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README.md
浏览文件 @ 95fcf4b9
......@@ -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 0.0.26 (as of 27/01/2020). A full release is targetted in the first week of February 2020.
The current release is beta 0.0.27 (as of 28/01/2020). A full release is targetted in the first week of February 2020.
## Features Currently Available
As per beta 0.0.26 following modules are generally available:
As per beta 0.0.27 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 0.0.26 classification, regression, nlp, arules, anomaly and clustering modules are available.
As of beta 0.0.27 classification, regression, nlp, arules, anomaly and clustering modules are available.
### Classification / Regression
......
anomaly.py
浏览文件 @ 95fcf4b9
......@@ -15,7 +15,12 @@ def setup(data,
transformation = False,
transformation_method = 'yeo-johnson',
pca = False,
pca_components = 0.99,
pca_method = 'linear', #new
pca_components = None,
ignore_low_variance = False, #new
combine_rare_levels = False, #new
rare_level_threshold = 0.10, #new
bin_numeric_features = None, #new
supervised = False,
supervised_target = None,
session_id = None,
......@@ -46,66 +51,91 @@ def setup(data,
categorical_features: string, default = None
If the inferred data types are not correct, categorical_features can be used to
overwrite the inferred type. For example upon running setup if type of column1
is inferred as numeric instead of categorical, this parameter can be used to
overwrite by passing categorical_features = 'column1'
overwrite the inferred type. If when running setup the type of 'column1' is
is inferred as numeric instead of categorical, then this parameter can be used
to overwrite the type by passing categorical_features = ['column1'].
categorical_imputation: string, default = 'constant'
If missing values are found in categorical features, it will be imputed with a
constant 'not_available' value. Other option available is 'mode' in which case
imputation is done by most frequent value.
If missing values are found in categorical features, they will be imputed with
a constant 'not_available' value. The other available option available is 'mode'
which imputes the value using most frequent value in the training dataset.
numeric_features: string, default = None
If the inferred data types are not correct, numeric_features can be used to
overwrite the inferred type. For example upon running setup if type of column1
is inferred as categorical instead of numeric, this parameter can be used to
overwrite by passing numeric_features = 'column1'
overwrite the inferred type. If when running setup the type of 'column1' is
inferred as categorical instead of numeric, then this parameter can be used
to overwrite by passing numeric_features = ['column1'].
numeric_imputation: string, default = 'mean'
If missing values are found in numeric features, it will be imputed with mean
value of feature. Other option available is 'median' in which case imputation
will be done by median value.
If missing values are found in numeric features, they will be imputed with the
mean value of the feature. The other available option is 'median' which imputes
the value using median value in the training dataset.
date_features: string, default = None
If data has DateTime column and is not automatically detected when running
setup, this parameter can be used to define date_feature by passing
setup, this parameter can be used to define date_feature by passing
data_features = 'date_column_name'. It can work with multiple date columns.
Date columns is not used in modeling, instead feature extraction is performed
and date column is dropped from the dataset. Incase the date column as time
stamp, it will also extract features related to time / hours.
Date columns are not used in modeling. Instead, feature extraction is performed
and date columns are dropped from the dataset. If the date column includes time
stamp, it will also extract features related to time.
ignore_features: string, default = None
If any feature has to be ignored for modeling, it can be passed in the param
ignore_features. ID and DateTime column when inferred, is automatically set
ignore for modeling.
If any feature should be ignored for modeling, it can be passed in the param
ignore_features. The ID and DateTime columns when inferred, are automatically
set to ignore for modeling.
normalize: bool, default = False
When set to True, transform feature space using normalize_method param defined.
Normally, linear algorithms perform better with normalized data. However, the
results may vary and it is advised to run multiple experiments to evaluate the
benefit of normalization.
When set to True, the feature space is transformed using the normalized_method
param defined. Generally, linear algorithms perform better with normalized data.
However, the results may vary and it is advised to run multiple experiments to
evaluate the benefit of normalization.
normalize_method: string, default = 'zscore'
Defines the method to be used for normalization. By default, normalize method
is set to 'zscore'. The other available option is 'minmax'.
is set to 'zscore'. The standard zscore is calculated as z = (x - u) / s. The
other available options are:
'minmax' : scales and translates each feature individually such that it is in
the range of 0 - 1.
'maxabs' : scales and translates each feature individually such that the maximal
absolute value of each feature will be 1.0. It does not shift/center
the data, and thus does not destroy any sparsity.
'robust' : scales and translates each feature according to the Interquartile range.
