6fe0a3ec
......@@ -3,6 +3,7 @@
# License: MIT
def setup(data,
target,
train_size = 0.7,
......@@ -18,6 +19,8 @@ def setup(data,
normalize_method = 'zscore',
transformation = False,
transformation_method = 'yeo-johnson',
handle_unknown_categorical = True, #new #create docstring and exception
unknown_categorical_method = 'least_frequent', #new #create docstring and exception
pca = False, #new
pca_method = 'linear', #new
pca_components = None, #new
......@@ -31,6 +34,17 @@ def setup(data,
multicollinearity_threshold = 0.9, #new
create_clusters = False, #new
cluster_iter = 20, #new
polynomial_features = False, #new #create checking exceptions and docstring
polynomial_degree = 2, #new #create checking exceptions and docstring
trigonometry_features = False, #new #create checking exceptions and docstring
polynomial_threshold = 0.1, #new #create checking exceptions and docstring
group_features = None, #new #create checking exceptions and docstring
group_names = None, #new #create checking exceptions and docstring
feature_selection = False, #new #create checking exceptions and docstring
feature_selection_threshold = 0.8, #new #create checking exceptions and docstring
feature_interaction = False, #new #create checking exceptions and docstring
feature_ratio = False, #new #create checking exceptions and docstring
interaction_threshold = 0.01, #new #create checking exceptions and docstring
session_id = None,
profile = False):
......@@ -150,7 +164,16 @@ def setup(data,
the transformation transforms the feature set to follow Gaussian-like or normal
distribution. Note that quantile transformer is non-linear and may distort linear
correlations between variables measured at the same scale.
handle_unknown_categorical: bool, default = True
When set to True, unknown categorical levels in new / unseen data is replaced by
most or least frequent level in as learned in training data. The method is defined
under unknown_categorical_method param.
unknown_categorical_method: string, default = 'least_frequent'
Method to be used to replace unknown categorical level in unseen data. Method can
be 'least_frequent' or 'most_frequent'.
pca: bool, default = False
When set to True, dimensionality reduction is applied to project the data into
lower dimensional space using the method defined in pca_method param. Generally,
......@@ -182,10 +205,11 @@ def setup(data,
common value.
combine_rare_levels: bool, default = False
When set to True, All levels in categorical features below the threshold defined in
rare_level_threshold param is combined together as a single level. rare_level_threshold
represents the percentile distribution of specific level. Generally, this features is
applied to limit the sparse matrix caused by high number of levels in categorical
When set to True, All levels in categorical features below the threshold defined
in rare_level_threshold param is combined together as a single level. There must be
atleast two levels under threshold for this to take effect. rare_level_threshold
represents the percentile distribution of level frequency. Generally, this features
is applied to limit the sparse matrix caused by high number of levels in categorical
features.
rare_level_threshold: float, default = 0.1
......@@ -200,10 +224,8 @@ def setup(data,
non-gaussian datasets.
remove_outliers: bool, default = False
When set to True, outliers from the training data is removed using ensemble of
Isolation Forest, K Nearest Neighbour and PCA Outlier detector. All of them are
unsupervised techniques. The contamination percentage is defined using the
outliers_threshold parameter.
When set to True, outliers from the training data is removed using PCA linear
dimensionality reduction using Singular Value Decomposition technique.
outliers_threshold: float, default = 0.05
The percentage / proportion of outliers in the dataset can be defined using
......@@ -229,6 +251,70 @@ def setup(data,
Number of iterations for creating cluster. Each iteration represent cluster size.
Only comes into effect when create_clusters param is set to True.
polynomial_features: bool, default = False
When set to True, it creates new features of all polynomial combinations of existing
numeric features in dataset with degree defined in polynomial_degree param.
polynomial_degree: int, default = 2
Degree of polynomial features. For example, if an input sample is two dimensional and
of the form [a, b], polynomial features with degree = 2 are: [1, a, b, a^2, ab, b^2].
trigonometry_features: bool, default = False
When set to True, it creates new features of all trigonometric combinations of existing
numeric features in dataset with degree defined in polynomial_degree param.
polynomial_threshold: float, default = 0.1
This is used to compress the sparse matrix of polynomial and trigonometric features.
