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Update 0.1.2-alpha-3
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- Fixed issues with Ensembler

Signed-off-by: Arsh <[email protected]>
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@d4rk-lucif3r
d4rk-lucif3r committed Jul 1, 2022
1 parent ac8f27f commit ae762ac
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161 changes: 80 additions & 81 deletions anai/supervised/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,7 +267,13 @@ def __init__(
optuna.logging.set_verbosity(optuna.logging.WARNING)
self.explainer = Explainer()
self.ensembler = Ensembler(
"classification", n_estimators=3, n_clusters=3)
"classification",
n_estimators=3,
n_clusters=3,
estimators=None,
verbose=False,
random_state=self.random_state
)
self.fit_params = {}
self.encoded_column_names = []
self.dimension_handler = DimensionHandler()
Expand Down Expand Up @@ -504,64 +510,61 @@ def __fitall(self):
self.result_df["Accuracy"] = self.acc
self.result_df["Cross Validated Accuracy"] = self.k_fold_accuracy
self.result_df["Model"] = self.classifier_model
if self.tune:
self.result_df["Best Parameters"] = self.best_params
self.result_df["Best Accuracy"] = self.bestacc
self.best_classifier = Best(
self.result_df.loc[self.result_df["Best Accuracy"].idxmax()],
self.tune,
)
else:
self.best_classifier = Best(
self.result_df.loc[self.result_df["Cross Validated Accuracy"].idxmax(
)],
self.tune,
)
self.result_df = self.result_df.sort_values(
by="Cross Validated Accuracy", ascending=False
)
by="Cross Validated Accuracy", ascending=False)
top_result = self.result_df.sort_values(
by=["Cross Validated Accuracy"], ascending=False
).head(5)
by=['Cross Validated Accuracy'], ascending=False).head(5)
estimators = []
est1 = []
for i in range(top_result.shape[0]):
if not top_result.iloc[i]["Name"] == "K-Nearest Neighbors":
if not top_result.iloc[i]['Name'] == 'K-Nearest Neighbors':
estimators.append(
(top_result.iloc[i]["Name"], top_result.iloc[i]["Model"])
)
print(Fore.YELLOW + "Ensembling on top {} models\n".format(5 if len(estimators) > 5 else len(estimators)))
top_result.iloc[i]['Model'])
est1.append(
(top_result.iloc[i]['Name'], top_result.iloc[i]['Model']))
print(Fore.YELLOW + "Ensembling on top {} models\n".format(
5 if len(estimators) > 5 else len(estimators)))
try:
ens_result = self.ensembler.ensemble(
self.X_train,
self.y_train,
self.X_val,
self.y_val,
cv_folds=self.cv_folds,
estimators=estimators,
)
self.result_df = pd.concat([self.result_df, ens_result], axis=0)
self.X_train, self.y_train, self.X_val, self.y_val, cv_folds=self.cv_folds, estimators=estimators, est=est1)
self.result_df = pd.concat(
[self.result_df, ens_result], axis=0)
except Exception as error:
print(Fore.RED + "Ensembling Failed with error: ", error, "\n")
print(traceback.format_exc())
print(Fore.RED+"Ensembling Failed with error: ", error, "\n")
self.result_df = self.result_df.sort_values(
by=["Cross Validated Accuracy"], ascending=False
).reset_index(drop=True)
by=['Cross Validated Accuracy'], ascending=False).reset_index(drop=True)
print(Fore.GREEN + "Training Done [", "\u2713", "]\n")
print(Fore.CYAN + "Results Below\n")
display(self.result_df.drop(["Model"], axis=1))
if self.tune:
self.result_df["Best Parameters"] = self.best_params
self.result_df["Best Accuracy"] = self.bestacc
self.best_classifier = Best(
self.result_df.loc[self.result_df["Best Accuracy"].idxmax()],
self.tune,
)
else:
self.best_classifier = Best(
self.result_df.loc[self.result_df["Cross Validated Accuracy"].idxmax(
)], self.tune
)
display(self.result_df.drop(['Model'], axis=1))
print(Fore.GREEN + "\nCompleted ANAI Run [", "\u2713", "]\n")
if len(self.model_to_predict) > 1:
self.best_classifier_path, self.scaler_path = self.save(
best=True, model=self.best_classifier.model, scaler=self.sc
self.model = self.best_classifier.model
self.model_name = self.best_classifier.name
self.end = time.time()
final_time = self.end - self.start
self.meta_path = self.save(
best=True
)
print(
Fore.CYAN
+ "Saved Best Model to {} and its scaler to {}".format(
self.best_classifier_path, self.scaler_path
+ "Saved Best Model at {} ".format(
self.meta_path
),
"\n",
)
self.end = time.time()
final_time = self.end - self.start
print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n")
return