When dataset consists of ourliers, robust scaler often gives better
results.
transformation: bool, default = False
When set to True, apply a power transformation to make data more Gaussian-like
This is useful for modeling issues related to heteroscedasticity or other
situations where normality is desired. The optimal parameter for stabilizing
When set to True, a power transformation is applied to make the data more normal /
Gaussian-like. This is useful for modeling issues related to heteroscedasticity or
other situations where normality is desired. The optimal parameter for stabilizing
variance and minimizing skewness is estimated through maximum likelihood.
transformation_method: string, default = 'yeo-johnson'
Defines the method for transformation. By default, transformation method is set
Defines the method for transformation. By default, the transformation method is set
to 'yeo-johnson'. The other available option is 'quantile' transformation. Both
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.
pca: bool, default = False
When set to True, it will perform Linear dimensionality reduction using Singular
Value Decomposition of the data to project it to a lower dimensional space. It
is recommended when dataset has mix of categorical and numeric features.
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,
in a supervised learning, pca is performed when dealing with very high feature
space and memory is a constraint. Note that, not all datasets can be decomposed
efficiently using linear PCA technique and applying PCA may result is loss of
information. As such, it is advised to run multiple experiments with different
pca_methods to evaluate the impact.
pca_method: string, default = 'linear'
'linear' method performs Linear dimensionality reduction using Singular Value
Decomposition. The other available options are:
kernel : dimensionality reduction through the use of RVF kernel.
incremental : replacement for 'linear' pca when the dataset to be decomposed is
too large to fit in memory
pca_components: int/float, default = 0.99
Number of components to keep. if pca_components is a float, it is treated as
......@@ -185,9 +215,9 @@ def setup(data,
sys.exit("(Value Error): numeric_imputation param only accepts 'mean' or 'median' ")
#checking normalize method
allowed_normalize_method = ['zscore', 'minmax']
allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']
if normalize_method not in allowed_normalize_method:
sys.exit("(Value Error): normalize_method param only accepts 'zscore' or 'minxmax' ")
sys.exit("(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. ")
#checking transformation method
allowed_transformation_method = ['yeo-johnson', 'quantile']
......@@ -225,6 +255,52 @@ def setup(data,
if type(pca) is not bool:
sys.exit('(Type Error): pca parameter only accepts True or False.')
#pca method check
allowed_pca_methods = ['linear', 'kernel', 'incremental']
if pca_method not in allowed_pca_methods:
sys.exit("(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. ")
#pca components check
if pca is True:
if pca_method is not 'linear':
if pca_components is not None:
if(type(pca_components)) is not int:
sys.exit("(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. ")
#pca components check 2
if pca is True:
if pca_method is not 'linear':
if pca_components is not None:
if pca_components > len(data.columns):
sys.exit("(Type Error): pca_components parameter cannot be greater than original features space.")
#pca components check 3
if pca is True:
if pca_method is 'linear':
if pca_components is not None:
if type(pca_components) is not float:
if pca_components > len(data.columns):
sys.exit("(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.")
#check ignore_low_variance
if type(ignore_low_variance) is not bool:
sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')
#check ignore_low_variance
if type(combine_rare_levels) is not bool:
sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')
#check rare_level_threshold
if type(rare_level_threshold) is not float:
sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')
#bin numeric features
if bin_numeric_features is not None:
all_cols = list(data.columns)
for i in bin_numeric_features:
if i not in all_cols:
sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")
"""
error handling ends here
......@@ -334,6 +410,41 @@ def setup(data,
elif transformation_method == 'quantile':
trans_method_pass = 'quantile'
#pass method
if pca_method == 'linear':
pca_method_pass = 'pca_liner'
elif pca_method == 'kernel':
pca_method_pass = 'pca_kernal'
elif pca_method == 'incremental':
pca_method_pass = 'incremental'
elif pca_method == 'pls':
pca_method_pass = 'pls'
#pca components
if pca is True:
if pca_components is None:
if pca_method == 'linear':
pca_components_pass = 0.99
else:
pca_components_pass = int((len(data.columns))*0.5)
else:
pca_components_pass = pca_components
else:
pca_components_pass = 0.99
if bin_numeric_features is None:
apply_binning_pass = False
features_to_bin_pass = []
else:
apply_binning_pass = True
features_to_bin_pass = bin_numeric_features
#display dtypes
if supervised is False:
display_types_pass = True
......@@ -356,7 +467,13 @@ def setup(data,
Power_transform_data = transformation,
Power_transform_method = trans_method_pass,
apply_pca = pca,
pca_variance_retained=pca_components,