Polynomial and trigonometric features whose feature importance based on the combination
of Random Forest, AdaBoost and Linear correlation is within the percentile of threshold
defined are kept in the dataset, remaining features are dropped before further processing.
group_features: list or list of list, default = None
When data contains features that contains related characteristics, it can be used for
statistical feature extraction. For example, if dataset has numeric features that are
related with each other such as 'Column1', 'Column2', 'Column3', a list containing
column names can be passed under group_features to extract statistical information
such as mean, median, mode and standard deviation.
group_names: list, default = None
When group_features is passed, a name of the group can be passed in group_names param
as a list containing string. The length of group_names list must equal to the length
of group_features. When the length doesn't match or name is not passed, new features
are sequentially named such as group_1, group_2 etc.
feature_selection: bool, default = False
When set to True, subset of features are selected using combination of various
permutation importance techniques including Random Forest, Adaboost and Linear
correlation with target variable. The size of subset is dependent on the
feature_selection_param. Generally, this is used to constraint the feature
space for efficiency in modeling. When polynomial_features and feature_interaction
is used, it is highly recommended to use feature_selection with lower values of
feature_selection_threshold.
feature_selection_threshold: float, default = 0.8
Threshold used for feature selection (including the newly created polynomial features)
Higher value will result in high feature space. It is recommended to do multiple trials
with different feature_selection_threshold specially in case where polynomial_features
and feature_interaction is used. Setting very low value may be efficient but may result
in under-fitting.
feature_interaction: bool, default = False
When set to True, it will create new features by interacting (a * b) all numeric
variables in the dataset (including polynomial and trigonometric (if created).
This feature is not scalable and may not work as expected on dataset with high
feature space.
feature_ratio: bool, default = False
When set to True, it will create new features by calculating ratios (a / b) all numeric
variables in the dataset. This feature is not scalable and may not work as expected on
dataset with high feature space.
interaction_threshold: bool, default = 0.01
Similar to polynomial_threshold, It is used to compress the sparse matrix of newly
features through interaction. Features whose importance based on the combination of
Random Forest, AdaBoost and Linear correlation is within the percentile of threshold
defined are kept in the dataset, remaining features are dropped before further processing.
session_id: int, default = None
If None, a random seed is generated and returned in the Information grid. The
unique number is then distributed as a seed in all functions used during the
......@@ -308,6 +394,16 @@ def setup(data,
if transformation_method not in allowed_transformation_method:
sys.exit("(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile'. ")
#handle unknown categorical
if type(handle_unknown_categorical) is not bool:
sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')
#unknown categorical method
unknown_categorical_method_available = ['least_frequent', 'most_frequent']
if unknown_categorical_method not in unknown_categorical_method_available:
sys.exit("(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.")
#check pca
if type(pca) is not bool:
sys.exit('(Type Error): PCA parameter only accepts True or False.')
