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提交 f4761c81 编写于 作者: P PyCaret
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pycaret-nightly 0.7

上级 4c588463
隐藏空白更改
内联 并排
Showing with 612 addition and 26 deletion
.gitignore
浏览文件 @ f4761c81
......@@ -16,3 +16,4 @@ catboost_info
/pycaret_nightly.egg-info
/pycaret.egg-info
/dist
.vscode/settings.json
pycaret/classification.py
浏览文件 @ f4761c81
......@@ -7928,8 +7928,6 @@ def create_stacknet(estimator_list,
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
mean_acc=np.mean(score_acc)
mean_auc=np.mean(score_auc)
mean_recall=np.mean(score_recall)
......
pycaret/regression.py
浏览文件 @ f4761c81
......@@ -1632,6 +1632,7 @@ def setup(data,
if logging_param:
import mlflow
from pathlib import Path
if experiment_name is None:
exp_name_ = 'clf-default-name'
......@@ -1677,6 +1678,9 @@ def setup(data,
# Log the transformation pipeline
save_model(prep_pipe, 'Transformation Pipeline', verbose=False)
mlflow.log_artifact('Transformation Pipeline' + '.pkl')
size_bytes = Path('Transformation Pipeline.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Transformation Pipeline.pkl')
# Log pandas profile
......@@ -2329,6 +2333,7 @@ def create_model(estimator = None,
#import mlflow
import mlflow
from pathlib import Path
import os
mlflow.set_experiment(exp_name_log)
......@@ -2410,10 +2415,13 @@ def create_model(estimator = None,
except:
pass
# Log model and transformation pipeline
save_model(model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
os.remove('Trained Model.pkl')
# Log model and transformation pipeline
save_model(model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
progress.value += 1
......@@ -2446,7 +2454,7 @@ def ensemble_model(estimator,
fold = 10,
n_estimators = 10,
round = 4,
choose_better = True, #added in pycaret==1.0.1
choose_better = False, #added in pycaret==1.0.1
optimize = 'r2', #added in pycaret==1.0.1
verbose = True):
"""
......@@ -2495,7 +2503,7 @@ def ensemble_model(estimator,
round: integer, default = 4
Number of decimal places the metrics in the score grid will be rounded to.
choose_better: Boolean, default = True
choose_better: Boolean, default = False
When set to set to True, base estimator is returned when the metric doesn't
improve by ensemble_model. This gurantees the returned object would perform
atleast equivalent to base estimator created using create_model or model
......@@ -2539,6 +2547,10 @@ def ensemble_model(estimator,
#exception checking
import sys
#run_time
import datetime, time
runtime_start = time.time()
#Check for allowed method
available_method = ['Bagging', 'Boosting']
if method not in available_method:
......@@ -2666,6 +2678,33 @@ def ensemble_model(estimator,
estimator__ = model_dict.get(mn)
model_dict_logging = {'ExtraTreesRegressor' : 'Extra Trees Regressor',
'GradientBoostingRegressor' : 'Gradient Boosting Regressor',
'RandomForestRegressor' : 'Random Forest',
'LGBMRegressor' : 'Light Gradient Boosting Machine',
'XGBRegressor' : 'Extreme Gradient Boosting',
'AdaBoostRegressor' : 'AdaBoost Regressor',
'DecisionTreeRegressor' : 'Decision Tree',
'Ridge' : 'Ridge Regression',
'TheilSenRegressor' : 'TheilSen Regressor',
'BayesianRidge' : 'Bayesian Ridge',
'LinearRegression' : 'Linear Regression',
'ARDRegression' : 'Automatic Relevance Determination',
'KernelRidge' : 'Kernel Ridge',
'RANSACRegressor' : 'Random Sample Consensus',
'HuberRegressor' : 'Huber Regressor',
'Lasso' : 'Lasso Regression',
'ElasticNet' : 'Elastic Net',
'Lars' : 'Least Angle Regression',
'OrthogonalMatchingPursuit' : 'Orthogonal Matching Pursuit',
'MLPRegressor' : 'Multi Level Perceptron',
'KNeighborsRegressor' : 'K Neighbors Regressor',
'SVR' : 'Support Vector Machine',
'LassoLars' : 'Lasso Least Angle Regression',
