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未验证 提交 c8abd30e 编写于 作者: P pycaret 提交者： GitHub
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anomaly.py
浏览文件 @ c8abd30e
# Module: Anomaly Detection
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
categorical_features = None,
categorical_imputation = 'constant',
......
arules.py
浏览文件 @ c8abd30e
# Module: Association Rules Mining
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
transaction_id,
item_id,
......@@ -334,3 +339,21 @@ def plot_model(model,
fig.show()
def get_rules(data,
transaction_id,
item_id,
ignore_items = None,
metric='confidence',
threshold = 0.5,
min_support = 0.05):
"""
Magic function to get Association Rules in Power Query / Power BI.
"""
s = setup(data=data, transaction_id=transaction_id, item_id=item_id, ignore_items = ignore_items)
dataset = create_model(metric=metric, threshold=threshold, min_support=min_support, round=4)
return dataset
\ No newline at end of file
classification.py
浏览文件 @ c8abd30e
# Module: Classification
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
target,
train_size = 0.7,
......@@ -3807,6 +3812,7 @@ def tune_model(estimator = None,
return best_model
def blend_models(estimator_list = 'All',
fold = 10,
round = 4,
......@@ -3931,6 +3937,12 @@ def blend_models(estimator_list = 'All',
if check >= 1:
sys.exit('(Type Error): Estimator list contains estimator that doesnt support probabilities and method is forced to soft. Either change the method or drop the estimator.')
#checking catboost:
if estimator_list != 'All':
for i in estimator_list:
if 'CatBoostClassifier' in str(i):
sys.exit('(Type Error): CatBoost Classifier not supported in this function.')
#checking fold parameter
if type(fold) is not int:
sys.exit('(Type Error): Fold parameter only accepts integer value.')
......@@ -4383,6 +4395,8 @@ def blend_models(estimator_list = 'All',
return model
def stack_models(estimator_list,
meta_model = None,
fold = 10,
......@@ -4580,6 +4594,7 @@ def stack_models(estimator_list,
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_predict
import seaborn as sns
from copy import deepcopy
progress.value += 1
......@@ -4589,12 +4604,15 @@ def stack_models(estimator_list,
elif method == 'hard':
predict_method = 'predict'
#copy estimator_list
estimator_list = deepcopy(estimator_list)
#Defining meta model. Logistic Regression hardcoded for now
if meta_model == None:
from sklearn.linear_model import LogisticRegression
meta_model = LogisticRegression()
else:
meta_model = meta_model
meta_model = deepcopy(meta_model)
#defining data_X and data_y
if finalize:
......@@ -4903,6 +4921,8 @@ def stack_models(estimator_list,
return models_
def create_stacknet(estimator_list,
meta_model = None,
fold = 10,
......@@ -5095,9 +5115,13 @@ def create_stacknet(estimator_list,
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_predict
from copy import deepcopy
progress.value += 1
#copy estimator_list
estimator_list = deepcopy(estimator_list)
base_level = estimator_list[0]
base_level_names = []
......@@ -5150,7 +5174,7 @@ def create_stacknet(estimator_list,
from sklearn.linear_model import LogisticRegression
meta_model = LogisticRegression()
else:
meta_model = meta_model
meta_model = deepcopy(meta_model)
#Capturing the method of stacking required by user. method='soft' means 'predict_proba' else 'predict'
if method == 'soft':
......@@ -5472,6 +5496,10 @@ def create_stacknet(estimator_list,
return models_
def interpret_model(estimator,
plot = 'summary',
feature = None,
......@@ -6462,6 +6490,7 @@ def load_experiment(experiment_name):
return exp
def predict_model(estimator,
data=None):
......@@ -6538,7 +6567,7 @@ def predict_model(estimator,
X_test_.reset_index(drop=True, inplace=True)
y_test_.reset_index(drop=True, inplace=True)
model = estimator
model = deepcopy(estimator)
else:
......@@ -6555,19 +6584,19 @@ def predict_model(estimator,
else:
prep_pipe_transformer = prep_pipe
model = estimator
estimator = estimator
model = deepcopy(estimator)
estimator = deepcopy(estimator)
else:
prep_pipe_transformer = prep_pipe
model = estimator
estimator = estimator
