import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.preprocessing as sp
import sklearn.model_selection as sms
import sklearn.metrics as sm
import sklearn.feature_selection as sfs
import sklearn.neighbors as sn
import warnings
__author__ = "TechLeo"
__email__ = "techleo.ng@outlook.com"
__copyright__ = "Copyright (c) 2023 TechLeo"
__license__ = "MIT"
[docs]
def select_features(x, y, strategy: str, estimator: str, number_of_features: int, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
types = ["rfe", "selectkbest", "selectfrommodel", "selectpercentile"]
rfe_possible_estimator = "A regression or classification algorithm that can implement 'fit'."
kbest_possible_score_functions = ["f_regression", "f_classif", "f_oneway", "chi2"]
frommodel_possible_estimator = "A regression or classification algorithm that can implement 'fit'."
percentile_possible_score_functions = ["f_regression", "f_classif", "f_oneway", "chi2"]
strategy = strategy.lower()
if strategy in types:
if strategy == "rfe" and estimator != None:
technique = sfs.RFE(estimator = estimator, n_features_to_select = number_of_features)
x = technique.fit_transform(x, y)
x = pd.DataFrame(x, columns = technique.get_feature_names_out())
return x
elif strategy == "selectkbest" and estimator != None:
technique = sfs.SelectKBest(score_func = estimator, k = number_of_features)
x = technique.fit_transform(x, y)
x = pd.DataFrame(x, columns = technique.get_feature_names_out())
best_features = pd.DataFrame({"Features": technique.feature_names_in_, "Scores": technique.scores_, "P_Values": technique.pvalues_})
return {"Dataset ---> Features Selected": x, "Selection Metrics": best_features}
elif strategy == "selectfrommodel" and estimator != None:
technique = sfs.SelectFromModel(estimator = estimator, max_features = number_of_features)
x = technique.fit_transform(x, y)
x = pd.DataFrame(x, columns = technique.get_feature_names_out())
return x
elif strategy == "selectpercentile" and estimator != None:
technique = sfs.SelectPercentile(score_func = estimator, percentile = number_of_features)
x = technique.fit_transform(x, y)
x = pd.DataFrame(x, columns = technique.get_feature_names_out())
best_features = pd.DataFrame({"Features": technique.feature_names_in_, "Scores": technique.scores_, "P_Values": technique.pvalues_})
return {"Dataset ---> Features Selected": x, "Selection Metrics": best_features}
elif estimator == None:
raise TypeError("You must specify an estimator or score function to use feature selection processes")
else:
raise TypeError(f"Select a feature selection technique from the following: {types}. \n\nRFE Estimator = {rfe_possible_estimator} e.g XGBoost, RandomForest, SVM etc\nSelectKBest Score Function = {kbest_possible_score_functions}\nSelectFromModel Estimator = {frommodel_possible_estimator} e.g XGBoost, RandomForest, SVM etc.\nSelectPercentile Score Function = {percentile_possible_score_functions}")
[docs]
def split_data(x, y, test_size, random_state, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
x_train, x_test, y_train, y_test = sms.train_test_split(x, y, test_size = test_size, random_state = random_state)
return {"Training X": x_train, "Test X": x_test, "Training Y": y_train, "Test Y": y_test}
[docs]
def build_regressor_model(regressor, x_train, y_train, x_test, y_test, kfold: int = None, cross_validation: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
model = regressor.fit(x_train, y_train)
y_pred = model.predict(x_train)
y_pred1 = model.predict(x_test)
if kfold == None and cross_validation == False:
training_rsquared = sm.r2_score(y_train, y_pred)
test_rsquared = sm.r2_score(y_test, y_pred1)
training_rmse = np.sqrt(sm.mean_squared_error(y_train, y_pred))
test_rmse = np.sqrt(sm.mean_squared_error(y_test, y_pred1))
return {"Model": model, "Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1}, "Training Evaluation": {"Training R2": training_rsquared, "Training RMSE": training_rmse}, "Test Evaluation": {"Test R2": test_rsquared, "Test RMSE": test_rmse}}
elif kfold != None and cross_validation == False:
raise TypeError("KFold cannot work when cross validation is set to FALSE")
elif kfold == None and cross_validation == True:
training_rsquared = sm.r2_score(y_train, y_pred)
test_rsquared = sm.r2_score(y_test, y_pred1)
training_rmse = np.sqrt(sm.mean_squared_error(y_train, y_pred))
