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提交 657ed04e 编写于 作者: P PyCaret
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pycaret-nightly==0.23 update part 2

上级 7c19e999
隐藏空白更改
内联 并排
Showing with 478 addition and 84 deletion
pycaret/anomaly.py
浏览文件 @ 657ed04e
......@@ -298,6 +298,38 @@ def setup(data,
#exception checking
import sys
from pycaret.utils import __version__
ver = __version__()
import logging
# create logger
global logger
logger = logging.getLogger('logs')
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
if logger.hasHandlers():
logger.handlers.clear()
ch = logging.FileHandler('logs.log')
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
logger.info("PyCaret Anomaly Detection Module")
logger.info('version ' + str(ver))
logger.info("Initializing setup()")
logger.info("Checking Exceptions")
#run_time
import datetime, time
runtime_start = time.time()
......@@ -512,6 +544,7 @@ def setup(data,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import pandas as pd
import ipywidgets as ipw
......@@ -529,6 +562,8 @@ def setup(data,
#create html_param
html_param = html
logger.info("Preparing display monitor")
#progress bar
max_steps = 4
......@@ -546,6 +581,7 @@ def setup(data,
display(progress)
display(monitor, display_id = 'monitor')
logger.info("Importing libraries")
#general dependencies
import numpy as np
import pandas as pd
......@@ -560,10 +596,12 @@ def setup(data,
import warnings
warnings.filterwarnings('ignore')
logger.info("Declaring global variables")
#defining global variables
global data_, X, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, exp_name_log, logging_param, log_plots_param, USI
logger.info("Copying data for preprocessing")
#copy original data for pandas profiler
data_before_preprocess = data.copy()
......@@ -600,6 +638,8 @@ def setup(data,
#define parameters for preprocessor
logger.info("Declaring preprocessing parameters")
#categorical features
if categorical_features is None:
cat_features_pass = []
......@@ -751,9 +791,13 @@ def setup(data,
if silent:
display_types_pass = False
logger.info("Importing preprocessing module")
#import library
from pycaret import preprocess
logger.info("Creating preprocessing pipeline")
X = preprocess.Preprocess_Path_Two(train_data = data_for_preprocess,
categorical_features = cat_features_pass,
apply_ordinal_encoding = apply_ordinal_encoding_pass, #new
......@@ -789,7 +833,8 @@ def setup(data,
random_state = seed)
progress.value += 1
logger.info("Preprocessing pipeline created successfully")
try:
res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']
res = preprocess.dtypes.response
......@@ -803,6 +848,8 @@ def setup(data,
prep_pipe = preprocess.pipe
prep_param = preprocess
logger.info("Creating grid variables")
#generate values for grid show
missing_values = data_before_preprocess.isna().sum().sum()
if missing_values > 0:
......@@ -883,6 +930,8 @@ def setup(data,
pd.reset_option("display.max_rows")
pd.reset_option("display.max_columns")
logger.info("Creating global containers")
#create an empty list for pickling later.