......@@ -375,11 +471,6 @@ def setup(data,
#multicollinearity_threshold
if type(multicollinearity_threshold) is not float:
sys.exit('(Type Error): multicollinearity_threshold must be a float between 0 and 1. ')
#multicollinearity and multiclass check
if data[target].value_counts().count() > 2:
if remove_multicollinearity is True:
sys.exit('(Type Error): remove_multicollinearity cannot be used when target is multiclass. ')
#create_clusters
if type(create_clusters) is not bool:
......@@ -389,10 +480,56 @@ def setup(data,
if type(cluster_iter) is not int:
sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ')
#polynomial_features
if type(polynomial_features) is not bool:
sys.exit('(Type Error): polynomial_features only accepts True or False. ')
#polynomial_degree
if type(polynomial_degree) is not int:
sys.exit('(Type Error): polynomial_degree must be an integer. ')
#polynomial_features
if type(trigonometry_features) is not bool:
sys.exit('(Type Error): trigonometry_features only accepts True or False. ')
#polynomial threshold
if type(polynomial_threshold) is not float:
sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ')
#group features
if group_features is not None:
if type(group_features) is not list:
sys.exit('(Type Error): group_features must be of type list. ')
if group_names is not None:
if type(group_names) is not list:
sys.exit('(Type Error): group_names must be of type list. ')
#cannot drop target
if ignore_features is not None:
if target in ignore_features:
sys.exit("(Value Error): cannot drop target column. ")
#feature_selection
if type(feature_selection) is not bool:
sys.exit('(Type Error): feature_selection only accepts True or False. ')
#feature_selection_threshold
if type(feature_selection_threshold) is not float:
sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ')
#feature_interaction
if type(feature_interaction) is not bool:
sys.exit('(Type Error): feature_interaction only accepts True or False. ')
#feature_ratio
if type(feature_ratio) is not bool:
sys.exit('(Type Error): feature_ratio only accepts True or False. ')
#interaction_threshold
if type(interaction_threshold) is not float:
sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ')
#forced type check
all_cols = list(data.columns)
......@@ -555,6 +692,68 @@ def setup(data,
else:
apply_binning_pass = True
features_to_bin_pass = bin_numeric_features
#trignometry
if trigonometry_features is False:
trigonometry_features_pass = []
else:
trigonometry_features_pass = ['sin', 'cos', 'tan']
#group features
#=============#
#apply grouping
if group_features is not None:
apply_grouping_pass = True
else:
apply_grouping_pass = False
#group features listing
if apply_grouping_pass is True:
if type(group_features[0]) is str:
group_features_pass = []
group_features_pass.append(group_features)
else:
group_features_pass = group_features
else:
group_features_pass = [[]]
#group names
if apply_grouping_pass is True:
if (group_names is None) or (len(group_names) != len(group_features_pass)):
group_names_pass = list(np.arange(len(group_features_pass)))
group_names_pass = ['group_' + str(i) for i in group_names_pass]
else:
group_names_pass = group_names
else:
group_names_pass = []
#feature interactions
if feature_interaction or feature_ratio:
apply_feature_interactions_pass = True
else:
apply_feature_interactions_pass = False
interactions_to_apply_pass = []
if feature_interaction:
interactions_to_apply_pass.append('multiply')
if feature_ratio:
interactions_to_apply_pass.append('divide')
#uknown categorical
if unknown_categorical_method == 'least_frequent':
unknown_categorical_method_pass = 'least frequent'
elif unknown_categorical_method == 'most_frequent':
unknown_categorical_method_pass = 'most frequent'
#import library
from pycaret import preprocess
......@@ -571,6 +770,8 @@ def setup(data,
scaling_method = normalize_method,
Power_transform_data = transformation,
Power_transform_method = trans_method_pass,
apply_untrained_levels_treatment= handle_unknown_categorical, #new
untrained_levels_treatment_method = unknown_categorical_method_pass, #new
apply_pca = pca, #new
pca_method = pca_method_pass, #new
pca_variance_retained_or_number_of_components = pca_components_pass, #new
......@@ -581,10 +782,23 @@ def setup(data,
features_to_binn = features_to_bin_pass, #new
remove_outliers = remove_outliers, #new
outlier_contamination_percentage = outliers_threshold, #new
outlier_methods = ['pca'], #pca hardcoded
remove_multicollinearity = remove_multicollinearity, #new
maximum_correlation_between_features = multicollinearity_threshold, #new
cluster_entire_data = create_clusters, #new
range_of_clusters_to_try = cluster_iter, #new
apply_polynomial_trigonometry_features = polynomial_features, #new
max_polynomial = polynomial_degree, #new
trigonometry_calculations = trigonometry_features_pass, #new
top_poly_trig_features_to_select_percentage = polynomial_threshold, #new
apply_grouping = apply_grouping_pass, #new
features_to_group_ListofList = group_features_pass, #new
group_name = group_names_pass, #new
apply_feature_selection = feature_selection, #new
feature_selection_top_features_percentage = feature_selection_threshold, #new
apply_feature_interactions = apply_feature_interactions_pass, #new
feature_interactions_to_apply = interactions_to_apply_pass, #new
feature_interactions_top_features_to_select_percentage=interaction_threshold, #new
display_types = True, #this is for inferred input box
target_transformation = False, #not needed for classification
random_state = seed)
......