'PassiveAggressiveRegressor' : 'Passive Aggressive Regressor',
'CatBoostRegressor' : 'CatBoost Regressor',
'BaggingRegressor' : 'Bagging Regressor'}
'''
MONITOR UPDATE STARTS
'''
......@@ -2893,7 +2932,12 @@ def ensemble_model(estimator,
if html_param:
update_display(monitor, display_id = 'monitor')
model_fit_start = time.time()
model.fit(data_X, data_y)
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
#storing results in create_model_container
create_model_container.append(model_results.data)
......@@ -2934,6 +2978,9 @@ def ensemble_model(estimator,
else:
model = base_model
#re-instate display_constainer state
display_container.pop(-1)
#storing into experiment
model_name = str(model).split("(")[0]
tup = (model_name,model)
......@@ -2943,6 +2990,114 @@ def ensemble_model(estimator,
tup = (nam, model_results)
experiment__.append(tup)
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
if logging_param:
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
monitor.iloc[2,1:] = 'Almost Finished'
if verbose:
if html_param:
update_display(monitor, display_id = 'monitor')
import mlflow
from pathlib import Path
import os
mlflow.set_experiment(exp_name_log)
full_name = model_dict_logging.get(mn)
with mlflow.start_run(run_name=full_name) as run:
# Get active run to log as tag
RunID = mlflow.active_run().info.run_id
params = model.get_params()
for i in list(params):
v = params.get(i)
if len(str(v)) > 250:
params.pop(i)
mlflow.log_params(params)
mlflow.log_metrics({"MAE": avgs_mae[0], "MSE": avgs_mse[0], "RMSE": avgs_rmse[0], "R2" : avgs_r2[0],
"RMSLE": avgs_rmsle[0], "MAPE": avgs_mape[0]})
# Log internal parameters
mlflow.log_param('ensemble_model_estimator', full_name)
mlflow.log_param('ensemble_model_method', method)
mlflow.log_param('ensemble_model_fold', fold)
mlflow.log_param('ensemble_model_n_estimators', n_estimators)
mlflow.log_param('ensemble_model_round', round)
mlflow.log_param('ensemble_model_choose_better', choose_better)
mlflow.log_param('ensemble_model_optimize', optimize)
mlflow.log_param('ensemble_model_verbose', verbose)
#set tag of compare_models
mlflow.set_tag("Source", "ensemble_model")
import secrets
URI = secrets.token_hex(nbytes=4)
mlflow.set_tag("URI", URI)
mlflow.set_tag("USI", USI)
mlflow.set_tag("Run Time", runtime)
mlflow.set_tag("Run ID", RunID)
# Log training time in seconds
mlflow.log_metric("TT", model_fit_time)
# Log model and transformation pipeline
save_model(model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
# Generate hold-out predictions and save as html
holdout = predict_model(model, verbose=False)
holdout_score = pull()
display_container.pop(-1)
holdout_score.to_html('Holdout.html', col_space=65, justify='left')
mlflow.log_artifact('Holdout.html')
os.remove('Holdout.html')
# Log AUC and Confusion Matrix plot
if log_plots_param:
try:
plot_model(model, plot = 'residuals', verbose=False, save=True, system=False)
mlflow.log_artifact('Residuals.png')
os.remove("Residuals.png")
except:
pass
try:
plot_model(model, plot = 'error', verbose=False, save=True, system=False)
mlflow.log_artifact('Prediction Error.png')
os.remove("Prediction Error.png")
except:
pass
try:
plot_model(model, plot = 'feature', verbose=False, save=True, system=False)
mlflow.log_artifact('Feature Importance.png')
os.remove("Feature Importance.png")
except:
pass
# Log the CV results as model_results.html artifact
model_results.data.to_html('Results.html', col_space=65, justify='left')
mlflow.log_artifact('Results.html')
os.remove('Results.html')
if verbose:
clear_output()
if html_param:
......@@ -3153,6 +3308,9 @@ def compare_models(blacklist = None,
else:
n_select_num = abs(n_select)
if n_select_num > len_mod:
n_select_num = len_mod
if whitelist is not None:
wl = len(whitelist)