pca_method = pca_method_pass, #new
pca_variance_retained_or_number_of_components = pca_components_pass, #new
apply_zero_nearZero_variance = ignore_low_variance, #new
club_rare_levels = combine_rare_levels, #new
rara_level_threshold_percentage = rare_level_threshold, #new
apply_binning = apply_binning_pass, #new
features_to_binn = features_to_bin_pass, #new
random_state = seed)
progress.value += 1
......@@ -391,12 +508,25 @@ def setup(data,
else:
transformation_grid = 'None'
pca_grid = pca
if pca is True:
pca_method_grid = pca_method
else:
pca_method_grid = 'None'
if pca is True:
pca_components_grid = pca_components_pass
else:
pca_components_grid = 'None'
if combine_rare_levels:
rare_level_threshold_grid = rare_level_threshold
else:
rare_level_threshold_grid = 'None'
if pca_grid is False:
pca_comp_grid = None
if bin_numeric_features is None:
numeric_bin_grid = 'False'
else:
pca_comp_grid = pca_components
numeric_bin_grid = 'True'
learned_types = preprocess.dtypes.learent_dtypes
#learned_types.drop(target, inplace=True)
......@@ -444,15 +574,20 @@ def setup(data,
functions = pd.DataFrame ( [ ['session_id ', seed ],
['Original Data ', shape ],
['Transformed Data ', shape_transformed ],
['Numeric Features ', float_type-1 ],
['Categorical Features ', cat_type ],
['Numeric Features ', float_type ],
['Normalize ', normalize ],
['Normalize Method ', normalize_grid ],
['Transformation ', transformation ],
['Transformation Method ', transformation_grid ],
['Missing Values ', missing_flag],
['PCA ', pca_grid],
['PCA components ', pca_comp_grid],
['PCA ', pca],
['PCA Method ', pca_method_grid],
['PCA components ', pca_components_grid],
['Ignore Low Variance ', ignore_low_variance],
['Combine Rare Levels ', combine_rare_levels],
['Rare Level Threshold ', rare_level_threshold_grid],
['Numeric Binning ', numeric_bin_grid],
['Numeric Imputer ', numeric_imputation],
['Categorical Imputer ', categorical_imputation],
], columns = ['Description', 'Value'] )
......@@ -498,8 +633,6 @@ def setup(data,
def create_model(model = None,
fraction = 0.05,
verbose = True):
......@@ -727,7 +860,6 @@ def create_model(model = None,
def assign_model(model,
transformation=False,
score=True,
......@@ -1263,9 +1395,18 @@ def tune_model(model=None,
if 'Empty' in str(prep_param.pca):
pca_pass = False
pca_method_pass = 'linear'
else:
pca_pass = True
if prep_param.pca.method == 'pca_liner':
pca_method_pass = 'linear'
elif prep_param.pca.method == 'pca_kernal':
pca_method_pass = 'kernel'
elif prep_param.pca.method == 'incremental':
pca_method_pass = 'incremental'
if pca_pass is True:
pca_comp_pass = prep_param.pca.variance_retained
else:
......@@ -1303,6 +1444,26 @@ def tune_model(model=None,
else:
transformation_method_pass = 'yeo-johnson'
if 'Empty' in str(prep_param.binn):
features_to_bin_pass = []
apply_binning_pass = False
else:
features_to_bin_pass = prep_param.binn.features_to_discretize
apply_binning_pass = True
if 'Empty' in str(prep_param.club_R_L):
combine_rare_levels_pass = False
combine_rare_threshold_pass = 0.1
else:
combine_rare_levels_pass = True
combine_rare_threshold_pass = prep_param.club_R_L.threshold
if 'Empty' in str(prep_param.znz):
ignore_low_variance_pass = False
else:
ignore_low_variance_pass = True
global setup_without_target
setup_without_target = setup(data = data_,
......@@ -1317,7 +1478,12 @@ def tune_model(model=None,
transformation = transformation_pass,
transformation_method = transformation_method_pass,
pca = pca_pass,
pca_components = pca_comp_pass,
pca_components = pca_comp_pass, #new
pca_method = pca_method_pass, #new
ignore_low_variance = ignore_low_variance_pass, #new
combine_rare_levels = combine_rare_levels_pass, #new
rare_level_threshold = combine_rare_threshold_pass, #new
bin_numeric_features = features_to_bin_pass, #new
supervised = True,
supervised_target = supervised_target,
session_id = seed,
......@@ -2093,6 +2259,10 @@ def save_model(model, model_name, verbose=True):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
model_ = []
model_.append(prep_pipe)
model_.append(model)
......@@ -2104,7 +2274,6 @@ def save_model(model, model_name, verbose=True):
print('Transformation Pipeline and Model Succesfully Saved')
def load_model(model_name,
platform = None,
authentication = None,
......@@ -2140,7 +2309,11 @@ def load_model(model_name,
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#exception checking
import sys
......@@ -2171,8 +2344,6 @@ def load_model(model_name,
return joblib.load(model_name)
def save_experiment(experiment_name=None):
......@@ -2211,6 +2382,10 @@ def save_experiment(experiment_name=None):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import joblib
global experiment__
......@@ -2229,7 +2404,6 @@ def save_experiment(experiment_name=None):
def load_experiment(experiment_name):
"""
......@@ -2262,6 +2436,10 @@ def load_experiment(experiment_name):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import joblib
import pandas as pd
......@@ -2283,101 +2461,6 @@ def load_experiment(experiment_name):
def deploy_model(model,
model_name,
authentication,
platform = 'aws'):
"""
Description:
------------
This function deploys the transformation pipeline and trained model object
for production use. Platform of deployment can be defined under platform
param along with applicable authentication tokens to be passed as dictionary
in authentication param.