model = deepcopy(estimator)
estimator = deepcopy(estimator)
try:
model = finalize_model(estimator)
except:
model = estimator
model = deepcopy(estimator)
Xtest = prep_pipe_transformer.transform(data)
X_test_ = data.copy() #original concater
......@@ -6584,7 +6613,7 @@ def predict_model(estimator,
"""
#utility
stacker = model.copy()
stacker = deepcopy(model)
restack = stacker.pop()
stacker_method = stacker.pop()
#stacker_method = stacker_method[0]
......@@ -6786,7 +6815,7 @@ def predict_model(estimator,
"""
#copy
stacker = model.copy()
stacker = deepcopy(model)
#restack
restack = stacker.pop()
......@@ -6828,7 +6857,7 @@ def predict_model(estimator,
if method == 'hard':
#print('done')
p = i.predict(Xtest) #change
else:
try:
p = i.predict_proba(Xtest) #change
......@@ -6995,5 +7024,4 @@ def predict_model(estimator,
pass
return X_test_
return X_test_
\ No newline at end of file
clustering.py
浏览文件 @ c8abd30e
# Module: Clustering
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
categorical_features = None,
categorical_imputation = 'constant',
......
datasets.py
浏览文件 @ c8abd30e
# Module: Datasets
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def get_data(dataset, save_copy=False, profile = False):
"""
......
nlp.py
浏览文件 @ c8abd30e
# Module: Natural Language Processing
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
target=None,
custom_stopwords=None,
......
preprocess.py
浏览文件 @ c8abd30e
# Module: Preprocess
# Author: Fahad Akbar <m.akbar@queensu.ca>
# License: MIT
import pandas as pd
import numpy as np
import ipywidgets as wg
......
regression.py
浏览文件 @ c8abd30e
# Module: Regression
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
def setup(data,
target,
train_size=0.7,
......@@ -3707,464 +3711,6 @@ def tune_model(estimator = None,
def stack_models(estimator_list,
meta_model = None,
fold = 10,
round = 4,
restack = False,
plot = False,
finalize = False,
verbose = True):
"""
Description:
------------
This function creates a meta model and scores it using Kfold Cross Validation.
The predictions from the base level models as passed in the estimator_list param
are used as input features for the meta model. The restacking parameter controls
the ability to expose raw features to the meta model when set to True
(default = False).
The output prints a score grid that shows MAE, MSE, RMSE, R2 and ME by fold
(default = 10 Folds).
This function returns a container which is the list of all models in stacking.
Example:
--------
from pycaret.datasets import get_data
boston = get_data('boston')
experiment_name = setup(data = boston, target = 'medv')
dt = create_model('dt')
rf = create_model('rf')
ada = create_model('ada')
ridge = create_model('ridge')
knn = create_model('knn')
stacked_models = stack_models(estimator_list=[dt,rf,ada,ridge,knn])
This will create a meta model that will use the predictions of all the
models provided in estimator_list param. By default, the meta model is
Linear Regression but can be changed with meta_model param.
Parameters
----------
estimator_list : list of object
meta_model : object, default = None
if set to None, Linear Regression is used as a meta model.
fold: integer, default = 10
Number of folds to be used in Kfold CV. Must be at least 2.
round: integer, default = 4
Number of decimal places the metrics in the score grid will be rounded to.
restack: Boolean, default = False
When restack is set to True, raw data will be exposed to meta model when
making predictions, otherwise when False, only the predicted label is passed
to meta model when making final predictions.
plot: Boolean, default = False
When plot is set to True, it will return the correlation plot of prediction
from all base models provided in estimator_list.
finalize: Boolean, default = False
When finalize is set to True, it will fit the stacker on entire dataset
including the hold-out sample created during the setup() stage. It is not
recommended to set this to True here, If you would like to fit the stacker
on the entire dataset including the hold-out, use finalize_model().
verbose: Boolean, default = True
Score grid is not printed when verbose is set to False.
