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未验证 提交 abe71486 编写于 作者: P pycaret 提交者： GitHub
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Showing with 146 addition and 94 deletion
classification.py
浏览文件 @ abe71486
......@@ -321,7 +321,7 @@ def setup(data,
model_results = pd.DataFrame({'Sample Size' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})
fig = px.line(model_results, x='Sample Size', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])
fig.update_layout(plot_bgcolor='rgb(245,245,245)')
title= str(model_name) + ' Metrics and Fraction %'
title= str(model_name) + ' Metrics and Sample %'
fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})
fig.show()
......@@ -5099,7 +5099,6 @@ def create_stacknet(estimator_list,
return models_
def automl(qualifier = 5,
target_metric = 'Accuracy',
fold = 10,
......@@ -5130,7 +5129,8 @@ def automl(qualifier = 5,
qualifier : integer, default = None
Number of top models considered for experimentation to return the best model.
Higher number will result in longer training time.
Higher number will result in longer training time. qualifier param has to be
greater than 3.
target_metric : String, default = 'Accuracy'
Metric to use for qualifying models and tuning the hyperparameters.
......@@ -5194,7 +5194,12 @@ def automl(qualifier = 5,
if y.value_counts().count() > 2:
if target_metric == 'AUC':
sys.exit('(Type Error): AUC metric not supported for multiclass problems. See docstring for list of other optimization parameters.')
#checking qualifier parameter
if qualifier <3:
sys.exit('(Value Error): Qualifier parameter cannot be less than 3.')
#checking fold parameter
if type(fold) is not int:
sys.exit('(Type Error): Fold parameter only accepts integer value.')
......@@ -7620,7 +7625,6 @@ def automl(qualifier = 5,
return best_model
def interpret_model(estimator,
plot = 'summary',
feature = None,
......@@ -8664,23 +8668,32 @@ def predict_model(estimator,
#dataset
if data is None:
Xtest = X_test
ytest = y_test
Xtest = X_test.copy()
ytest = y_test.copy()
X_test_ = X_test.copy()
y_test_ = y_test.copy()
Xtest.reset_index(drop=True, inplace=True)
ytest.reset_index(drop=True, inplace=True)
X_test_.reset_index(drop=True, inplace=True)
y_test_.reset_index(drop=True, inplace=True)
model = estimator
else:
Xtest = data
model = finalize_model(estimator)
Xtest.reset_index(drop=True, inplace=True)
ytest.reset_index(drop=True, inplace=True)
#copy X_test
X_test_ = X_test.copy()
X_test_ = X_test_.reset_index(drop=True)
y_test_ = y_test.copy()
y_test_ = y_test_.reset_index(drop=True)
Xtest = data.copy()
X_test_ = data.copy()
Xtest.reset_index(drop=True, inplace=True)
X_test_.reset_index(drop=True, inplace=True)
try:
model = finalize_model(estimator)
except:
model = estimator
if type(estimator) is list:
if type(estimator[0]) is list:
......@@ -8731,12 +8744,12 @@ def predict_model(estimator,
for i in stacker_base:
if stacker_method == 'soft':
try:
a = i.predict_proba(X_test)
a = i.predict_proba(Xtest) #change
a = a[:,1]
except:
a = i.predict(X_test)
a = i.predict(Xtest) #change
else:
a = i.predict(X_test)
a = i.predict(Xtest) #change
base_pred.append(a)
base_pred_df = pd.DataFrame()
......@@ -8842,41 +8855,41 @@ def predict_model(estimator,
#print('Success')
#if data is None:
sca = metrics.accuracy_score(ytest,pred_)