Example:
--------
from pycaret.datasets import get_data
anomaly = get_data('anomaly')
experiment_name = setup(data = anomaly, normalize=True)
knn = create_model('knn')
deploy_model(model = knn, model_name = 'deploy_knn', platform = 'aws',
authentication = {'bucket' : 'pycaret-test'})
This will deploy the model on AWS S3 account under bucket 'pycaret-test'
For AWS users:
--------------
Before deploying a model to AWS S3 ('aws'), environment variables must be
configured using command line interface. To configure AWS environment variables,
type aws configure in your python command line, it requires following information
that can be generated using Identity and Access Management (IAM) portal of your
amazon console account:
- AWS Access Key ID
- AWS Secret Key Access
- Default Region Name (can be seen under Global settings on your AWS console)
- Default output format (must be left blank)
Parameters
----------
model : object
A trained model object should be passed as an estimator.
model_name : string
Name of model to be passed as a string.
authentication : dict
dictionary of applicable authentication tokens.
When platform = 'aws':
{'bucket' : 'Name of Bucket on S3'}
platform: string, default = 'aws'
Name of platform for deployment. Current available options are: 'aws'.
Returns:
--------
Success Message
Warnings:
---------
None
"""
#general dependencies
import ipywidgets as ipw
import pandas as pd
from IPython.display import clear_output, update_display
try:
model = finalize_model(model)
except:
pass
if platform == 'aws':
import boto3
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
s3 = boto3.client('s3')
filename = str(model_name)+'.pkl'
key = str(model_name)+'.pkl'
bucket_name = authentication.get('bucket')
s3.upload_file(filename,bucket_name,key)
clear_output()
print("Model Succesfully Deployed on AWS S3")
def predict_model(model,
data,
platform=None,
......@@ -2424,6 +2507,10 @@ def predict_model(model,
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#testing
#no active tests
......@@ -2481,6 +2568,104 @@ def predict_model(model,
def deploy_model(model,
model_name,
authentication,
platform = 'aws'):
"""
Description:
------------
This function deploys the transformation pipeline and trained model object for
production use. The platform of deployment can be defined under the platform
param along with the applicable authentication tokens which are passed as a
dictionary to the authentication param.
Example:
--------
from pycaret.datasets import get_data
anomaly = get_data('anomaly')
experiment_name = setup(data = anomaly, normalize=True)
knn = create_model('knn')
deploy_model(model = knn, model_name = 'deploy_knn', platform = 'aws',
authentication = {'bucket' : 'pycaret-test'})
This will deploy the model on an AWS S3 account under bucket 'pycaret-test'
For AWS users:
--------------
Before deploying a model to an AWS S3 ('aws'), environment variables must be
configured using the command line interface. To configure AWS env. variables,
type aws configure in your python command line. The following information is
required which can be generated using the Identity and Access Management (IAM)
portal of your amazon console account:
- AWS Access Key ID
- AWS Secret Key Access
- Default Region Name (can be seen under Global settings on your AWS console)
- Default output format (must be left blank)
Parameters
----------
model : object
A trained model object should be passed as an estimator.
model_name : string
Name of model to be passed as a string.
authentication : dict
dictionary of applicable authentication tokens.
When platform = 'aws':
{'bucket' : 'Name of Bucket on S3'}
platform: string, default = 'aws'
Name of platform for deployment. Current available options are: 'aws'.
Returns:
--------
Success Message
Warnings:
---------
None
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import ipywidgets as ipw
import pandas as pd
from IPython.display import clear_output, update_display
try:
model = finalize_model(model)
except:
pass
if platform == 'aws':
import boto3
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
s3 = boto3.client('s3')
filename = str(model_name)+'.pkl'
key = str(model_name)+'.pkl'
bucket_name = authentication.get('bucket')
s3.upload_file(filename,bucket_name,key)
clear_output()
print("Model Succesfully Deployed on AWS S3")
def get_outliers(data,
model = None,
fraction=0.05,
......@@ -2529,3 +2714,4 @@ def get_outliers(data,
dataset = assign_model(c, verbose=False)
return dataset
clustering.py
浏览文件 @ 95fcf4b9
......@@ -2,6 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
categorical_features = None,
categorical_imputation = 'constant',
......@@ -14,7 +15,12 @@ def setup(data,
transformation = False,
transformation_method = 'yeo-johnson',
pca = False,
pca_components = 0.99,
pca_method = 'linear', #new
pca_components = None,
ignore_low_variance = False, #new
combine_rare_levels = False, #new
rare_level_threshold = 0.10, #new
bin_numeric_features = None, #new
supervised = False,
supervised_target = None,
session_id = None,
......@@ -45,66 +51,91 @@ def setup(data,
categorical_features: string, default = None
If the inferred data types are not correct, categorical_features can be used to
overwrite the inferred type. For example upon running setup if type of column1
is inferred as numeric instead of categorical, this parameter can be used to
overwrite by passing categorical_features = 'column1'
overwrite the inferred type. If when running setup the type of 'column1' is
is inferred as numeric instead of categorical, then this parameter can be used
to overwrite the type by passing categorical_features = ['column1'].
categorical_imputation: string, default = 'constant'
If missing values are found in categorical features, it will be imputed with a
constant 'not_available' value. Other option available is 'mode' in which case
imputation is done by most frequent value.