if supervised is False:
experiment__ = []
......@@ -997,6 +1046,8 @@ def setup(data,
USI = secrets.token_hex(nbytes=2)
if logging_param:
logger.info("Logging experiment in MLFlow")
import mlflow
from pathlib import Path
......@@ -1078,9 +1129,10 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
return X, data_, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, exp_name_log, logging_param, log_plots_param, USI
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
def create_model(model = None,
fraction = 0.05,
......@@ -1153,8 +1205,9 @@ def create_model(model = None,
"""
#testing
#no test available
import logging
logger.info("Initializing create_model()")
logger.info("Checking exceptions")
#exception checking
import sys
......@@ -1193,6 +1246,8 @@ def create_model(model = None,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import pandas as pd
import numpy as np
......@@ -1204,6 +1259,8 @@ def create_model(model = None,
monitor starts
"""
logger.info("Preparing display monitor")
#progress bar and monitor control
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
progress = ipw.IntProgress(value=0, min=0, max=4, step=1 , description='Processing: ')
......@@ -1230,6 +1287,8 @@ def create_model(model = None,
progress.value += 1
#create model
logger.info("Importing untrained model")
if model == 'abod':
from pyod.models.abod import ABOD
model = ABOD(contamination=fraction, **kwargs)
......@@ -1299,6 +1358,8 @@ def create_model(model = None,
model = SOS(contamination=fraction, **kwargs)
full_name = 'Stochastic Outlier Selection'
logger.info(str(full_name) + ' Imported succesfully')
#monitor update
monitor.iloc[1,1:] = 'Fitting the Model'
progress.value += 1
......@@ -1308,16 +1369,12 @@ def create_model(model = None,
#fitting the model
model_fit_start = time.time()
logger.info("Fitting Model")
model.fit(X)
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
#storing in experiment__
if system:
tup = (full_name,model)
experiment__.append(tup)
#end runtime
runtime_end = time.time()
runtime = np.array(runtime_end - runtime_start).round(2)
......@@ -1325,6 +1382,8 @@ def create_model(model = None,
#mlflow logging
if logging_param and system:
logger.info("Creating MLFlow logs")
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
if verbose:
......@@ -1393,8 +1452,9 @@ def create_model(model = None,
if verbose:
clear_output()
return model
logger.info("create_models() succesfully completed")
return model
def assign_model(model,
transformation=False,
......@@ -1455,6 +1515,9 @@ def assign_model(model,
#exception checking
import sys
import logging
logger.info("Initializing assign_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -1489,6 +1552,7 @@ def assign_model(model,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import numpy as np
import pandas as pd
......@@ -1496,12 +1560,14 @@ def assign_model(model,
from IPython.display import display, HTML, clear_output, update_display
import datetime, time
logger.info("Copying data")
#copy data_
if transformation:
data__ = X.copy()
else:
data__ = data_.copy()
logger.info("Preparing display monitor")
#progress bar and monitor control
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
progress = ipw.IntProgress(value=0, min=0, max=3, step=1 , description='Processing: ')
......@@ -1536,6 +1602,8 @@ def assign_model(model,
progress.value += 1
logger.info("Determining Trained Model")
mod_type = str(model).split("(")[0]
if 'ABOD' in mod_type:
......@@ -1578,17 +1646,15 @@ def assign_model(model,
name_ = 'Unknown Anomaly Detector'
name_ = 'Assigned ' + str(name_)
#storing in experiment__
if verbose:
tup = (name_,data__)
experiment__.append(tup)
logger.info("Trained Model : " + str(name_))
if verbose:
clear_output()
return data__
logger.info("assign_model() succesfully completed")
return data__
def tune_model(model=None,
supervised_target=None,
......@@ -2860,9 +2926,6 @@ def tune_model(model=None,
return best_model
def plot_model(model,
plot = 'tsne',
feature = None,
......@@ -3020,8 +3083,6 @@ def plot_model(model,
if save:
fig.write_html("UMAP.html")
def save_model(model, model_name, verbose=True):
"""
......@@ -3062,10 +3123,14 @@ def save_model(model, model_name, verbose=True):
"""
import logging
