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提交 6e9224a5 编写于 作者: P PyCaret
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pycaret-nightly==0.34 part 3

上级 db4876c4
隐藏空白更改
内联 并排
Showing with 168 addition and 66 deletion
pycaret/anomaly.py
浏览文件 @ 6e9224a5
......@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 21/07/2020
# Last modified : 23/07/2020
def setup(data,
categorical_features = None,
......@@ -1096,7 +1096,9 @@ def setup(data,
mlflow.set_tag("Run ID", RunID)
# Log the transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(prep_pipe, 'Transformation Pipeline', verbose=False)
logger.info("SubProcess save_model() end ==================================")
mlflow.log_artifact('Transformation Pipeline' + '.pkl')
size_bytes = Path('Transformation Pipeline.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
......@@ -1130,7 +1132,8 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
logger.info("setup() succesfully completed")
logger.info(str(prep_pipe))
logger.info("setup() succesfully completed......................................")
return X, data_, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, html_param, exp_name_log, logging_param, log_plots_param, USI
......@@ -1441,11 +1444,6 @@ def create_model(model = None,
params.pop(i)
mlflow.log_params(params)
# Log internal parameters
mlflow.log_param("create_model_model", model)
mlflow.log_param("create_model_fraction", fraction)
mlflow.log_param("create_model_verbose", verbose)
#set tag of compare_models
mlflow.set_tag("Source", "create_model")
......@@ -1462,15 +1460,22 @@ def create_model(model = None,
# Log AUC and Confusion Matrix plot
if log_plots_param:
logger.info("SubProcess plot_model() called ==================================")
try:
plot_model(model, plot = 'tsne', save=True, system=False)
mlflow.log_artifact('TSNE.html')
os.remove("TSNE.html")
except:
pass
logger.info("SubProcess plot_model() end ==================================")
# Log model and transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(model, 'Trained Model', verbose=False)
logger.info("SubProcess save_model() end ==================================")
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
......@@ -1482,7 +1487,8 @@ def create_model(model = None,
if verbose:
clear_output()
logger.info("create_models() succesfully completed")
logger.info(str(model))
logger.info("create_models() succesfully completed......................................")
return model
......@@ -1678,7 +1684,8 @@ def assign_model(model,
if verbose:
clear_output()
logger.info("assign_model() succesfully completed")
logger.info(str(data__.shape))
logger.info("assign_model() succesfully completed......................................")
return data__
......@@ -2985,14 +2992,6 @@ def tune_model(model=None,
params.pop(i)
mlflow.log_params(params)
# Log internal parameters
mlflow.log_param("tune_model_model", model)
mlflow.log_param("tune_model_supervised_target", supervised_target)
mlflow.log_param("tune_model_estimator", estimator)
mlflow.log_param("tune_model_optimize", optimize)
mlflow.log_param("tune_model_fold", fold)
mlflow.log_param("tune_model_verbose", verbose)
#set tag of compare_models
mlflow.set_tag("Source", "tune_model")
......@@ -3013,14 +3012,17 @@ def tune_model(model=None,
os.remove('Iterations.html')
# Log model and transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(best_model, 'Trained Model', verbose=False)
logger.info("SubProcess save_model() end ==================================")
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')
logger.info("tune_model() succesfully completed")
logger.info(str(best_model))
logger.info("tune_model() succesfully completed......................................")
return best_model
......@@ -3234,7 +3236,8 @@ def save_model(model, model_name, verbose=True):
print('Transformation Pipeline and Model Succesfully Saved')
logger.info(str(model_name) + ' saved in current working directory')
logger.info("save_model() succesfully completed")
logger.info(str(model_))
logger.info("save_model() succesfully completed......................................")
def load_model(model_name,
platform = None,
......@@ -3509,7 +3512,9 @@ def deploy_model(model,
import boto3
logger.info("Saving model in current working directory")
logger.info("SubProcess save_model() called ==================================")
save_model(model, model_name = model_name, verbose=False)
logger.info("SubProcess save_model() end ==================================")
#initiaze s3
logger.info("Initializing S3 client")
......@@ -3520,8 +3525,9 @@ def deploy_model(model,
s3.upload_file(filename,bucket_name,key)
clear_output()
os.remove(filename)
logger.info("deploy_model() succesfully completed")
print("Model Succesfully Deployed on AWS S3")
logger.info(str(model))
logger.info("deploy_model() succesfully completed......................................")