If missing values are found in categorical features, they will be imputed with
a constant 'not_available' value. The other available option available is 'mode'
which imputes the value using most frequent value in the training dataset.
numeric_features: string, default = None
If the inferred data types are not correct, numeric_features can be used to
overwrite the inferred type. For example upon running setup if type of column1
is inferred as categorical instead of numeric, this parameter can be used to
overwrite by passing numeric_features = 'column1'
overwrite the inferred type. If when running setup the type of 'column1' is
inferred as categorical instead of numeric, then this parameter can be used
to overwrite by passing numeric_features = ['column1'].
numeric_imputation: string, default = 'mean'
If missing values are found in numeric features, it will be imputed with mean
value of feature. Other option available is 'median' in which case imputation
will be done by median value.
If missing values are found in numeric features, they will be imputed with the
mean value of the feature. The other available option is 'median' which imputes
the value using median value in the training dataset.
date_features: string, default = None
If data has DateTime column and is not automatically detected when running
setup, this parameter can be used to define date_feature by passing
setup, this parameter can be used to define date_feature by passing
data_features = 'date_column_name'. It can work with multiple date columns.
Date columns is not used in modeling, instead feature extraction is performed
and date column is dropped from the dataset. Incase the date column as time
stamp, it will also extract features related to time / hours.
Date columns are not used in modeling. Instead, feature extraction is performed
and date columns are dropped from the dataset. If the date column includes time
stamp, it will also extract features related to time.
ignore_features: string, default = None
If any feature has to be ignored for modeling, it can be passed in the param
ignore_features. ID and DateTime column when inferred, is automatically set
ignore for modeling.
If any feature should be ignored for modeling, it can be passed in the param
ignore_features. The ID and DateTime columns when inferred, are automatically
set to ignore for modeling.
normalize: bool, default = False
When set to True, transform feature space using normalize_method param defined.
Normally, linear algorithms perform better with normalized data. However, the
results may vary and it is advised to run multiple experiments to evaluate the
benefit of normalization.
When set to True, the feature space is transformed using the normalized_method
param defined. Generally, linear algorithms perform better with normalized data.
However, the results may vary and it is advised to run multiple experiments to
evaluate the benefit of normalization.
normalize_method: string, default = 'zscore'
Defines the method to be used for normalization. By default, normalize method
is set to 'zscore'. The other available option is 'minmax'.
is set to 'zscore'. The standard zscore is calculated as z = (x - u) / s. The
other available options are:
'minmax' : scales and translates each feature individually such that it is in
the range of 0 - 1.
'maxabs' : scales and translates each feature individually such that the maximal
absolute value of each feature will be 1.0. It does not shift/center
the data, and thus does not destroy any sparsity.
'robust' : scales and translates each feature according to the Interquartile range.
When dataset consists of ourliers, robust scaler often gives better
results.
transformation: bool, default = False
When set to True, apply a power transformation to make data more Gaussian-like
This is useful for modeling issues related to heteroscedasticity or other
situations where normality is desired. The optimal parameter for stabilizing
When set to True, a power transformation is applied to make the data more normal /
Gaussian-like. This is useful for modeling issues related to heteroscedasticity or
other situations where normality is desired. The optimal parameter for stabilizing
variance and minimizing skewness is estimated through maximum likelihood.
transformation_method: string, default = 'yeo-johnson'
Defines the method for transformation. By default, transformation method is set
Defines the method for transformation. By default, the transformation method is set
to 'yeo-johnson'. The other available option is 'quantile' transformation. Both
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.
pca: bool, default = False
When set to True, it will perform Linear dimensionality reduction using Singular
Value Decomposition of the data to project it to a lower dimensional space. It
is recommended when dataset has mix of categorical and numeric features.