logger.info("Initializing save_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
logger.info("Appending prep pipeline")
model_ = []
model_.append(prep_pipe)
model_.append(model)
......@@ -3076,6 +3141,8 @@ def save_model(model, model_name, verbose=True):
if verbose:
print('Transformation Pipeline and Model Succesfully Saved')
logger.info(str(model_name) + ' saved in current working directory')
logger.info("save_model() succesfully completed")
def load_model(model_name,
platform = None,
......@@ -3112,7 +3179,10 @@ def load_model(model_name,
"""
import logging
logger.info("Initializing load_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -3127,6 +3197,8 @@ def load_model(model_name,
#cloud provider
if platform == 'aws':
logger.info("Importing model from AWS-S3")
import boto3
bucketname = authentication.get('bucket')
filename = str(model_name) + '.pkl'
......@@ -3137,16 +3209,18 @@ def load_model(model_name,
if verbose:
print('Transformation Pipeline and Model Sucessfully Loaded')
logger.info("load_model() succesfully completed")
return model
import joblib
model_name = model_name + '.pkl'
if verbose:
print('Transformation Pipeline and Model Sucessfully Loaded')
logger.info("load_model() succesfully completed")
return joblib.load(model_name)
def predict_model(model,
data,
platform=None,
......@@ -3252,8 +3326,6 @@ def predict_model(model,
return data__
def deploy_model(model,
model_name,
authentication,
......@@ -3323,6 +3395,9 @@ def deploy_model(model,
"""
import logging
logger.info("Initializing deploy_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -3331,29 +3406,26 @@ def deploy_model(model,
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':
logger.info("Platform : AWS S3")
import boto3
logger.info("Saving model in current working directory")
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
logger.info("Initializing S3 client")
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()
logger.info("deploy_model() succesfully completed")
print("Model Succesfully Deployed on AWS S3")
def get_outliers(data,
model = None,
fraction=0.05,
......@@ -3430,6 +3502,9 @@ def models():
"""
import logging
logger.info("Initializing models()")
import pandas as pd
model_id = ['abod', 'iforest', 'cluster', 'cof', 'histogram', 'knn', 'lof', 'svm', 'pca', 'mcd', 'sod', 'sos']
......@@ -3466,6 +3541,8 @@ def models():
df.set_index('ID', inplace=True)
logger.info("models() succesfully completed")
return df
def get_logs(experiment_name = None, save = False):
......@@ -3494,6 +3571,9 @@ def get_logs(experiment_name = None, save = False):
"""
import logging
logger.info("Initializing get_logs()")
import sys
if experiment_name is None:
......@@ -3507,12 +3587,121 @@ def get_logs(experiment_name = None, save = False):
client = MlflowClient()
if client.get_experiment_by_name(exp_name_log_) is None:
logger.info("No active run found.")
sys.exit('No active run found. Check logging parameter in setup or to get logs for inactive run pass experiment_name.')
exp_id = client.get_experiment_by_name(exp_name_log_).experiment_id
logger.info("Searching runs")
runs = mlflow.search_runs(exp_id)
if save:
logger.info("Saving logs as csv")
file_name = str(exp_name_log_) + '_logs.csv'
runs.to_csv(file_name, index=False)
return runs
\ No newline at end of file
logger.info("get_logs() succesfully completed")
return runs
def get_config(variable):
"""
get global environment variable
"""
import logging
logger.info("Initializing get_config()")
if variable == 'X':
global_var = X
if variable == 'data_':
global_var = data_
if variable == 'seed':
global_var = seed
if variable == 'prep_pipe':
global_var = prep_pipe
if variable == 'prep_param':
global_var = prep_param
if variable == 'n_jobs_param':
global_var = n_jobs_param
if variable == 'html_param':
global_var = html_param
if variable == 'exp_name_log':
global_var = exp_name_log
if variable == 'logging_param':
global_var = logging_param
if variable == 'log_plots_param':
global_var = log_plots_param
if variable == 'USI':
global_var = USI
logger.info("Global variable: " + str(variable) + ' returned')
logger.info("get_config() succesfully completed")
return global_var
def set_config(variable,value):
"""
set global environment variable
"""
import logging
logger.info("Initializing set_config()")
