def run(self):
"""Performs various stages in predictive modeling"""
#Path to Data set.
path = "../../neeraj/resource/pima-indians-diabetes.data"
#Column names of Data set.
column_names = [ ' preg ' , ' plas ' , ' pres ' , ' skin ' , ' test ' , ' mass ' , ' pedi ' , ' age ' , ' class ' ]
#Loading Data set using class DatasetLoader.
load_data = DatasetLoader(path, column_names)
data = load_data.load()
load_data.print_shape(data)
#Understanding data using class DataExplorer.
explore_data = DataExplorer()
explore_data.print_data_statistics(data)
explore_data.visualize(data)
#Performing data preprocessing.
process_data = DataPreprocessor()
input_set, output_set = process_data.split_dataset(data,0,8,8)
process_data.display_dataset()
process_data.summarize(input_set, 0, 5, 3)
#Model evaluation using class Evaluator.
evaluator = Evaluator()
evaluator.validate(LogisticRegression(), input_set, output_set, 10, 7)
evaluator.evaluate(LogisticRegression(), input_set, output_set, 10, 7,'log_loss')
#Selecting best model using class ModelSelector.
model = ModelSelector()
#A set of models for selection.
models = []
models.append(( ' LR ' , LogisticRegression()))
models.append(( ' LDA ' , LinearDiscriminantAnalysis()))
models.append(( ' RF ' , RandomForestClassifier(n_estimators=100, max_features=3)))
selected_model = model.select_model(models, input_set, output_set, 10, 7)
print("\nSelected Model:\n %s") % (selected_model)
#Improving Accuracy using class AccuracyImprover.
improve_accuracy = AccuracyImprover()
improve_accuracy.tuning(Ridge(),input_set, output_set)
improve_accuracy.ensemble_prediction(RandomForestClassifier(n_estimators=100, max_features=3), input_set, output_set, 10, 7)
#Finalizing the model and performing prediction.
finalize_model = ModelFinalizer()
input_train, input_test, output_train, output_test = finalize_model.split_train_test_sets(input_set, output_set, 0.33, 7)
finalize_model.finalize_and_save(LogisticRegression(), "../../neeraj/resource/pima_model.sav", input_train, output_train)
finalize_model.predict("../../neeraj/resource/pima_model.sav", input_test, output_test)
import argparse
# Parse arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
help="# of CPU cores used for classification")
args = vars(ap.parse_args())
# Load images
print("[INFO] Loading images")
image_paths = list(paths.list_images(args["dataset"]))
sdl = DatasetLoader()
(data, labels) = sdl.load(image_paths, verbose=500)
data = data.reshape((data.shape[0], 32*32*3))
# Encode labels as intergers
le = LabelEncoder()
labels = le.fit_transform(labels)
# Partition the data.
# training: 75%, testing: 25%
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
# Evaluate k-NN classifier
model = KNeighborsClassifier(n_neighbors=args["neighbors"], n_jobs=args["jobs"], weights = 'distance')
model.fit(trainX, trainY)
print(classification_report(testY, model.predict(testX), target_names=le.classes_))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Main.
@author: neeraj
"""
from dataset_loader import DatasetLoader
from data_preprocessor import DataPreprocessor
from model_builder import ModelBuilder
#Loading dataset
data_loader = DatasetLoader('../resources/ann-train.data')
train = data_loader.load()
data_loader = DatasetLoader('../resources/ann-test.data')
test = data_loader.load()
#Preprocessing data
dp = DataPreprocessor()
train, test = dp.preprocess(train, test)
#Splitting data to predictors and target vaiables
train_X, train_y = dp.split_predictors(train)
test_X, test_y = dp.split_predictors(test)
#splitting data for validation set
X_train, X_val, y_train, y_val = dp.validation_split(train_X, train_y)
#scaling train and validation data
X_train, X_val = dp.scale_data(X_train, X_val)