bl = len_of_blacklist
......@@ -3527,12 +3685,19 @@ def compare_models(blacklist = None,
mask = actual != 0
return (np.fabs(actual - prediction)/actual)[mask].mean()
#create URI (before loop)
import secrets
URI = secrets.token_hex(nbytes=4)
name_counter = 0
model_store = []
for model in model_library:
#run_time
runtime_start = time.time()
progress.value += 1
'''
......@@ -3575,6 +3740,7 @@ def compare_models(blacklist = None,
ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
time_start=time.time()
model_store_by_fold.append(model.fit(Xtrain,ytrain))
time_end=time.time()
pred_ = model.predict(Xtest)
try:
......@@ -3586,7 +3752,6 @@ def compare_models(blacklist = None,
except:
pass
time_end=time.time()
mae = metrics.mean_absolute_error(ytest,pred_)
mse = metrics.mean_squared_error(ytest,pred_)
rmse = np.sqrt(mse)
......@@ -3642,13 +3807,12 @@ def compare_models(blacklist = None,
avgs_rmsle = np.append(avgs_rmsle,np.mean(score_rmsle))
avgs_r2 = np.append(avgs_r2,np.mean(score_r2))
avgs_mape = np.append(avgs_mape,np.mean(score_mape))
avgs_training_time = np.append(avgs_training_time,np.sum(score_training_time))
avgs_training_time = np.append(avgs_training_time,np.mean(score_training_time))
compare_models_ = pd.DataFrame({'Model':model_names[name_counter], 'MAE':avgs_mae, 'MSE':avgs_mse,
'RMSE':avgs_rmse, 'R2':avgs_r2, 'RMSLE':avgs_rmsle, 'MAPE':avgs_mape, 'TT (Sec)':avgs_training_time})
master_display = pd.concat([master_display, compare_models_],ignore_index=True)
master_display = master_display.round(round)
#master_display.loc[:,'TT (Sec)'] = master_display.loc[:,'TT (Sec)'].round(2)
if sort == 'R2':
master_display = master_display.sort_values(by=sort,ascending=False)
......@@ -3661,6 +3825,61 @@ def compare_models(blacklist = None,
if html_param:
update_display(master_display, display_id = display_id)
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
"""
MLflow logging starts here
"""
if logging_param:
import mlflow
from pathlib import Path
import os
run_name = model_names[name_counter]
with mlflow.start_run(run_name=run_name) as run:
# Get active run to log as tag
RunID = mlflow.active_run().info.run_id
params = model.get_params()
for i in list(params):
v = params.get(i)
if len(str(v)) > 250:
params.pop(i)
mlflow.log_params(params)
#set tag of compare_models
mlflow.set_tag("Source", "compare_models")
mlflow.set_tag("URI", URI)
mlflow.set_tag("USI", USI)
mlflow.set_tag("Run Time", runtime)
mlflow.set_tag("Run ID", RunID)
#Log top model metrics
mlflow.log_metric("MAE", avgs_mae[0])
mlflow.log_metric("MSE", avgs_mse[0])
mlflow.log_metric("RMSE", avgs_rmse[0])
mlflow.log_metric("R2", avgs_r2[0])
mlflow.log_metric("RMSLE", avgs_rmsle[0])
mlflow.log_metric("MAPE", avgs_mape[0])
mlflow.log_metric("TT", avgs_training_time[0])
# Log model and transformation pipeline
save_model(model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
score_mae =np.empty((0,0))
score_mse =np.empty((0,0))
score_rmse =np.empty((0,0))
......@@ -3726,7 +3945,7 @@ def compare_models(blacklist = None,
update_display(monitor, display_id = 'monitor')
progress.value += 1
k = model_dict.get(i)
m = create_model(estimator=k, verbose = False)
m = create_model(estimator=k, verbose = False, system=False)
model_store_final.append(m)
if len(model_store_final) == 1:
......@@ -3747,7 +3966,7 @@ def compare_models(blacklist = None,
def blend_models(estimator_list = 'All',
fold = 10,
round = 4,
choose_better = True, #added in pycaret==1.0.1
choose_better = False, #added in pycaret==1.0.1
optimize = 'r2', #added in pycaret==1.0.1
turbo = True,
verbose = True):
......@@ -3797,7 +4016,7 @@ def blend_models(estimator_list = 'All',
round: integer, default = 4
Number of decimal places the metrics in the score grid will be rounded to.