test_rmse = np.sqrt(sm.mean_squared_error(y_test, y_pred1))
cross_val = sms.cross_val_score(model, x_train, y_train, cv = 10)
score_mean = round((cross_val.mean() * 100), 2)
score_std_dev = round((cross_val.std() * 100), 2)
return {"Model": model, "Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1}, "Training Evaluation": {"Training R2": training_rsquared, "Training RMSE": training_rmse}, "Test Evaluation": {"Test R2": test_rsquared, "Test RMSE": test_rmse}, "Cross Validation": {"Cross Validation Mean": score_mean, "Cross Validation Standard Deviation": score_std_dev}}
elif kfold != None and cross_validation == True:
training_rsquared = sm.r2_score(y_train, y_pred)
test_rsquared = sm.r2_score(y_test, y_pred1)
training_rmse = np.sqrt(sm.mean_squared_error(y_train, y_pred))
test_rmse = np.sqrt(sm.mean_squared_error(y_test, y_pred1))
cross_val = sms.cross_val_score(model, x_train, y_train, cv = kfold)
score_mean = round((cross_val.mean() * 100), 2)
score_std_dev = round((cross_val.std() * 100), 2)
return {"Model": model, "Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1}, "Training Evaluation": {"Training R2": training_rsquared, "Training RMSE": training_rmse}, "Test Evaluation": {"Test R2": test_rsquared, "Test RMSE": test_rmse}, "Cross Validation": {"Cross Validation Mean": score_mean, "Cross Validation Standard Deviation": score_std_dev}}
[docs]
def classifier_model_testing(classifier_model, variables_values: list, scaling: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
scaler = sp.StandardScaler()
if scaling == False:
prediction = classifier_model.predict([variables_values])
return prediction
elif scaling == True:
variables_values = scaler.transform([variables_values])
prediction = classifier_model.predict(variables_values)
return prediction
[docs]
def regressor_model_testing(regressor_model, variables_values: list, scaling: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
scaler = sp.StandardScaler()
if scaling == False:
prediction = regressor_model.predict([variables_values])
return prediction
elif scaling == True:
variables_values = scaler.transform([variables_values])
prediction = regressor_model.predict(variables_values)
return prediction
[docs]
def build_classifier_model(classifier, x_train, y_train, x_test, y_test, kfold: int = None, cross_validation: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
model = classifier.fit(x_train, y_train)
y_pred = model.predict(x_train)
y_pred1 = model.predict(x_test)
if kfold == None and cross_validation == False:
training_analysis = sm.confusion_matrix(y_train, y_pred)
training_class_report = sm.classification_report(y_train, y_pred)
training_accuracy = sm.accuracy_score(y_train, y_pred)
training_precision = sm.precision_score(y_train, y_pred, average='weighted')
training_recall = sm.recall_score(y_train, y_pred, average='weighted')
training_f1_score = sm.f1_score(y_train, y_pred, average='weighted')
test_analysis = sm.confusion_matrix(y_test, y_pred1)
test_class_report = sm.classification_report(y_test, y_pred1)
test_accuracy = sm.accuracy_score(y_test, y_pred1)
test_precision = sm.precision_score(y_test, y_pred1, average='weighted')
test_recall = sm.recall_score(y_test, y_pred1, average='weighted')
test_f1_score = sm.f1_score(y_test, y_pred1, average='weighted')
return {
"Model": model,
"Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1},
"Training Evaluation": {
"Confusion Matrix": training_analysis,
"Classification Report": training_class_report,
"Model Accuracy": training_accuracy,
"Model Precision": training_precision,
"Model Recall": training_recall,
"Model F1 Score": training_f1_score,
},
"Test Evaluation": {
"Confusion Matrix": test_analysis,
"Classification Report": test_class_report,
"Model Accuracy": test_accuracy,
"Model Precision": test_precision,
"Model Recall": test_recall,
"Model F1 Score": test_f1_score,
},
}
elif kfold != None and cross_validation == False:
raise TypeError("KFold cannot work when cross validation is set to FALSE")
elif kfold == None and cross_validation == True:
training_analysis = sm.confusion_matrix(y_train, y_pred)
training_class_report = sm.classification_report(y_train, y_pred)
training_accuracy = sm.accuracy_score(y_train, y_pred)
training_precision = sm.precision_score(y_train, y_pred, average='weighted')
training_recall = sm.recall_score(y_train, y_pred, average='weighted')