if variable == 'X':
global X
X = value
if variable == 'data_':
global data_
data_ = value
if variable == 'seed':
global seed
seed = value
if variable == 'prep_pipe':
global prep_pipe
prep_pipe = value
if variable == 'prep_param':
global prep_param
prep_param = value
if variable == 'n_jobs_param':
global n_jobs_param
n_jobs_param = value
if variable == 'html_param':
global html_param
html_param = value
if variable == 'exp_name_log':
global exp_name_log
exp_name_log = value
if variable == 'logging_param':
global logging_param
logging_param = value
if variable == 'log_plots_param':
global log_plots_param
log_plots_param = value
if variable == 'USI':
global USI
USI = value
logger.info("Global variable: " + str(variable) + ' updated')
logger.info("set_config() succesfully completed")
\ No newline at end of file
pycaret/classification.py
浏览文件 @ 657ed04e
......@@ -437,9 +437,6 @@ def setup(data,
"""
#testing
#no active test
#exception checking
import sys
......@@ -455,6 +452,10 @@ def setup(data,
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
if logger.hasHandlers():
logger.handlers.clear()
ch = logging.FileHandler('logs.log')
ch.setLevel(logging.DEBUG)
......@@ -1118,7 +1119,7 @@ def setup(data,
random_state = seed)
progress.value += 1
logger.info("Preprocessing pipeline created successfully")
if hasattr(preprocess.dtypes, 'replacement'):
label_encoded = preprocess.dtypes.replacement
......
pycaret/clustering.py
浏览文件 @ 657ed04e
......@@ -298,6 +298,38 @@ def setup(data,
#exception checking
import sys
from pycaret.utils import __version__
ver = __version__()
import logging
# create logger
global logger
logger = logging.getLogger('logs')
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
if logger.hasHandlers():
logger.handlers.clear()
ch = logging.FileHandler('logs.log')
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
logger.info("PyCaret Clustering Module")
logger.info('version ' + str(ver))
logger.info("Initializing setup()")
logger.info("Checking Exceptions")
#run_time
import datetime, time
runtime_start = time.time()
......@@ -511,6 +543,8 @@ def setup(data,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import pandas as pd
import ipywidgets as ipw
......@@ -529,6 +563,8 @@ def setup(data,
#create html_param
html_param = html
logger.info("Preparing display monitor")
#progress bar
max_steps = 4
......@@ -546,6 +582,7 @@ def setup(data,
display(progress)
display(monitor, display_id = 'monitor')
logger.info("Importing libraries")
#general dependencies
import numpy as np
import pandas as pd
......@@ -560,10 +597,12 @@ def setup(data,
import warnings
warnings.filterwarnings('ignore')
logger.info("Declaring global variables")
#defining global variables
global data_, X, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, exp_name_log, logging_param, log_plots_param, USI
logger.info("Copying data for preprocessing")
#copy original data for pandas profiler
data_before_preprocess = data.copy()
......@@ -600,6 +639,8 @@ def setup(data,
#define parameters for preprocessor
logger.info("Declaring preprocessing parameters")
#categorical features
if categorical_features is None:
cat_features_pass = []
......@@ -746,9 +787,13 @@ def setup(data,
if silent:
display_types_pass = False
logger.info("Importing preprocessing module")
#import library
from pycaret import preprocess
logger.info("Creating preprocessing pipeline")
X = preprocess.Preprocess_Path_Two(train_data = data_for_preprocess,
categorical_features = cat_features_pass,
apply_ordinal_encoding = apply_ordinal_encoding_pass,
......@@ -784,7 +829,8 @@ def setup(data,
random_state = seed)
progress.value += 1
logger.info("Preprocessing pipeline created successfully")
try:
res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']
res = preprocess.dtypes.response
......@@ -798,6 +844,8 @@ def setup(data,
prep_pipe = preprocess.pipe
prep_param = preprocess
logger.info("Creating grid variables")
#generate values for grid show
missing_values = data_before_preprocess.isna().sum().sum()
if missing_values > 0:
......@@ -878,6 +926,8 @@ def setup(data,
pd.reset_option("display.max_rows")
pd.reset_option("display.max_columns")
logger.info("Creating global containers")
#create an empty list for pickling later.
