422 lines
15 KiB
Python
422 lines
15 KiB
Python
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#################################################
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### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
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#################################################
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# file to edit: Q3.ipynb
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import numpy as np
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import pandas as pd
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import time
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import gc
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import random
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from sklearn.model_selection import cross_val_score, GridSearchCV, cross_validate, train_test_split
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.svm import SVC
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from sklearn.linear_model import LinearRegression
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from sklearn.neural_network import MLPClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.preprocessing import StandardScaler, normalize
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from sklearn.decomposition import PCA
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from sklearn.impute import SimpleImputer
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class GaTech():
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# Change to your GA Tech Username
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def GTusername(self):
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gt_username = "helfayoumy3"
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return gt_username
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class Data():
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# points [1]
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def dataAllocation(self,path):
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# TODO: Separate out the x_data and y_data and return each
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# args: string path for .csv file
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# return: pandas dataframe, pandas series
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# -------------------------------
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# ADD CODE HERE
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data = pd.read_csv('data/pima-indians-diabetes.csv')
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x_data = data.loc[:, data.columns != "y"].to_numpy()
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y_data = data.loc[:, "y"].to_numpy()
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# -------------------------------
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return x_data,y_data
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# points [1]
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def trainSets(self,x_data,y_data):
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# TODO: Split 70% of the data into training and 30% into test sets. Call them x_train, x_test, y_train and y_test.
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# Use the train_test_split method in sklearn with the parameter 'shuffle' set to true and the 'random_state' set to 614.
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# args: pandas dataframe, pandas dataframe
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# return: pandas dataframe, pandas dataframe, pandas series, pandas series
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# -------------------------------
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# ADD CODE HERE
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x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.3, random_state = 614, shuffle=True)
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# -------------------------------
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return x_train, x_test, y_train, y_test
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##################################################
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##### Do not add anything below this line ########
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##################################################
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class LinearRegressionModel():
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# points [2]
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def linearClassifier(self,x_train, x_test, y_train):
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# TODO: Create a LinearRegression classifier and train it.
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# args: pandas dataframe, pandas dataframe, pandas series
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# return: numpy array, numpy array
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# -------------------------------
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# ADD CODE HERE
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linear = LinearRegression()
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linear.fit(x_train, y_train)
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y_predict_train = linear.predict(x_train)
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y_predict_test = linear.predict(x_test)
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# -------------------------------
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return y_predict_train, y_predict_test
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# points [1]
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def lgTrainAccuracy(self,y_train,y_predict_train):
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# TODO: Return accuracy (on the training set) using the accuracy_score method.
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# Note: Round the output values greater than or equal to 0.5 to 1 and those less than 0.5 to 0. You can use any method that satisfies the requriements.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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train_accuracy = accuracy_score(y_train, (y_predict_train + 0.5).astype(int))
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# -------------------------------
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return train_accuracy
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# points [1]
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def lgTestAccuracy(self,y_test,y_predict_test):
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# TODO: Return accuracy (on the testing set) using the accuracy_score method.
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# Note: Round the output values greater than or equal to 0.5 to 1 and those less than 0.5 to 0. You can use any method that satisfies the requriements.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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test_accuracy = accuracy_score(y_test, (y_predict_test + 0.5).astype(int))
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# -------------------------------
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return test_accuracy
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##################################################
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##### Do not add anything below this line ########
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##################################################
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class RFClassifier():
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# points [2]
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def randomForestClassifier(self,x_train,x_test, y_train):
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# TODO: Create a RandomForestClassifier and train it. Set Random state to 614.
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# args: pandas dataframe, pandas dataframe, pandas series
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# return: RandomForestClassifier object, numpy array, numpy array
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# -------------------------------
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# ADD CODE HERE
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rf_clf = RandomForestClassifier(random_state = 614)
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rf_clf.fit(x_train, y_train)
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y_predict_train = rf_clf.predict(x_train)
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y_predict_test = rf_clf.predict(x_test)
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# -------------------------------
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return rf_clf, y_predict_train, y_predict_test
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# points [1]
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def rfTrainAccuracy(self,y_train,y_predict_train):
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# TODO: Return accuracy on the training set using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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train_accuracy = accuracy_score(y_train, y_predict_train)
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# -------------------------------
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return train_accuracy
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# points [1]
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def rfTestAccuracy(self,y_test,y_predict_test):
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# TODO: Return accuracy on the test set using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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test_accuracy = accuracy_score(y_test, y_predict_test)
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# -------------------------------
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return test_accuracy
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# Q3.3.1 Feature Importance
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# points [1]
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def rfFeatureImportance(self,rf_clf):
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# TODO: Determine the feature importance as evaluated by the Random Forest Classifier.