training_f1_score = sm.f1_score(y_train, y_pred, average='weighted')
test_analysis = sm.confusion_matrix(y_test, y_pred1)
test_class_report = sm.classification_report(y_test, y_pred1)
test_accuracy = sm.accuracy_score(y_test, y_pred1)
test_precision = sm.precision_score(y_test, y_pred1, average='weighted')
test_recall = sm.recall_score(y_test, y_pred1, average='weighted')
test_f1_score = sm.f1_score(y_test, y_pred1, average='weighted')
cross_val = sms.cross_val_score(model, x_train, y_train, cv = 10)
score_mean = round((cross_val.mean() * 100), 2)
score_std_dev = round((cross_val.std() * 100), 2)
return {
"Model": model,
"Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1},
"Training Evaluation": {
"Confusion Matrix": training_analysis,
"Classification Report": training_class_report,
"Model Accuracy": training_accuracy,
"Model Precision": training_precision,
"Model Recall": training_recall,
"Model F1 Score": training_f1_score,
},
"Test Evaluation": {
"Confusion Matrix": test_analysis,
"Classification Report": test_class_report,
"Model Accuracy": test_accuracy,
"Model Precision": test_precision,
"Model Recall": test_recall,
"Model F1 Score": test_f1_score,
},
"Cross Validation": {
"Cross Validation Mean": score_mean,
"Cross Validation Standard Deviation": score_std_dev
}
}
elif kfold != None and cross_validation == True:
training_analysis = sm.confusion_matrix(y_train, y_pred)
training_class_report = sm.classification_report(y_train, y_pred)
training_accuracy = sm.accuracy_score(y_train, y_pred)
training_precision = sm.precision_score(y_train, y_pred, average='weighted')
training_recall = sm.recall_score(y_train, y_pred, average='weighted')
training_f1_score = sm.f1_score(y_train, y_pred, average='weighted')
test_analysis = sm.confusion_matrix(y_test, y_pred1)
test_class_report = sm.classification_report(y_test, y_pred1)
test_accuracy = sm.accuracy_score(y_test, y_pred1)
test_precision = sm.precision_score(y_test, y_pred1, average='weighted')
test_recall = sm.recall_score(y_test, y_pred1, average='weighted')
test_f1_score = sm.f1_score(y_test, y_pred1, average='weighted')
cross_val = sms.cross_val_score(model, x_train, y_train, cv = kfold)
score_mean = round((cross_val.mean() * 100), 2)
score_std_dev = round((cross_val.std() * 100), 2)
return {
"Model": model,
"Predictions": {"Actual Training Y": y_train, "Actual Test Y": y_test, "Predicted Training Y": y_pred, "Predicted Test Y": y_pred1},
"Training Evaluation": {
"Confusion Matrix": training_analysis,
"Classification Report": training_class_report,
"Model Accuracy": training_accuracy,
"Model Precision": training_precision,
"Model Recall": training_recall,
"Model F1 Score": training_f1_score,
},
"Test Evaluation": {
"Confusion Matrix": test_analysis,
"Classification Report": test_class_report,
"Model Accuracy": test_accuracy,
"Model Precision": test_precision,
"Model Recall": test_recall,
"Model F1 Score": test_f1_score,
},
"Cross Validation": {
"Cross Validation Mean": score_mean,
"Cross Validation Standard Deviation": score_std_dev
}
}
[docs]
def build_multiple_regressors(regressors: list or tuple, x_train, y_train, x_test, y_test, kfold: int = None, cross_validation: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
if isinstance(regressors, list) or isinstance(regressors, tuple):
multiple_regressor_models = {}
for algorithms in regressors:
multiple_regressor_models[f"{algorithms.__class__.__name__}"] = build_regressor_model(regressor = algorithms, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold , cross_validation = cross_validation)
return multiple_regressor_models
[docs]
def build_multiple_classifiers(classifiers: list or tuple, x_train, y_train, x_test, y_test, kfold: int = None, cross_validation: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
if isinstance(classifiers, list) or isinstance(classifiers, tuple):
multiple_classifier_models = {}
for algorithms in classifiers:
multiple_classifier_models[f"{algorithms.__class__.__name__}"] = build_classifier_model(classifier = algorithms, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold , cross_validation = cross_validation)
return multiple_classifier_models
[docs]
def build_single_regressor_from_features(x, y, regressor, test_size: float, random_state: int, strategy: str, estimator: str, max_num_features: int = None, min_num_features: int = None, kfold: int = None, cv: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