choose_better: Boolean, default = True
choose_better: Boolean, default = False
When set to True, base estimator is returned when the metric doesn't
improve by ensemble_model. This gurantees the returned object would perform
atleast equivalent to base estimator created using create_model or model
......@@ -3847,8 +4066,11 @@ def blend_models(estimator_list = 'All',
#exception checking
import sys
#run_time
import datetime, time
runtime_start = time.time()
#checking error for estimator_list (string)
if estimator_list != 'All':
for i in estimator_list:
if 'sklearn' not in str(type(i)) and 'CatBoostRegressor' not in str(type(i)):
......@@ -4182,6 +4404,7 @@ def blend_models(estimator_list = 'All',
except:
pass
time_end=time.time()
mae = metrics.mean_absolute_error(ytest,pred_)
mse = metrics.mean_squared_error(ytest,pred_)
......@@ -4308,7 +4531,11 @@ def blend_models(estimator_list = 'All',
if html_param:
update_display(monitor, display_id = 'monitor')
model_fit_start = time.time()
model.fit(data_X, data_y)
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
progress.value += 1
......@@ -4350,11 +4577,14 @@ def blend_models(estimator_list = 'All',
base_models_ = []
for i in estimator_list:
m = create_model(i,verbose=False)
m = create_model(i,verbose=False, system=False)
s = create_model_container[-1][compare_dimension][-2:][0]
scorer.append(s)
base_models_.append(m)
#re-instate display_constainer state
display_container.pop(-1)
if optimize == 'r2':
index_scorer = scorer.index(max(scorer))
else:
......@@ -4365,6 +4595,101 @@ def blend_models(estimator_list = 'All',
else:
model = base_models_[index_scorer-1]
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
if logging_param:
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
monitor.iloc[2,1:] = 'Almost Finished'
if verbose:
if html_param:
update_display(monitor, display_id = 'monitor')
import mlflow
from pathlib import Path
import os
with mlflow.start_run(run_name='Voting Regressor') as run:
# Get active run to log as tag
RunID = mlflow.active_run().info.run_id
mlflow.log_metrics({"MAE": avgs_mae[0], "MSE": avgs_mse[0], "RMSE": avgs_rmse[0], "R2" : avgs_r2[0],
"RMSLE": avgs_rmsle[0], "MAPE": avgs_mape[0]})
# Log internal parameters
mlflow.log_param("blend_models_estimator_list", model_names_final)
mlflow.log_param("blend_models_fold", fold)
mlflow.log_param("blend_models_round", round)
mlflow.log_param("blend_models_choose_better", choose_better)
mlflow.log_param("blend_models_optimize", optimize)
mlflow.log_param("blend_models_turbo", turbo)
mlflow.log_param("blend_models_verbose", verbose)
# Log model and transformation pipeline
save_model(model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
# Generate hold-out predictions and save as html
holdout = predict_model(model, verbose=False)
holdout_score = pull()
display_container.pop(-1)
holdout_score.to_html('Holdout.html', col_space=65, justify='left')
mlflow.log_artifact('Holdout.html')
os.remove('Holdout.html')
#set tag of compare_models
mlflow.set_tag("Source", "blend_models")
import secrets
URI = secrets.token_hex(nbytes=4)
mlflow.set_tag("URI", URI)
mlflow.set_tag("USI", USI)
mlflow.set_tag("Run Time", runtime)
mlflow.set_tag("Run ID", RunID)
# Log training time of compare_models
mlflow.log_metric("TT", model_fit_time)