if data is None:
sca = metrics.accuracy_score(ytest,pred_)
try:
sc = metrics.roc_auc_score(ytest,pred_prob,average='weighted')
except:
sc = 0
if y.value_counts().count() > 2:
recall = metrics.recall_score(ytest,pred_, average='macro')
precision = metrics.precision_score(ytest,pred_, average = 'weighted')
f1 = metrics.f1_score(ytest,pred_, average='weighted')
else:
recall = metrics.recall_score(ytest,pred_)
precision = metrics.precision_score(ytest,pred_)
f1 = metrics.f1_score(ytest,pred_)
kappa = metrics.cohen_kappa_score(ytest,pred_)
df_score = pd.DataFrame( {'Model' : 'Stacking Classifier', 'Accuracy' : [sca], 'AUC' : [sc], 'Recall' : [recall], 'Prec.' : [precision],
'F1' : [f1], 'Kappa' : [kappa]})
df_score = df_score.round(4)
display(df_score)
try:
sc = metrics.roc_auc_score(ytest,pred_prob,average='weighted')
except:
sc = 0
if y.value_counts().count() > 2:
recall = metrics.recall_score(ytest,pred_, average='macro')
precision = metrics.precision_score(ytest,pred_, average = 'weighted')
f1 = metrics.f1_score(ytest,pred_, average='weighted')
else:
recall = metrics.recall_score(ytest,pred_)
precision = metrics.precision_score(ytest,pred_)
f1 = metrics.f1_score(ytest,pred_)
kappa = metrics.cohen_kappa_score(ytest,pred_)
df_score = pd.DataFrame( {'Model' : 'Stacking Classifier', 'Accuracy' : [sca], 'AUC' : [sc], 'Recall' : [recall], 'Prec.' : [precision],
'F1' : [f1], 'Kappa' : [kappa]})
df_score = df_score.round(4)
display(df_score)
label = pd.DataFrame(pred_)
label.columns = ['Label']
label['Label']=label['Label'].astype(int)
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1)
else:
X_test_ = pd.concat([Xtest,label], axis=1)
if hasattr(model,'predict_proba'):
if hasattr(stacker_meta,'predict_proba'):
try:
score = pd.DataFrame(pred_prob)
score.columns = ['Score']
......@@ -8933,13 +8946,14 @@ def predict_model(estimator,
for i in stacker:
if method == 'hard':
#print('done')
p = i.predict(X_test)
p = i.predict(Xtest) #change
else:
try:
p = i.predict_proba(X_test)
p = i.predict_proba(Xtest) #change
p = p[:,1]
except:
p = i.predict(X_test)
p = i.predict(Xtest) #change
base_pred.append(p)
......@@ -8950,9 +8964,9 @@ def predict_model(estimator,
df.columns = model_names
df_restack = pd.concat([X_test_,df], axis=1)
df_restack = pd.concat([Xtest,df], axis=1) #change
ytest = y_test
#ytest = ytest #change
#meta predictions starts here
......@@ -8974,6 +8988,7 @@ def predict_model(estimator,
pass
if data is None:
sca = metrics.accuracy_score(ytest,pred_)
try:
......@@ -8997,18 +9012,15 @@ def predict_model(estimator,
df_score = df_score.round(4)
display(df_score)
else:
pass
label = pd.DataFrame(pred_)
label.columns = ['Label']
label['Label']=label['Label'].astype(int)
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1) #changed
else:
X_test_ = pd.concat([Xtest,label], axis=1) #changed
if hasattr(meta_model,'predict_proba'):
try:
score = pd.DataFrame(pred_prob)
......@@ -9082,18 +9094,15 @@ def predict_model(estimator,
'F1' : [f1], 'Kappa' : [kappa]})
df_score = df_score.round(4)
display(df_score)
else:
pass
label = pd.DataFrame(pred_)
label.columns = ['Label']
label['Label']=label['Label'].astype(int)
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1)
else:
X_test_ = pd.concat([Xtest,label], axis=1)
if hasattr(model,'predict_proba'):
try:
......