Expand Down Expand Up @@ -1006,7 +1009,7 @@ def __init__(
n_clusters=3,
estimators=None,
verbose=False,
result_df=None,
random_state=self.random_state
)
self.fit_params = {}
self.dimension_handler = DimensionHandler()
Expand Down Expand Up @@ -1281,72 +1284,68 @@ def __fitall(self):
self.result_df["Root Mean Squared Error"] = self.rmse
self.result_df["Cross Validated Accuracy"] = self.k_fold_accuracy
self.result_df["Model"] = self.regressor_model
if self.tune:
self.result_df["Best Parameters"] = self.best_params
self.result_df["Best Accuracy"] = self.bestacc
self.result_df["Trained Model"] = self.tuned_trained_model
self.best_regressor = Best(
self.result_df.loc[self.result_df["Best Accuracy"].idxmax()],
self.tune,
isReg=True,
)
else:
self.best_regressor = Best(
self.result_df.loc[self.result_df["Cross Validated Accuracy"].idxmax(
)],
self.tune,
isReg=True,
)
self.result_df = self.result_df.sort_values(
by=["Cross Validated Accuracy"], ascending=False
)
by=['Cross Validated Accuracy'], ascending=False)
top_result = self.result_df.sort_values(
by=["Cross Validated Accuracy"], ascending=False
).head(5)
by=['Cross Validated Accuracy'], ascending=False).head(5)
estimators = []
est1 = []
for i in range(top_result.shape[0]):
if not top_result.iloc[i]["Name"] == "K-Nearest Neighbors":
if not top_result.iloc[i]['Name'] == 'K-Nearest Neighbors':
estimators.append(
(top_result.iloc[i]["Name"], top_result.iloc[i]["Model"])
)
top_result.iloc[i]['Model'])
est1.append(
(top_result.iloc[i]['Name'], top_result.iloc[i]['Model']))
self.estimators = estimators
print(Fore.YELLOW + "Ensembling on top {} models\n".format(
5 if len(estimators) > 5 else len(estimators)))
try:
ens_result = self.ensembler.ensemble(
self.X_train,
self.y_train,
self.X_val,
self.y_val,
cv_folds=self.cv_folds,
estimators=estimators,
)
self.result_df = pd.concat([self.result_df, ens_result], axis=0)
self.X_train, self.y_train, self.X_val, self.y_val, cv_folds=self.cv_folds, estimators=estimators, est=est1)
self.result_df = pd.concat(
[self.result_df, ens_result], axis=0)
except Exception as error:
print(
Fore.RED + "Ensembling Failed with error: ",
error,
"\n",
)
self.result_df = self.result_df.sort_values(
by=["Cross Validated Accuracy"], ascending=False
).reset_index(drop=True)
by=['Cross Validated Accuracy'], ascending=False).reset_index(drop=True)
print(Fore.GREEN + "Training Done [", "\u2713", "]\n")
print(Fore.CYAN + "Results Below\n")
display(self.result_df.drop(["Model"], axis=1))
if self.tune:
self.result_df["Best Parameters"] = self.best_params
self.result_df["Best Accuracy"] = self.bestacc
self.result_df["Trained Model"] = self.tuned_trained_model
self.best_regressor = Best(
self.result_df.loc[self.result_df["Best Accuracy"].idxmax()],
self.tune,
isReg=True,
)
else:
self.best_regressor = Best(
self.result_df.loc[self.result_df["Cross Validated Accuracy"].idxmax(
)],
self.tune,
isReg=True,
)
display(self.result_df.drop(['Model'], axis=1))
print(Fore.GREEN + "\nCompleted ANAI Run [", "\u2713", "]\n")
if len(self.model_to_predict) > 1:
self.best_regressor_path, self.scaler_path = self.save(
best=True, model=self.best_regressor.model, scaler=self.sc
self.model = self.best_regressor.model
self.end = time.time()
final_time = self.end - self.start
self.meta_path = self.save(
best=True,
)
print(
Fore.CYAN
+ "Saved Best Model to {} and its scaler to {}".format(
self.best_regressor_path, self.scaler_path
+ "Saved Best Model at {} ".format(
self.meta_path
),
"\n",
)
self.end = time.time()
final_time = self.end - self.start
print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n")
return

Expand Down
2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
],
include=["anai.*", "anai"],
),
version="0.1.2-alpha-2",
version="0.1.2-alpha-3",
license="Apache License 2.0",
description="Automated ML",
url="https://github.com/Revca-ANAI/ANAI",
Expand Down

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