# Log AUC and Confusion Matrix plot
if log_plots_param:
try:
plot_model(model, plot = 'residuals', verbose=False, save=True, system=False)
mlflow.log_artifact('Residuals.png')
os.remove("Residuals.png")
except:
pass
try:
plot_model(model, plot = 'error', verbose=False, save=True, system=False)
mlflow.log_artifact('Prediction Error.png')
os.remove("Prediction Error.png")
except:
pass
try:
plot_model(model, plot = 'feature', verbose=False, save=True, system=False)
mlflow.log_artifact('Feature Importance.png')
os.remove("Feature Importance.png")
except:
pass
# Log the CV results as model_results.html artifact
model_results.data.to_html('Results.html', col_space=65, justify='left')
mlflow.log_artifact('Results.html')
os.remove('Results.html')
if verbose:
clear_output()
if html_param:
......@@ -4380,7 +4705,7 @@ def tune_model(estimator,
n_iter = 10,
custom_grid = None, #added in pycaret==1.0.1
optimize = 'r2',
choose_better = True, #added in pycaret==1.0.1
choose_better = False, #added in pycaret==1.0.1
verbose = True):
......@@ -4460,7 +4785,7 @@ def tune_model(estimator,
'rmsle', 'mape'. When using 'rmse' or 'rmsle' the base scorer is 'mse' and when using
'mape' the base scorer is 'mae'.
choose_better: Boolean, default = True
choose_better: Boolean, default = False
When set to set to True, base estimator is returned when the metric doesn't improve
by tune_model. This gurantees the returned object would perform atleast equivalent
to base estimator created using create_model or model returned by compare_models.
......@@ -4499,6 +4824,10 @@ def tune_model(estimator,
#exception checking
import sys
#run_time
import datetime, time
runtime_start = time.time()
#checking estimator if string
if type(estimator) is str:
sys.exit('(Type Error): The behavior of tune_model in version 1.0.1 is changed. Please pass trained model object.')
......@@ -4637,6 +4966,33 @@ def tune_model(estimator,
'CatBoostRegressor' : 'catboost',
'BaggingRegressor' : 'Bagging'}
model_dict_logging = {'ExtraTreesRegressor' : 'Extra Trees Regressor',
'GradientBoostingRegressor' : 'Gradient Boosting Regressor',
'RandomForestRegressor' : 'Random Forest',
'LGBMRegressor' : 'Light Gradient Boosting Machine',
'XGBRegressor' : 'Extreme Gradient Boosting',
'AdaBoostRegressor' : 'AdaBoost Regressor',
'DecisionTreeRegressor' : 'Decision Tree',
'Ridge' : 'Ridge Regression',
'TheilSenRegressor' : 'TheilSen Regressor',
'BayesianRidge' : 'Bayesian Ridge',
'LinearRegression' : 'Linear Regression',
'ARDRegression' : 'Automatic Relevance Determination',
'KernelRidge' : 'Kernel Ridge',
'RANSACRegressor' : 'Random Sample Consensus',
'HuberRegressor' : 'Huber Regressor',
'Lasso' : 'Lasso Regression',
'ElasticNet' : 'Elastic Net',
'Lars' : 'Least Angle Regression',
'OrthogonalMatchingPursuit' : 'Orthogonal Matching Pursuit',
'MLPRegressor' : 'Multi Level Perceptron',
'KNeighborsRegressor' : 'K Neighbors Regressor',
'SVR' : 'Support Vector Machine',
'LassoLars' : 'Lasso Least Angle Regression',
'PassiveAggressiveRegressor' : 'Passive Aggressive Regressor',
'CatBoostRegressor' : 'CatBoost Regressor',
'BaggingRegressor' : 'Bagging Regressor'}
_estimator_ = estimator
estimator = model_dict.get(mn)
......@@ -4680,7 +5036,7 @@ def tune_model(estimator,
MONITOR UPDATE STARTS
'''
monitor.iloc[1,1:] = 'Searching Hyperparameters Grid'