def get_outliers(data,
model = None,
......@@ -3779,7 +3785,7 @@ def get_config(variable):
global_var = USI
logger.info("Global variable: " + str(variable) + ' returned')
logger.info("get_config() succesfully completed")
logger.info("get_config() succesfully completed......................................")
return global_var
......@@ -3859,7 +3865,7 @@ def set_config(variable,value):
USI = value
logger.info("Global variable: " + str(variable) + ' updated')
logger.info("set_config() succesfully completed")
logger.info("set_config() succesfully completed......................................")
def get_system_logs():
......
pycaret/classification.py
浏览文件 @ 6e9224a5
......@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 22/07/2020
# Last modified : 23/07/2020
def setup(data,
target,
......@@ -2079,6 +2079,10 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
logger.info("create_model_container " + str(len(create_model_container)))
logger.info("master_model_container " + str(len(master_model_container)))
logger.info("display_container " + str(len(display_container)))
logger.info(str(prep_pipe))
logger.info("setup() succesfully completed......................................")
......@@ -2600,6 +2604,10 @@ def create_model(estimator = None,
if verbose:
clear_output()
logger.info("create_model_container " + str(len(create_model_container)))
logger.info("master_model_container " + str(len(master_model_container)))
logger.info("display_container " + str(len(display_container)))
logger.info(str(model))
logger.info("create_models() succesfully completed......................................")
......@@ -2952,7 +2960,7 @@ def create_model(estimator = None,
display_container.append(model_results.data)
#storing results in master_model_container
logger.info("Uploading model into container")
logger.info("Uploading model into container now")
master_model_container.append(model)
if verbose:
......@@ -2963,6 +2971,10 @@ def create_model(estimator = None,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("create_model() succesfully completed......................................")
return model
......@@ -3712,6 +3724,10 @@ def ensemble_model(estimator,
else:
clear_output()
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("ensemble_model() succesfully completed......................................")
......@@ -5138,6 +5154,10 @@ def compare_models(blacklist = None,
#store in display container
display_container.append(compare_models_.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model_store_final))
logger.info("compare_models() succesfully completed......................................")
......@@ -6361,6 +6381,10 @@ def tune_model(estimator = None,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(best_model))
logger.info("tune_model() succesfully completed......................................")
......@@ -7178,6 +7202,10 @@ def blend_models(estimator_list = 'All',
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("blend_models() succesfully completed......................................")
......@@ -7962,6 +7990,10 @@ def stack_models(estimator_list,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(models_))
logger.info("stack_models() succesfully completed......................................")
......@@ -8805,6 +8837,10 @@ def create_stacknet(estimator_list,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(models_))
logger.info("create_stacknet() succesfully completed......................................")
......@@ -9631,6 +9667,10 @@ def calibrate_model(estimator,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("calibrate_model() succesfully completed......................................")
......@@ -10014,6 +10054,10 @@ def finalize_model(estimator):
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model_final))
logger.info("finalize_model() succesfully completed......................................")
......
pycaret/clustering.py
浏览文件 @ 6e9224a5
......@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 22/07/2020
# Last modified : 23/07/2020
def setup(data,
categorical_features = None,
......@@ -1099,7 +1099,9 @@ def setup(data,
mlflow.set_tag("Run ID", RunID)
# Log the transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(prep_pipe, 'Transformation Pipeline', verbose=False)
logger.info("SubProcess save_model() end ==================================")
mlflow.log_artifact('Transformation Pipeline' + '.pkl')
size_bytes = Path('Transformation Pipeline.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
......@@ -1133,7 +1135,8 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
logger.info("setup() succesfully completed")
logger.info(str(prep_pipe))
logger.info("setup() succesfully completed......................................")
return X, data_, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, html_param, exp_name_log, logging_param, log_plots_param, USI
......@@ -1507,12 +1510,6 @@ def create_model(model = None,
params.pop(i)
mlflow.log_params(params)
# Log internal parameters
mlflow.log_param("create_model_model", model)
mlflow.log_param("create_model_num_clusters", num_clusters)
mlflow.log_param("create_model_verbose", verbose)
mlflow.log_param("create_model_system", system)
#set tag of compare_models
mlflow.set_tag("Source", "create_model")
......@@ -1533,6 +1530,9 @@ def create_model(model = None,
# Log Cluster, Distribution Plot and Elbow Plot
if log_plots_param:
logger.info("SubProcess plot_model() called ==================================")
try:
plot_model(model, plot = 'cluster', save=True, system=False)
mlflow.log_artifact('Cluster.html')
......@@ -1554,8 +1554,12 @@ def create_model(model = None,
except:
pass
logger.info("SubProcess plot_model() end ==================================")
# Log model and transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(model, 'Trained Model', verbose=False)
logger.info("SubProcess save_model() end ==================================")
mlflow.log_artifact('Trained Model' + '.pkl')
size_bytes = Path('Trained Model.pkl').stat().st_size
size_kb = np.round(size_bytes/1000, 2)
......@@ -1571,7 +1575,8 @@ def create_model(model = None,
except:
pass
logger.info("create_models() succesfully completed")
logger.info(str(model))
logger.info("create_models() succesfully completed......................................")