clustering.py
浏览文件 @ 6fe0a3ec
......@@ -557,7 +557,11 @@ def setup(data,
if supervised is False:
experiment__ = []
else:
pass
try:
experiment__.append('dummy')
experiment__.remove('dummy')
except:
experiment__ = []
progress.value += 1
......
preprocess.py
浏览文件 @ 6fe0a3ec
此差异已折叠。
regression.py
浏览文件 @ 6fe0a3ec
......@@ -3,6 +3,7 @@
# License: MIT
def setup(data,
target,
train_size=0.7,
......@@ -18,6 +19,8 @@ def setup(data,
normalize_method = 'zscore',
transformation = False,
transformation_method = 'yeo-johnson',
handle_unknown_categorical = True, #new #create docstring and exception
unknown_categorical_method = 'least_frequent', #new #create docstring and exception
pca = False, #new
pca_method = 'linear', #new
pca_components = None, #new
......@@ -31,6 +34,17 @@ def setup(data,
multicollinearity_threshold = 0.9, #new
create_clusters = False, #new
cluster_iter = 20, #new
polynomial_features = False, #new #create checking exceptions and docstring
polynomial_degree = 2, #new #create checking exceptions and docstring
trigonometry_features = False, #new #create checking exceptions and docstring
polynomial_threshold = 0.1, #new #create checking exceptions and docstring
group_features = None, #new #create checking exceptions and docstring
group_names = None, #new #create checking exceptions and docstring
feature_selection = False, #new #create checking exceptions and docstring
feature_selection_threshold = 0.8, #new #create checking exceptions and docstring
feature_interaction = False, #new #create checking exceptions and docstring
feature_ratio = False, #new #create checking exceptions and docstring
interaction_threshold = 0.01, #new #create checking exceptions and docstring
transform_target = False, #new
transform_target_method = 'box-cox', #new
session_id = None,
......@@ -151,6 +165,15 @@ def setup(data,
distribution. Note that quantile transformer is non-linear and may distort linear
correlations between variables measured at the same scale.
handle_unknown_categorical: bool, default = True
When set to True, unknown categorical levels in new / unseen data is replaced by
most or least frequent level in as learned in training data. The method is defined
under unknown_categorical_method param.
unknown_categorical_method: string, default = 'least_frequent'
Method to be used to replace unknown categorical level in unseen data. Method can
be 'least_frequent' or 'most_frequent'.
pca: bool, default = False
When set to True, dimensionality reduction is applied to project the data into
lower dimensional space using the method defined in pca_method param. Generally,
......@@ -228,7 +251,71 @@ def setup(data,
cluster_iter: int, default = 20
Number of iterations for creating cluster. Each iteration represent cluster size.
Only comes into effect when create_clusters param is set to True.
polynomial_features: bool, default = False
When set to True, it creates new features of all polynomial combinations of existing
numeric features in dataset with degree defined in polynomial_degree param.
polynomial_degree: int, default = 2
Degree of polynomial features. For example, if an input sample is two dimensional and
of the form [a, b], polynomial features with degree = 2 are: [1, a, b, a^2, ab, b^2].
trigonometry_features: bool, default = False
When set to True, it creates new features of all trigonometric combinations of existing
numeric features in dataset with degree defined in polynomial_degree param.
polynomial_threshold: float, default = 0.1
This is used to compress the sparse matrix of polynomial and trigonometric features.