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# args: RandomForestClassifier object
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# return: float array
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# -------------------------------
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# ADD CODE HERE
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feature_importance = rf_clf.feature_importances_
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# -------------------------------
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return feature_importance
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# points [1]
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def sortedRFFeatureImportanceIndicies(self,rf_clf):
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# TODO: Sort them in the ascending order and return the feature numbers[0 to ...].
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# Hint: There is a direct function available in sklearn to achieve this. Also checkout argsort() function in Python.
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# args: RandomForestClassifier object
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# return: int array
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# -------------------------------
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# ADD CODE HERE
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sorted_indices = np.argsort(rf_clf.feature_importances_)[::-1]
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# -------------------------------
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return sorted_indices
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# Q3.3.2 Hyper-parameter Tuning
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# points [2]
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def hyperParameterTuning(self,rf_clf,x_train,y_train):
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# TODO: Tune the hyper-parameters 'n_estimators' and 'max_depth'.
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# args: RandomForestClassifier object, pandas dataframe, pandas series
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# return: GridSearchCV object
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# 'n_estimators': [4, 16, 256]
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# 'max_depth': [2, 8, 16]
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# -------------------------------
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# ADD CODE HERE
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n_estimators = [4, 16, 256]
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max_depth = [2, 8, 16]
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param_grid = {'n_estimators': n_estimators,
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'max_depth': max_depth}
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gscv_rfc = GridSearchCV(estimator = rf_clf, param_grid = param_grid, cv = 10)
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gscv_rfc.fit(x_train, y_train)
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# -------------------------------
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return gscv_rfc
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# points [1]
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def bestParams(self,gscv_rfc):
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# TODO: Get the best params, using .best_params_
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# args: GridSearchCV object
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# return: parameter dict
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# -------------------------------
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# ADD CODE HERE
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best_params = gscv_rfc.best_params_
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# -------------------------------
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return best_params
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# points [1]
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def bestScore(self,gscv_rfc):
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# TODO: Get the best score, using .best_score_.
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# args: GridSearchCV object
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# return: float
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# -------------------------------
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# ADD CODE HERE
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best_score = gscv_rfc.best_score_
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# -------------------------------
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return best_score
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##################################################
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##### Do not add anything below this line ########
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##################################################
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class SupportVectorMachine():
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# Q3.4.1 Pre-process
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# points [1]
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def dataPreProcess(self,x_train,x_test):
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# TODO: Pre-process the data to standardize it, otherwise the grid search will take much longer.
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# args: pandas dataframe, pandas dataframe
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# return: pandas dataframe, pandas dataframe
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# -------------------------------
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# ADD CODE HERE
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scaled_x_train = normalize(x_train)
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scaled_x_test = normalize(x_test)
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# -------------------------------
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return scaled_x_train, scaled_x_test
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# Q3.4.2 Classification
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# points [1]
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def SVCClassifier(self,scaled_x_train,scaled_x_test, y_train):
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# TODO: Create a SVC classifier and train it. Set gamma = 'auto'
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# args: pandas dataframe, pandas dataframe, pandas series
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# return: numpy array, numpy array
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# -------------------------------
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# ADD CODE HERE
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supportVM = SVC(gamma='auto')
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supportVM.fit(scaled_x_train, y_train)
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y_predict_train = supportVM.predict(scaled_x_train)
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y_predict_test = supportVM.predict(scaled_x_test)
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# -------------------------------
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return y_predict_train,y_predict_test
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# points [1]
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def SVCTrainAccuracy(self,y_train,y_predict_train):
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# TODO: Return accuracy on the training set using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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train_accuracy = accuracy_score(y_train, y_predict_train)
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# -------------------------------
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return train_accuracy
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# points [1]
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def SVCTestAccuracy(self,y_test,y_predict_test):
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# TODO: Return accuracy on the test set using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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test_accuracy = accuracy_score(y_test,y_predict_test)
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# -------------------------------
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return test_accuracy
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# Q3.4.3 Hyper-parameter Tuning
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# points [1]
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def SVMBestScore(self, scaled_x_train, y_train):
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# TODO: Tune the hyper-parameters 'C' and 'kernel' (use rbf and linear).