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,
in a supervised learning, pca is performed when dealing with very high feature
space and memory is a constraint. Note that, not all datasets can be decomposed
efficiently using linear PCA technique and applying PCA may result is loss of
information. As such, it is advised to run multiple experiments with different
pca_methods to evaluate the impact.
pca_method: string, default = 'linear'
'linear' method performs Linear dimensionality reduction using Singular Value
Decomposition. The other available options are:
kernel : dimensionality reduction through the use of RVF kernel.
incremental : replacement for 'linear' pca when the dataset to be decomposed is
too large to fit in memory
pca_components: int/float, default = 0.99
Number of components to keep. if pca_components is a float, it is treated as
......@@ -184,9 +215,9 @@ def setup(data,
sys.exit("(Value Error): numeric_imputation param only accepts 'mean' or 'median' ")
#checking normalize method
allowed_normalize_method = ['zscore', 'minmax']
allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']
if normalize_method not in allowed_normalize_method:
sys.exit("(Value Error): normalize_method param only accepts 'zscore' or 'minxmax' ")
sys.exit("(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. ")
#checking transformation method
allowed_transformation_method = ['yeo-johnson', 'quantile']
......@@ -220,10 +251,57 @@ def setup(data,
if i not in all_cols:
sys.exit("(Value Error): Feature ignored is either target column or doesn't exist in the dataset.")
#checking pca parameter
#check pca
if type(pca) is not bool:
sys.exit('(Type Error): pca parameter only accepts True or False.')
sys.exit('(Type Error): PCA parameter only accepts True or False.')
#pca method check
allowed_pca_methods = ['linear', 'kernel', 'incremental']
if pca_method not in allowed_pca_methods:
sys.exit("(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. ")
#pca components check
if pca is True:
if pca_method is not 'linear':
if pca_components is not None:
if(type(pca_components)) is not int:
sys.exit("(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. ")
#pca components check 2
if pca is True:
if pca_method is not 'linear':
if pca_components is not None:
if pca_components > len(data.columns):
sys.exit("(Type Error): pca_components parameter cannot be greater than original features space.")
#pca components check 3
if pca is True:
if pca_method is 'linear':
if pca_components is not None:
if type(pca_components) is not float:
if pca_components > len(data.columns):
sys.exit("(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.")
#check ignore_low_variance
if type(ignore_low_variance) is not bool:
sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')
#check ignore_low_variance
if type(combine_rare_levels) is not bool:
sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')
#check rare_level_threshold
if type(rare_level_threshold) is not float:
sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')
#bin numeric features
if bin_numeric_features is not None:
all_cols = list(data.columns)
for i in bin_numeric_features:
if i not in all_cols:
sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")
"""
error handling ends here
......@@ -333,6 +411,41 @@ def setup(data,
elif transformation_method == 'quantile':
trans_method_pass = 'quantile'
#pass method
if pca_method == 'linear':
pca_method_pass = 'pca_liner'
elif pca_method == 'kernel':
pca_method_pass = 'pca_kernal'
elif pca_method == 'incremental':
pca_method_pass = 'incremental'
elif pca_method == 'pls':
pca_method_pass = 'pls'
#pca components
if pca is True:
if pca_components is None:
if pca_method == 'linear':
pca_components_pass = 0.99
else:
pca_components_pass = int((len(data.columns))*0.5)
else:
pca_components_pass = pca_components
else:
pca_components_pass = 0.99
if bin_numeric_features is None:
apply_binning_pass = False
features_to_bin_pass = []
else:
apply_binning_pass = True
features_to_bin_pass = bin_numeric_features
#display dtypes
if supervised is False:
display_types_pass = True
......@@ -355,7 +468,13 @@ def setup(data,
Power_transform_data = transformation,
Power_transform_method = trans_method_pass,
apply_pca = pca,
pca_variance_retained=pca_components,
pca_method = pca_method_pass, #new
pca_variance_retained_or_number_of_components = pca_components_pass, #new
apply_zero_nearZero_variance = ignore_low_variance, #new
club_rare_levels = combine_rare_levels, #new
rara_level_threshold_percentage = rare_level_threshold, #new
apply_binning = apply_binning_pass, #new
features_to_binn = features_to_bin_pass, #new
random_state = seed)
progress.value += 1
......@@ -391,13 +510,26 @@ def setup(data,
else:
transformation_grid = 'None'
pca_grid = pca
if pca_grid is False:
pca_comp_grid = None
if pca is True:
pca_method_grid = pca_method
else:
pca_method_grid = 'None'
if pca is True:
pca_components_grid = pca_components_pass
else:
pca_components_grid = 'None'
if combine_rare_levels:
rare_level_threshold_grid = rare_level_threshold
else:
pca_comp_grid = pca_components
rare_level_threshold_grid = 'None'
if bin_numeric_features is None:
numeric_bin_grid = 'False'
else:
numeric_bin_grid = 'True'
learned_types = preprocess.dtypes.learent_dtypes
#learned_types.drop(target, inplace=True)
......@@ -444,15 +576,20 @@ def setup(data,