return model
......@@ -1744,7 +1749,8 @@ def assign_model(model,
if verbose:
clear_output()
logger.info("assign_model() succesfully completed")
logger.info(done__.shape)
logger.info("assign_model() succesfully completed......................................")
return data__
......@@ -2330,17 +2336,17 @@ def tune_model(model=None,
#create and assign the model to dataset d
model_fit_start = time.time()
logger.info("SubProcess create_model() called")
logger.info("SubProcess create_model() called==================================")
m = create_model(model=model, num_clusters=i, verbose=False, system=False)
logger.info("SubProcess create_model() end")
logger.info("SubProcess create_model() end==================================")
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
model_fit_time_list.append(model_fit_time)
logger.info("Generating labels")
logger.info("SubProcess assign_model() called")
logger.info("SubProcess assign_model() called==================================")
d = assign_model(m, transformation=True, verbose=False)
logger.info("SubProcess assign_model() ends")
logger.info("SubProcess assign_model() ends==================================")
d[str(supervised_target)] = target_
master.append(m)
......@@ -3036,14 +3042,6 @@ def tune_model(model=None,
params.pop(i)
mlflow.log_params(params)
# Log internal parameters
mlflow.log_param("tune_model_model", model)
mlflow.log_param("tune_model_supervised_target", supervised_target)
mlflow.log_param("tune_model_estimator", estimator)
mlflow.log_param("tune_model_optimize", optimize)
mlflow.log_param("tune_model_fold", fold)
mlflow.log_param("tune_model_verbose", verbose)
#set tag of compare_models
mlflow.set_tag("Source", "tune_model")
......@@ -3064,14 +3062,17 @@ def tune_model(model=None,
os.remove('Iterations.html')
# Log model and transformation pipeline
logger.info("SubProcess save_model() called ==================================")
save_model(best_model, 'Trained Model', verbose=False)
logger.info("SubProcess save_model() end ==================================")
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')
logger.info("tune_model() succesfully completed")
logger.info(str(best_model))
logger.info("tune_model() succesfully completed......................................")
return best_model
......@@ -3453,7 +3454,8 @@ def save_model(model, model_name, verbose=True):
print('Transformation Pipeline and Model Succesfully Saved')
logger.info(str(model_name) + ' saved in current working directory')
logger.info("save_model() succesfully completed")
logger.info(str(model_))
logger.info("save_model() succesfully completed......................................")
def load_model(model_name,
platform = None,
......@@ -3734,7 +3736,9 @@ def deploy_model(model,
import boto3
logger.info("Saving model in current working directory")
logger.info("SubProcess save_model() called ==================================")
save_model(model, model_name = model_name, verbose=False)
logger.info("SubProcess save_model() end ==================================")
#initiaze s3
logger.info("Initializing S3 client")
......@@ -3745,9 +3749,10 @@ def deploy_model(model,
s3.upload_file(filename,bucket_name,key)
clear_output()
os.remove(filename)
logger.info("deploy_model() succesfully completed")
print("Model Succesfully Deployed on AWS S3")
logger.info(str(model))
logger.info("deploy_model() succesfully completed......................................")
def get_clusters(data,
model = None,
num_clusters = 4,
......@@ -3997,7 +4002,7 @@ def get_config(variable):
global_var = USI
logger.info("Global variable: " + str(variable) + ' returned')
logger.info("get_config() succesfully completed")
logger.info("get_config() succesfully completed......................................")
return global_var
......@@ -4077,7 +4082,7 @@ def set_config(variable,value):
USI = value
logger.info("Global variable: " + str(variable) + ' updated')
logger.info("set_config() succesfully completed")
logger.info("set_config() succesfully completed......................................")
def get_system_logs():
......