if supervised is False:
experiment__ = []
......@@ -991,6 +1041,8 @@ def setup(data,
USI = secrets.token_hex(nbytes=2)
if logging_param:
logger.info("Logging experiment in MLFlow")
import mlflow
from pathlib import Path
......@@ -1072,9 +1124,10 @@ def setup(data,
mlflow.log_artifact("input.txt")
os.remove('input.txt')
logger.info("setup() succesfully completed")
return X, data_, seed, prep_pipe, prep_param, experiment__,\
n_jobs_param, exp_name_log, logging_param, log_plots_param, USI
n_jobs_param, html_param, exp_name_log, logging_param, log_plots_param, USI
def create_model(model = None,
num_clusters = None,
......@@ -1159,8 +1212,9 @@ def create_model(model = None,
"""
#testing
#no test available
import logging
logger.info("Initializing create_model()")
logger.info("Checking exceptions")
#exception checking
import sys
......@@ -1211,6 +1265,8 @@ def create_model(model = None,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import pandas as pd
import numpy as np
......@@ -1218,6 +1274,8 @@ def create_model(model = None,
from IPython.display import display, HTML, clear_output, update_display
import datetime, time
logger.info("Setting num_cluster param")
#determine num_clusters
if num_clusters is None:
num_clusters = 4
......@@ -1228,6 +1286,8 @@ def create_model(model = None,
monitor starts
"""
logger.info("Preparing display monitor")
#progress bar and monitor control
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
progress = ipw.IntProgress(value=0, min=0, max=3, step=1 , description='Processing: ')
......@@ -1245,6 +1305,8 @@ def create_model(model = None,
monitor ends
"""
logger.info("Importing untrained model")
if model == 'kmeans':
from sklearn.cluster import KMeans
model = KMeans(n_clusters = num_clusters, random_state=seed, n_jobs=n_jobs_param, **kwargs)
......@@ -1289,7 +1351,9 @@ def create_model(model = None,
from kmodes.kmodes import KModes
model = KModes(n_clusters=num_clusters, n_jobs=n_jobs_param, random_state=seed, **kwargs)
full_name = 'K-Modes Clustering'
logger.info(str(full_name) + ' Imported succesfully')
#monitor update
monitor.iloc[1,1:] = 'Fitting ' + str(full_name) + ' Model'
progress.value += 1
......@@ -1299,17 +1363,20 @@ def create_model(model = None,
#fitting the model
model_fit_start = time.time()
logger.info("Fitting Model")
model.fit(X)
model_fit_end = time.time()
model_fit_time = np.array(model_fit_end - model_fit_start).round(2)
#Calculate unsupervised metrics
logger.info("Evaluating Metrics")
from sklearn import metrics
metric = []
metric_value = []
try:
silhouette = metrics.silhouette_score(X,model.labels_)
silhouette = silhouette.round(4)
......@@ -1338,6 +1405,8 @@ def create_model(model = None,
if ground_truth is not None:
logger.info("ground_truth parameter set to " + str(ground_truth))
gt = np.array(data_[ground_truth])
try:
......@@ -1366,18 +1435,12 @@ def create_model(model = None,
pass
try:
logger.info("Creating Metrics dataframe")
model_results = pd.DataFrame(metric_value)
model_results.columns = ['Metric']
model_results.set_index([metric], inplace=True)
except:
pass
#storing in experiment__
full_name_ = str(full_name) + ' Model'
if system:
tup = (full_name_,model)
experiment__.append(tup)
#end runtime
runtime_end = time.time()
......@@ -1386,6 +1449,8 @@ def create_model(model = None,
#mlflow logging
if logging_param and system:
logger.info("Creating MLFlow logs")
#Creating Logs message monitor
monitor.iloc[1,1:] = 'Creating Logs'
if verbose:
......@@ -1477,6 +1542,8 @@ def create_model(model = None,
except:
pass
logger.info("create_models() succesfully completed")
return model
def assign_model(model,
......@@ -1530,6 +1597,10 @@ def assign_model(model,
#exception checking
import sys
import logging
logger.info("Initializing assign_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -1538,6 +1609,8 @@ def assign_model(model,
error handling starts here
"""
logger.info("Checking exceptions")