monitor.iloc[1,1:] = 'Searching Hyperparameters'
if verbose:
if html_param:
update_display(monitor, display_id = 'monitor')
......@@ -5493,7 +5849,11 @@ def tune_model(estimator,
if html_param:
update_display(monitor, display_id = 'monitor')
model_fit_start = time.time()
best_model.fit(data_X, data_y)
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
progress.value += 1
......@@ -5520,7 +5880,7 @@ def tune_model(estimator,
#creating base model for comparison
if estimator in ['Bagging', 'ada']:
base_model = create_model(estimator=_estimator_, verbose = False)
base_model = create_model(estimator=_estimator_, verbose = False, system=False)
else:
base_model = create_model(estimator=estimator, verbose = False)
base_model_results = create_model_container[-1][compare_dimension][-2:][0]
......@@ -5537,6 +5897,9 @@ def tune_model(estimator,
else:
best_model = base_model
#re-instate display_constainer state
display_container.pop(-1)
#storing into experiment
model_name = 'Tuned ' + str(model).split("(")[0]
tup = (model_name,best_model)
......@@ -5544,6 +5907,123 @@ def tune_model(estimator,
nam = str(model_name) + ' Score Grid'
tup = (nam, model_results)
experiment__.append(tup)
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
#mlflow logging
if logging_param:
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
monitor.iloc[2,1:] = 'Almost Finished'
if verbose:
if html_param:
update_display(monitor, display_id = 'monitor')
import mlflow
from pathlib import Path
import os
mlflow.set_experiment(exp_name_log)
full_name = model_dict_logging.get(mn)
with mlflow.start_run(run_name=full_name) as run:
# Get active run to log as tag
RunID = mlflow.active_run().info.run_id
params = best_model.get_params()
# Log model parameters
params = model.get_params()
for i in list(params):
v = params.get(i)
if len(str(v)) > 250:
params.pop(i)
mlflow.log_params(params)
mlflow.log_metrics({"MAE": avgs_mae[0], "MSE": avgs_mse[0], "RMSE": avgs_rmse[0], "R2" : avgs_r2[0],
"RMSLE": avgs_rmsle[0], "MAPE": avgs_mape[0]})
# Log internal parameters
mlflow.log_param("tune_model_fold", fold)
mlflow.log_param("tune_model_round", round)
mlflow.log_param("tune_model_n_iter", n_iter)
mlflow.log_param("tune_model_optimize", optimize)
mlflow.log_param("tune_model_choose_better", choose_better)
mlflow.log_param("tune_model_verbose", verbose)
#set tag of compare_models
mlflow.set_tag("Source", "tune_model")
import secrets
URI = secrets.token_hex(nbytes=4)
mlflow.set_tag("URI", URI)
mlflow.set_tag("USI", USI)
mlflow.set_tag("Run Time", runtime)
mlflow.set_tag("Run ID", RunID)
# Log training time in seconds
mlflow.log_metric("TT", model_fit_time)
# Log model and transformation pipeline
save_model(best_model, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
# Log the CV results as model_results.html artifact
model_results.data.to_html('Results.html', col_space=65, justify='left')
mlflow.log_artifact('Results.html')
os.remove('Results.html')
# Generate hold-out predictions and save as html
holdout = predict_model(best_model, verbose=False)
holdout_score = pull()
display_container.pop(-1)
holdout_score.to_html('Holdout.html', col_space=65, justify='left')
mlflow.log_artifact('Holdout.html')
os.remove('Holdout.html')
# Log AUC and Confusion Matrix plot
if log_plots_param:
try:
plot_model(model, plot = 'residuals', verbose=False, save=True, system=False)