types1 = ["selectkbest", "selectpercentile"]
types2 = ["rfe", "selectfrommodel"]
if not (isinstance(regressor, list) or isinstance(regressor, tuple)) and cv == False:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
multiple_regressor_models[f"{regressor.__class__.__name__}"] = build_regressor_model(regressor, x_train, y_train, x_test, y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Model"].__class__.__name__,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test RMSE"]
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"] = multiple_regressor_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
multiple_regressor_models[f"{regressor.__class__.__name__}"] = build_regressor_model(regressor, x_train, y_train, x_test, y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Model"].__class__.__name__,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test RMSE"]
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"] = multiple_regressor_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
elif not (isinstance(regressor, list) or isinstance(regressor, tuple)) and cv == True:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
multiple_regressor_models[f"{regressor.__class__.__name__}"] = build_regressor_model(regressor, x_train, y_train, x_test, y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Model"].__class__.__name__,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"] = multiple_regressor_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
multiple_regressor_models[f"{regressor.__class__.__name__}"] = build_regressor_model(regressor, x_train, y_train, x_test, y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Model"].__class__.__name__,
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Test Evaluation"]["Test RMSE"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_regressor_models[f"{regressor.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"] = multiple_regressor_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
dataset_features = dataset_features.reset_index(drop = True)
return {"Feature Metrics": dataset_features, "More Info": store}
[docs]
def build_single_classifier_from_features(x, y, classifier, test_size: float, random_state: int, strategy: str, estimator: str, max_num_features: int = None, min_num_features: int = None, kfold: int = None, cv: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
types1 = ["selectkbest", "selectpercentile"]
types2 = ["rfe", "selectfrommodel"]
if not (isinstance(classifier, list) or isinstance(classifier, tuple)) and cv == False:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
multiple_classifier_models[f"{classifier.__class__.__name__}"] = build_classifier_model(classifier = classifier, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Model"].__class__.__name__,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"] = multiple_classifier_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
multiple_classifier_models[f"{classifier.__class__.__name__}"] = build_classifier_model(classifier = classifier, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Model"].__class__.__name__,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"] = multiple_classifier_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
elif not (isinstance(classifier, list) or isinstance(classifier, tuple)) and cv == True:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range((length_col - 1), 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
multiple_classifier_models[f"{classifier.__class__.__name__}"] = build_classifier_model(classifier = classifier, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Model"].__class__.__name__,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"] = multiple_classifier_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
multiple_classifier_models[f"{classifier.__class__.__name__}"] = build_classifier_model(classifier = classifier, x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Model"].__class__.__name__,
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_classifier_models[f"{classifier.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"] = multiple_classifier_models
store[f"{num}"]["Feature Info"] = feature_info