#determine model type and store in string
mod_type = str(type(model))
......@@ -1558,6 +1631,7 @@ def assign_model(model,
error handling ends here
"""
logger.info("Preloading libraries")
#pre-load libraries
import numpy as np
import pandas as pd
......@@ -1565,12 +1639,14 @@ def assign_model(model,
from IPython.display import display, HTML, clear_output, update_display
import datetime, time
logger.info("Copying data")
#copy data_
if transformation:
data__ = X.copy()
else:
data__ = data_.copy()
logger.info("Preparing display monitor")
#progress bar and monitor control
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
progress = ipw.IntProgress(value=0, min=0, max=3, step=1 , description='Processing: ')
......@@ -1601,10 +1677,11 @@ def assign_model(model,
labels.append(a)
data__['Cluster'] = labels
progress.value += 1
logger.info("Determining Trained Model")
mod_type = str(model).split("(")[0]
if 'KMeans' in mod_type:
......@@ -1636,16 +1713,14 @@ def assign_model(model,
else:
name_ = 'Unknown Clustering'
name_ = 'Assigned ' + str(name_)
#storing in experiment__
if verbose:
tup = (name_,data__)
experiment__.append(tup)
logger.info("Trained Model : " + str(name_))
if verbose:
clear_output()
logger.info("assign_model() succesfully completed")
return data__
def tune_model(model=None,
......@@ -2894,7 +2969,6 @@ def tune_model(model=None,
return best_model
def plot_model(model,
plot='cluster',
feature = None,
......@@ -3239,9 +3313,6 @@ def plot_model(model,
except:
sys.exit('(Type Error): Plot Type not supported for this model.')
def save_model(model, model_name, verbose=True):
"""
......@@ -3285,10 +3356,14 @@ def save_model(model, model_name, verbose=True):
"""
import logging
logger.info("Initializing save_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
logger.info("Appending prep pipeline")
model_ = []
model_.append(prep_pipe)
model_.append(model)
......@@ -3299,6 +3374,8 @@ def save_model(model, model_name, verbose=True):
if verbose:
print('Transformation Pipeline and Model Succesfully Saved')
logger.info(str(model_name) + ' saved in current working directory')
logger.info("save_model() succesfully completed")
def load_model(model_name,
platform = None,
......@@ -3348,7 +3425,10 @@ def load_model(model_name,
"""
import logging
logger.info("Initializing load_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -3363,6 +3443,8 @@ def load_model(model_name,
#cloud provider
if platform == 'aws':
logger.info("Importing model from AWS-S3")
import boto3
bucketname = authentication.get('bucket')
filename = str(model_name) + '.pkl'
......@@ -3373,16 +3455,18 @@ def load_model(model_name,
if verbose:
print('Transformation Pipeline and Model Sucessfully Loaded')
logger.info("load_model() succesfully completed")
return model
import joblib
model_name = model_name + '.pkl'
if verbose:
print('Transformation Pipeline and Model Sucessfully Loaded')
logger.info("load_model() succesfully completed")
return joblib.load(model_name)
def predict_model(model,
data,
platform=None,
......@@ -3502,8 +3586,6 @@ def predict_model(model,
return data__
def deploy_model(model,
model_name,
authentication,
......@@ -3572,6 +3654,9 @@ def deploy_model(model,
"""
import logging
logger.info("Initializing deploy_model()")
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
......@@ -3580,29 +3665,26 @@ def deploy_model(model,
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':
logger.info("Platform : AWS S3")
import boto3
logger.info("Saving model in current working directory")
save_model(model, model_name = model_name, verbose=False)
#initiaze s3
logger.info("Initializing S3 client")
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()
logger.info("deploy_model() succesfully completed")
print("Model Succesfully Deployed on AWS S3")
def get_clusters(data,
model = None,
num_clusters = 4,
......@@ -3677,6 +3759,9 @@ def models():
models and their metadata.