functions = pd.DataFrame ( [ ['session_id ', seed ],
['Original Data ', shape ],
['Transformed Data ', shape_transformed ],
['Numeric Features ', float_type-1 ],
['Categorical Features ', cat_type ],
['Numeric Features ', float_type ],
['Normalize ', normalize ],
['Normalize Method ', normalize_grid ],
['Transformation ', transformation ],
['Transformation Method ', transformation_grid ],
['Missing Values ', missing_flag],
['PCA ', pca_grid],
['PCA components ', pca_comp_grid],
['PCA ', pca],
['PCA Method ', pca_method_grid],
['PCA components ', pca_components_grid],
['Ignore Low Variance ', ignore_low_variance],
['Combine Rare Levels ', combine_rare_levels],
['Rare Level Threshold ', rare_level_threshold_grid],
['Numeric Binning ', numeric_bin_grid],
['Numeric Imputer ', numeric_imputation],
['Categorical Imputer ', categorical_imputation],
], columns = ['Description', 'Value'] )
......@@ -497,8 +634,6 @@ def setup(data,
return X, data_, seed, prep_pipe, prep_param, experiment__
def create_model(model = None,
num_clusters = None,
verbose=True):
......@@ -1210,9 +1345,18 @@ def tune_model(model=None,
if 'Empty' in str(prep_param.pca):
pca_pass = False
pca_method_pass = 'linear'
else:
pca_pass = True
if prep_param.pca.method == 'pca_liner':
pca_method_pass = 'linear'
elif prep_param.pca.method == 'pca_kernal':
pca_method_pass = 'kernel'
elif prep_param.pca.method == 'incremental':
pca_method_pass = 'incremental'
if pca_pass is True:
pca_comp_pass = prep_param.pca.variance_retained
else:
......@@ -1250,6 +1394,27 @@ def tune_model(model=None,
else:
transformation_method_pass = 'yeo-johnson'
if 'Empty' in str(prep_param.binn):
features_to_bin_pass = []
apply_binning_pass = False
else:
features_to_bin_pass = prep_param.binn.features_to_discretize
apply_binning_pass = True
if 'Empty' in str(prep_param.club_R_L):
combine_rare_levels_pass = False
combine_rare_threshold_pass = 0.1
else:
combine_rare_levels_pass = True
combine_rare_threshold_pass = prep_param.club_R_L.threshold
if 'Empty' in str(prep_param.znz):
ignore_low_variance_pass = False
else:
ignore_low_variance_pass = True
global setup_without_target
setup_without_target = setup(data = data_,
......@@ -1264,7 +1429,12 @@ def tune_model(model=None,
transformation = transformation_pass,
transformation_method = transformation_method_pass,
pca = pca_pass,
pca_components = pca_comp_pass,
pca_components = pca_comp_pass, #new
pca_method = pca_method_pass, #new
ignore_low_variance = ignore_low_variance_pass, #new
combine_rare_levels = combine_rare_levels_pass, #new
rare_level_threshold = combine_rare_threshold_pass, #new
bin_numeric_features = features_to_bin_pass, #new
supervised = True,
supervised_target = supervised_target,
session_id = seed,
......@@ -1862,7 +2032,6 @@ def tune_model(model=None,
def plot_model(model, plot='cluster', feature=None):
......@@ -2127,8 +2296,6 @@ def plot_model(model, plot='cluster', feature=None):
sys.exit('(Type Error): Plot Type not supported for this model.')
def save_model(model, model_name, verbose=True):
"""
......@@ -2169,6 +2336,10 @@ def save_model(model, model_name, verbose=True):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
model_ = []
model_.append(prep_pipe)
model_.append(model)
......@@ -2229,7 +2400,11 @@ def load_model(model_name,
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#exception checking
import sys
......@@ -2261,7 +2436,6 @@ def load_model(model_name,
def save_experiment(experiment_name=None):
......@@ -2300,6 +2474,10 @@ def save_experiment(experiment_name=None):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import joblib
global experiment__
......@@ -2350,6 +2528,10 @@ def load_experiment(experiment_name):
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import joblib
import pandas as pd
......@@ -2371,99 +2553,6 @@ def load_experiment(experiment_name):
def deploy_model(model,
model_name,
authentication,
platform = 'aws'):
"""
Description:
------------
This function deploys the transformation pipeline and trained model object
for production use. Platform of deployment can be defined under platform
param along with applicable authentication tokens to be passed as dictionary
in authentication param.
Example:
--------
from pycaret.datasets import get_data
jewellery = get_data('jewellery')
experiment_name = setup(data = jewellery, normalize = True)
kmeans = create_model('kmeans')
deploy_model(model = kmeans, model_name = 'deploy_kmeans', platform = 'aws',
authentication = {'bucket' : 'pycaret-test'})
This will deploy the model on AWS S3 account under bucket 'pycaret-test'
For AWS users:
--------------
Before deploying a model to AWS S3 ('aws'), environment variables must be
configured using command line interface. To configure AWS environment variables,
type aws configure in your python command line, it requires following information
that can be generated using Identity and Access Management (IAM) portal of your
amazon console account:
- AWS Access Key ID
- AWS Secret Key Access
- Default Region Name (can be seen under Global settings on your AWS console)
- Default output format (must be left blank)
Parameters
----------
model : object
A trained model object should be passed as an estimator.
model_name : string
Name of model to be passed as a string.
authentication : dict
dictionary of applicable authentication tokens.