mlflow.log_artifact('Residuals.png')
os.remove("Residuals.png")
except:
pass
try:
plot_model(model, plot = 'error', verbose=False, save=True, system=False)
mlflow.log_artifact('Prediction Error.png')
os.remove("Prediction Error.png")
except:
pass
try:
plot_model(model, plot = 'feature', verbose=False, save=True, system=False)
mlflow.log_artifact('Feature Importance.png')
os.remove("Feature Importance.png")
except:
pass
# Log hyperparameter tuning grid
d1 = model_grid.cv_results_.get('params')
dd = pd.DataFrame.from_dict(d1)
dd['Score'] = model_grid.cv_results_.get('mean_test_score')
dd.to_html('Iterations.html', col_space=75, justify='left')
mlflow.log_artifact('Iterations.html')
os.remove('Iterations.html')
if verbose:
clear_output()
......@@ -5562,7 +6042,7 @@ def stack_models(estimator_list,
round = 4,
restack = True,
plot = False,
choose_better = True, #added in pycaret==1.0.1
choose_better = False, #added in pycaret==1.0.1
optimize = 'r2', #added in pycaret==1.0.1
finalize = False,
verbose = True):
......@@ -5622,7 +6102,7 @@ def stack_models(estimator_list,
When plot is set to True, it will return the correlation plot of prediction
from all base models provided in estimator_list.
choose_better: Boolean, default = True
choose_better: Boolean, default = False
When set to True, base estimator is returned when the metric doesn't
improve by ensemble_model. This gurantees the returned object would perform
atleast equivalent to base estimator created using create_model or model
......@@ -5670,6 +6150,10 @@ def stack_models(estimator_list,
#exception checking
import sys
#run_time
import datetime, time
runtime_start = time.time()
#checking error for estimator_list
for i in estimator_list:
if 'sklearn' not in str(type(i)) and 'CatBoostRegressor' not in str(type(i)):
......@@ -5824,6 +6308,8 @@ def stack_models(estimator_list,
counter = 0
model_fit_start = time.time()
for model in estimator_list:
'''
......@@ -5897,6 +6383,7 @@ def stack_models(estimator_list,
avgs_r2 =np.empty((0,0))
avgs_mape =np.empty((0,0))
avgs_training_time=np.empty((0,0))
def calculate_mape(actual, prediction):
mask = actual != 0
return (np.fabs(actual - prediction)/actual)[mask].mean()
......@@ -6023,6 +6510,9 @@ def stack_models(estimator_list,
'''
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
mean_mae=np.mean(score_mae)
mean_mse=np.mean(score_mse)
mean_rmse=np.mean(score_rmse)
......@@ -6110,6 +6600,9 @@ def stack_models(estimator_list,
s = create_model_container[-1][compare_dimension][-2:][0]
scorer.append(s)
#re-instate display_constainer state
display_container.pop(-1)
#returning better model
if optimize == 'r2':
index_scorer = scorer.index(max(scorer))
......@@ -6136,6 +6629,100 @@ def stack_models(estimator_list,
linewidths=1)
ax.set_ylim(sorted(ax.get_xlim(), reverse=True))
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
if logging_param and not finalize:
import mlflow
from pathlib import Path
import os
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
monitor.iloc[2,1:] = 'Almost Finished'
if verbose:
if html_param:
update_display(monitor, display_id = 'monitor')
with mlflow.start_run(run_name='Stacking Regressor') as run:
# Get active run to log as tag
RunID = mlflow.active_run().info.run_id
params = meta_model.get_params()