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# Note: Set n_jobs = -1 and return_train_score = True and gamma = 'auto'
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# args: pandas dataframe, pandas series
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# return: GridSearchCV object, float
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# -------------------------------
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svm_parameters = {'kernel':('linear', 'rbf'), 'C':[0.01, 0.1, 1.0]}
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# ADD CODE HERE
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supportVM = SVC(gamma = 'auto')
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svm_cv = GridSearchCV(estimator = supportVM, param_grid = svm_parameters, n_jobs=-1, return_train_score=True)
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svm_cv.fit(scaled_x_train, y_train)
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best_score = svm_cv.best_score_
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# -------------------------------
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return svm_cv, best_score
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# points [1]
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def SVCClassifierParam(self,svm_cv,scaled_x_train,scaled_x_test,y_train):
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# TODO: Calculate the training and test set accuracy values after hyperparameter tuning and standardization.
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# args: GridSearchCV object, pandas dataframe, pandas dataframe, pandas series
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# return: numpy series, numpy series
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# -------------------------------
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# ADD CODE HERE
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y_predict_train = svm_cv.predict(scaled_x_train)
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y_predict_test = svm_cv.predict(scaled_x_test)
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# -------------------------------
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return y_predict_train,y_predict_test
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# points [1]
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def svcTrainAccuracy(self,y_train,y_predict_train):
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# TODO: Return accuracy (on the training set) using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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train_accuracy = accuracy_score(y_train,y_predict_train)
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# -------------------------------
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return train_accuracy
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# points [1]
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def svcTestAccuracy(self,y_test,y_predict_test):
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# TODO: Return accuracy (on the test set) using the accuracy_score method.
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# args: pandas series, numpy array
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# return: float
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# -------------------------------
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# ADD CODE HERE
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test_accuracy = accuracy_score(y_test,y_predict_test)
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# -------------------------------
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return test_accuracy
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# Q3.4.4 Cross Validation Results
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# points [1]
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def SVMRankTestScore(self,svm_cv):
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# TODO: Return the rank test score for all hyperparameter values that you obtained in Q3.4.3. The
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# GridSearchCV class holds a ‘cv_results_’ dictionary that should help you report these metrics easily.
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# args: GridSearchCV object
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# return: int array
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# -------------------------------
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# ADD CODE HERE
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rank_test_score = svm_cv.cv_results_['rank_test_score']
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# -------------------------------
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return rank_test_score
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# points [1]
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def SVMMeanTestScore(self,svm_cv):
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# TODO: Return mean test score for all of hyperparameter values that you obtained in Q3.4.3. The
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# GridSearchCV class holds a ‘cv_results_’ dictionary that should help you report these metrics easily.
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# args: GridSearchCV object
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# return: float array
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# -------------------------------
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# ADD CODE HERE
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mean_test_score = svm_cv.cv_results_['mean_test_score']
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# -------------------------------
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return mean_test_score
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##################################################
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##### Do not add anything below this line ########
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##################################################
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class PCAClassifier():
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# points [2]
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def pcaClassifier(self,x_data):
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# TODO: Perform dimensionality reduction of the data using PCA.
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# Set parameters n_components to 8 and svd_solver to 'full'. Keep other parameters at their default value.
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# args: pandas dataframe
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# return: pca_object
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# -------------------------------
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# ADD CODE HERE
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pca = PCA(n_components = 8, svd_solver = 'full')
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pca.fit(x_data)
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# -------------------------------
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return pca
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# points [1]
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def pcaExplainedVarianceRatio(self, pca):
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# TODO: Return percentage of variance explained by each of the selected components
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# args: pca_object
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# return: float array
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# -------------------------------
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# ADD CODE HERE
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explained_variance_ratio = pca.explained_variance_ratio_
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# -------------------------------
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return explained_variance_ratio
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# points [1]
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def pcaSingularValues(self, pca):
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# TODO: Return the singular values corresponding to each of the selected components.
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# args: pca_object
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# return: float array
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# -------------------------------
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# ADD CODE HERE
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singular_values = pca.singular_values_
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# -------------------------------
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return singular_values
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##################################################
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##### Do not add anything below this line ########
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################################################## |