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
dataset_features = dataset_features.reset_index(drop = True)
return {"Feature Metrics": dataset_features, "More Info": store}
[docs]
def build_multiple_regressors_from_features(x, y, regressors: list or tuple, test_size: float, random_state: int, strategy: str, estimator: str, max_num_features: int = None, min_num_features: int = None, kfold: int = None, cv: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
types1 = ["selectkbest", "selectpercentile"]
types2 = ["rfe", "selectfrommodel"]
if (isinstance(regressors, list) or isinstance(regressors, tuple)) and cv == False:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
for algorithms in regressors:
multiple_regressor_models[f"{algorithms.__class__.__name__}"] = build_regressor_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, regressor = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test RMSE"]
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_regressor_models
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
for algorithms in regressors:
multiple_regressor_models[f"{algorithms.__class__.__name__}"] = build_regressor_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, regressor = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test RMSE"]
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_regressor_models
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
elif (isinstance(regressors, list) or isinstance(regressors, tuple)) and cv == True:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
for algorithms in regressors:
multiple_regressor_models[f"{algorithms.__class__.__name__}"] = build_regressor_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, regressor = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_regressor_models
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_regressor_models = {}
store_data = []
for algorithms in regressors:
multiple_regressor_models[f"{algorithms.__class__.__name__}"] = build_regressor_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, regressor = algorithms, kfold = kfold, cross_validation = True)
info = [
num,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Training RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test R2"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Test RMSE"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_regressor_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_regressors = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training R2", "Training RMSE", "Test R2", "Test RMSE", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_regressors
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_regressor_models
dataset2 = dataset_regressors
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
dataset_features = dataset_features.reset_index(drop = True)
return {"Feature Metrics": dataset_features, "More Info": store}
[docs]
def build_multiple_classifiers_from_features(x, y, classifiers: list or tuple, test_size: float, random_state: int, strategy: str, estimator: str, max_num_features: int = None, min_num_features: int = None, kfold: int = None, cv: bool = False, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
types1 = ["selectkbest", "selectpercentile"]
types2 = ["rfe", "selectfrommodel"]
if (isinstance(classifiers, list) or isinstance(classifiers, tuple)) and cv == False:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range(length_col, 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
for algorithms in classifiers:
multiple_classifier_models[f"{algorithms.__class__.__name__}"] = build_classifier_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, classifier = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_classifier_models
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
for algorithms in classifiers:
multiple_classifier_models[f"{algorithms.__class__.__name__}"] = build_classifier_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, classifier = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score",])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_classifier_models
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
elif (isinstance(classifiers, list) or isinstance(classifiers, tuple)) and cv == True:
data_columns = [col for col in x.columns]
length_col = len(data_columns)