pycaret/nlp.py
浏览文件 @ 6e9224a5
......@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 21/07/2020
# Last modified : 23/07/2020
def setup(data,
target=None,
......@@ -749,7 +749,9 @@ def setup(data,
else:
print(functions_.data)
logger.info("setup() succesfully completed")
logger.info('Corpus: ' + str(len(corpus)))
logger.info('Vocab: ' + str(len(id2word.keys())))
logger.info("setup() succesfully completed......................................")
return text, data_, corpus, id2word, seed, target_, experiment__,\
exp_name_log, logging_param, log_plots_param, USI, html_param
......@@ -1126,7 +1128,8 @@ def create_model(model=None,
if verbose:
clear_output()
logger.info("create_model() succesfully completed")
logger.info(str(model))
logger.info("create_model() succesfully completed......................................")
return model
......@@ -1449,7 +1452,8 @@ def assign_model(model,
if verbose:
clear_output()
logger.info("assign_model() succesfully completed")
logger.info(str(bb_.shape))
logger.info("assign_model() succesfully completed......................................")
return bb_
......@@ -3065,7 +3069,8 @@ def tune_model(model=None,
p = 'Best Model: ' + topic_model_name + ' |' + ' # Topics: ' + str(best_k) + ' | ' + str(optimize) + ' : ' + str(best_m)
print(p)
logger.info("tune_model() succesfully completed")
logger.info(str(best_model))
logger.info("tune_model() succesfully completed......................................")
return best_model
......@@ -3194,7 +3199,8 @@ def save_model(model, model_name,
if verbose:
print('Model Succesfully Saved')
logger.info("save_model() succesfully completed")
logger.info(str(model))
logger.info("save_model() succesfully completed......................................")
def load_model(model_name,
verbose=True): #added in pycaret==2.0.0
......@@ -3403,7 +3409,7 @@ def get_config(variable):
global_var = USI
logger.info("Global variable: " + str(variable) + ' returned')
logger.info("get_config() succesfully completed")
logger.info("get_config() succesfully completed......................................")
return global_var
......@@ -3479,7 +3485,7 @@ def set_config(variable,value):
USI = value
logger.info("Global variable: " + str(variable) + ' updated')
logger.info("set_config() succesfully completed")
logger.info("set_config() succesfully completed......................................")
def get_system_logs():
......
pycaret/regression.py
浏览文件 @ 6e9224a5
......@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 22/07/2020
# Last modified : 23/07/2020
def setup(data,
target,
......@@ -2019,12 +2019,16 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
logger.info("setup() succesfully completed......................................")
return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\
experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\
master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info("setup() succesfully completed......................................")
return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\
experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\
master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI
def create_model(estimator = None,
ensemble = False,
......@@ -2542,6 +2546,10 @@ def create_model(estimator = None,
if verbose:
clear_output()
logger.info("create_model_container " + str(len(create_model_container)))
logger.info("master_model_container " + str(len(master_model_container)))
logger.info("display_container " + str(len(display_container)))
logger.info(str(model))
logger.info("create_models() succesfully completed......................................")
return model
......@@ -2851,6 +2859,10 @@ def create_model(estimator = None,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("create_model() succesfully completed......................................")
return model
......@@ -3531,6 +3543,10 @@ def ensemble_model(estimator,
else:
clear_output()
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("ensemble_model() succesfully completed......................................")
......@@ -4398,6 +4414,10 @@ def compare_models(blacklist = None,
#store in display container
display_container.append(compare_models_.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model_store_final))
logger.info("compare_models() succesfully completed......................................")
......@@ -5151,6 +5171,10 @@ def blend_models(estimator_list = 'All',
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model))
logger.info("blend_models() succesfully completed......................................")
......@@ -6488,6 +6512,10 @@ def tune_model(estimator,
else:
clear_output()
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(best_model))
logger.info("tune_model() succesfully completed......................................")
......@@ -7198,6 +7226,10 @@ def stack_models(estimator_list,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(models_))
logger.info("stack_models() succesfully completed......................................")
......@@ -7964,6 +7996,10 @@ def create_stacknet(estimator_list,
else:
print(model_results.data)
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(models_))
logger.info("create_stacknet() succesfully completed......................................")
......@@ -8830,7 +8866,12 @@ def finalize_model(estimator):
mlflow.set_tag("Size KB", size_kb)
os.remove('Trained Model.pkl')
logger.info("create_model_container: " + str(len(create_model_container)))
logger.info("master_model_container: " + str(len(master_model_container)))
logger.info("display_container: " + str(len(display_container)))
logger.info(str(model_final))
logger.info("finalize_model() succesfully completed......................................")
return model_final
......
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