Returns:
--------
score grid: A table containing the scores of the model across the kfolds.
----------- Scoring metrics used are MAE, MSE, RMSE, R2 and ME. Mean and
standard deviation of the scores across the folds are also
returned.
container: list of all the models where last element is meta model.
----------
Warnings:
---------
None
"""
'''
ERROR HANDLING STARTS HERE
'''
#exception checking
import sys
#checking error for estimator_list
for i in estimator_list:
if 'sklearn' not in str(type(i)) and 'CatBoostRegressor' not in str(type(i)):
sys.exit("(Value Error): estimator_list parameter only trained model object")
#checking meta model
if meta_model is not None:
if 'sklearn' not in str(type(meta_model)) and 'CatBoostRegressor' not in str(type(meta_model)):
sys.exit("(Value Error): estimator_list parameter only trained model object")
#checking fold parameter
if type(fold) is not int:
sys.exit('(Type Error): Fold parameter only accepts integer value.')
#checking round parameter
if type(round) is not int:
sys.exit('(Type Error): Round parameter only accepts integer value.')
#checking restack parameter
if type(restack) is not bool:
sys.exit('(Type Error): Restack parameter can only take argument as True or False.')
#checking plot parameter
if type(restack) is not bool:
sys.exit('(Type Error): Plot parameter can only take argument as True or False.')
#checking verbose parameter
if type(verbose) is not bool:
sys.exit('(Type Error): Verbose parameter can only take argument as True or False.')
'''
ERROR HANDLING ENDS HERE
'''
#testing
#no active tests
#pre-load libraries
import pandas as pd
import ipywidgets as ipw
from IPython.display import display, HTML, clear_output, update_display
import time, datetime
#progress bar
max_progress = len(estimator_list) + fold + 4
progress = ipw.IntProgress(value=0, min=0, max=max_progress, step=1 , description='Processing: ')
master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'ME'])
display(progress)
#display monitor
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ],
['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
['ETC' , '. . . . . . . . . . . . . . . . . .', 'Calculating ETC'] ],
columns=['', ' ', ' ']).set_index('')
display(monitor, display_id = 'monitor')
if verbose:
display_ = display(master_display, display_id=True)
display_id = display_.display_id
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#dependencies
import numpy as np
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_predict
import seaborn as sns
progress.value += 1
#Defining meta model. Linear Regression hardcoded for now
if meta_model == None:
from sklearn.linear_model import LinearRegression
meta_model = LinearRegression()
else:
meta_model = meta_model
#defining data_X and data_y
if finalize:
data_X = X.copy()
data_y = y.copy()
else:
data_X = X_train.copy()
data_y = y_train.copy()
#reset index
data_X.reset_index(drop=True,inplace=True)
data_y.reset_index(drop=True,inplace=True)
#models_ for appending
models_ = []
#defining model_library model names
model_names = np.zeros(0)
for item in estimator_list:
model_names = np.append(model_names, str(item).split("(")[0])
model_names_fixed = []
for i in model_names:
if 'CatBoostRegressor' in i:
a = 'CatBoostRegressor'
model_names_fixed.append(a)
else:
model_names_fixed.append(i)
model_names = model_names_fixed
model_names_fixed = []
counter = 0
for i in model_names:
s = str(i) + '_' + str(counter)
model_names_fixed.append(s)
counter += 1
base_array = np.zeros((0,0))
base_prediction = pd.DataFrame(data_y) #changed to data_y
base_prediction = base_prediction.reset_index(drop=True)
counter = 0
for model in estimator_list:
'''
MONITOR UPDATE STARTS
'''
monitor.iloc[1,1:] = 'Evaluating ' + model_names[counter]
update_display(monitor, display_id = 'monitor')
'''
MONITOR UPDATE ENDS
'''
#fitting and appending
model.fit(data_X, data_y)
models_.append(model)
progress.value += 1
base_array = cross_val_predict(model,data_X,data_y,cv=fold, method='predict')
base_array_df = pd.DataFrame(base_array)
base_prediction = pd.concat([base_prediction,base_array_df],axis=1)
base_array = np.empty((0,0))
counter += 1
#defining column names now
target_col_name = np.array(base_prediction.columns[0])
model_names = np.append(target_col_name, model_names_fixed) #adding fixed column names now
base_prediction.columns = model_names #defining colum names now
#defining data_X and data_y dataframe to be used in next stage.