store = {}
dataset_features = pd.DataFrame(columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
if (max_num_features != None) and isinstance(max_num_features, int):
length_col = max_num_features
if (min_num_features == None):
for num in range((length_col - 1), 0, -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
for algorithms in classifiers:
multiple_classifier_models[f"{algorithms.__class__.__name__}"] = build_classifier_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, classifier = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_classifier_models
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
elif (min_num_features != None) and isinstance(min_num_features, int):
if (min_num_features <= length_col):
for num in range(length_col, (min_num_features - 1), -1):
feature_info = {}
features = select_features(x = x, y = y, strategy = strategy, estimator = estimator, number_of_features = num)
strategy = strategy.lower()
if strategy in types2:
x = features
elif strategy in types1:
x = features["Dataset ---> Features Selected"]
x_train, x_test, y_train, y_test = split_data(x = x, y = y, test_size = test_size, random_state = random_state).values()
multiple_classifier_models = {}
store_data = []
for algorithms in classifiers:
multiple_classifier_models[f"{algorithms.__class__.__name__}"] = build_classifier_model(x_train = x_train, y_train = y_train, x_test = x_test, y_test = y_test, classifier = algorithms, kfold = kfold, cross_validation = cv)
info = [
num,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Built Model"].__class__.__name__,
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Training Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Accuracy"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Precision"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model Recall"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Test Evaluation"]["Model F1 Score"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Mean"],
multiple_classifier_models[f"{algorithms.__class__.__name__}"]["Cross Validation"]["Cross Validation Standard Deviation"],
]
store_data.append(info)
dataset_classifiers = pd.DataFrame(store_data, columns = ["No. of features selected", "Algorithm", "Training Accuracy", "Training Precision", "Training Recall", "Training F1 Score", "Test Accuracy", "Test Precision", "Test Recall", "Test F1 Score", "Cross Validation Mean", "Cross Validation Standard Deviation"])
feature_info[f"{num} Feature(s) Selected"] = features
feature_info[f"Model Trained with {num} Feature(s)"] = dataset_classifiers
store[f"{num}"] = {}
store[f"{num}"]["Feature Info"] = feature_info
store[f"{num}"]["More Info"] = multiple_classifier_models
dataset2 = dataset_classifiers
dataset_features = pd.concat([dataset_features, dataset2], axis = 0)
else:
raise TypeError("The parameter 'min_num_features' cannot be more than the number of features in our dataset.")
dataset_features = dataset_features.reset_index(drop = True)
return {"Feature Metrics": dataset_features, "More Info": store}
[docs]
def classifier_graph(classifier, x_train, y_train, cmap_train = "viridis", cmap_test = "viridis", size_train_marker: float = 10, size_test_marker: float = 10, x_test=None, y_test=None, resolution=100, plot_title="Decision Boundary", warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
feature1 = x_train.iloc[:, 0].name
feature2 = x_train.iloc[:, 1].name
le = sp.LabelEncoder()
y_train_encoded = le.fit_transform(y_train)
if isinstance(x_train, pd.DataFrame):
x1_vals_train, x2_vals_train = np.meshgrid(np.linspace((x_train.iloc[:, 0].min() - (x_train.iloc[:, 0].min() / 8)), (x_train.iloc[:, 0].max() + (x_train.iloc[:, 0].max() / 8)), resolution),
np.linspace((x_train.iloc[:, 1].min() - (x_train.iloc[:, 1].min() / 8)), (x_train.iloc[:, 1].max() + (x_train.iloc[:, 1].max() / 8)), resolution))
elif isinstance(x_train, np.ndarray):
x1_vals_train, x2_vals_train = np.meshgrid(np.linspace((x_train.iloc[:, 0].min() - (x_train.iloc[:, 0].min() / 8)), (x_train.iloc[:, 0].max() + (x_train.iloc[:, 0].max() / 8)), resolution),
np.linspace((x_train.iloc[:, 1].min() - (x_train.iloc[:, 1].min() / 8)), (x_train.iloc[:, 1].max() + (x_train.iloc[:, 1].max() / 8)), resolution))
else:
raise TypeError("Unsupported input type for x_train. Use either Pandas DataFrame or NumPy array.")