regression.py
浏览文件 @ abe71486
......@@ -247,7 +247,7 @@ def setup(data,
model_results = pd.DataFrame({'Sample Size' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})
fig = px.line(model_results, x='Sample Size', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])
fig.update_layout(plot_bgcolor='rgb(245,245,245)')
title= str(model_name) + ' Metric and Fraction %'
title= str(model_name) + ' Metric and Sample %'
fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})
fig.show()
......@@ -5110,6 +5110,7 @@ def load_experiment(experiment_name):
return exp
def predict_model(estimator,
data=None,
round=4):
......@@ -5174,21 +5175,31 @@ def predict_model(estimator,
#dataset
if data is None:
Xtest = X_test
ytest = y_test
Xtest = X_test.copy()
ytest = y_test.copy()
X_test_ = X_test.copy()
y_test_ = y_test.copy()
Xtest.reset_index(drop=True, inplace=True)
ytest.reset_index(drop=True, inplace=True)
X_test_.reset_index(drop=True, inplace=True)
y_test_.reset_index(drop=True, inplace=True)
model = estimator
else:
Xtest = data
model = finalize_model(estimator)
Xtest.reset_index(drop=True, inplace=True)
ytest.reset_index(drop=True, inplace=True)
#copy X_test
X_test_ = X_test.copy()
X_test_ = X_test_.reset_index(drop=True)
y_test_ = y_test.copy()
y_test_ = y_test_.reset_index(drop=True)
Xtest = data.copy()
X_test_ = data.copy()
Xtest.reset_index(drop=True, inplace=True)
X_test_.reset_index(drop=True, inplace=True)
try:
model = finalize_model(estimator)
except:
model = estimator
if type(estimator) is list:
......@@ -5239,7 +5250,7 @@ def predict_model(estimator,
"""
base_pred = []
for i in stacker_base:
a = i.predict(X_test)
a = i.predict(Xtest) #change
base_pred.append(a)
base_pred_df = pd.DataFrame()
......@@ -5320,7 +5331,7 @@ def predict_model(estimator,
df_score = pd.DataFrame( {'Model' : 'Stacking Regressor', 'MAE' : [mae], 'MSE' : [mse], 'RMSE' : [rmse],
'R2' : [r2], 'ME' : [max_error_]})
df_score = df_score.round(4)
df_score = df_score.round(round)
display(df_score)
label = pd.DataFrame(pred_)
......@@ -5329,9 +5340,9 @@ def predict_model(estimator,
label['Label']=label['Label']
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1)
else:
X_test_ = pd.concat([Xtest,label], axis=1)
else:
......@@ -5376,7 +5387,7 @@ def predict_model(estimator,
base_pred = []
for i in stacker:
p = i.predict(X_test)
p = i.predict(Xtest) #change
base_pred.append(p)
df = pd.DataFrame()
......@@ -5386,9 +5397,9 @@ def predict_model(estimator,
df.columns = model_names
df_restack = pd.concat([X_test_,df], axis=1)
df_restack = pd.concat([Xtest,df], axis=1) #change
ytest = y_test
#ytest = y_test
#meta predictions starts here
......@@ -5409,7 +5420,7 @@ def predict_model(estimator,
df_score = pd.DataFrame( {'Model' : 'Stacking Regressor', 'MAE' : [mae], 'MSE' : [mse], 'RMSE' : [rmse],
'R2' : [r2], 'ME' : [max_error_]})
df_score = df_score.round(4)
df_score = df_score.round(round)
display(df_score)
label = pd.DataFrame(pred_)
......@@ -5418,9 +5429,9 @@ def predict_model(estimator,
label['Label']=label['Label']
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1)
else:
X_test_ = pd.concat([Xtest,label], axis=1)
else:
......@@ -5484,12 +5495,13 @@ def predict_model(estimator,
label['Label']=label['Label']
if data is None:
X_test_ = pd.concat([X_test_,y_test_], axis=1)
X_test_ = pd.concat([X_test_,label], axis=1)
X_test_ = pd.concat([Xtest,ytest,label], axis=1)
else:
X_test_ = pd.concat([Xtest,label], axis=1)
return X_test_
def automl(qualifier = 5,
target_metric = 'R2',
fold = 10,
......@@ -5561,6 +5573,37 @@ def automl(qualifier = 5,
import pandas as pd
import random
import sys
from sklearn import metrics
"""
error handling
"""
#checking target_metric
allowed_metrics = ['MAE', 'MSE', 'RMSE', 'R2', 'ME']
if target_metric not in allowed_metrics:
sys.exit('(Value Error): target_metric not valid. See docstring for list of metrics that can be optimized.')
#checking qualifier parameter
if qualifier <3:
sys.exit('(Value Error): Qualifier parameter cannot be less than 3.')
#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 verbose parameter
if type(turbo) is not bool:
sys.exit('(Type Error): Turbo parameter can only take argument as True or False.')
"""
error handling ends here
"""
#master collector
#This is being used for appending throughout the process
......
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