#drop column from base_prediction
base_prediction.drop(base_prediction.columns[0],axis=1,inplace=True)
if restack:
data_X = pd.concat([data_X, base_prediction], axis=1)
else:
data_X = base_prediction
#data_y = base_prediction[base_prediction.columns[0]]
#Correlation matrix of base_prediction
base_prediction_cor = base_prediction.drop(base_prediction.columns[0],axis=1)
base_prediction_cor = base_prediction_cor.corr()
#Meta Modeling Starts Here
model = meta_model #this defines model to be used below as model = meta_model (as captured above)
#appending in models
model.fit(data_X, data_y)
models_.append(model)
kf = KFold(fold, random_state=seed) #capturing fold requested by user
score_mae =np.empty((0,0))
score_mse =np.empty((0,0))
score_rmse =np.empty((0,0))
score_r2 =np.empty((0,0))
score_max_error =np.empty((0,0))
avgs_mae =np.empty((0,0))
avgs_mse =np.empty((0,0))
avgs_rmse =np.empty((0,0))
avgs_r2 =np.empty((0,0))
avgs_max_error =np.empty((0,0))
progress.value += 1
fold_num = 1
for train_i , test_i in kf.split(data_X,data_y):
t0 = time.time()
'''
MONITOR UPDATE STARTS
'''
monitor.iloc[1,1:] = 'Fitting Meta Model Fold ' + str(fold_num) + ' of ' + str(fold)
update_display(monitor, display_id = 'monitor')
'''
MONITOR UPDATE ENDS
'''
progress.value += 1
Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
model.fit(Xtrain,ytrain)
pred_ = model.predict(Xtest)
mae = metrics.mean_absolute_error(ytest,pred_)
mse = metrics.mean_squared_error(ytest,pred_)
rmse = np.sqrt(mse)
r2 = metrics.r2_score(ytest,pred_)
max_error_ = metrics.max_error(ytest,pred_)
score_mae = np.append(score_mae,mae)
score_mse = np.append(score_mse,mse)
score_rmse = np.append(score_rmse,rmse)
score_r2 =np.append(score_r2,r2)
score_max_error = np.append(score_max_error,max_error_)
'''
This section handles time calculation and is created to update_display() as code loops through
the fold defined.
'''
fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse],
'R2': [r2], 'ME': [max_error_]}).round(round)
master_display = pd.concat([master_display, fold_results],ignore_index=True)
fold_results = []
'''
TIME CALCULATION SUB-SECTION STARTS HERE
'''
t1 = time.time()
tt = (t1 - t0) * (fold-fold_num) / 60
tt = np.around(tt, 2)
if tt < 1:
tt = str(np.around((tt * 60), 2))
ETC = tt + ' Seconds Remaining'
else:
tt = str (tt)
ETC = tt + ' Minutes Remaining'
'''
MONITOR UPDATE STARTS
'''
monitor.iloc[2,1:] = ETC
update_display(monitor, display_id = 'monitor')
'''
MONITOR UPDATE ENDS
'''
#update_display(ETC, display_id = 'ETC')
fold_num += 1
'''
TIME CALCULATION ENDS HERE
'''
if verbose:
update_display(master_display, display_id = display_id)
'''
Update_display() ends here
'''
mean_mae=np.mean(score_mae)
mean_mse=np.mean(score_mse)
mean_rmse=np.mean(score_rmse)
mean_r2=np.mean(score_r2)
mean_max_error=np.mean(score_max_error)
std_mae=np.std(score_mae)
std_mse=np.std(score_mse)
std_rmse=np.std(score_rmse)
std_r2=np.std(score_r2)
std_max_error=np.std(score_max_error)
avgs_mae = np.append(avgs_mae, mean_mae)
avgs_mae = np.append(avgs_mae, std_mae)
avgs_mse = np.append(avgs_mse, mean_mse)
avgs_mse = np.append(avgs_mse, std_mse)
avgs_rmse = np.append(avgs_rmse, mean_rmse)
avgs_rmse = np.append(avgs_rmse, std_rmse)
avgs_r2 = np.append(avgs_r2, mean_r2)
avgs_r2 = np.append(avgs_r2, std_r2)
avgs_max_error = np.append(avgs_max_error, mean_max_error)
avgs_max_error = np.append(avgs_max_error, std_max_error)