grid_points_train = np.c_[x1_vals_train.ravel(), x2_vals_train.ravel()]
predictions_train = classifier.predict(grid_points_train)
predictions_train = le.inverse_transform(predictions_train)
plt.figure(figsize = (15, 10))
plt.contourf(x1_vals_train, x2_vals_train, le.transform(predictions_train).reshape(x1_vals_train.shape), alpha=0.3, cmap = cmap_train)
if isinstance(x_train, pd.DataFrame):
plt.scatter(x_train.iloc[:, 0], x_train.iloc[:, 1], c=y_train_encoded, cmap=cmap_train, edgecolors='k', s=size_train_marker, marker='o')
elif isinstance(x_train, np.ndarray):
plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train_encoded, cmap=cmap_train, edgecolors='k', s=size_train_marker, marker='o')
plt.title(f"{classifier.__class__.__name__} Training Classification Graph")
plt.xlabel(feature1)
plt.ylabel(feature2)
plt.tight_layout()
plt.show()
if x_test is not None and y_test is not None:
plt.figure(figsize = (15, 10))
x1_vals_test, x2_vals_test = np.meshgrid(np.linspace((x_test.iloc[:, 0].min() - (x_test.iloc[:, 0].min() / 8)), (x_test.iloc[:, 0].max() + (x_test.iloc[:, 0].max() / 8)), resolution),
np.linspace((x_test.iloc[:, 1].min() - (x_test.iloc[:, 1].min() / 8)), (x_test.iloc[:, 1].max() + (x_test.iloc[:, 1].max() / 8)), resolution))
grid_points_test = np.c_[x1_vals_test.ravel(), x2_vals_test.ravel()]
predictions_test = classifier.predict(grid_points_test)
predictions_test = le.inverse_transform(predictions_test)
plt.contourf(x1_vals_test, x2_vals_test, le.transform(predictions_test).reshape(x1_vals_test.shape), alpha=0.3, cmap=cmap_test)
if isinstance(x_test, pd.DataFrame):
plt.scatter(x_test.iloc[:, 0], x_test.iloc[:, 1], c=le.transform(y_test), cmap=cmap_test, edgecolors='k', s=size_test_marker, marker='o')
elif isinstance(x_test, np.ndarray):
plt.scatter(x_test[:, 0], x_test[:, 1], c=le.transform(y_test), cmap=cmap_test, edgecolors='k', s=size_test_marker, marker='o')
plt.title(f"{classifier.__class__.__name__} Test Classification Graph")
plt.xlabel(feature1)
plt.ylabel(feature2)
plt.tight_layout()
plt.show()
[docs]
def FindK_KNN_Classifier(x_train, y_train, weight = "uniform", algorithm = "auto", metric = "minkowski", max_k_range: int = 31, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
algorithms = ['auto', 'ball_tree', 'kd_tree', 'brute']
weights = ['uniform', 'distance']
a = algorithm.lower()
b = weight.lower()
if (a in algorithms) or (b in weights):
k = [num for num in range(1, max_k_range)]
scores_knn = []
scores_store = {}
for num in k:
classifier = sn.KNeighborsClassifier(n_neighbors = num, weights = weight, algorithm = algorithm, metric = metric)
model = classifier.fit(x_train, y_train)
# Model Evaluation
scores_knn.append(model.score(x_train, y_train))
scores_store[num] = (model.score(x_train, y_train))
# Plotting a graph
plt.figure(figsize = (15, 10))
plt.plot(k, scores_knn)
plt.title('KNN graph for values of K and their scores')
plt.xlabel('Ranges of K values')
plt.ylabel('Scores')
plt.show()
# Getting the best score
b = (0, 0)
for key, value in scores_store.items():
if value > b[1]:
b = (key, value)
print(f'\n\nKNN CLASSIFIER ------> Finding the besk K value:\nThe best k-value is {b[0]} with a score of {b[1]}.')