Polynomial and trigonometric features whose feature importance based on the combination
of Random Forest, AdaBoost and Linear correlation is within the percentile of threshold
defined are kept in the dataset, remaining features are dropped before further processing.
group_features: list or list of list, default = None
When data contains features that contains related characteristics, it can be used for
statistical feature extraction. For example, if dataset has numeric features that are
related with each other such as 'Column1', 'Column2', 'Column3', a list containing
column names can be passed under group_features to extract statistical information
such as mean, median, mode and standard deviation.
group_names: list, default = None
When group_features is passed, a name of the group can be passed in group_names param
as a list containing string. The length of group_names list must equal to the length
of group_features. When the length doesn't match or name is not passed, new features
are sequentially named such as group_1, group_2 etc.
feature_selection: bool, default = False
When set to True, subset of features are selected using combination of various
permutation importance techniques including Random Forest, Adaboost and Linear
correlation with target variable. The size of subset is dependent on the
feature_selection_param. Generally, this is used to constraint the feature
space for efficiency in modeling. When polynomial_features and feature_interaction
is used, it is highly recommended to use feature_selection with lower values of
feature_selection_threshold.
feature_selection_threshold: float, default = 0.8
Threshold used for feature selection (including the newly created polynomial features)
Higher value will result in high feature space. It is recommended to do multiple trials
with different feature_selection_threshold specially in case where polynomial_features
and feature_interaction is used. Setting very low value may be efficient but may result
in under-fitting.
feature_interaction: bool, default = False
When set to True, it will create new features by interacting (a * b) all numeric
variables in the dataset (including polynomial and trigonometric (if created).
This feature is not scalable and may not work as expected on dataset with high
feature space.
feature_ratio: bool, default = False
When set to True, it will create new features by calculating ratios (a / b) all numeric
variables in the dataset. This feature is not scalable and may not work as expected on
dataset with high feature space.
interaction_threshold: bool, default = 0.01
Similar to polynomial_threshold, It is used to compress the sparse matrix of newly
features through interaction. Features whose importance based on the combination of
Random Forest, AdaBoost and Linear correlation is within the percentile of threshold
defined are kept in the dataset, remaining features are dropped before further processing.
transform_target: bool, default = False
When set to True, target variable is transformed using the method defined in
transform_target_method param. Target transformation is applied separately from
......@@ -288,7 +375,6 @@ def setup(data,
if type(profile) is not bool:
sys.exit('(Type Error): profile parameter only accepts True or False.')
#checking normalize parameter
if type(normalize) is not bool:
sys.exit('(Type Error): normalize parameter only accepts True or False.')
......@@ -316,7 +402,17 @@ def setup(data,
allowed_transformation_method = ['yeo-johnson', 'quantile']
if transformation_method not in allowed_transformation_method:
sys.exit("(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile' ")
#handle unknown categorical
if type(handle_unknown_categorical) is not bool:
sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')
#unknown categorical method
unknown_categorical_method_available = ['least_frequent', 'most_frequent']
if unknown_categorical_method not in unknown_categorical_method_available:
sys.exit("(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.")
#check pca
if type(pca) is not bool:
sys.exit('(Type Error): PCA parameter only accepts True or False.')
......@@ -401,7 +497,58 @@ def setup(data,
#cluster_iter
if type(cluster_iter) is not int:
sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ')
#polynomial_features
if type(polynomial_features) is not bool:
sys.exit('(Type Error): polynomial_features only accepts True or False. ')
#polynomial_degree
if type(polynomial_degree) is not int:
sys.exit('(Type Error): polynomial_degree must be an integer. ')
#polynomial_features
if type(trigonometry_features) is not bool:
sys.exit('(Type Error): trigonometry_features only accepts True or False. ')
#polynomial threshold
if type(polynomial_threshold) is not float:
sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ')
#group features
if group_features is not None:
if type(group_features) is not list:
sys.exit('(Type Error): group_features must be of type list. ')
if group_names is not None:
if type(group_names) is not list:
sys.exit('(Type Error): group_names must be of type list. ')
#cannot drop target
if ignore_features is not None:
if target in ignore_features:
sys.exit("(Value Error): cannot drop target column. ")
#feature_selection
if type(feature_selection) is not bool:
sys.exit('(Type Error): feature_selection only accepts True or False. ')
#feature_selection_threshold
if type(feature_selection_threshold) is not float:
sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ')
#feature_interaction
if type(feature_interaction) is not bool:
sys.exit('(Type Error): feature_interaction only accepts True or False. ')
#feature_ratio
if type(feature_ratio) is not bool:
sys.exit('(Type Error): feature_ratio only accepts True or False. ')
#interaction_threshold
if type(interaction_threshold) is not float:
sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ')
#cannot drop target
if ignore_features is not None:
if target in ignore_features:
......@@ -570,6 +717,68 @@ def setup(data,
apply_binning_pass = True
features_to_bin_pass = bin_numeric_features
#trignometry
if trigonometry_features is False:
trigonometry_features_pass = []
else:
trigonometry_features_pass = ['sin', 'cos', 'tan']
#group features
#=============#
#apply grouping
if group_features is not None:
apply_grouping_pass = True
else:
apply_grouping_pass = False
#group features listing
if apply_grouping_pass is True:
if type(group_features[0]) is str:
group_features_pass = []
group_features_pass.append(group_features)
else:
group_features_pass = group_features
else:
group_features_pass = [[]]
#group names
if apply_grouping_pass is True:
if (group_names is None) or (len(group_names) != len(group_features_pass)):
group_names_pass = list(np.arange(len(group_features_pass)))
group_names_pass = ['group_' + str(i) for i in group_names_pass]
else:
group_names_pass = group_names
else:
group_names_pass = []
#feature interactions
if feature_interaction or feature_ratio:
apply_feature_interactions_pass = True
else:
apply_feature_interactions_pass = False
interactions_to_apply_pass = []
if feature_interaction:
interactions_to_apply_pass.append('multiply')
if feature_ratio:
interactions_to_apply_pass.append('divide')
#uknown categorical
if unknown_categorical_method == 'least_frequent':
unknown_categorical_method_pass = 'least frequent'
elif unknown_categorical_method == 'most_frequent':
unknown_categorical_method_pass = 'most frequent'
#transform target method
if transform_target_method == 'box-cox':
transform_target_method_pass = 'bc'
......@@ -591,6 +800,8 @@ def setup(data,
scaling_method = normalize_method,
Power_transform_data = transformation,
Power_transform_method = trans_method_pass,
apply_untrained_levels_treatment= handle_unknown_categorical, #new
untrained_levels_treatment_method = unknown_categorical_method_pass, #new
apply_pca = pca, #new
pca_method = pca_method_pass, #new
pca_variance_retained_or_number_of_components = pca_components_pass, #new
......@@ -601,10 +812,23 @@ def setup(data,
features_to_binn = features_to_bin_pass, #new
remove_outliers = remove_outliers, #new
outlier_contamination_percentage = outliers_threshold, #new
outlier_methods = ['pca'], #pca hardcoded
remove_multicollinearity = remove_multicollinearity, #new
maximum_correlation_between_features = multicollinearity_threshold, #new
cluster_entire_data = create_clusters, #new
range_of_clusters_to_try = cluster_iter, #new
apply_polynomial_trigonometry_features = polynomial_features, #new
max_polynomial = polynomial_degree, #new
trigonometry_calculations = trigonometry_features_pass, #new
top_poly_trig_features_to_select_percentage = polynomial_threshold, #new
apply_grouping = apply_grouping_pass, #new
features_to_group_ListofList = group_features_pass, #new
group_name = group_names_pass, #new
apply_feature_selection = feature_selection, #new
feature_selection_top_features_percentage = feature_selection_threshold, #new
apply_feature_interactions = apply_feature_interactions_pass, #new
feature_interactions_to_apply = interactions_to_apply_pass, #new
feature_interactions_top_features_to_select_percentage=interaction_threshold, #new
display_types = True, #new #to be parameterized in setup later.
target_transformation = transform_target, #new
target_transformation_method = transform_target_method_pass, #new
......@@ -1075,6 +1299,7 @@ def setup(data,
def create_model(estimator = None,
ensemble = False,
method = None,
......
setup.py
浏览文件 @ 6fe0a3ec
......@@ -27,7 +27,7 @@ def readme():
setup(
name="pycaret",
version="0.0.28",
version="0.0.29",
description="A Python package for supervised and unsupervised machine learning.",
long_description=readme(),
long_description_content_type="text/markdown",
......
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