for i in list(params):
v = params.get(i)
if len(str(v)) > 250:
params.pop(i)
mlflow.log_params(params)
mlflow.log_metrics({"MAE": avgs_mae[0], "MSE": avgs_mse[0], "RMSE": avgs_rmse[0], "R2" : avgs_r2[0],
"RMSLE": avgs_rmsle[0], "MAPE": avgs_mape[0]})
# Log internal parameters
mlflow.log_param("stack_models_estimator_list", estimator_list)
mlflow.log_param("stack_models_fold", fold)
mlflow.log_param("stack_models_round", round)
mlflow.log_param("stack_models_restack", restack)
mlflow.log_param("stack_models_plot", plot)
mlflow.log_param("stack_models_choose_better", choose_better)
mlflow.log_param("stack_models_optimize", optimize)
mlflow.log_param("stack_models_finalize", finalize)
mlflow.log_param("stack_models_verbose", verbose)
#set tag of stack_models
mlflow.set_tag("Source", "stack_models")
import secrets
URI = secrets.token_hex(nbytes=4)
mlflow.set_tag("URI", URI)
mlflow.set_tag("USI", USI)
mlflow.set_tag("Run Time", runtime)
mlflow.set_tag("Run ID", RunID)
# Log model and transformation pipeline
save_model(models_, 'Trained Model', verbose=False)
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
# Log training time of compare_models
mlflow.log_metric("TT", model_fit_time)
# Log the CV results as model_results.html artifact
model_results.data.to_html('Results.html', col_space=65, justify='left')
mlflow.log_artifact('Results.html')
os.remove('Results.html')
if log_plots_param:
plt.subplots(figsize=(15,7))
ax = sns.heatmap(base_prediction_cor, vmin=0.2, vmax=1, center=0,cmap='magma', square=True, annot=True,
linewidths=1)
ax.set_ylim(sorted(ax.get_xlim(), reverse=True))
plt.savefig("Stacking Heatmap.png")
mlflow.log_artifact('Stacking Heatmap.png')
os.remove('Stacking Heatmap.png')
plt.close()
# Generate hold-out predictions and save as html
holdout = predict_model(models_, verbose=False)
holdout_score = pull()
display_container.pop(-1)
holdout_score.to_html('Holdout.html', col_space=65, justify='left')
mlflow.log_artifact('Holdout.html')
os.remove('Holdout.html')
if verbose:
clear_output()
if html_param:
......@@ -6150,7 +6737,7 @@ def create_stacknet(estimator_list,
fold = 10,
round = 4,
restack = True,
choose_better = True, #added in pycaret==1.0.1
choose_better = False, #added in pycaret==1.0.1
optimize = 'r2', #added in pycaret==1.0.1
finalize = False,
verbose = True):
......@@ -6203,7 +6790,7 @@ def create_stacknet(estimator_list,
the predicted label of last layer is passed to meta model when making final
predictions.
choose_better: Boolean, default = True
choose_better: Boolean, default = False
When set to True, base estimator is returned when the metric doesn't
improve by ensemble_model. This gurantees the returned object would perform
atleast equivalent to base estimator created using create_model or model
......
pycaret/utils.py
浏览文件 @ f4761c81
......@@ -3,7 +3,7 @@
# License: MIT
def version():
print("pycaret-nightly-0.6")
print("pycaret-nightly-0.7")
def check_metric(actual, prediction, metric, round=4):
......
setup.py
浏览文件 @ f4761c81
......@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
setup(
name="pycaret-nightly",
version="0.6",
version="0.7",
description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
long_description=readme(),
long_description_content_type="text/markdown",
......
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