else:
raise TypeError(f"Check that the parameter 'algorithm' is one of the following: {algorithms}. Also, check that the parameter 'weight' is one of the following: {weights}")
[docs]
def FindK_KNN_Regressor(x_train, y_train, weight = "uniform", algorithm = "auto", metric = "minkowski", max_k_range: int = 31, warning: bool = False):
if warning == False:
warnings.filterwarnings("ignore")
algorithms = ['auto', 'ball_tree', 'kd_tree', 'brute']
weights = ['uniform', 'distance']
a = algorithm.lower()
b = weight.lower()
if (a in algorithms) or (b in weights):
k = [num for num in range(1, max_k_range)]
scores_knn = []
scores_store = {}
for num in k:
regressor = sn.KNeighborsRegressor(n_neighbors = num, weights = weight, algorithm = algorithm, metric = metric)
model = regressor.fit(x_train, y_train)
# Model Evaluation
scores_knn.append(model.score(x_train, y_train))
scores_store[num] = (model.score(x_train, y_train))
# Plotting a graph
plt.figure(figsize = (15, 10))
plt.plot(k, scores_knn)
plt.title('KNN graph for values of K and their scores')
plt.xlabel('Ranges of K values')
plt.ylabel('Scores')
plt.show()
# Getting the best score
b = (0, 0)
for key, value in scores_store.items():
if value > b[1]:
b = (key, value)
print(f'\n\nKNN REGRESSOR ------> Finding the besk K value:\nThe best k-value is {b[0]} with a score of {b[1]}.')
else:
raise TypeError(f"Check that the parameter 'algorithm' is one of the following: {algorithms}. Also, check that the parameter 'weight' is one of the following: {weights}")
[docs]
def simple_linregres_graph(x, y, regressor, title: str, line_style: str = "dashed", line_width: float = 2, line_marker: str = "o", line_marker_size: float = 12, train_color_marker: str = "red", test_color_marker: str = "red", line_color: str = "green", size_train_marker: float = 10, size_test_marker: float = 10, whole_dataset: bool = False, test_size: float = 0.2):
name_x = [col for col in x.columns]
name_y = y.name
if not (isinstance(regressor, list) or isinstance(regressor, tuple)):
if len(name_x) == 1:
if not whole_dataset:
x_train, x_test, y_train, y_test = sms.train_test_split(x, y, test_size = test_size, random_state = 0)
# Visualising the Training set results
plt.figure(figsize = (15, 10))
plt.scatter(x_train, y_train, color = train_color_marker, s=size_train_marker)
plt.plot(x_train, regressor.fit(x_train, y_train).predict(x_train), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(f"{title} (Training Dataset)")
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
# Visualising the Test set results
plt.figure(figsize = (15, 10))
plt.scatter(x_test, y_test, color = test_color_marker, s=size_test_marker)
plt.plot(x_train, regressor.fit(x_train, y_train).predict(x_train), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(f"{title} (Test Dataset)")
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
else:
plt.figure(figsize = (15, 10))
plt.scatter(x, y, color = train_color_marker, s=size_train_marker)
plt.plot(x, regressor.fit(x, y).predict(x), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(title)
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
else:
raise TypeError("Simple Linear Regression involves only one independent variable. Ensure that your dataframe for x has just one column.")
else:
for each_regressor in regressor:
if len(name_x) == 1:
if not whole_dataset:
x_train, x_test, y_train, y_test = sms.train_test_split(x, y, test_size = test_size, random_state = 0)
# Visualising the Training set results
plt.figure(figsize = (15, 10))
plt.scatter(x_train, y_train, color = train_color_marker, s=size_train_marker)
plt.plot(x_train, each_regressor.fit(x_train, y_train).predict(x_train), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(f"{title} (Training Dataset) for {each_regressor.__class__.__name__}")
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
# Visualising the Test set results
plt.figure(figsize = (15, 10))
plt.scatter(x_test, y_test, color = test_color_marker, s=size_test_marker)
plt.plot(x_train, each_regressor.fit(x_train, y_train).predict(x_train), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(f"{title} (Test Dataset) for {each_regressor.__class__.__name__}")
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
else:
plt.figure(figsize = (15, 10))
plt.scatter(x, y, color = train_color_marker, s=size_train_marker)
plt.plot(x, each_regressor.fit(x, y).predict(x), color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_marker_size)
plt.title(f"{title} for {each_regressor.__class__.__name__}")
plt.xlabel(name_x[0])
plt.ylabel(name_y)
plt.show()
else:
raise TypeError("Simple Linear Regression involves only one independent variable. Ensure that your dataframe for x has just one column.")