When platform = 'aws':
{'bucket' : 'Name of Bucket on S3'}
platform: string, default = 'aws'
Name of platform for deployment. Current available options are: 'aws'.
Returns:
--------
Success Message
Warnings:
---------
None
"""
#general dependencies
import ipywidgets as ipw
import pandas as pd
from IPython.display import clear_output, update_display
try:
model = finalize_model(model)
except:
pass
if platform == 'aws':
import boto3
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
s3 = boto3.client('s3')
filename = str(model_name)+'.pkl'
key = str(model_name)+'.pkl'
bucket_name = authentication.get('bucket')
s3.upload_file(filename,bucket_name,key)
clear_output()
print("Model Succesfully Deployed on AWS S3")
def predict_model(model,
data,
platform=None,
......@@ -2520,6 +2609,10 @@ def predict_model(model,
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#testing
#no active tests
......@@ -2581,6 +2674,102 @@ def predict_model(model,
def deploy_model(model,
model_name,
authentication,
platform = 'aws'):
"""
Description:
------------
This function deploys the transformation pipeline and trained model object for
production use. The platform of deployment can be defined under the platform
param along with the applicable authentication tokens which are passed as a
dictionary to the authentication param.
Example:
--------
from pycaret.datasets import get_data
jewellery = get_data('jewellery')
experiment_name = setup(data = jewellery, normalize = True)
kmeans = create_model('kmeans')
deploy_model(model = kmeans, model_name = 'deploy_kmeans', platform = 'aws',
authentication = {'bucket' : 'pycaret-test'})
This will deploy the model on an AWS S3 account under bucket 'pycaret-test'
For AWS users:
--------------
Before deploying a model to an AWS S3 ('aws'), environment variables must be
configured using the command line interface. To configure AWS env. variables,
type aws configure in your python command line. The following information is
required which can be generated using the Identity and Access Management (IAM)
portal of your amazon console account:
- AWS Access Key ID
- AWS Secret Key Access
- Default Region Name (can be seen under Global settings on your AWS console)
- Default output format (must be left blank)
Parameters
----------
model : object
A trained model object should be passed as an estimator.
model_name : string
Name of model to be passed as a string.
authentication : dict
dictionary of applicable authentication tokens.
When platform = 'aws':
{'bucket' : 'Name of Bucket on S3'}
platform: string, default = 'aws'
Name of platform for deployment. Current available options are: 'aws'.
Returns:
--------
Success Message
Warnings:
---------
None
"""
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#general dependencies
import ipywidgets as ipw
import pandas as pd
from IPython.display import clear_output, update_display
try:
model = finalize_model(model)
except:
pass
if platform == 'aws':
import boto3
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
s3 = boto3.client('s3')
filename = str(model_name)+'.pkl'
key = str(model_name)+'.pkl'
bucket_name = authentication.get('bucket')
s3.upload_file(filename,bucket_name,key)
clear_output()
print("Model Succesfully Deployed on AWS S3")
def get_clusters(data,
model = None,
num_clusters = 4,
......@@ -2630,4 +2819,3 @@ def get_clusters(data,
dataset = assign_model(c, verbose=False)
return dataset
requirements.txt
浏览文件 @ 95fcf4b9
......@@ -28,7 +28,5 @@ datefinder==0.7.0
datetime
DateTime==4.3
tqdm==4.36.1
awscli==1.17.9
boto3
s3transfer==0.3.2
botocore==1.14.9
\ No newline at end of file
awscli
boto3
\ No newline at end of file
setup.py
浏览文件 @ 95fcf4b9
......@@ -27,7 +27,7 @@ def readme():
setup(
name="pycaret",
version="0.0.26",
version="0.0.27",
description="A Python package for supervised and unsupervised machine learning.",
long_description=readme(),
long_description_content_type="text/markdown",
......@@ -47,6 +47,5 @@ setup(
"wordcloud", "textblob", "plotly==4.4.1", "cufflinks==0.17.0", "umap-learn",
"lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk", "mlxtend",
"pyod", "catboost==0.20.2", "pandas-profiling==2.3.0", "kmodes==0.10.1",
"datefinder==0.7.0", "datetime", "DateTime==4.3", "tqdm==4.36.1", "awscli==1.17.9", "boto3",
"s3transfer==0.3.2", "botocore==1.14.9"]
"datefinder==0.7.0", "datetime", "DateTime==4.3", "tqdm==4.36.1", "awscli", "boto3"]
)
\ No newline at end of file
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