model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2 ,
'ME' : score_max_error})
model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2,
'ME' : avgs_max_error},index=['Mean', 'SD'])
model_results = model_results.append(model_avgs)
model_results = model_results.round(round)
progress.value += 1
#appending method into models_
models_.append(restack)
#storing into experiment
model_name = 'Stacking Regressor (Single Layer)'
tup = (model_name,models_)
experiment__.append(tup)
nam = str(model_name) + ' Score Grid'
tup = (nam, model_results)
experiment__.append(tup)
if plot:
clear_output()
ax = sns.heatmap(base_prediction_cor, vmin=1, vmax=1, center=0,cmap='magma', square=True, annot=True,
linewidths=1)
if verbose:
clear_output()
display(model_results)
return models_
else:
clear_output()
return models_
def create_stacknet(estimator_list,
meta_model = None,
......@@ -4331,19 +3877,23 @@ def create_stacknet(estimator_list,
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_predict
from copy import deepcopy
#models_ list
models_ = []
progress.value += 1
#copy estimator_list
estimator_list = deepcopy(estimator_list)
base_level = estimator_list[0]
base_level_names = []
#defining base_level_names
for item in base_level:
base_level_names = np.append(base_level_names, str(item).split("(")[0])
base_level_fixed = []
for i in base_level_names:
......@@ -4352,9 +3902,9 @@ def create_stacknet(estimator_list,
base_level_fixed.append(a)
else:
base_level_fixed.append(i)
base_level_names = base_level_fixed
base_level_fixed_2 = []
counter = 0
......@@ -4396,7 +3946,7 @@ def create_stacknet(estimator_list,
from sklearn.linear_model import LinearRegression
meta_model = LinearRegression()
else:
meta_model = meta_model
meta_model = deepcopy(meta_model)
base_array = np.zeros((0,0))
base_array_df = pd.DataFrame()
......@@ -4666,6 +4216,8 @@ def create_stacknet(estimator_list,
return models_
def plot_model(estimator,
plot = 'residuals'):
......@@ -5481,7 +5033,6 @@ def load_experiment(experiment_name):
def predict_model(estimator,
data=None,
round=4):
......@@ -5562,7 +5113,7 @@ def predict_model(estimator,
X_test_.reset_index(drop=True, inplace=True)
y_test_.reset_index(drop=True, inplace=True)
model = estimator
model = deepcopy(estimator)
else:
......@@ -5579,19 +5130,19 @@ def predict_model(estimator,
else:
prep_pipe_transformer = prep_pipe
model = estimator
estimator = estimator
model = deepcopy(estimator)
estimator = deepcopy(estimator)
else:
prep_pipe_transformer = prep_pipe
model = estimator
estimator = estimator
model = deepcopy(estimator)
estimator = deepcopy(estimator)
try:
model = finalize_model(estimator)
except:
model = estimator
model = deepcopy(estimator)
Xtest = prep_pipe_transformer.transform(data)
X_test_ = data.copy() #original concater
......@@ -5609,7 +5160,7 @@ def predict_model(estimator,
"""
#utility
stacker = model.copy()
stacker = deepcopy(model)
restack = stacker.pop()
#stacker_method = stacker.pop()
#stacker_method = stacker_method[0]
......@@ -5749,7 +5300,7 @@ def predict_model(estimator,
"""
#copy
stacker = model.copy()
stacker = deepcopy(model)
#restack
restack = stacker.pop()
......@@ -5897,4 +5448,6 @@ def predict_model(estimator,
else:
X_test_ = pd.concat([X_test_,label], axis=1)
return X_test_
\ No newline at end of file
return X_test_
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