"""
import logging
logger.info("Initializing models()")
import pandas as pd
......@@ -3708,6 +3793,8 @@ def models():
df.set_index('ID', inplace=True)
logger.info("models() succesfully completed")
return df
def get_logs(experiment_name = None, save = False):
......@@ -3736,6 +3823,9 @@ def get_logs(experiment_name = None, save = False):
"""
import logging
logger.info("Initializing get_logs()")
import sys
if experiment_name is None:
......@@ -3749,12 +3839,121 @@ def get_logs(experiment_name = None, save = False):
client = MlflowClient()
if client.get_experiment_by_name(exp_name_log_) is None:
logger.info("No active run found.")
sys.exit('No active run found. Check logging parameter in setup or to get logs for inactive run pass experiment_name.')
exp_id = client.get_experiment_by_name(exp_name_log_).experiment_id
logger.info("Searching runs")
runs = mlflow.search_runs(exp_id)
if save:
logger.info("Saving logs as csv")
file_name = str(exp_name_log_) + '_logs.csv'
runs.to_csv(file_name, index=False)
return runs
\ No newline at end of file
logger.info("get_logs() succesfully completed")
return runs
def get_config(variable):
"""
get global environment variable
"""
import logging
logger.info("Initializing get_config()")
if variable == 'X':
global_var = X
if variable == 'data_':
global_var = data_
if variable == 'seed':
global_var = seed
if variable == 'prep_pipe':
global_var = prep_pipe
if variable == 'prep_param':
global_var = prep_param
if variable == 'n_jobs_param':
global_var = n_jobs_param
if variable == 'html_param':
global_var = html_param
if variable == 'exp_name_log':
global_var = exp_name_log
if variable == 'logging_param':
global_var = logging_param
if variable == 'log_plots_param':
global_var = log_plots_param
if variable == 'USI':
global_var = USI
logger.info("Global variable: " + str(variable) + ' returned')
logger.info("get_config() succesfully completed")
return global_var
def set_config(variable,value):
"""
set global environment variable
"""
import logging
logger.info("Initializing set_config()")
if variable == 'X':
global X
X = value
if variable == 'data_':
global data_
data_ = value
if variable == 'seed':
global seed
seed = value
if variable == 'prep_pipe':
global prep_pipe
prep_pipe = value
if variable == 'prep_param':
global prep_param
prep_param = value
if variable == 'n_jobs_param':
global n_jobs_param
n_jobs_param = value
if variable == 'html_param':
global html_param
html_param = value
if variable == 'exp_name_log':
global exp_name_log
exp_name_log = value
if variable == 'logging_param':
global logging_param
logging_param = value
if variable == 'log_plots_param':
global log_plots_param
log_plots_param = value
if variable == 'USI':
global USI
USI = value
logger.info("Global variable: " + str(variable) + ' updated')
logger.info("set_config() succesfully completed")
\ No newline at end of file
pycaret/regression.py
浏览文件 @ 657ed04e
......@@ -451,6 +451,10 @@ def setup(data,
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
if logger.hasHandlers():
logger.handlers.clear()
ch = logging.FileHandler('logs.log')
ch.setLevel(logging.DEBUG)
......@@ -1109,7 +1113,8 @@ def setup(data,
random_state = seed)
progress.value += 1
logger.info("Preprocessing pipeline created successfully")
if hasattr(preprocess.dtypes, 'replacement'):
label_encoded = preprocess.dtypes.replacement
label_encoded = str(label_encoded).replace("'", '')
......
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